Remote Sensing

37 pages, 24105 KB

Open AccessEditor’s ChoiceArticle

Estimating Tree Health Decline Caused by Ips typographus L. from UAS RGB Images Using a Deep One-Stage Object Detection Neural Network

by Heini Kanerva, Eija Honkavaara, Roope Näsi, Teemu Hakala, Samuli Junttila, Kirsi Karila, Niko Koivumäki, Raquel Alves Oliveira, Mikko Pelto-Arvo, Ilkka Pölönen, Johanna Tuviala, Madeleine Östersund and Päivi Lyytikäinen-Saarenmaa

Remote Sens. 2022, 14(24), 6257; https://doi.org/10.3390/rs14246257 - 10 Dec 2022

Cited by 12 | Viewed by 3571

Abstract

Various biotic and abiotic stresses are causing decline in forest health globally. Presently, one of the major biotic stress agents in Europe is the European spruce bark beetle (Ips typographus L.) which is increasingly causing widespread tree mortality in northern latitudes as [...] Read more.

Various biotic and abiotic stresses are causing decline in forest health globally. Presently, one of the major biotic stress agents in Europe is the European spruce bark beetle (Ips typographus L.) which is increasingly causing widespread tree mortality in northern latitudes as a consequence of the warming climate. Remote sensing using unoccupied aerial systems (UAS) together with evolving machine learning techniques provide a powerful tool for fast-response monitoring of forest health. The aim of this study was to investigate the performance of a deep one-stage object detection neural network in the detection of damage by I. typographus in Norway spruce trees using UAS RGB images. A Scaled-YOLOv4 (You Only Look Once) network was implemented and trained for tree health analysis. Datasets for model training were collected during 2013–2020 from three different areas, using four different RGB cameras, and under varying weather conditions. Different model training options were evaluated, including two different symptom rules, different partitions of the dataset, fine-tuning, and hyperparameter optimization. Our study showed that the network was able to detect and classify spruce trees that had visually separable crown symptoms, but it failed to separate spruce trees with stem symptoms and a green crown from healthy spruce trees. For the best model, the overall F-score was 89%, and the F-scores for the healthy, infested, and dead trees were 90%, 79%, and 98%, respectively. The method adapted well to the diverse dataset, and the processing results with different options were consistent. The results indicated that the proposed method could enable implementation of low-cost tools for management of I. typographus outbreaks. Full article

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22 pages, 54888 KB

Open AccessEditor’s ChoiceArticle

Landslide Risk Assessment in Eastern Kentucky, USA: Developing a Regional Scale, Limited Resource Approach

by Matthew M. Crawford, Jason M. Dortch, Hudson J. Koch, Yichuan Zhu, William C. Haneberg, Zhenming Wang and L. Sebastian Bryson

Remote Sens. 2022, 14(24), 6246; https://doi.org/10.3390/rs14246246 - 9 Dec 2022

Cited by 15 | Viewed by 3396

Abstract

Rapidly changing remote sensing technologies (lidar, aerial photography, satellites) provide opportunities to improve regional-scale landslide risk mapping. However, data limitations regarding landslide hazard and exposure data influence how landslide risk is calculated. To develop risk assessments for a landslide-prone region of eastern Kentucky, [...] Read more.

Rapidly changing remote sensing technologies (lidar, aerial photography, satellites) provide opportunities to improve regional-scale landslide risk mapping. However, data limitations regarding landslide hazard and exposure data influence how landslide risk is calculated. To develop risk assessments for a landslide-prone region of eastern Kentucky, USA, we assessed risk modeling and applicability using variable quality data. First, we used a risk equation that incorporated the hazard as a logistic regression landslide susceptibility model using geomorphic variables derived from lidar data. Susceptibility is calculated as a probability of occurrence. The exposure data included population, roads, railroads, and land class. Our vulnerability value was assumed to equal one (worst-case scenario for a degree of loss) and consequence data was economic cost. Results indicate 64.1 percent of the study area is classified as moderate to high socioeconomic risk. To develop a more data-limited approach, we used a 30 m slope-angle map as the hazard input and simplified exposure data. Results for the slope-based approach show the distribution of risk that is less uniform, with large areas of over-and under-prediction. Changes in the hazard and exposure inputs result in significant changes in the quality and applicability of the maps and demonstrate the broad range of risk modelling approaches. Full article

(This article belongs to the Special Issue Geohazard Mapping for Community Resilience: Susceptibility, Impact, and Recovery)

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20 pages, 4905 KB

Open AccessEditor’s ChoiceArticle

High-Resolution Deformation Monitoring from DInSAR: Implications for Geohazards and Ground Stability in the Metropolitan Area of Santiago, Chile

by Felipe Orellana, Marcos Moreno and Gonzalo Yáñez

Remote Sens. 2022, 14(23), 6115; https://doi.org/10.3390/rs14236115 - 2 Dec 2022

Cited by 14 | Viewed by 5419

Abstract

Large urban areas are vulnerable to various geological hazards and anthropogenic activities that affect ground stability—a key factor in structural performance, such as buildings and infrastructure, in an inherently expanding context. Time series data from synthetic aperture radar (SAR) satellites make it possible [...] Read more.

Large urban areas are vulnerable to various geological hazards and anthropogenic activities that affect ground stability—a key factor in structural performance, such as buildings and infrastructure, in an inherently expanding context. Time series data from synthetic aperture radar (SAR) satellites make it possible to identify small rates of motion over large areas of the Earth’s surface with high spatial resolution, which is key to detecting high-deformation areas. Santiago de Chile’s metropolitan region comprises a large Andean foothills basin in one of the most seismically active subduction zones worldwide. The Santiago basin and its surroundings are prone to megathrust and shallow crustal earthquakes, landslides, and constant anthropogenic effects, such as the overexploitation of groundwater and land use modification, all of which constantly affect the ground stability. Here, we recorded ground deformations in the Santiago basin using a multi-temporal differential interferometric synthetic aperture radar (DInSAR) from Sentinel 1, obtaining high-resolution ground motion rates between 2018 and 2021. GNSS stations show a constant regional uplift in the metropolitan area (~10 mm/year); meanwhile, DInSAR allows for the identification of areas with anomalous local subsistence (rates < −15 mm/year) and mountain sectors with landslides with unprecedented detail. Ground deformation patterns vary depending on factors such as soil type, basin geometry, and soil/soil heterogeneities. Thus, the areas with high subsidence rates are concentrated in sectors with fine sedimentary cover and a depressing shallow water table as well as in cropping areas with excess water withdrawal. There is no evidence of detectable movement on the San Ramon Fault (the major quaternary fault in the metropolitan area) over the observational period. Our results highlight the mechanical control of the sediment characteristics of the basin and the impact of anthropogenic processes on ground stability. These results are essential to assess the stability of the Santiago basin and contribute to future infrastructure development and hazard management in highly populated areas. Full article

(This article belongs to the Special Issue Mapping and Monitoring of Geohazards with Remote Sensing Technologies)

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29 pages, 8926 KB

Open AccessFeature PaperEditor’s ChoiceArticle

A Sentinel-2 Based Multi-Temporal Monitoring Framework for Wind and Bark Beetle Detection and Damage Mapping

by Anna Candotti, Michaela De Giglio, Marco Dubbini and Enrico Tomelleri

Remote Sens. 2022, 14(23), 6105; https://doi.org/10.3390/rs14236105 - 1 Dec 2022

Cited by 32 | Viewed by 6023

Abstract

The occurrence of extreme windstorms and increasing heat and drought events induced by climate change leads to severe damage and stress in coniferous forests, making trees more vulnerable to spruce bark beetle infestations. The combination of abiotic and biotic disturbances in forests can [...] Read more.

The occurrence of extreme windstorms and increasing heat and drought events induced by climate change leads to severe damage and stress in coniferous forests, making trees more vulnerable to spruce bark beetle infestations. The combination of abiotic and biotic disturbances in forests can cause drastic environmental and economic losses. The first step to containing such damage is establishing a monitoring framework for the early detection of vulnerable plots and distinguishing the cause of forest damage at scales from the management unit to the region. To develop and evaluate the functionality of such a monitoring framework, we first selected an area of interest affected by windthrow damage and bark beetles at the border between Italy and Austria in the Friulian Dolomites, Carnic and Julian Alps and the Carinthian Gailtal. Secondly, we implemented a framework for time-series analysis with open-access Sentinel-2 data over four years (2017–2020) by quantifying single-band sensitivity to disturbances. Additionally, we enhanced the framework by deploying vegetation indices to monitor spectral changes and perform supervised image classification for change detection. A mean overall accuracy of 89% was achieved; thus, Sentinel-2 imagery proved to be suitable for distinguishing stressed stands, bark-beetle-attacked canopies and wind-felled patches. The advantages of our methodology are its large-scale applicability to monitoring forest health and forest-cover changes and its usability to support the development of forest management strategies for dealing with massive bark beetle outbreaks. Full article

(This article belongs to the Special Issue Remote Sensing for Mountain Ecosystems)

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22 pages, 12227 KB

Open AccessEditor’s ChoiceArticle

A Novel Technique Using Planar Area and Ground Shadows Calculated from UAV RGB Imagery to Estimate Pistachio Tree (Pistacia vera L.) Canopy Volume

by Sergio Vélez, Rubén Vacas, Hugo Martín, David Ruano-Rosa and Sara Álvarez

Remote Sens. 2022, 14(23), 6006; https://doi.org/10.3390/rs14236006 - 27 Nov 2022

Cited by 16 | Viewed by 4941

Abstract

Interest in pistachios has increased in recent years due to their healthy nutritional profile and high profitability. In pistachio trees, as in other woody crops, the volume of the canopy is a key factor that affects the pistachio crop load, water requirements, and [...] Read more.

Interest in pistachios has increased in recent years due to their healthy nutritional profile and high profitability. In pistachio trees, as in other woody crops, the volume of the canopy is a key factor that affects the pistachio crop load, water requirements, and quality. However, canopy/crown monitoring is time-consuming and labor-intensive, as it is traditionally carried out by measuring tree dimensions in the field. Therefore, methods for rapid tree canopy characterization are needed for providing accurate information that can be used for management decisions. The present study focuses on developing a new, fast, and low-cost technique, based on two main steps, for estimating the canopy volume in pistachio trees. The first step is based on adequately planning the UAV (unmanned aerial vehicle) flight according to light conditions and segmenting the RGB (Red, Green, Blue) imagery using machine learning methods. The second step is based on measuring vegetation planar area and ground shadows using two methodological approaches: a pixel-based classification approach and an OBIA (object-based image analysis) approach. The results show statistically significant linear relationships (p < 0.05) between the ground-truth data and the estimated volume of pistachio tree crowns, with R² > 0.8 (pixel-based classification) and R² > 0.9 (OBIA). The proposed methodologies show potential benefits for accurately monitoring the vegetation of the trees. Moreover, the method is compatible with other remote sensing techniques, usually performed at solar noon, so UAV operators can plan a flexible working day. Further research is needed to verify whether these results can be extrapolated to other woody crops. Full article

(This article belongs to the Special Issue Proximal and Remote Sensing for Precision Crop Management)

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22 pages, 1732 KB

Open AccessEditor’s ChoiceReview

Detection of Surface Water and Floods with Multispectral Satellites

by Cinzia Albertini, Andrea Gioia, Vito Iacobellis and Salvatore Manfreda

Remote Sens. 2022, 14(23), 6005; https://doi.org/10.3390/rs14236005 - 27 Nov 2022

Cited by 54 | Viewed by 10758

Abstract

The use of multispectral satellite imagery for water monitoring is a fast and cost-effective method that can benefit from the growing availability of medium–high-resolution and free remote sensing data. Since the 1970s, multispectral satellite imagery has been exploited by adopting different techniques and [...] Read more.

The use of multispectral satellite imagery for water monitoring is a fast and cost-effective method that can benefit from the growing availability of medium–high-resolution and free remote sensing data. Since the 1970s, multispectral satellite imagery has been exploited by adopting different techniques and spectral indices. The high number of available sensors and their differences in spectral and spatial characteristics led to a proliferation of outcomes that depicts a nice picture of the potential and limitations of each. This paper provides a review of satellite remote sensing applications for water extent delineation and flood monitoring, highlighting trends in research studies that adopted freely available optical imagery. The performances of the most common spectral indices for water segmentation are qualitatively analyzed and assessed according to different land cover types to provide guidance for targeted applications in specific contexts. The comparison is carried out by collecting evidence obtained from several applications identifying the overall accuracy (OA) obtained with each specific configuration. In addition, common issues faced when dealing with optical imagery are discussed, together with opportunities offered by new-generation passive satellites. Full article

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16 pages, 8305 KB

Open AccessEditor’s ChoiceArticle

Investigating the Long-Term Variation Trends of Absorbing Aerosols over Asia by Using Multiple Satellites

by Ding Li, Yong Xue, Kai Qin, Han Wang, Hanshu Kang and Lizhang Wang

Remote Sens. 2022, 14(22), 5832; https://doi.org/10.3390/rs14225832 - 17 Nov 2022

Cited by 5 | Viewed by 2508

Abstract

Absorbing aerosols, consisting of smoke (black carbon (BC) and other organics) and dust (from windblown sources), can have a strong warming effect on the climate and impact atmospheric circulation due to localized heating. To investigate the spatiotemporal and vertical changes of absorbing aerosols [...] Read more.

