Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.3
4.2
Journals
Remote Sensing
Editor’s Choice
remotesensing-logo

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Editor’s Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Order results
Result details
Results per page
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
19 pages, 5440 KiB  
Article
Sentinel-1 Response to Canopy Moisture in Mediterranean Forests before and after Fire Events
by Francesco Pirotti, Opeyemi Adedipe and Brigitte Leblon
Remote Sens. 2023, 15(3), 823; https://doi.org/10.3390/rs15030823 - 1 Feb 2023
Cited by 6 | Viewed by 2647
Abstract
This study investigates the sensibility of Sentinel-1 C-band backscatter to the moisture content of tree canopies over an area of about 500 km2 in north-western Portugal, with specific analysis over burnt areas. Sentinel-1 C-VV and C-VH backscatter values from 276 images acquired [...] Read more.
This study investigates the sensibility of Sentinel-1 C-band backscatter to the moisture content of tree canopies over an area of about 500 km2 in north-western Portugal, with specific analysis over burnt areas. Sentinel-1 C-VV and C-VH backscatter values from 276 images acquired between January 2018 and December 2020 were assigned to five classes depending on the Drought Code (DC) scenario over several unburned and burned sites with total (>90%) forest canopy cover. Confounding variables such as tree cover and incidence angle were accounted for by masking using specific thresholds. The following results are discussed: (a) C-VV and C-VH backscatter values are inversely correlated (R2 = 0.324 to 0.438 −p < 0.001) with local incidence angle over canopies; (b) correlation is significantly stronger over very wet scenarios (DC class = 0 to 1); (c) C-VV and C-VH backscatter values can discriminate wet to dry forest environments, but they are less sensitive to the transition between dry (DC classes = 1 to 10, 10 to 100) and extremely dry environments (DC classes = 100 to 1000); (d) C-VH is more sensible than C-VV to capture burnt canopy; and (e) the C-VH polarization captures post-fire recovery after an average minimum period of 360 days after the fire event, although with less distinction for extremely wet soils. We conclude that C-band VH backscatter intensity decreases from wet to dry canopy conditions, that this behavior of the backscatter signal with respect to canopy dryness is lost after a fire event, and that after one year it is recovered. Full article
(This article belongs to the Special Issue Remote Sensing in Geomatics)
►▼ Show Figures

Graphical abstract

27 pages, 6203 KiB  
Article
Improved Neural Network with Spatial Pyramid Pooling and Online Datasets Preprocessing for Underwater Target Detection Based on Side Scan Sonar Imagery
by Jinrui Li, Libin Chen, Jian Shen, Xiongwu Xiao, Xiaosong Liu, Xin Sun, Xiao Wang and Deren Li
Remote Sens. 2023, 15(2), 440; https://doi.org/10.3390/rs15020440 - 11 Jan 2023
Cited by 30 | Viewed by 6194
Abstract
Fast and high-accuracy detection of underwater targets based on side scan sonar images has great potential for marine fisheries, underwater security, marine mapping, underwater engineering and other applications. The following problems, however, must be addressed when using low-resolution side scan sonar images for [...] Read more.
Fast and high-accuracy detection of underwater targets based on side scan sonar images has great potential for marine fisheries, underwater security, marine mapping, underwater engineering and other applications. The following problems, however, must be addressed when using low-resolution side scan sonar images for underwater target detection: (1) the detection performance is limited due to the restriction on the input of multi-scale images; (2) the widely used deep learning algorithms have a low detection effect due to their complex convolution layer structures; (3) the detection performance is limited due to insufficient model complexity in training process; and (4) the number of samples is not enough because of the bad dataset preprocessing methods. To solve these problems, an improved neural network for underwater target detection—which is based on side scan sonar images and fully utilizes spatial pyramid pooling and online dataset preprocessing based on the You Look Only Once version three (YOLO V3) algorithm—is proposed. The methodology of the proposed approach is as follows: (1) the AlexNet, GoogleNet, VGGNet and the ResNet networks and an adopted YOLO V3 algorithm were the backbone networks. The structure of the YOLO V3 model is more mature and compact and has higher target detection accuracy and better detection efficiency than the other models; (2) spatial pyramid pooling was added at the end of the convolution layer to improve detection performance. Spatial pyramid pooling breaks the scale restrictions when inputting images to improve feature extraction because spatial pyramid pooling enables the backbone network to learn faster at high accuracy; and (3) online dataset preprocessing based on YOLO V3 with spatial pyramid pooling increases the number of samples and improves the complexity of the model to further improve detection process performance. Three-side scan imagery datasets were used for training and were tested in experiments. The quantitative evaluation using Accuracy, Recall, Precision, mAP and F1-Score metrics indicates that: for the AlexNet, GoogleNet, VGGNet and ResNet algorithms, when spatial pyramid pooling is added to their backbone networks, the average detection accuracy of the three sets of data was improved by 2%, 4%, 2% and 2%, respectively, as compared to their original formulations. Compared with the original YOLO V3 model, the proposed ODP+YOLO V3+SPP underwater target detection algorithm model has improved detection performance through the mAP qualitative evaluation index has increased by 6%, the Precision qualitative evaluation index has increased by 13%, and the detection efficiency has increased by 9.34%. These demonstrate that adding spatial pyramid pooling and online dataset preprocessing can improve the target detection accuracy of these commonly used algorithms. The proposed, improved neural network with spatial pyramid pooling and online dataset preprocessing based on the YOLO V3 method achieves the highest scores for underwater target detection results for sunken ships, fish flocks and seafloor topography, with mAP scores of 98%, 91% and 96% for the above three kinds of datasets, respectively. Full article
(This article belongs to the Special Issue Artificial Intelligence-Driven Methods for Remote Sensing Target and Object Detection)
►▼ Show Figures