Absorbing aerosols, consisting of smoke (black carbon (BC) and other organics) and dust (from windblown sources), can have a strong warming effect on the climate and impact atmospheric circulation due to localized heating. To investigate the spatiotemporal and vertical changes of absorbing aerosols across Asia, collocation data from OMI, MODIS, and CALIPSO were used to compare two periods: 2006–2013 and 2014–2021. This study revealed a significant temporal and spatial contrast of aerosol loading over the study region, with a drop in total aerosol concentration and anthropogenic smoke concentration recorded across the Eastern China region (all seasons) and a concurrent increase in the Indian sub-continent region (especially in autumn). The range of aerosol diffusion is affected by the height of the smoke and aerosol plumes, as well as the wind force, and is dispersed eastwards because of the Hadley circulation patterns in the Northern Hemisphere. Smoke from Southeast Asia typically rises to a height of 3 km and affects the largest area in contrast to other popular anthropogenic zones, where it is found to be around 1.5–2 km. The dust in Inner Mongolia had the lowest plume height of 2 km (typically in spring) compared to other locations across the study region where it reached 2–5 km in the summer. This study showed, by comparison with AERONET measurements, that combining data from MODIS and OMI generates more accuracy in detecting aerosol AOD from smoke than using the instruments singularly. This study has provided a comprehensive assessment of absorbing aerosol in Asia by utilizing multiplatform remote-sensed data and has summarized long-term changes in the spatiotemporal distribution and vertical structure of absorbing aerosols. Full article

(This article belongs to the Special Issue Satellite Observations of Air Pollution, Analyses with Models and Applications)

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20 pages, 2714 KB

Open AccessEditor’s ChoiceArticle

Data-Driven Short-Term Daily Operational Sea Ice Regional Forecasting

by Timofey Grigoryev, Polina Verezemskaya, Mikhail Krinitskiy, Nikita Anikin, Alexander Gavrikov, Ilya Trofimov, Nikita Balabin, Aleksei Shpilman, Andrei Eremchenko, Sergey Gulev, Evgeny Burnaev and Vladimir Vanovskiy

Remote Sens. 2022, 14(22), 5837; https://doi.org/10.3390/rs14225837 - 17 Nov 2022

Cited by 14 | Viewed by 5175

Abstract

Global warming has made the Arctic increasingly available for marine operations and created a demand for reliable operational sea ice forecasts to increase safety. Because ocean-ice numerical models are highly computationally intensive, relatively lightweight ML-based methods may be more efficient for sea ice [...] Read more.

Global warming has made the Arctic increasingly available for marine operations and created a demand for reliable operational sea ice forecasts to increase safety. Because ocean-ice numerical models are highly computationally intensive, relatively lightweight ML-based methods may be more efficient for sea ice forecasting. Many studies have exploited different deep learning models alongside classical approaches for predicting sea ice concentration in the Arctic. However, only a few focus on daily operational forecasts and consider the real-time availability of data needed for marine operations. In this article, we aim to close this gap and investigate the performance of the U-Net model trained in two regimes for predicting sea ice for up to the next 10 days. We show that this deep learning model can outperform simple baselines by a significant margin, and we can improve the model’s quality by using additional weather data and training on multiple regions to ensure its generalization abilities. As a practical outcome, we build a fast and flexible tool that produces operational sea ice forecasts in the Barents Sea, the Labrador Sea, and the Laptev Sea regions. Full article

(This article belongs to the Section AI Remote Sensing)

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28 pages, 37926 KB

Open AccessEditor’s ChoiceArticle

AICCA: AI-Driven Cloud Classification Atlas

by Takuya Kurihana, Elisabeth J. Moyer and Ian T. Foster

Remote Sens. 2022, 14(22), 5690; https://doi.org/10.3390/rs14225690 - 10 Nov 2022

Cited by 8 | Viewed by 4058

Abstract

Clouds play an important role in the Earth’s energy budget, and their behavior is one of the largest uncertainties in future climate projections. Satellite observations should help in understanding cloud responses, but decades and petabytes of multispectral cloud imagery have to date received [...] Read more.

Clouds play an important role in the Earth’s energy budget, and their behavior is one of the largest uncertainties in future climate projections. Satellite observations should help in understanding cloud responses, but decades and petabytes of multispectral cloud imagery have to date received only limited use. This study describes a new analysis approach that reduces the dimensionality of satellite cloud observations by grouping them via a novel automated, unsupervised cloud classification technique based on a convolutional autoencoder, an artificial intelligence (AI) method good at identifying patterns in spatial data. Our technique combines a rotation-invariant autoencoder and hierarchical agglomerative clustering to generate cloud clusters that capture meaningful distinctions among cloud textures, using only raw multispectral imagery as input. Cloud classes are therefore defined based on spectral properties and spatial textures without reliance on location, time/season, derived physical properties, or pre-designated class definitions. We use this approach to generate a unique new cloud dataset, the AI-driven cloud classification atlas (AICCA), which clusters 22 years of ocean images from the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Aqua and Terra instruments—198 million patches, each roughly 100 km × 100 km (128 × 128 pixels)—into 42 AI-generated cloud classes, a number determined via a newly-developed stability protocol that we use to maximize richness of information while ensuring stable groupings of patches. AICCA thereby translates 801 TB of satellite images into 54.2 GB of class labels and cloud top and optical properties, a reduction by a factor of 15,000. The 42 AICCA classes produce meaningful spatio-temporal and physical distinctions and capture a greater variety of cloud types than do the nine International Satellite Cloud Climatology Project (ISCCP) categories—for example, multiple textures in the stratocumulus decks along the West coasts of North and South America. We conclude that our methodology has explanatory power, capturing regionally unique cloud classes and providing rich but tractable information for global analysis. AICCA delivers the information from multi-spectral images in a compact form, enables data-driven diagnosis of patterns of cloud organization, provides insight into cloud evolution on timescales of hours to decades, and helps democratize climate research by facilitating access to core data. Full article

(This article belongs to the Special Issue AI for Marine, Ocean and Climate Change Monitoring)

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11 pages, 2257 KB

Open AccessEditor’s ChoiceTechnical Note

Development of Snow Cover Frequency Maps from MODIS Snow Cover Products

by George Riggs, Dorothy Hall, Carrie Vuyovich and Nicolo DiGirolamo

Remote Sens. 2022, 14(22), 5661; https://doi.org/10.3390/rs14225661 - 9 Nov 2022

Cited by 10 | Viewed by 3434

Abstract

With a decade scale record of global snow cover extent (SCE) at up to 500 m from the Moderate-resolution Imaging Spectroradiometer (MODIS), the dynamics of snow cover can be mapped at local to global scales. We developed daily snow cover frequency maps from [...] Read more.

With a decade scale record of global snow cover extent (SCE) at up to 500 m from the Moderate-resolution Imaging Spectroradiometer (MODIS), the dynamics of snow cover can be mapped at local to global scales. We developed daily snow cover frequency maps from 2001–2020 using a ~5 km resolution MODIS snow cover map. For each day of the year the maps show the frequency of snow cover for the 20-year period on a per-grid cell basis. Following on from other work to develop snow frequency maps using MODIS snow cover products, we include spatial filtering to reduce errors caused by ‘false snow’ that occurs primarily due to cloud-snow confusion. On our snow frequency maps, there were no regions or time periods with a noticeable absence of snow where snow was expected. In one example, the MODIS derived frequency of snow cover on 25 December compares well with NOAA’s historical probability of snow on the same day. Though the MODIS derived snow frequency and NOAA probabilities are computed from very different data sources, they compare well. Though this preliminary research is promising, much future evaluation is needed. Full article

(This article belongs to the Special Issue Remote Sensing of Environmental Changes in Cold Regions Ⅱ)

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24 pages, 8065 KB

Open AccessEditor’s ChoiceArticle

Processing and Validation of the STAR COSMIC-2 Temperature and Water Vapor Profiles in the Neutral Atmosphere

by Shu-peng Ho, Stanislav Kireev, Xi Shao, Xinjia Zhou and Xin Jing

Remote Sens. 2022, 14(21), 5588; https://doi.org/10.3390/rs14215588 - 5 Nov 2022

Cited by 7 | Viewed by 3185

Abstract

The global navigation satellite system (GNSS) radio occultation (RO) is becoming an essential component of National Oceanic and Atmospheric Administration (NOAA) observation systems. The constellation observing system for meteorology, ionosphere, and climate (COSMIC) 2 mission and the Formosa satellite mission 7, a COSMIC [...] Read more.

The global navigation satellite system (GNSS) radio occultation (RO) is becoming an essential component of National Oceanic and Atmospheric Administration (NOAA) observation systems. The constellation observing system for meteorology, ionosphere, and climate (COSMIC) 2 mission and the Formosa satellite mission 7, a COSMIC follow-on mission, is now the NOAA’s backbone RO mission. The NOAA’s dedicated GNSS RO SAtellite processing and science Application Center (RO-SAAC) was established at the Center for Satellite Applications and Research (STAR). To better quantify how the observation uncertainty from clock error and geometry determination may propagate to bending angle and refractivity profiles, STAR has developed the GNSS RO data processing and validation system. This study describes the COSMIC-2 neutral atmospheric temperature and moisture profile inversion algorithms at STAR. We used RS41 and ERA5, and UCAR 1D-Var products (wetPrf2) to validate the accuracy and uncertainty of the STAR 1D-Var thermal profiles. The STAR-RS41 temperature differences are less than a few tenths of 1 K from 8 km to 30 km altitude with a standard deviation (std) of 1.5–2 K. The mean STAR-RS41 water vapor specific humidity difference and the standard deviation are −0.35 g/kg and 1.2 g/kg, respectively. We also used the 1D-Var-derived temperature and water vapor profiles to compute the simulated brightness temperature (BTs) for advanced technology microwave sounder (ATMS) and cross-track infrared sounder (CrIS) channels and compared them to the collocated ATMS and CrIS measurements. The BT differences of STAR COSMIC-2-simulated BTs relative to SNPP ATMS are less than 0.1 K over all ATMS channels. Full article

(This article belongs to the Special Issue GNSS in Meteorology and Climatology)

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20 pages, 13336 KB

Open AccessEditor’s ChoiceArticle

Pre-Launch Polarization Assessment of JPSS-2 VIIRS VNIR Bands

by David Moyer, Jeff McIntire and Xiaoxiong Xiong

Remote Sens. 2022, 14(21), 5547; https://doi.org/10.3390/rs14215547 - 3 Nov 2022

Cited by 2 | Viewed by 2035

Abstract

The Visible Infrared Imaging Radiometer Suite (VIIRS) instruments on-board the Suomi National Polar-orbiting Partnership (S-NPP), National Oceanic and Atmospheric Administration 20 (NOAA-20) and Joint Polar Satellite System (JPSS-2) spacecraft, with launch dates of October 2011, November 2017 and late 2022, respectively, have polarization [...] Read more.

The Visible Infrared Imaging Radiometer Suite (VIIRS) instruments on-board the Suomi National Polar-orbiting Partnership (S-NPP), National Oceanic and Atmospheric Administration 20 (NOAA-20) and Joint Polar Satellite System (JPSS-2) spacecraft, with launch dates of October 2011, November 2017 and late 2022, respectively, have polarization sensitivity that affects the at-aperture radiometric Sensor Data Record (SDR) calibration in the Visible Near InfraRed (VNIR) spectral region. These SDRs are used as inputs into the VIIRS atmospheric, land, and water Environmental Data Records (EDRs) that are integral to climate and weather applications. Pre-launch characterization of the VIIRS polarization sensitivity was performed that provides an SDR radiance correction factor to enable high fidelity EDR products for the user community. The pre-launch polarization sensitivity used an external source that consisted of a 100 cm diameter Spherical Integrating Source (SIS) in combination with several sheet polarizers. These sheet polarizers were illuminated by the SIS and viewed by the VIIRS instrument. The sheet was then rotated to measure the variation in the VIIRS response relative to the at-aperture polarization orientation. There are sensor requirements that define the maximum allowed polarization amplitude to be below 2.5–3.0% depending on the band and have an uncertainty in both amplitude and phase of less than 0.5%. The pre-launch data analysis evaluated the VIIRS response through the rotating sheet polarizer to quantify each VNIR bands polarization amplitude, phase, and uncertainty. These parameters were compared with the sensor requirements and used to create on-orbit Look-Up Tables (LUTs) for EDR ground processing. The results of the analysis showed that all bands met the uncertainty requirement of 0.5%, but band M1 failed the 3% polarization amplitude requirement. A root-cause analysis identified the optical element responsible for the non-compliance and has been modified for JPSS-3 and -4 builds. The large polarization amplitudes observed in the NOAA-20 VIIRS build, for bands M1-M4, are greatly reduced for JPSS-2 VIIRS. This improved polarization performance was due to modifications to the band M1-M4 bandpass filters between these sensor builds. Full article

(This article belongs to the Special Issue Frontiers in Remote Sensing Techniques and Applications Using Visible Infrared Imaging Radiometer Suites)

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16 pages, 958 KB

Open AccessEditor’s ChoiceArticle

How to Improve the Reproducibility, Replicability, and Extensibility of Remote Sensing Research

by Peter Kedron and Amy E. Frazier

Remote Sens. 2022, 14(21), 5471; https://doi.org/10.3390/rs14215471 - 31 Oct 2022

Cited by 8 | Viewed by 4961

Abstract

The field of remote sensing has undergone a remarkable shift where vast amounts of imagery are now readily available to researchers. New technologies, such as uncrewed aircraft systems, make it possible for anyone with a moderate budget to gather their own remotely sensed [...] Read more.

The field of remote sensing has undergone a remarkable shift where vast amounts of imagery are now readily available to researchers. New technologies, such as uncrewed aircraft systems, make it possible for anyone with a moderate budget to gather their own remotely sensed data, and methodological innovations have added flexibility for processing and analyzing data. These changes create both the opportunity and need to reproduce, replicate, and compare remote sensing methods and results across spatial contexts, measurement systems, and computational infrastructures. Reproducing and replicating research is key to understanding the credibility of studies and extending recent advances into new discoveries. However, reproducibility and replicability (R&R) remain issues in remote sensing because many studies cannot be independently recreated and validated. Enhancing the R&R of remote sensing research will require significant time and effort by the research community. However, making remote sensing research reproducible and replicable does not need to be a burden. In this paper, we discuss R&R in the context of remote sensing and link the recent changes in the field to key barriers hindering R&R while discussing how researchers can overcome those barriers. We argue for the development of two research streams in the field: (1) the coordinated execution of organized sequences of forward-looking replications, and (2) the introduction of benchmark datasets that can be used to test the replicability of results and methods. Full article

(This article belongs to the Special Issue Reproducibility and Replicability in Remote Sensing Workflows)

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27 pages, 13124 KB

Open AccessEditor’s ChoiceArticle

Numerical Modeling and Parameter Sensitivity Analysis for Understanding Scale-Dependent Topographic Effects Governing Anisotropic Reflectance Correction of Satellite Imagery

by Michael P. Bishop, Brennan W. Young and Jeffrey D. Colby

Remote Sens. 2022, 14(21), 5339; https://doi.org/10.3390/rs14215339 - 25 Oct 2022

Cited by 3 | Viewed by 2632

Abstract

Anisotropic reflectance correction (ARC) of satellite imagery is required to remove multi-scale topographic effects in imagery. Commonly utilized ARC approaches have not effectively accounted for atmosphere-topographic coupling. Furthermore, it is not clear which topographic effects need to be formally accounted for. Consequently, we [...] Read more.

Anisotropic reflectance correction (ARC) of satellite imagery is required to remove multi-scale topographic effects in imagery. Commonly utilized ARC approaches have not effectively accounted for atmosphere-topographic coupling. Furthermore, it is not clear which topographic effects need to be formally accounted for. Consequently, we simulate the direct and diffuse-skylight irradiance components and formally account for multi-scale topographic effects. A sensitivity analysis was used to determine if characterization schemes can account for a collective treatment of effects, using our parameterization scheme as a basis for comparison. We found that commonly used assumptions could not account for topographic modulation in our simulations. We also found that the use of isotropic diffuse irradiance and a topographic shielding parameter also failed to characterize topographic modulation. Our results reveal that topographic effects govern irradiance variations in a synergistic way, and that issues of ARC need to be formally addressed given atmosphere-topography coupling. Collectively, our results suggest that empirical ARC methods cannot be used to effectively address topographic effects, given inadequate parameterization schemes. Characterizing and removing spectral variation from multispectral imagery will most likely require numerical modeling efforts. More research is warranted to develop/evaluate parameterization schemes that better characterize the anisotropic nature of atmosphere-topography coupling. Full article

(This article belongs to the Section Environmental Remote Sensing)

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18 pages, 2776 KB

Open AccessEditor’s ChoiceArticle

Evaluation of the Landsat-8 Albedo Product across the Circumpolar Domain

by Angela M. Erb, Zhan Li, Qingsong Sun, Ian Paynter, Zhuosen Wang and Crystal Schaaf

Remote Sens. 2022, 14(21), 5320; https://doi.org/10.3390/rs14215320 - 24 Oct 2022

Cited by 5 | Viewed by 4159

Abstract

Land surface albedo plays an extremely important role in the surface energy budget, by determining the proportion of incoming solar radiation, which is available to drive photosynthesis and surface heating, and that which is reflected directly back to space. In northern high latitude [...] Read more.

Land surface albedo plays an extremely important role in the surface energy budget, by determining the proportion of incoming solar radiation, which is available to drive photosynthesis and surface heating, and that which is reflected directly back to space. In northern high latitude regions, the albedo of snow-covered vegetation and open, leafless forest canopies in winter, is quite high, while the albedo of boreal evergreen conifers can either be quite low (even with extensive snow lying under the canopy) or rather bright depending on the structure and density of the canopy. Here, we present the further development and evaluation of a 30 m Landsat albedo product, including an operational blue-sky albedo product, for application in the circumpolar domain. The surface reflectances from the Landsat satellite constellation are coupled with surface anisotropy information (Bidirectional Reflectance Distribution Function, BRDF) from the MODerate-resolution Imaging Spectroradiometer (MODIS). The product is extensively validated across diverse land cover and conditions and performs well with root mean squared error of 0.0395 and negligible bias when compared to coincident tower-based albedo measurements. The development of this Landsat albedo products allows for better capture of ephemeral, heterogeneous and dynamic surface conditions at the landscape scale across the circumpolar domain. Full article

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20 pages, 5605 KB

Open AccessEditor’s ChoiceArticle

Benchmarking Different SfM-MVS Photogrammetric and iOS LiDAR Acquisition Methods for the Digital Preservation of a Short-Lived Excavation: A Case Study from an Area of Sinkhole Related Subsidence

by Amerigo Corradetti, Thomas Seers, Marco Mercuri, Chiara Calligaris, Alice Busetti and Luca Zini

Remote Sens. 2022, 14(20), 5187; https://doi.org/10.3390/rs14205187 - 17 Oct 2022

Cited by 36 | Viewed by 4959

Abstract

We are witnessing a digital revolution in geoscientific field data collection and data sharing, driven by the availability of low-cost sensory platforms capable of generating accurate surface reconstructions as well as the proliferation of apps and repositories which can leverage their data products. [...] Read more.

We are witnessing a digital revolution in geoscientific field data collection and data sharing, driven by the availability of low-cost sensory platforms capable of generating accurate surface reconstructions as well as the proliferation of apps and repositories which can leverage their data products. Whilst the wider proliferation of 3D close-range remote sensing applications is welcome, improved accessibility is often at the expense of model accuracy. To test the accuracy of consumer-grade close-range 3D model acquisition platforms commonly employed for geo-documentation, we have mapped a 20-m-wide trench using aerial and terrestrial photogrammetry, as well as iOS LiDAR. The latter was used to map the trench using both the 3D Scanner App and PIX4Dcatch applications. Comparative analysis suggests that only in optimal scenarios can geotagged field-based photographs alone result in models with acceptable scaling errors, though even in these cases, the orientation of the transformed model is not sufficiently accurate for most geoscientific applications requiring structural metric data. The apps tested for iOS LiDAR acquisition were able to produce accurately scaled models, though surface deformations caused by simultaneous localization and mapping (SLAM) errors are present. Finally, of the tested apps, PIX4Dcatch is the iOS LiDAR acquisition tool able to produce correctly oriented models. Full article

(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)

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19 pages, 4180 KB

Open AccessEditor’s ChoiceArticle

Spatially Continuous Mapping of Forest Canopy Height in Canada by Combining GEDI and ICESat-2 with PALSAR and Sentinel

by Camile Sothe, Alemu Gonsamo, Ricardo B. Lourenço, Werner A. Kurz and James Snider

Remote Sens. 2022, 14(20), 5158; https://doi.org/10.3390/rs14205158 - 15 Oct 2022

Cited by 58 | Viewed by 9515

Abstract

Continuous large-scale mapping of forest canopy height is crucial for estimating and reporting forest carbon content, analyzing forest degradation and restoration, or to model ecosystem variables such as aboveground biomass. Over the last years, the spaceborne Light Detection and Ranging (LiDAR) sensor specifically [...] Read more.

Continuous large-scale mapping of forest canopy height is crucial for estimating and reporting forest carbon content, analyzing forest degradation and restoration, or to model ecosystem variables such as aboveground biomass. Over the last years, the spaceborne Light Detection and Ranging (LiDAR) sensor specifically designed to acquire forest structure information, Global Ecosystem Dynamics Investigation (GEDI), has been used to extract forest canopy height information over large areas. Yet, GEDI has no spatial coverage for most forested areas in Canada and other high latitude regions. On the other hand, the spaceborne LiDAR called Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) provides a global coverage but was not specially developed to study forested ecosystems. Nonetheless, both spaceborne LiDAR sensors obtain point-based information, making spatially continuous forest canopy height estimation very challenging. This study compared the performance of both spaceborne LiDAR, GEDI and ICESat-2, combined with ALOS-2/PALSAR-2 and Sentinel-1 and -2 data to produce continuous canopy height maps in Canada for the year 2020. A set-aside dataset and airborne LiDAR (ALS) from a national LiDAR campaign were used for accuracy assessment. Both maps overestimated canopy height in relation to ALS data, but GEDI had a better performance than ICESat-2 with a mean difference (MD) of 0.9 m and 2.9 m, and a root mean square error (RMSE) of 4.2 m and 5.2 m, respectively. However, as both GEDI and ALS have no coverage in most of the hemi-boreal forests, ICESat-2 captures the tall canopy heights expected for these forests better than GEDI. PALSAR-2 HV polarization was the most important covariate to predict canopy height, showing the great potential of L-band in comparison to C-band from Sentinel-1 or optical data from Sentinel-2. The approach proposed here can be used operationally to produce annual canopy height maps for areas that lack GEDI and ICESat-2 coverage. Full article

(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)

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25 pages, 715 KB

Open AccessEditor’s ChoiceReview

Monitoring and Mapping Vegetation Cover Changes in Arid and Semi-Arid Areas Using Remote Sensing Technology: A Review

by Raid Almalki, Mehdi Khaki, Patricia M. Saco and Jose F. Rodriguez

Remote Sens. 2022, 14(20), 5143; https://doi.org/10.3390/rs14205143 - 14 Oct 2022

Cited by 82 | Viewed by 19425

Abstract

Vegetation cover change is one of the key indicators used for monitoring environmental quality. It can accurately reflect changes in hydrology, climate, and human activities, especially in arid and semi-arid regions. The main goal of this paper is to review the remote sensing [...] Read more.

Vegetation cover change is one of the key indicators used for monitoring environmental quality. It can accurately reflect changes in hydrology, climate, and human activities, especially in arid and semi-arid regions. The main goal of this paper is to review the remote sensing satellite sensors and the methods used for monitoring and mapping vegetation cover changes in arid and semi-arid. Arid and semi-arid lands are eco-sensitive environments with limited water resources and vegetation cover. Monitoring vegetation changes are especially important in arid and semi-arid regions due to the scarce and sensitive nature of the plant cover. Due to expected changes in vegetation cover, land productivity and biodiversity might be affected. Thus, early detection of vegetation cover changes and the assessment of their extent and severity at the local and regional scales become very important in preventing future biodiversity loss. Remote sensing data are useful for monitoring and mapping vegetation cover changes and have been used extensively for identifying, assessing, and mapping such changes in different regions. Remote sensing data, such as satellite images, can be obtained from satellite-based and aircraft-based sensors to monitor and detect vegetation cover changes. By combining remotely sensed images, e.g., from satellites and aircraft, with ground truth data, it is possible to improve the accuracy of monitoring and mapping techniques. Additionally, satellite imagery data combined with ancillary data such as slope, elevation, aspect, water bodies, and soil characteristics can detect vegetation cover changes at the species level. Using analytical methods, the data can then be used to derive vegetation indices for mapping and monitoring vegetation. Full article

(This article belongs to the Section Environmental Remote Sensing)

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20 pages, 3616 KB

Open AccessEditor’s ChoiceArticle

Hyperspectral Reconnaissance: Joint Characterization of the Spectral Mixture Residual Delineates Geologic Unit Boundaries in the White Mountains, CA

by Francis J. Sousa and Daniel J. Sousa

Remote Sens. 2022, 14(19), 4914; https://doi.org/10.3390/rs14194914 - 1 Oct 2022

Cited by 14 | Viewed by 3777

Abstract

We use a classic locale for geology education in the White Mountains, CA, to demonstrate a novel approach for using imaging spectroscopy (hyperspectral imaging) to generate base maps for the purpose of geologic mapping. The base maps produced in this fashion are complementary [...] Read more.

We use a classic locale for geology education in the White Mountains, CA, to demonstrate a novel approach for using imaging spectroscopy (hyperspectral imaging) to generate base maps for the purpose of geologic mapping. The base maps produced in this fashion are complementary to, but distinct from, maps of mineral abundance. The approach synthesizes two concepts in imaging spectroscopy data analysis: the spectral mixture residual and joint characterization. First, the mixture residual uses a linear, generalizable, and physically based continuum removal model to mitigate the confounding effects of terrain and vegetation. Then, joint characterization distinguishes spectrally distinct geologic units by isolating residual, absorption-driven spectral features as nonlinear manifolds. Compared to most traditional classifiers, important strengths of this approach include physical basis, transparency, and near-uniqueness of result. Field validation confirms that this approach can identify regions of interest that contribute significant complementary information to PCA alone when attempting to accurately map spatial boundaries between lithologic units. For a geologist, this new type of base map can complement existing algorithms in exploiting the coming availability of global hyperspectral data for pre-field reconnaissance and geologic unit delineation. Full article

(This article belongs to the Special Issue New Trends on Remote Sensing Applications to Mineral Deposits)

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23 pages, 20371 KB

Open AccessEditor’s ChoiceArticle

A Methodology for National Scale Coastal Landcover Mapping in New Zealand

by Benedict Collings, Murray Ford and Mark Dickson

Remote Sens. 2022, 14(19), 4827; https://doi.org/10.3390/rs14194827 - 27 Sep 2022

Cited by 2 | Viewed by 5513

Abstract

Satellite earth observation data has become fundamental in efforts to map coastal change at large geographic scales. Research has generally focussed on extracting the instantaneous waterline position from time-series of satellite images to interpret long-term trends. The use of this proxy can, however, [...] Read more.

Satellite earth observation data has become fundamental in efforts to map coastal change at large geographic scales. Research has generally focussed on extracting the instantaneous waterline position from time-series of satellite images to interpret long-term trends. The use of this proxy can, however, be uncertain because the waterline is sensitive to marine conditions and beach gradient. In addition, the technique disregards potentially useful data stored in surrounding pixels. In this paper, we describe a pixel-based technique to analyse coastal change. A hybrid rule-based and machine learning methodology was developed using a combination of Sentinel multispectral and Synthetic Aperture Radar composite imagery. The approach was then used to provide the first national-scale pixel-based landcover classification for the open coast of New Zealand. Nine landcover types were identified including vegetation, rock, and sedimentary classes that are common on beaches (dark sand, light sand, and gravel). Accuracy was assessed at national scale (overall accuracy: 86%) and was greater than 90% when normalised for class area. Using a combination of optical and Synthetic Aperture Radar data improved overall accuracy by 14% and enhanced the separation of coastal sedimentary classes. Comparison against a previous classification approach of sandy coasts indicated improvements of 30% in accuracy. The outputs and code are freely available and open-source providing a new framework for per-pixel coastal landcover mapping for all regions where public earth observation data is available. Full article

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24 pages, 9479 KB

Open AccessEditor’s ChoiceArticle

A Comparison of Processing Schemes for Automotive MIMO SAR Imaging

by Marco Manzoni, Stefano Tebaldini, Andrea Virgilio Monti-Guarnieri, Claudio Maria Prati and Ivan Russo

Remote Sens. 2022, 14(19), 4696; https://doi.org/10.3390/rs14194696 - 20 Sep 2022

Cited by 18 | Viewed by 3877

Abstract

Synthetic Aperture Radar (SAR) imaging is starting to play an essential role in the automotive industry. Its day and night sensing capability, fine resolution, and high flexibility are key aspects making SAR a very compelling instrument in this field. This paper describes and [...] Read more.