Figure 1

20 pages, 4461 KiB  
Article
Spatio-Temporal Evolution of Glacial Lakes in the Tibetan Plateau over the Past 30 Years
by Xiangyang Dou, Xuanmei Fan, Xin Wang, Ali P. Yunus, Junlin Xiong, Ran Tang, Marco Lovati, Cees van Westen and Qiang Xu
Remote Sens. 2023, 15(2), 416; https://doi.org/10.3390/rs15020416 - 10 Jan 2023
Cited by 26 | Viewed by 4127
Abstract
As the Third Pole of the Earth and the Water Tower of Asia, the Tibetan Plateau (TP) nurtures large numbers of glacial lakes, which are sensitive to global climate change. These lakes modulate the freshwater ecosystem in the region but concurrently pose severe [...] Read more.
As the Third Pole of the Earth and the Water Tower of Asia, the Tibetan Plateau (TP) nurtures large numbers of glacial lakes, which are sensitive to global climate change. These lakes modulate the freshwater ecosystem in the region but concurrently pose severe threats to the valley population by means of sudden glacial lake outbursts and consequent floods (GLOFs). The lack of high-resolution multi-temporal inventory of glacial lakes in TP hampers a better understanding and prediction of the future trend and risk of glacial lakes. Here, we created a multi-temporal inventory of glacial lakes in TP using a 30-year record of 42,833 satellite images (1990–2019), and we discussed their characteristics and spatio-temporal evolution over the years. Results showed that their number and area had increased by 3285 and 258.82 km2 in the last 3 decades, respectively. We noticed that different regions of the TP exhibited varying change rates in glacial lake size; most regions show a trend of expansion and increase in glacial lakes, while some regions show a trend of decreasing such as the western Pamir and the eastern Hindu Kush. The mapping uncertainty is about 17.5%, which is lower than other available datasets, thus making our inventory reliable for the spatio-temporal evolution analysis of glacial lakes in the TP. Our lake inventory data are publicly published, it can help to study climate change–glacier–glacial lake–GLOF interactions in the Third Pole and serve as input to various hydro-climatic studies. Full article
(This article belongs to the Special Issue Study on Cryospheric Sciences Using Remote Sensing Technology)
►▼ Show Figures

Graphical abstract

20 pages, 6714 KiB  
Article
Forcing Mechanisms of the Interannual Sea Level Variability in the Midlatitude South Pacific during 2004–2020
by C. Germineaud, D. L. Volkov, S. Cravatte and W. Llovel
Remote Sens. 2023, 15(2), 352; https://doi.org/10.3390/rs15020352 - 6 Jan 2023
Cited by 2 | Viewed by 2139
Abstract
Over the past few decades, the global mean sea level rise and superimposed regional fluctuations of sea level have exerted considerable stress on coastal communities, especially in low-elevation regions such as the Pacific Islands in the western South Pacific Ocean. This made it [...] Read more.
Over the past few decades, the global mean sea level rise and superimposed regional fluctuations of sea level have exerted considerable stress on coastal communities, especially in low-elevation regions such as the Pacific Islands in the western South Pacific Ocean. This made it necessary to have the most comprehensive understanding of the forcing mechanisms that are responsible for the increasing rates of extreme sea level events. In this study, we explore the causes of the observed sea level variability in the midlatitude South Pacific on interannual time scales using observations and atmospheric reanalyses combined with a 1.5 layer reduced-gravity model. We focus on the 2004–2020 period, during which the Argo’s global array allowed us to assess year-to-year changes in steric sea level caused by thermohaline changes in different depth ranges (from the surface down to 2000 m). We find that during the 2015–2016 El Niño and the following 2017–2018 La Niña, large variations in thermosteric sea level occurred due to temperature changes within the 100–500 dbar layer in the midlatitude southwest Pacific. In the western boundary region (from 30°S to 40°S), the variations in halosteric sea level between 100 and 500 dbar were significant and could have partially balanced the corresponding changes in thermosteric sea level. We show that around 35°S, baroclinic Rossby waves forced by the open-ocean wind-stress forcing account for 40 to 75% of the interannual sea level variance between 100°W and 180°, while the influence of remote sea level signals generated near the Chilean coast is limited to the region east of 100°W. The contribution of surface heat fluxes on interannual time scales is also considered and shown to be negligible. Full article
(This article belongs to the Section Ocean Remote Sensing)
►▼ Show Figures