Synthetic Aperture Radar (SAR) imaging is starting to play an essential role in the automotive industry. Its day and night sensing capability, fine resolution, and high flexibility are key aspects making SAR a very compelling instrument in this field. This paper describes and compares three algorithms used to combine low-resolution images acquired by a Multiple-Input Multiple-Output (MIMO) automotive radar to form an SAR image of the environment. The first is the well-known Fast Factorized Back-Projection (FFBP), which focuses the image in different stages. The second one will be called 3D2D, and it is a simple 3D interpolation used to extract the SAR image from the Range-Angle-Velocity (RAV) data cube. The third will be called Quick&Dirty (Q&D), and it is a fast alternative to the 3D2D scheme that exploits the same intuition. A rigorous mathematical description of each algorithm is derived, and their limits are addressed. We then provide simulated results assessing different interpolation kernels, proving which one performs better. A rough estimation of the number of operations proves that both algorithms can be deployed using a real-time implementation. Finally, we will present some experimental results based on open road campaign data acquired using an eight-channel MIMO radar at 77 GHz, considering the case of a forward-looking geometry. Full article

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20 pages, 5059 KB

Open AccessEditor’s ChoiceArticle

Vegetation Mapping with Random Forest Using Sentinel 2 and GLCM Texture Feature—A Case Study for Lousã Region, Portugal

by Pegah Mohammadpour, Domingos Xavier Viegas and Carlos Viegas

Remote Sens. 2022, 14(18), 4585; https://doi.org/10.3390/rs14184585 - 14 Sep 2022

Cited by 84 | Viewed by 12560

Abstract

Vegetation mapping requires accurate information to allow its use in applications such as sustainable forest management against the effects of climate change and the threat of wildfires. Remote sensing provides a powerful resource of fundamental data at different spatial resolutions and spectral regions, [...] Read more.

Vegetation mapping requires accurate information to allow its use in applications such as sustainable forest management against the effects of climate change and the threat of wildfires. Remote sensing provides a powerful resource of fundamental data at different spatial resolutions and spectral regions, making it an essential tool for vegetation mapping and biomass management. Due to the ever-increasing availability of free data and software, satellites have been predominantly used to map, analyze, and monitor natural resources for conservation purposes. This study aimed to map vegetation from Sentinel-2 (S2) data in a complex and mixed vegetation cover of the Lousã district in Portugal. We used ten multispectral bands with a spatial resolution of 10 m, and four vegetation indices, including Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Enhanced Vegetation Index (EVI), and Soil Adjusted Vegetation Index (SAVI). After applying principal component analysis (PCA) on the 10 S2A bands, four texture features, including mean (ME), homogeneity (HO), correlation (CO), and entropy (EN), were derived for the first three principal components. Textures were obtained using the Gray-Level Co-Occurrence Matrix (GLCM). As a result, 26 independent variables were extracted from S2. After defining the land use classes using an object-based approach, the Random Forest (RF) classifier was applied. The map accuracy was evaluated by the confusion matrix, using the metrics of overall accuracy (OA), producer accuracy (PA), user accuracy (UA), and kappa coefficient (Kappa). The described classification methodology showed a high OA of 90.5% and kappa of 89% for vegetation mapping. Using GLCM texture features and vegetation indices increased the accuracy by up to 2%; however, classification using GLCM texture features and spectral bands achieved the highest OA (92%), indicating the texture features′ capability in detecting the variability of forest species at stand level. The ME and CO showed the highest contribution to the classification accuracy among the GLCM textures. GNDVI outperformed other vegetation indices in variable importance. Moreover, using only S2A spectral bands, especially bands 11, 12, and 2, showed a high potential to classify the map with an OA of 88%. This study showed that adding at least one GLCM texture feature and at least one vegetation index into the S2A spectral bands may effectively increase the accuracy metrics and tree species discrimination. Full article

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33 pages, 9288 KB

Open AccessEditor’s ChoiceReview

Review on the Geophysical and UAV-Based Methods Applied to Landslides

by Yawar Hussain, Romy Schlögel, Agnese Innocenti, Omar Hamza, Roberto Iannucci, Salvatore Martino and Hans-Balder Havenith

Remote Sens. 2022, 14(18), 4564; https://doi.org/10.3390/rs14184564 - 13 Sep 2022

Cited by 61 | Viewed by 9724

Abstract

Landslides (LS) represent geomorphological processes that can induce changes over time in the physical, hydrogeological, and mechanical properties of the involved materials. For geohazard assessment, the variations of these properties might be detected by a wide range of non-intrusive techniques, which can sometimes [...] Read more.

Landslides (LS) represent geomorphological processes that can induce changes over time in the physical, hydrogeological, and mechanical properties of the involved materials. For geohazard assessment, the variations of these properties might be detected by a wide range of non-intrusive techniques, which can sometimes be confusing due to their significant variation in accuracy, suitability, coverage area, logistics, timescale, cost, and integration potential; this paper reviews common geophysical methods (GM) categorized as Emitted Seismic and Ambient Noise based and proposes an integrated approach between them for improving landslide studies; this level of integration (among themselves) is an important step ahead of integrating geophysical data with remote sensing data. The aforementioned GMs help to construct a framework based on physical properties that may be linked with site characterization (e.g., a landslide and its subsurface channel geometry, recharge pathways, rock fragments, mass flow rate, etc.) and dynamics (e.g., quantification of the rheology, saturation, fracture process, toe erosion, mass flow rate, deformation marks and spatiotemporally dependent geogenic pore-water pressure feedback through a joint analysis of geophysical time series, displacement and hydrometeorological measurements from the ground, air and space). A review of the use of unmanned aerial vehicles (UAV) based photogrammetry for the investigation of landslides was also conducted to highlight the latest advancement and discuss the synergy between UAV and geophysical in four possible broader areas: (i) survey planning, (ii) LS investigation, (iii) LS dynamics and (iv) presentation of results in GIS environment. Additionally, endogenous source mechanisms lead to the appearance of deformation marks on the surface and provide ground for the integrated use of UAV and geophysical monitoring for landslide early warning systems. Further development in this area requires UAVs to adopt more multispectral and other advanced sensors where their data are integrated with the geophysical one as well as the climatic data to enable Artificial Intelligent based prediction of LS. Full article

(This article belongs to the Special Issue Landslide Studies Integrating Remote Sensing and Geophysical Data)

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18 pages, 4730 KB

Open AccessEditor’s ChoiceArticle

A CNN-LSTM Model for Soil Organic Carbon Content Prediction with Long Time Series of MODIS-Based Phenological Variables

by Lei Zhang, Yanyan Cai, Haili Huang, Anqi Li, Lin Yang and Chenghu Zhou

Remote Sens. 2022, 14(18), 4441; https://doi.org/10.3390/rs14184441 - 6 Sep 2022

Cited by 83 | Viewed by 9738

Abstract

The spatial distribution of soil organic carbon (SOC) serves as critical geographic information for assessing ecosystem services, climate change mitigation, and optimal agriculture management. Digital mapping of SOC is challenging due to the complex relationships between the soil and its environment. Except for [...] Read more.

The spatial distribution of soil organic carbon (SOC) serves as critical geographic information for assessing ecosystem services, climate change mitigation, and optimal agriculture management. Digital mapping of SOC is challenging due to the complex relationships between the soil and its environment. Except for the well-known terrain and climate environmental covariates, vegetation that interacts with soils influences SOC significantly over long periods. Although several remote-sensing-based vegetation indices have been widely adopted in digital soil mapping, variables indicating long term vegetation growth have been less used. Vegetation phenology, an indicator of vegetation growth characteristics, can be used as a potential time series environmental covariate for SOC prediction. A CNN-LSTM model was developed for SOC prediction with inputs of static and dynamic environmental variables in Xuancheng City, China. The spatially contextual features in static variables (e.g., topographic variables) were extracted by the convolutional neural network (CNN), while the temporal features in dynamic variables (e.g., vegetation phenology over a long period of time) were extracted by a long short-term memory (LSTM) network. The ten-year phenological variables derived from moderate-resolution imaging spectroradiometer (MODIS) observations were adopted as predictors with historical temporal changes in vegetation in addition to the commonly used static variables. The random forest (RF) model was used as a reference model for comparison. Our results indicate that adding phenological variables can produce a more accurate map, as tested by the five-fold cross-validation, and demonstrate that CNN-LSTM is a potentially effective model for predicting SOC at a regional spatial scale with long-term historical vegetation phenology information as an extra input. We highlight the great potential of hybrid deep learning models, which can simultaneously extract spatial and temporal features from different types of environmental variables, for future applications in digital soil mapping. Full article

(This article belongs to the Special Issue Remote Sensing for Soil Mapping and Monitoring)

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30 pages, 83951 KB

Open AccessEditor’s ChoiceArticle

Apple LiDAR Sensor for 3D Surveying: Tests and Results in the Cultural Heritage Domain

by Lorenzo Teppati Losè, Alessandra Spreafico, Filiberto Chiabrando and Fabio Giulio Tonolo

Remote Sens. 2022, 14(17), 4157; https://doi.org/10.3390/rs14174157 - 24 Aug 2022

Cited by 75 | Viewed by 22638

Abstract

The launch of the new iPad Pro by Apple in March 2020 generated high interest and expectations for different reasons; nevertheless, one of the new features that developers and users were interested in testing was the LiDAR sensor integrated into this device (and, [...] Read more.

The launch of the new iPad Pro by Apple in March 2020 generated high interest and expectations for different reasons; nevertheless, one of the new features that developers and users were interested in testing was the LiDAR sensor integrated into this device (and, later on, in the iPhone 12 and 13 Pro series). The implications of using this technology are mainly related to augmented and mixed reality applications, but its deployment for surveying tasks also seems promising. In particular, the potentialities of this miniaturized and low-cost sensor embedded in a mobile device have been assessed for documentation from the cultural heritage perspective—a domain where this solution may be particularly innovative. Over the last two years, an increasing number of mobile apps using the Apple LiDAR sensor for 3D data acquisition have been released. However, their performance and the 3D positional accuracy and precision of the acquired 3D point clouds have not yet been fully validated. Among the solutions available, as of September 2021, three iOS apps (SiteScape, EveryPoint, and 3D Scanner App) were tested. They were compared in different surveying scenarios, considering the overall accuracy of the sensor, the best acquisition strategies, the operational limitations, and the 3D positional accuracy of the final products achieved. Full article

(This article belongs to the Special Issue 3D Modeling and GIS for Archaeology and Cultural Heritage)

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19 pages, 15925 KB

Open AccessEditor’s ChoiceArticle

GeoDLS: A Deep Learning-Based Corn Disease Tracking and Location System Using RTK Geolocated UAS Imagery

by Aanis Ahmad, Varun Aggarwal, Dharmendra Saraswat, Aly El Gamal and Gurmukh S. Johal

Remote Sens. 2022, 14(17), 4140; https://doi.org/10.3390/rs14174140 - 23 Aug 2022

Cited by 14 | Viewed by 4147

Abstract

Deep learning-based solutions for precision agriculture have recently achieved promising results. Deep learning has been used to identify crop diseases at the initial stages of disease development in an effort to create effective disease management systems. However, the use of deep learning and [...] Read more.

Deep learning-based solutions for precision agriculture have recently achieved promising results. Deep learning has been used to identify crop diseases at the initial stages of disease development in an effort to create effective disease management systems. However, the use of deep learning and unmanned aerial system (UAS) imagery to track the spread of diseases, identify diseased regions within cornfields, and notify users with actionable information remains a research gap. Therefore, in this study, high-resolution, UAS-acquired, real-time kinematic (RTK) geotagged, RGB imagery at an altitude of 12 m above ground level (AGL) was used to develop the Geo Disease Location System (GeoDLS), a deep learning-based system for tracking diseased regions in corn fields. UAS images (resolution 8192 × 5460 pixels) were acquired in cornfields located at Purdue University’s Agronomy Center for Research and Education (ACRE), using a DJI Matrice 300 RTK UAS mounted with a 45-megapixel DJI Zenmuse P1 camera during corn stages V14 to R4. A dataset of 5076 images was created by splitting the UAS-acquired images using tile and simple linear iterative clustering (SLIC) segmentation. For tile segmentation, the images were split into tiles of sizes 250 × 250 pixels, 500 × 500 pixels, and 1000 × 1000 pixels, resulting in 1804, 1112, and 570 image tiles, respectively. For SLIC segmentation, 865 and 725 superpixel images were obtained using compactness (m) values of 5 and 10, respectively. Five deep neural network architectures, VGG16, ResNet50, InceptionV3, DenseNet169, and Xception, were trained to identify diseased, healthy, and background regions in corn fields. DenseNet169 identified diseased, healthy, and background regions with the highest testing accuracy of 100.00% when trained on images of tile size 1000 × 1000 pixels. Using a sliding window approach, the trained DenseNet169 model was then used to calculate the percentage of diseased regions present within each UAS image. Finally, the RTK geolocation information for each image was used to update users with the location of diseased regions with an accuracy of within 2 cm through a web application, a smartphone application, and email notifications. The GeoDLS could be a potential tool for an automated disease management system to track the spread of crop diseases, identify diseased regions, and provide actionable information to the users. Full article

(This article belongs to the Special Issue Advances of Remote Sensing in Precision Agriculture)

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17 pages, 5462 KB

Open AccessEditor’s ChoiceArticle

Radiological Surveillance Using a Fixed-Wing UAV Platform

by Stef Geelen, Johan Camps, Geert Olyslaegers, Greet Ilegems and Wouter Schroeyers

Remote Sens. 2022, 14(16), 3908; https://doi.org/10.3390/rs14163908 - 12 Aug 2022

Cited by 5 | Viewed by 5827

Abstract

A drone–detector system was designed, developed, and tested for radiological monitoring. The system was tailored to perform measurements during the threat, release, and post-release phases of a nuclear or radiological event. This allows the surveillance of large areas, with an autonomy of up [...] Read more.

A drone–detector system was designed, developed, and tested for radiological monitoring. The system was tailored to perform measurements during the threat, release, and post-release phases of a nuclear or radiological event. This allows the surveillance of large areas, with an autonomy of up to 12 h, in a large range of altitudes above ground level. The detector system was optimized for gamma spectroscopy, taking into account the available payload for maximum endurance and maximum detection efficiency using ‘PENELOPE (2018)’ Monte Carlo simulations. A generic methodology was used to derive quantitative information on radioactivity levels from the raw measured gamma-ray spectra at different altitudes. Based on the methodology, it was demonstrated that the drone–detector system can measure the concentration of potassium-40 (K-40) that is naturally present in the soil. These measurements complied within 30% of the soil sampling results taking into account the uncertainties. The functioning of the system was tested during test flights, which demonstrated that radionuclide identification and quantification of radioactivity concentrations are possible. Full article

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14 pages, 17655 KB

Open AccessEditor’s ChoiceArticle

Wide-Area GNSS Corrections for Precise Positioning and Navigation in Agriculture

by Manuel Hernández-Pajares, Germán Olivares-Pulido, Victoria Graffigna, Alberto García-Rigo, Haixia Lyu, David Roma-Dollase, M. Clara de Lacy, Carles Fernández-Prades, Javier Arribas, Marc Majoral, Zizis Tisropoulos, Panagiotis Stamatelopoulos, Machi Symeonidou, Michael Schmidt, Andreas Goss, Eren Erdogan, Frits K. van Evert, Pieter M. Blok, Juan Grosso, Emiliano Spaltro, Jacobo Domínguez, Esther López and Alina Hriscu Show full author list Hide full author list

Remote Sens. 2022, 14(16), 3845; https://doi.org/10.3390/rs14163845 - 9 Aug 2022

Cited by 6 | Viewed by 5474

Abstract

This paper characterizes, with static and roving GNSS receivers in the context of precision agriculture research, the hybrid ionospheric-geodetic GNSS model Wide-Area Real-Time Kinematics (WARTK), which computes and broadcasts real-time corrections for high-precision GNSS positioning and navigation within sparse GNSS receiver networks. This [...] Read more.

This paper characterizes, with static and roving GNSS receivers in the context of precision agriculture research, the hybrid ionospheric-geodetic GNSS model Wide-Area Real-Time Kinematics (WARTK), which computes and broadcasts real-time corrections for high-precision GNSS positioning and navigation within sparse GNSS receiver networks. This research is motivated by the potential benefits of the low-cost precise WARTK technique on mass-market applications such as precision agriculture. The results from two experiments summarized in this work, the second one involving a working spraying tractor, show, firstly, that the corrections from the model are in good agreement with the corrections provided by IGS (International GNSS Services) analysis centers computed in post-processing from global GNSS data. Moreover, secondly and most importantly, we have shown that WARTK provides navigation solutions at decimeter-level accuracy, and the ionospheric corrections significantly reduce the computational time for ambiguity estimation: up to convergence times for the 50%, 75% and 95% of cases equal or below 30 s (single-epoch), 150 s and 600 s approximately, vs. 1000 s, 2750 s and 4850 s without ionospheric corrections, everything for a roving receiver at more than 100 km far away from the nearest permanent receiver. The real-time horizontal position errors reach up to 3 cm, 5 cm and 12 cm for 50%, 75% and 95% of cases, respectively, by constraining and continuously updating the ambiguities without updating the permanent receiver coordinates, vs. the 6 cm, 12 cm and 32 cm, respectively, in the same conditions but without WARTK ionospheric corrections. Full article

(This article belongs to the Special Issue GNSS Atmospheric Modelling)

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22 pages, 18108 KB

Open AccessEditor’s ChoiceArticle

Video-Based Nearshore Bathymetric Inversion on a Geologically Constrained Mesotidal Beach during Storm Events

by Isaac Rodríguez-Padilla, Bruno Castelle, Vincent Marieu and Denis Morichon

Remote Sens. 2022, 14(16), 3850; https://doi.org/10.3390/rs14163850 - 9 Aug 2022

Cited by 9 | Viewed by 4177

Abstract

Although geologically constrained sandy beaches are ubiquitous along wave-exposed coasts, there is still a limited understanding of their morphological response, particularly under storm conditions, which is mainly due to a critical lack of nearshore bathymetry observations. This paper examines the potential to derive [...] Read more.

Although geologically constrained sandy beaches are ubiquitous along wave-exposed coasts, there is still a limited understanding of their morphological response, particularly under storm conditions, which is mainly due to a critical lack of nearshore bathymetry observations. This paper examines the potential to derive bathymetries from video imagery under challenging wave conditions in order to investigate headland control on morphological beach response. For this purpose, a video-based linear depth inversion algorithm is applied to three consecutive weeks of frames collected during daylight hours from a single fixed camera located at La Petite Chambre d’Amour beach (Anglet, SW France). Video-derived bathymetries are compared against in situ topo-bathymetric surveys carried out at the beginning and end of the field experiment in order to assess the performance of the bathymetric estimates. The results show that the rates of accretion/erosion within the surf zone are strongly influenced by the headland, whereas the beach morphological response can be classified into three main regimes depending on the angle of wave incidence

θ_{p}

: (1) under deflection configuration (

θ_{p} > 0 °

), the alongshore sediment transport was trapped at the updrift side of the headland, promoting sand accretion. (2) Under shadowed configuration (

θ_{p} < 0 °

), the interruption of the longshore current drove a deficit of sand supply at the downdrift side of the headland, leading to an overall erosion in the surf zone. (3) Under shore-normal configuration (

θ_{p} = 0 °

), rip channels developed, and up-state beach transition was observed. A comparison between video-derived bathymetries and surveys shows an overall root mean square error (RMSE) around 0.49 to 0.57 m with a bias ranging between −0.36 and −0.29 m. The results show that video-derived bathymetries can provide new insight into the morphological change driven by storm events. The combination of such inferred bathymetry with video-derived surface current data is discussed, showing great potential to address the coupled morphodynamics system under time-varying wave conditions. Full article

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31 pages, 10136 KB

Open AccessEditor’s ChoiceArticle

Sen2Like: Paving the Way towards Harmonization and Fusion of Optical Data

by Sébastien Saunier, Bringfried Pflug, Italo Moletto Lobos, Belen Franch, Jérôme Louis, Raquel De Los Reyes, Vincent Debaecker, Enrico G. Cadau, Valentina Boccia, Ferran Gascon and Sultan Kocaman

Remote Sens. 2022, 14(16), 3855; https://doi.org/10.3390/rs14163855 - 9 Aug 2022

Cited by 24 | Viewed by 7894

Abstract

Satellite Earth Observation (EO) sensors are becoming a vital source of information for land surface monitoring. The concept of the Virtual Constellation (VC) is gaining interest within the science community owing to the increasing number of satellites/sensors in operation with similar characteristics. The [...] Read more.

Satellite Earth Observation (EO) sensors are becoming a vital source of information for land surface monitoring. The concept of the Virtual Constellation (VC) is gaining interest within the science community owing to the increasing number of satellites/sensors in operation with similar characteristics. The establishment of a VC out of individual missions offers new possibilities for many application domains, in particular in the fields of land surface monitoring and change detection. In this context, this paper describes the Copernicus Sen2Like algorithms and software, a solution for harmonizing and fusing Landsat 8/Landsat 9 data with Sentinel-2 data. Developed under the European Union Copernicus Program, the Sen2Like software processes a large collection of Level 1/Level 2A products and generates high quality Level 2 Analysis Ready Data (ARD) as part of harmonized (Level 2H) and/or fused (Level 2F) products providing high temporal resolutions. For this purpose, we have re-used and developed a broad spectrum of data processing and analysis methodologies, including geometric and spectral co-registration, atmospheric and Bi-Directional Reflectance Distribution Function (BRDF) corrections and upscaling to 10 m for relevant Landsat bands. The Sen2Like software and the algorithms have been developed within a VC establishment framework, and the tool can conveniently be used to compare processing algorithms in combinations. It also has the potential to integrate new missions from spaceborne and airborne platforms including unmanned aerial vehicles. The validation activities show that the proposed approach improves the temporal consistency of the multi temporal data stack, and output products are interoperable with the subsequent thematic analysis processes. Full article

(This article belongs to the Section Biogeosciences Remote Sensing)

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32 pages, 12352 KB

Open AccessEditor’s ChoiceArticle

Global Mangrove Extent Change 1996–2020: Global Mangrove Watch Version 3.0

by Pete Bunting, Ake Rosenqvist, Lammert Hilarides, Richard M. Lucas, Nathan Thomas, Takeo Tadono, Thomas A. Worthington, Mark Spalding, Nicholas J. Murray and Lisa-Maria Rebelo

Remote Sens. 2022, 14(15), 3657; https://doi.org/10.3390/rs14153657 - 30 Jul 2022

Cited by 280 | Viewed by 32593

Abstract

Mangroves are a globally important ecosystem that provides a wide range of ecosystem system services, such as carbon capture and storage, coastal protection and fisheries enhancement. Mangroves have significantly reduced in global extent over the last 50 years, primarily as a result of [...] Read more.

Mangroves are a globally important ecosystem that provides a wide range of ecosystem system services, such as carbon capture and storage, coastal protection and fisheries enhancement. Mangroves have significantly reduced in global extent over the last 50 years, primarily as a result of deforestation caused by the expansion of agriculture and aquaculture in coastal environments. However, a limited number of studies have attempted to estimate changes in global mangrove extent, particularly into the 1990s, despite much of the loss in mangrove extent occurring pre-2000. This study has used L-band Synthetic Aperture Radar (SAR) global mosaic datasets from the Japan Aerospace Exploration Agency (JAXA) for 11 epochs from 1996 to 2020 to develop a long-term time-series of global mangrove extent and change. The study used a map-to-image approach to change detection where the baseline map (GMW v2.5) was updated using thresholding and a contextual mangrove change mask. This approach was applied between all image-date pairs producing 10 maps for each epoch, which were summarised to produce the global mangrove time-series. The resulting mangrove extent maps had an estimated accuracy of 87.4% (95th conf. int.: 86.2–88.6%), although the accuracies of the individual gain and loss change classes were lower at 58.1% (52.4–63.9%) and 60.6% (56.1–64.8%), respectively. Sources of error included misregistration in the SAR mosaic datasets, which could only be partially corrected for, but also confusion in fragmented areas of mangroves, such as around aquaculture ponds. Overall, 152,604 km² (133,996–176,910) of mangroves were identified for 1996, with this decreasing by −5245 km² (−13,587–1444) resulting in a total extent of 147,359 km² (127,925–168,895) in 2020, and representing an estimated loss of 3.4% over the 24-year time period. The Global Mangrove Watch Version 3.0 represents the most comprehensive record of global mangrove change achieved to date and is expected to support a wide range of activities, including the ongoing monitoring of the global coastal environment, defining and assessments of progress toward conservation targets, protected area planning and risk assessments of mangrove ecosystems worldwide. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Land-Sea Ecosystems)

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32 pages, 10402 KB

Open AccessEditor’s ChoiceArticle

A Spatial Long-Term Trend Analysis of Estimated Chlorophyll-a Concentrations in Utah Lake Using Earth Observation Data

by Kaylee Brook Tanner, Anna Catherine Cardall and Gustavious Paul Williams

Remote Sens. 2022, 14(15), 3664; https://doi.org/10.3390/rs14153664 - 30 Jul 2022

Cited by 11 | Viewed by 5502

Abstract

We analyzed chlorophyll-a (chl-a) concentrations in shallow, turbid Utah Lake using Landsat data from 1984 to 2021. Utah Lake is ~40 km by 21 km, has a surface area of ~390 km², an average depth of ~3 m, and loses ~50% [...] Read more.

We analyzed chlorophyll-a (chl-a) concentrations in shallow, turbid Utah Lake using Landsat data from 1984 to 2021. Utah Lake is ~40 km by 21 km, has a surface area of ~390 km², an average depth of ~3 m, and loses ~50% of inflow to evaporation. This limits spatial mixing, allowing us to evaluate impacts on smaller lake regions. We evaluated long-term trends at the pixel level and for areas related to boundary conditions. We created 17 study areas based on differences in shoreline development and nutrient inflows. We expected impacted areas to exhibit increasing chl-a trends, as population growth and development in the Utah Lake watershed have been significant. We used the non-parametric Mann–Kendall test to evaluate trends. The majority of the lake exhibited decreasing trends, with a few pixels in Provo and Goshen Bays exhibiting slight increasing or no trends. We estimated trend magnitudes using Sen’s slope and fitted linear regression models. Trend magnitudes in all pixels (and regions), both decreasing and increasing, were small; with the largest decreasing and increasing trends being about −0.05 and −0.005 µg/L/year, and about 0.1 and 0.005 µg/L/year for the Sen’s slope and linear regression slope, respectively. Over the ~40 year-period, this would result in average decreases of 2 to 0.2 µg/L or increases of 4 and 0.2 µg/L. All the areas exhibited decreasing trends, but the monthly trends in some areas exhibited no trends rather than decreasing trends. Monthly trends for some areas showed some indications that algal blooms are occurring earlier, though evidence is inconclusive. We found essentially no change in algal concentrations in Utah Lake at either the pixel level or for the analysis regions since the 1980′s; despite significant population expansion; increased nutrient inflows; and land-use changes. This result matches prior research and supports the hypothesis that algal growth in Utah Lake is not limited by direct nutrient inflows but limited by other factors. Full article

(This article belongs to the Special Issue Environmental Monitoring Using Satellite Remote Sensing)

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28 pages, 22500 KB

Open AccessEditor’s ChoiceArticle

The Influence of Image Properties on High-Detail SfM Photogrammetric Surveys of Complex Geometric Landforms: The Application of a Consumer-Grade UAV Camera in a Rock Glacier Survey

by Adrián Martínez-Fernández, Enrique Serrano, Alfonso Pisabarro, Manuel Sánchez-Fernández, José Juan de Sanjosé, Manuel Gómez-Lende, Gizéh Rangel-de Lázaro and Alfonso Benito-Calvo

Remote Sens. 2022, 14(15), 3528; https://doi.org/10.3390/rs14153528 - 23 Jul 2022

Cited by 8 | Viewed by 4829

Abstract

The detailed description of processing workflows in Structure from Motion (SfM) surveys using unmanned aerial vehicles (UAVs) is not common in geomorphological research. One of the aspects frequently overlooked in photogrammetric reconstruction is image characteristics. In this context, the present study aims to [...] Read more.

The detailed description of processing workflows in Structure from Motion (SfM) surveys using unmanned aerial vehicles (UAVs) is not common in geomorphological research. One of the aspects frequently overlooked in photogrammetric reconstruction is image characteristics. In this context, the present study aims to determine whether the format or properties (e.g., exposure, sharpening, lens corrections) of the images used in the SfM process can affect high-detail surveys of complex geometric landforms such as rock glaciers. For this purpose, images generated (DNG and JPEG) and derived (TIFF) from low-cost UAV systems widely used by the scientific community are applied. The case study is carried out through a comprehensive flight plan with ground control and differences among surveys are assessed visually and geometrically. Thus, geometric evaluation is based on 2.5D and 3D perspectives and a ground-based LiDAR benchmark. The results show that the lens profiles applied by some low-cost UAV cameras to the images can significantly alter the geometry among photo-reconstructions, to the extent that they can influence monitoring activities with variations of around ±5 cm in areas with close control and over ±20 cm (10 times the ground sample distance) on surfaces outside the ground control surroundings. The terrestrial position of the laser scanner measurements and the scene changing topography results in uneven surface sampling, which makes it challenging to determine which set of images best fit the LiDAR benchmark. Other effects of the image properties are found in minor variations scattered throughout the survey or modifications to the RGB values of the point clouds or orthomosaics, with no critical impact on geomorphological studies. Full article

(This article belongs to the Special Issue UAV Photogrammetry for Environmental Monitoring)

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27 pages, 28374 KB

Open AccessEditor’s ChoiceArticle

Spectral Analysis to Improve Inputs to Random Forest and Other Boosted Ensemble Tree-Based Algorithms for Detecting NYF Pegmatites in Tysfjord, Norway

by Douglas Santos, Joana Cardoso-Fernandes, Alexandre Lima, Axel Müller, Marco Brönner and Ana Cláudia Teodoro

Remote Sens. 2022, 14(15), 3532; https://doi.org/10.3390/rs14153532 - 23 Jul 2022

Cited by 48 | Viewed by 6429

Abstract

As an important source of lithium and rare earth elements (REE) and other critical elements, pegmatites are of great strategic economic interest for present and future technological development. Identifying new pegmatite deposits is a strategy adopted by the European Union (EU) to decrease [...] Read more.

As an important source of lithium and rare earth elements (REE) and other critical elements, pegmatites are of great strategic economic interest for present and future technological development. Identifying new pegmatite deposits is a strategy adopted by the European Union (EU) to decrease its import dependence on non-European countries for these raw materials. It is in this context that the GREENPEG project was established, an EU project whose main objective is to identify new deposits of pegmatites in Europe in an environmentally friendly way. Remote sensing is a non-contact exploration tool that allows for identifying areas of interest for exploration at the early stage of exploration campaigns. Several RS methods have been developed to identify Li-Cs-Ta (LCT) pegmatites, but in this study, a new methodology was developed to detect Nb-Y-F (NYF) pegmatites in the Tysfjord area in Norway. This methodology is based on spectral analysis to select bands of the Sentinel 2 satellite and adapt RS methods, such as Band Ratios and Principal Component Analysis (PCA), to be used as input in the Random Forest (RF) and other tree-based ensemble algorithms to improve the classification accuracy. The results obtained are encouraging, and the algorithm was able to successfully identify the pegmatite areas already known and new locations of interest for exploration were also defined. Full article

(This article belongs to the Special Issue New Trends on Remote Sensing Applications to Mineral Deposits)

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26 pages, 7602 KB

Open AccessEditor’s ChoiceArticle

JPSS VIIRS SST Reanalysis Version 3

by Olafur Jonasson, Alexander Ignatov, Victor Pryamitsyn, Boris Petrenko and Yury Kihai

Remote Sens. 2022, 14(14), 3476; https://doi.org/10.3390/rs14143476 - 20 Jul 2022

Cited by 9 | Viewed by 4554

Abstract

The 3rd full-mission reanalysis (RAN3) of global sea surface temperature (SST) with a 750 m resolution at nadir is available from VIIRS instruments flown onboard two JPSS satellites: NPP (February 2012–present) and N20 (January 2018–present). Two SSTs, ‘subskin’ (sensitive to skin SST) and [...] Read more.

The 3rd full-mission reanalysis (RAN3) of global sea surface temperature (SST) with a 750 m resolution at nadir is available from VIIRS instruments flown onboard two JPSS satellites: NPP (February 2012–present) and N20 (January 2018–present). Two SSTs, ‘subskin’ (sensitive to skin SST) and ‘depth’ (proxy for in situ SST at depth of 20 cm), were produced from brightness temperatures (BTs) in the VIIRS bands centered at 8.6, 11 and 12 µm during the daytime and an additional 3.7 µm band at night, using the NOAA Advanced Clear Sky Processor for Ocean (ACSPO) enterprise SST system. The RAN3 dataset is fully archived at NASA JPL PO.DAAC and NOAA CoastWatch, and routinely supplemented in near real time (NRT) with a latency of a few hours. Delayed mode (DM) processing with a 2 months latency follows NRT, resulting in a more uniform science quality SST record. This paper documents and evaluates the performance of the VIIRS RAN3 dataset. Comparisons with in situ SSTs from drifters and tropical moorings (D+TM) as well as Argo floats (AFs) (both available from the NOAA iQuam system) show good agreement, generally within the NOAA specifications for accuracy (±0.2 K) and precision (0.6 K), in a clear-sky domain covering 18–20% of the global ocean. The nighttime SSTs compare with in situ data more closely, as expected due to the reduced diurnal thermocline. The daytime SSTs are also generally within NOAA specs but show some differences between the (D+TM) and AF validations as well as residual drift on the order of −0.1 K/decade. BT comparisons between two VIIRSs and MODIS-Aqua show good consistency in the 3.7 and 12 µm bands. The 11 µm band, while consistent between NPP and N20, shows residual drift with respect to MODIS-Aqua. Similar analyses of the 8.6 µm band are inconclusive, as the performance of the MODIS band 29 centered at 8.6 µm is degraded and unstable in time and cannot be used for comparisons. Full article

(This article belongs to the Special Issue VIIRS 2011–2021: Ten Years of Success in Earth Observations)

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15 pages, 2158 KB

Open AccessEditor’s ChoiceArticle

An Investigation of the Ice Cloud Detection Sensitivity of Cloud Radars Using the Raman Lidar at the ARM SGP Site

by Mingcheng Wang, Kelly A. Balmes, Tyler J. Thorsen, Dylan Willick and Qiang Fu

Remote Sens. 2022, 14(14), 3466; https://doi.org/10.3390/rs14143466 - 19 Jul 2022

Viewed by 2208

Abstract

The ice cloud detection sensitivity of the millimeter cloud radar (MMCR) and the Ka-band Zenith radar (KAZR) is investigated using a collocated Raman lidar (RL) at the Atmospheric Radiation Measurement Program Southern Great Plains site. Only profiles that are transparent to the RL [...] Read more.

The ice cloud detection sensitivity of the millimeter cloud radar (MMCR) and the Ka-band Zenith radar (KAZR) is investigated using a collocated Raman lidar (RL) at the Atmospheric Radiation Measurement Program Southern Great Plains site. Only profiles that are transparent to the RL with ice clouds only are considered in this study. The MMCR underestimates the RL ice cloud optical depth (COD) by 20%. The MMCR detects no ice clouds in 37% of the profiles. These profiles where ice cloud goes undetected by the MMCR typically contain very optically thin clouds, with a mean RL ice COD of 0.03. Higher ice cloud detection sensitivity is found for the KAZR, which underestimates the RL ice COD by 15%. The decrease in the ice COD bias for the KAZR compared to the MMCR is largely due to a decrease in the ice COD bias for the situation where the transparent profiles with ice clouds are detected by both the RL and cloud radar. The climatic net ice cloud radiative effects (CREs) from the RL at the top of the atmosphere (TOA) and the surface are 3.2 W m⁻² and −0.6 W m⁻², respectively. The ice CREs at the TOA and surface are underestimated for the MMCR by 0.7 W m⁻² and 0.16 W m⁻² (21% and 29%) and underestimated for the KAZR by 0.6 W m⁻² and 0.14 W m⁻² (17% and 24%). The ice clouds undetected by the cloud radars led to underestimating the climatic net cloud heating rates below 150 hPa by about 0–0.04 K day⁻¹. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Terrestrial Atmosphere)

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27 pages, 11106 KB

Open AccessEditor’s ChoiceArticle

Aerosol Mineralogical Study Using Laboratory and IASI Measurements: Application to East Asian Deserts

by Perla Alalam, Lise Deschutter, Antoine Al Choueiry, Denis Petitprez and Hervé Herbin

Remote Sens. 2022, 14(14), 3422; https://doi.org/10.3390/rs14143422 - 16 Jul 2022

Cited by 6 | Viewed by 2947

Abstract

East Asia is the second-largest mineral dust source in the world, after the Sahara. When dispersed in the atmosphere, mineral dust can alter the Earth’s radiation budget by changing the atmosphere’s absorption and scattering properties. Therefore, the mineralogical composition of dust is key [...] Read more.

East Asia is the second-largest mineral dust source in the world, after the Sahara. When dispersed in the atmosphere, mineral dust can alter the Earth’s radiation budget by changing the atmosphere’s absorption and scattering properties. Therefore, the mineralogical composition of dust is key to understanding the impact of mineral dust on the atmosphere. This paper presents new information on mineralogical dust during East Asian dust events that were obtained from laboratory dust measurements combined with satellite remote sensing dust detections from the Infrared Atmospheric Sounding Interferometer (IASI). However, the mineral dust in this region is lifted above the continent in the lower troposphere, posing constraints due to the large variability in the Land Surface Emissivity (LSE). First, a new methodology was developed to correct the LSE from a mean monthly emissivity dataset. The results show an adjustment in the IASI spectra by acquiring aerosol information. Then, the experimental extinction coefficients of pure minerals were linearly combined to reproduce a Gobi dust spectrum, which allowed for the determination of the mineralogical mass weights. In addition, from the IASI radiances, a spectral dust optical thickness was calculated, displaying features identical to the optical thickness of the Gobi dust measured in the laboratory. The linear combination of pure minerals spectra was also applied to the IASI optical thickness, providing mineralogical mass weights. Finally, the method was applied after LSE optimization, and mineralogical evolution maps were obtained for two dust events in two different seasons and years, May 2017 and March 2021. The mean dust weights originating from the Gobi Desert, Taklamakan Desert, and Horqin Sandy Land are close to the mass weights in the literature. In addition, the spatial variability was linked to possible dust sources, and it was examined with a backward trajectory model. Moreover, a comparison between two IASI instruments on METOP-A and -B proved the method’s applicability to different METOP platforms. Due to all of the above, the applied method is a powerful tool for exploiting dust mineralogy and dust sources using both laboratory optical properties and IASI detections. Full article

(This article belongs to the Special Issue Remote Sensing of Atmospheric Aerosol Using Spaceborne Observations)

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19 pages, 2235 KB

Open AccessEditor’s ChoiceArticle

Evaluation of Global Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Products at 500 m Spatial Resolution

by Yajie Zheng, Zhiqiang Xiao, Juan Li, Hua Yang and Jinling Song

Remote Sens. 2022, 14(14), 3304; https://doi.org/10.3390/rs14143304 - 8 Jul 2022

Cited by 6 | Viewed by 2959

Abstract

The fraction of absorbed photosynthetically active radiation (FAPAR) is a key biophysical variable directly associated with the photosynthetic activity of plants. Several global FAPAR products with different spatial resolutions have been generated from remote sensing data, and much work has focused on validating [...] Read more.

The fraction of absorbed photosynthetically active radiation (FAPAR) is a key biophysical variable directly associated with the photosynthetic activity of plants. Several global FAPAR products with different spatial resolutions have been generated from remote sensing data, and much work has focused on validating them. However, those studies have primarily evaluated global FAPAR products at a spatial resolution of 1 km or more, whereas few studies have evaluated the global 500 m resolution FAPAR product distributed in recent years. Furthermore, there are a few FAPAR products, including black-sky, white-sky and blue-sky FAPAR datasets, and almost no studies have evaluated these products. In this article, three global FAPAR products at 500 m resolution, namely MODIS (only black-sky FAPAR), MUSES and EBR (black-sky, white-sky and blue-sky FAPAR) were compared to evaluate their temporal and spatial discrepancies and direct validation was conducted to compare these FAPAR products with the FAPAR values derived from the high-resolution reference maps from the Validation of Land European Remote Sensing Instrument (VALERI) and Implementing Multi-Scale Agricultural Indicators Exploiting Sentinels (IMAGINES) projects. The results showed that the MUSES FAPAR product exhibited the best spatial integrity, whereas the MODIS and EBR FAPAR products had many missing pixels in the equatorial rainforest regions and at high latitudes in the Northern Hemisphere. The MODIS, MUSES and EBR FAPAR products were generally consistent in their spatial patterns. However, a relatively large discrepancy among these FAPAR products was present in the equatorial rainforest regions and the middle and high latitude regions where the main vegetation type was forest. The differences between the black-sky and white-sky FAPAR datasets at the global scale were significant. In January, the MUSES and EBR black-sky FAPAR values were larger than their white-sky FAPAR values in the region north of 30° north latitude but they were smaller than their white-sky FAPAR values in the region south of 30° north latitude. In July, the MUSES and EBR black-sky FAPAR values were lower than their white-sky FAPAR values in the region north of 30° south latitude and they were larger than their white-sky FAPAR values in the region south of 30° south latitude. The temporal profiles of the MUSES FAPAR product were continuous and smooth, whereas those of the MODIS and EBR FAPAR products showed many fluctuations, particularly during the growing seasons. Direct validation indicated that the MUSES FAPAR product had the best accuracy (R² = 0.6932, RMSE = 0.1495) compared to the MODIS FAPAR product (R² = 0.6202, RMSE = 0.1710) and the EBR FAPAR product (R² = 0.5746, RMSE = 0.1912). Full article

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110 pages, 11090 KB

Open AccessEditor’s ChoiceReview

Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review

by Liping Yang, Joshua Driscol, Sarigai Sarigai, Qiusheng Wu, Haifei Chen and Christopher D. Lippitt

Remote Sens. 2022, 14(14), 3253; https://doi.org/10.3390/rs14143253 - 6 Jul 2022

Cited by 164 | Viewed by 49876

Abstract

Remote sensing (RS) plays an important role gathering data in many critical domains (e.g., global climate change, risk assessment and vulnerability reduction of natural hazards, resilience of ecosystems, and urban planning). Retrieving, managing, and analyzing large amounts of RS imagery poses substantial challenges. [...] Read more.

Remote sensing (RS) plays an important role gathering data in many critical domains (e.g., global climate change, risk assessment and vulnerability reduction of natural hazards, resilience of ecosystems, and urban planning). Retrieving, managing, and analyzing large amounts of RS imagery poses substantial challenges. Google Earth Engine (GEE) provides a scalable, cloud-based, geospatial retrieval and processing platform. GEE also provides access to the vast majority of freely available, public, multi-temporal RS data and offers free cloud-based computational power for geospatial data analysis. Artificial intelligence (AI) methods are a critical enabling technology to automating the interpretation of RS imagery, particularly on object-based domains, so the integration of AI methods into GEE represents a promising path towards operationalizing automated RS-based monitoring programs. In this article, we provide a systematic review of relevant literature to identify recent research that incorporates AI methods in GEE. We then discuss some of the major challenges of integrating GEE and AI and identify several priorities for future research. We developed an interactive web application designed to allow readers to intuitively and dynamically review the publications included in this literature review. Full article

(This article belongs to the Special Issue The Future of Remote Sensing: Harnessing the Data Revolution)

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33 pages, 6146 KB

Open AccessEditor’s ChoiceArticle

Empirical Models for Estimating Air Temperature Using MODIS Land Surface Temperature (and Spatiotemporal Variables) in the Hurd Peninsula of Livingston Island, Antarctica, between 2000 and 2016

by Carmen Recondo, Alejandro Corbea-Pérez, Juanjo Peón, Enrique Pendás, Miguel Ramos, Javier F. Calleja, Miguel Ángel de Pablo, Susana Fernández and José Antonio Corrales

Remote Sens. 2022, 14(13), 3206; https://doi.org/10.3390/rs14133206 - 4 Jul 2022

Cited by 12 | Viewed by 3448

Abstract

In this article, we present empirical models for estimating daily mean air temperature (T_a) in the Hurd Peninsula of Livingston Island (Antarctica) using Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) data and spatiotemporal variables. The models were obtained [...] Read more.

In this article, we present empirical models for estimating daily mean air temperature (T_a) in the Hurd Peninsula of Livingston Island (Antarctica) using Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) data and spatiotemporal variables. The models were obtained and validated using the daily mean T_a from three Spanish in situ meteorological stations (AEMET stations), Juan Carlos I (JCI), Johnsons Glacier (JG), and Hurd Glacier (HG), and three stations in our team’s monitoring sites, Incinerador (INC), Reina Sofía (SOF), and Collado Ramos (CR), as well as daytime and nighttime Terra-MODIS LST and Aqua-MODIS LST data between 2000 and 2016. Two types of multiple linear regression (MLR) models were obtained: models for each individual station (for JCI, INC, SOF, and CR—not for JG and HG due to a lack of data) and global models using all stations. In the study period, the JCI and INC stations were relocated, so we analyzed the data from both locations separately (JCI1 and JCI2; INC1 and INC2). In general, the best individual T_a models were obtained using daytime Terra LST data, the best results for CR being followed by JCI2, SOF, and INC2 (R² = 0.5–0.7 and RSE = 2 °C). Model cross validation (CV) yielded results similar to those of the models (for the daytime Terra LST data: R²_CV = 0.4–0.6, RMSE_CV = 2.5–2.7 °C, and bias = −0.1 to 0.1 °C). The best global T_a model was also obtained using daytime Terra LST data (R² = 0.6 and RSE = 2 °C; in its validation: R²_CV = 0.5, RMSE_CV = 3, and bias = −0.03), along with the significant (p < 0.05) variables: linear time (t) and two time harmonics (sine-cosine), distance to the coast (d), slope (s), curvature (c), and hour of LST observation (H). T_a and LST data were carefully corrected and filtered, respectively, prior to its analysis and comparison. The analysis of the T_a time series revealed different cooling/warming trends in the locations, indicating a complex climatic variability at a spatial scale in the Hurd Peninsula. The variation of T_a in each station was obtained by the Locally Weighted Regression (LOESS) method. LST data that was not “good quality” usually underestimated T_a and were filtered, which drastically reduced the LST data (<5% of the studied days). Despite the shortage of “good” MODIS LST data in these cold environments, all months were represented in the final dataset, demonstrating that the MODIS LST data, through the models obtained in this article, are useful for estimating long-term trends in T_a and generating mean T_a maps at a global level (1 km² spatial resolution) in the Hurd Peninsula of Livingston Island. Full article

(This article belongs to the Special Issue Remote Sensing Monitoring of Land Surface Temperature (LST) II)

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21 pages, 10826 KB

Open AccessEditor’s ChoiceArticle

Subsurface Temperature Reconstruction for the Global Ocean from 1993 to 2020 Using Satellite Observations and Deep Learning

by Hua Su, Jinwen Jiang, An Wang, Wei Zhuang and Xiao-Hai Yan

Remote Sens. 2022, 14(13), 3198; https://doi.org/10.3390/rs14133198 - 3 Jul 2022

Cited by 38 | Viewed by 6869

Abstract

The reconstruction of the ocean’s 3D thermal structure is essential to the study of ocean interior processes and global climate change. Satellite remote sensing technology can collect large-scale, high-resolution ocean observation data, but only at the surface layer. Based on empirical statistical and [...] Read more.

The reconstruction of the ocean’s 3D thermal structure is essential to the study of ocean interior processes and global climate change. Satellite remote sensing technology can collect large-scale, high-resolution ocean observation data, but only at the surface layer. Based on empirical statistical and artificial intelligence models, deep ocean remote sensing techniques allow us to retrieve and reconstruct the 3D ocean temperature structure by combining surface remote sensing observations with in situ float observations. This study proposed a new deep learning method, Convolutional Long Short-Term Memory (ConvLSTM) neural networks, which combines multisource remote sensing observations and Argo gridded data to reconstruct and produce a new long-time-series global ocean subsurface temperature (ST) dataset for the upper 2000 m from 1993 to 2020, which is named the Deep Ocean Remote Sensing (DORS) product. The data-driven ConvLSTM model can learn the spatiotemporal features of ocean observation data, significantly improves the model’s robustness and generalization ability, and outperforms the LighGBM model for the data reconstruction. The validation results show our DORS dataset has high accuracy with an average R² and RMSE of 0.99/0.34 °C compared to the Argo gridded dataset, and the average R² and NRMSE validated by the EN4-Profile dataset over the time series are 0.94/0.05 °C. Furthermore, the ST structure between DORS and Argo has good consistency in the 3D spatial morphology and distribution pattern, indicating that the DORS dataset has high quality and strong reliability, and well fills the pre-Argo data gaps. We effectively track the global ocean warming in the upper 2000 m from 1993 to 2020 based on the DORS dataset, and we further examine and understand the spatial patterns, evolution trends, and vertical characteristics of global ST changes. From 1993 to 2020, the average global ocean temperature warming trend is 0.063 °C/decade for the upper 2000 m. The 3D temperature trends revealed significant spatial heterogeneity across different ocean basins. Since 2005, the warming signal has become more significant in the subsurface and deeper ocean. From a remote sensing standpoint, the DORS product can provide new and robust data support for ocean interior process and climate change studies. Full article

(This article belongs to the Section Ocean Remote Sensing)

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25 pages, 5811 KB

Open AccessEditor’s ChoiceArticle

Using a UAV Thermal Infrared Camera for Monitoring Floating Marine Plastic Litter

by Lonneke Goddijn-Murphy, Benjamin J. Williamson, Jason McIlvenny and Paolo Corradi

Remote Sens. 2022, 14(13), 3179; https://doi.org/10.3390/rs14133179 - 2 Jul 2022

Cited by 25 | Viewed by 7031

Abstract

In recent years, the remote sensing of marine plastic litter has been rapidly evolving and the technology is most advanced in the visible (VIS), near-infrared (NIR), and short-wave infrared (SWIR) wavelengths. It has become clear that sensing using VIS-SWIR bands, based on the [...] Read more.

In recent years, the remote sensing of marine plastic litter has been rapidly evolving and the technology is most advanced in the visible (VIS), near-infrared (NIR), and short-wave infrared (SWIR) wavelengths. It has become clear that sensing using VIS-SWIR bands, based on the surface reflectance of sunlight, would benefit from complementary measurements using different technologies. Thermal infrared (TIR) sensing shows potential as a novel method for monitoring macro plastic litter floating on the water surface, as the physics behind surface-leaving TIR is different. We assessed a thermal radiance model for floating plastic litter using a small UAV-grade FLIR Vue Pro R 640 thermal camera by flying it over controlled floating plastic litter targets during the day and night and in different seasons. Experiments in the laboratory supported the field measurements. We investigated the effects of environmental conditions, such as temperatures, light intensity, the presence of clouds, and biofouling. TIR sensing could complement observations from VIS, NIR, and SWIR in several valuable ways. For example, TIR sensing could be used for monitoring during the night, to detect plastics invisible to VIS-SWIR, to discriminate whitecaps from marine litter, and to detect litter pollution over clear, shallow waters. In this study, we have shown the previously unconfirmed potential of using TIR sensing for monitoring floating plastic litter. Full article

(This article belongs to the Special Issue Remote Sensing of Plastic Pollution)

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32 pages, 10477 KB

Open AccessEditor’s ChoiceArticle

New Reprocessing towards Life-Time Quality-Consistent Suomi NPP OMPS Nadir Sensor Data Records (SDR): Calibration Improvements and Impact Assessments on Long-Term Quality Stability of OMPS SDR Data Sets

by Banghua Yan, Chunhui Pan, Trevor Beck, Xin Jin, Likun Wang, Ding Liang, Lawrence Flynn, Junye Chen, Jingfeng Huang, Steven Buckner, Cheng-Zhi Zou, Ninghai Sun, Lin Lin, Alisa Young, Lihang Zhou and Wei Hao

Remote Sens. 2022, 14(13), 3125; https://doi.org/10.3390/rs14133125 - 29 Jun 2022

Cited by 4 | Viewed by 2842

Abstract

The Nadir Mapper (NM) and Nadir Profiler (NP) within the Ozone Mapping and Profiler Suites (OMPS) are ultraviolet spectrometers to measure Earth radiance and Solar irradiance spectra from 300–380 nm and 250–310 nm, respectively. The OMPS NM and NP instruments flying on the [...] Read more.

The Nadir Mapper (NM) and Nadir Profiler (NP) within the Ozone Mapping and Profiler Suites (OMPS) are ultraviolet spectrometers to measure Earth radiance and Solar irradiance spectra from 300–380 nm and 250–310 nm, respectively. The OMPS NM and NP instruments flying on the Suomi-NPP (SNPP) satellite have provided over ten years of operational Sensor Data Records (SDRs) data sets to support a variety of OMPS Environmental Data Record (EDR) applications. However, the discrepancies of quality remain in the operational OMPS SDR data prior to 28 June 2021 due to changes in calibration algorithms associated with the calibration coefficient look-up tables (LUTs) during this period. In this study, we present results for the newly (

v_{2}

) reprocessed SNPP OMPS NM and NP SDR data prior to 30 June 2021, which uses consistent calibration tables with improved accuracy. Compared with a previous (

v_{1}

) reprocessing, this new reprocessing includes the improvements associated with the following updated tables or error correction: an updated stray light correction table for the NM, an off-nadir geolocation error correction for the NM, an artificial offset error correction in the NM dark processing code, and biweekly solar wavelength LUTs for the NP. This study further analyzes the impact of each improvement on the quality of the OMPS SDR data by taking advantage of the existing OMPS SDR calibration/validation studies. Finally, this study compares the

v_{2}

reprocessed OMPS data sets with the operational and the

v_{1}

reprocessed data sets. The results demonstrate that the new reprocessing significantly improves the accuracy and consistency of the life-time SNPP OMPS NM and NP SDR data sets. It also advances the uniformity of the data over the dichroic range from 300 to 310 nm between the NM and NP. The normalized radiance differences at the same wavelength between the NM and NP observations are reduced from 0.001 order (

v_{1}

reprocessing) or 0.01 order (operational processing) to 0.001 order or smaller. The

v_{2}

reprocessed data are archived in the NOAA CLASS data center with the same format as the operational data. Full article

(This article belongs to the Section Satellite Missions for Earth and Planetary Exploration)

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27 pages, 9694 KB

Open AccessEditor’s ChoiceArticle

ELULC-10, a 10 m European Land Use and Land Cover Map Using Sentinel and Landsat Data in Google Earth Engine

by S. Mohammad Mirmazloumi, Mohammad Kakooei, Farzane Mohseni, Arsalan Ghorbanian, Meisam Amani, Michele Crosetto and Oriol Monserrat

Remote Sens. 2022, 14(13), 3041; https://doi.org/10.3390/rs14133041 - 24 Jun 2022

Cited by 29 | Viewed by 8830

Abstract

Land Use/Land Cover (LULC) maps can be effectively produced by cost-effective and frequent satellite observations. Powerful cloud computing platforms are emerging as a growing trend in the high utilization of freely accessible remotely sensed data for LULC mapping over large-scale regions using big [...] Read more.

Land Use/Land Cover (LULC) maps can be effectively produced by cost-effective and frequent satellite observations. Powerful cloud computing platforms are emerging as a growing trend in the high utilization of freely accessible remotely sensed data for LULC mapping over large-scale regions using big geodata. This study proposes a workflow to generate a 10 m LULC map of Europe with nine classes, ELULC-10, using European Sentinel-1/-2 and Landsat-8 images, as well as the LUCAS reference samples. More than 200 K and 300 K of in situ surveys and images, respectively, were employed as inputs in the Google Earth Engine (GEE) cloud computing platform to perform classification by an object-based segmentation algorithm and an Artificial Neural Network (ANN). A novel ANN-based data preparation was also presented to remove noisy reference samples from the LUCAS dataset. Additionally, the map was improved using several rule-based post-processing steps. The overall accuracy and kappa coefficient of 2021 ELULC-10 were 95.38% and 0.94, respectively. A detailed report of the classification accuracies was also provided, demonstrating an accurate classification of different classes, such as Woodland and Cropland. Furthermore, rule-based post processing improved LULC class identifications when compared with current studies. The workflow could also supply seasonal, yearly, and change maps considering the proposed integration of complex machine learning algorithms and large satellite and survey data. Full article

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17 pages, 11005 KB

Open AccessEditor’s ChoiceArticle

Using Support Vector Machine (SVM) with GPS Ionospheric TEC Estimations to Potentially Predict Earthquake Events

by Saed Asaly, Lee-Ad Gottlieb, Nimrod Inbar and Yuval Reuveni

Remote Sens. 2022, 14(12), 2822; https://doi.org/10.3390/rs14122822 - 12 Jun 2022

Cited by 42 | Viewed by 13589

Abstract

There are significant controversies surrounding the detection of precursors that may precede earthquakes. Natural hazard signatures associated with strong earthquakes can appear in the lithosphere, troposphere, and ionosphere, where current remote sensing technologies have become valuable tools for detecting and measuring early warning [...] Read more.

There are significant controversies surrounding the detection of precursors that may precede earthquakes. Natural hazard signatures associated with strong earthquakes can appear in the lithosphere, troposphere, and ionosphere, where current remote sensing technologies have become valuable tools for detecting and measuring early warning signals of stress build-up deep in the Earth’s crust (presumably associated with earthquake events). Here, we propose implementing a machine learning support vector machine (SVM) technique, applied with GPS ionospheric total electron content (TEC) pre-processed time series estimations, to evaluate potential precursors caused by earthquakes and manifested as disturbances in the TEC data. After filtering and screening our data for solar or geomagnetic influences at different time scales, our results indicate that for large earthquakes (>Mw 6), true negative predictions can be achieved with 85.7% accuracy, and true positive predictions with an accuracy of 80%. We tested our method with different skill scores, such as accuracy (0.83), precision (0.85), recall (0.8), the Heidke skill score (0.66), and true skill statistics (0.66). Full article

(This article belongs to the Special Issue Assessing Natural Hazards through Advanced Machine Learning Methods and Remote Sensing Technology)

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23 pages, 10044 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Urban Land Use and Land Cover Change Analysis Using Random Forest Classification of Landsat Time Series

by Saeid Amini, Mohsen Saber, Hamidreza Rabiei-Dastjerdi and Saeid Homayouni

Remote Sens. 2022, 14(11), 2654; https://doi.org/10.3390/rs14112654 - 1 Jun 2022

Cited by 197 | Viewed by 20315

Abstract

Efficient implementation of remote sensing image classification can facilitate the extraction of spatiotemporal information for land use and land cover (LULC) classification. Mapping LULC change can pave the way to investigate the impacts of different socioeconomic and environmental factors on the Earth’s surface. [...] Read more.

Efficient implementation of remote sensing image classification can facilitate the extraction of spatiotemporal information for land use and land cover (LULC) classification. Mapping LULC change can pave the way to investigate the impacts of different socioeconomic and environmental factors on the Earth’s surface. This study presents an algorithm that uses Landsat time-series data to analyze LULC change. We applied the Random Forest (RF) classifier, a robust classification method, in the Google Earth Engine (GEE) using imagery from Landsat 5, 7, and 8 as inputs for the 1985 to 2019 period. We also explored the performance of the pan-sharpening algorithm on Landsat bands besides the impact of different image compositions to produce a high-quality LULC map. We used a statistical pan-sharpening algorithm to increase multispectral Landsat bands’ (Landsat 7–9) spatial resolution from 30 m to 15 m. In addition, we checked the impact of different image compositions based on several spectral indices and other auxiliary data such as digital elevation model (DEM) and land surface temperature (LST) on final classification accuracy based on several spectral indices and other auxiliary data on final classification accuracy. We compared the classification result of our proposed method and the Copernicus Global Land Cover Layers (CGLCL) map to verify the algorithm. The results show that: (1) Using pan-sharpened top-of-atmosphere (TOA) Landsat products can produce more accurate results for classification instead of using surface reflectance (SR) alone; (2) LST and DEM are essential features in classification, and using them can increase final accuracy; (3) the proposed algorithm produced higher accuracy (94.438% overall accuracy (OA), 0.93 for Kappa, and 0.93 for F1-score) than CGLCL map (84.4% OA, 0.79 for Kappa, and 0.50 for F1-score) in 2019; (4) the total agreement between the classification results and the test data exceeds 90% (93.37–97.6%), 0.9 (0.91–0.96), and 0.85 (0.86–0.95) for OA, Kappa values, and F1-score, respectively, which is acceptable in both overall and Kappa accuracy. Moreover, we provide a code repository that allows classifying Landsat 4, 5, 7, and 8 within GEE. This method can be quickly and easily applied to other regions of interest for LULC mapping. Full article

(This article belongs to the Special Issue Urban Sensing Methods and Technologies)

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24 pages, 3501 KB

Open AccessEditor’s ChoiceArticle

SARCASTIC v2.0—High-Performance SAR Simulation for Next-Generation ATR Systems

by Michael Woollard, David Blacknell, Hugh Griffiths and Matthew A. Ritchie

Remote Sens. 2022, 14(11), 2561; https://doi.org/10.3390/rs14112561 - 27 May 2022

Cited by 15 | Viewed by 6918

Abstract

Synthetic aperture radar has been a mainstay of the remote sensing field for many years, with a wide range of applications across both civilian and military contexts. However, the lack of openly available datasets of comparable size and quality to those available for [...] Read more.

Synthetic aperture radar has been a mainstay of the remote sensing field for many years, with a wide range of applications across both civilian and military contexts. However, the lack of openly available datasets of comparable size and quality to those available for optical imagery has severely hampered work on open problems such as automatic target recognition, image understanding and inverse modelling. This paper presents a simulation and analysis framework based on the upgraded SARCASTIC v2.0 engine, along with a selection of case studies demonstrating its application to well-known and novel problems. In particular, we demonstrate that SARCASTIC v2.0 is capable of supporting complex phase-dependent processing such as interferometric height extraction whilst maintaining near-realtime performance on complex scenes. Full article

(This article belongs to the Special Issue New Technologies for Earth Remote Sensing)

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26 pages, 9524 KB

Open AccessEditor’s ChoiceArticle

Object Tracking and Geo-Localization from Street Images

by Daniel Wilson, Thayer Alshaabi, Colin Van Oort, Xiaohan Zhang, Jonathan Nelson and Safwan Wshah

Remote Sens. 2022, 14(11), 2575; https://doi.org/10.3390/rs14112575 - 27 May 2022

Cited by 13 | Viewed by 7486

Abstract

Object geo-localization from images is crucial to many applications such as land surveying, self-driving, and asset management. Current visual object geo-localization algorithms suffer from hardware limitations and impractical assumptions limiting their usability in real-world applications. Most of the current methods assume object sparsity, [...] Read more.

Object geo-localization from images is crucial to many applications such as land surveying, self-driving, and asset management. Current visual object geo-localization algorithms suffer from hardware limitations and impractical assumptions limiting their usability in real-world applications. Most of the current methods assume object sparsity, the presence of objects in at least two frames, and most importantly they only support a single class of objects. In this paper, we present a novel two-stage technique that detects and geo-localizes dense, multi-class objects such as traffic signs from street videos. Our algorithm is able to handle low frame rate inputs in which objects might be missing in one or more frames. We propose a detector that is not only able to detect objects in images, but also predicts a positional offset for each object relative to the camera GPS location. We also propose a novel tracker algorithm that is able to track a large number of multi-class objects. Many current geo-localization datasets require specialized hardware, suffer from idealized assumptions not representative of reality, and are often not publicly available. In this paper, we propose a public dataset called ARTSv2, which is an extension of ARTS dataset that covers a diverse set of roads in widely varying environments to ensure it is representative of real-world scenarios. Our dataset will both support future research and provide a crucial benchmark for the field. Full article

(This article belongs to the Topic Computational Intelligence in Remote Sensing)

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29 pages, 15239 KB

Open AccessEditor’s ChoiceReview

Detailed Three-Dimensional Building Façade Reconstruction: A Review on Applications, Data and Technologies

by Anna Klimkowska, Stefano Cavazzi, Richard Leach and Stephen Grebby

Remote Sens. 2022, 14(11), 2579; https://doi.org/10.3390/rs14112579 - 27 May 2022

Cited by 19 | Viewed by 6625

Abstract

Urban environments are regions of complex and diverse architecture. Their reconstruction and representation as three-dimensional city models have attracted the attention of many researchers and industry specialists, as they increasingly recognise the potential for new applications requiring detailed building models. Nevertheless, despite being [...] Read more.

Urban environments are regions of complex and diverse architecture. Their reconstruction and representation as three-dimensional city models have attracted the attention of many researchers and industry specialists, as they increasingly recognise the potential for new applications requiring detailed building models. Nevertheless, despite being investigated for a few decades, the comprehensive reconstruction of buildings remains a challenging task. While there is a considerable body of literature on this topic, including several systematic reviews summarising ways of acquiring and reconstructing coarse building structures, there is a paucity of in-depth research on the detection and reconstruction of façade openings (i.e., windows and doors). In this review, we provide an overview of emerging applications, data acquisition and processing techniques for building façade reconstruction, emphasising building opening detection. The use of traditional technologies from terrestrial and aerial platforms, along with emerging approaches, such as mobile phones and volunteered geography information, is discussed. The current status of approaches for opening detection is then examined in detail, separated into methods for three-dimensional and two-dimensional data. Based on the review, it is clear that a key limitation associated with façade reconstruction is process automation and the need for user intervention. Another limitation is the incompleteness of the data due to occlusion, which can be reduced by data fusion. In addition, the lack of available diverse benchmark datasets and further investigation into deep-learning methods for façade openings extraction present crucial opportunities for future research. Full article

(This article belongs to the Special Issue 3D Urban Modeling by Fusion of Lidar Point Clouds and Optical Imagery)

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24 pages, 1375 KB

Open AccessEditor’s ChoiceArticle

Green Area Index and Soil Moisture Retrieval in Maize Fields Using Multi-Polarized C- and L-Band SAR Data and the Water Cloud Model

by Jean Bouchat, Emma Tronquo, Anne Orban, Xavier Neyt, Niko E. C. Verhoest and Pierre Defourny

Remote Sens. 2022, 14(10), 2496; https://doi.org/10.3390/rs14102496 - 23 May 2022

Cited by 8 | Viewed by 3507

Abstract

The green area index (GAI) and the soil moisture under the canopy are two key variables for agricultural monitoring. The current most accurate GAI estimation methods exploit optical data and are rendered ineffective in the case of frequent cloud cover. Synthetic aperture radar [...] Read more.

The green area index (GAI) and the soil moisture under the canopy are two key variables for agricultural monitoring. The current most accurate GAI estimation methods exploit optical data and are rendered ineffective in the case of frequent cloud cover. Synthetic aperture radar (SAR) measurements could allow the remote estimation of both variables at the parcel level, on a large scale and regardless of clouds. In this study, several methods were implemented and tested for the simultaneous estimation of both variables using the water cloud model (WCM) and dual-polarized radar backscatter measurements. The methods were tested on the BELSAR-Campaign data set consisting of in-situ measurements of bio-geophysical variables of vegetation and soil in maize fields combined with multi-polarized C- and L-band SAR data from Sentinel-1 and BELSAR. Accurate GAI estimates were obtained using a random forest regressor for the inversion of a pair of WCMs calibrated using cross and vertical co-polarized SAR data in L- and C-band, with correlation coefficients of 0.79 and 0.65 and RMSEs of 0.77 m² m⁻² and 0.98 m² m⁻², respectively, between estimates and in-situ measurements. The WCM, however, proved inadequate for soil moisture monitoring in the conditions of the campaign. These promising results indicate that GAI retrieval in maize crops using only dual-polarized radar data could successfully substitute for estimates derived from optical data. Full article

(This article belongs to the Special Issue Innovative Belgian Earth Observation Research for the Environment)

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