Figure 1

20 pages, 6547 KiB  
Article
Monitoring Shoreline Changes along the Southwestern Coast of South Africa from 1937 to 2020 Using Varied Remote Sensing Data and Approaches
by Jennifer Murray, Elhadi Adam, Stephan Woodborne, Duncan Miller, Sifiso Xulu and Mary Evans
Remote Sens. 2023, 15(2), 317; https://doi.org/10.3390/rs15020317 - 5 Jan 2023
Cited by 25 | Viewed by 6351
Abstract
Shoreline analysis in response to the rapid erosion of sandy beaches has evolved along with geospatial and computer technology; it remains an essential task for sustainable coastal management. This severe and rapid erosion has been reported at several sandy beaches worldwide, including Yzerfontein [...] Read more.
Shoreline analysis in response to the rapid erosion of sandy beaches has evolved along with geospatial and computer technology; it remains an essential task for sustainable coastal management. This severe and rapid erosion has been reported at several sandy beaches worldwide, including Yzerfontein beaches, on the southwest coast of South Africa. We determined this vulnerability from 1937 to 2020 and predicted its change by 2040 by manually delineating shoreline positions from 1937, 1960, and 1977 from aerial photographs and Landsat products between 1985 and 2020 in an automated fashion using the CoastSat toolkit and Google Earth Engine. We then integrated these datasets to calculate the extent of shoreline dynamics over the past eight decades using the Digital Shoreline Analysis System (DSAS). Our results show that the coastline changed dynamically between 1937 and 2020, culminating in an average net erosion of 38 m, with the most extensive erosion occurring between 2015 and 2020. However, coastal projections indicate a slight change in shoreline position over the next two decades. Further studies should integrate additional high resolution remote sensing data and non-remote sensing data (e.g., field surveys) to improve our results and provide a more thorough understanding of the coastal environment and overcome some of remotely-sensed data underlying uncertainties. Full article
(This article belongs to the Special Issue Remote Sensing Observation on Coastal Change)
►▼ Show Figures

Figure 1

17 pages, 9291 KiB  
Article
Water-Quality Monitoring with a UAV-Mounted Multispectral Camera in Coastal Waters
by Alejandro Román, Antonio Tovar-Sánchez, Adam Gauci, Alan Deidun, Isabel Caballero, Emanuele Colica, Sebastiano D’Amico and Gabriel Navarro
Remote Sens. 2023, 15(1), 237; https://doi.org/10.3390/rs15010237 - 31 Dec 2022
Cited by 20 | Viewed by 8846
Abstract
Remote-sensing ocean colour studies have already been used to determine coastal water quality, coastal biodiversity, and nutrient availability. In recent years, Unmanned Aerial Vehicles (UAVs) equipped with multispectral sensors, originally designed for agriculture applications, have also enabled water-quality studies of coastal waters. However, [...] Read more.
Remote-sensing ocean colour studies have already been used to determine coastal water quality, coastal biodiversity, and nutrient availability. In recent years, Unmanned Aerial Vehicles (UAVs) equipped with multispectral sensors, originally designed for agriculture applications, have also enabled water-quality studies of coastal waters. However, since the sea surface is constantly changing, commonly used photogrammetric methods fail when applied to UAV images captured over water areas. In this work, we evaluate the applicability of a five-band multispectral sensor mounted on a UAV to derive scientifically valuable water parameters such as chlorophyll-a (Chl-a) concentration and total suspended solids (TSS), including a new Python workflow for the manual generation of an orthomosaic in aquatic areas exclusively based on the sensor’s metadata. We show water-quality details in two different sites along the Maltese coastline on the centimetre-scale, improving the existing approximations that are available for the region through Sentinel-3 OLCI imagery at a much lower spatial resolution of 300 m. The Chl-a and TSS values derived for the studied regions were within the expected ranges and varied between 0 to 3 mg/m3 and 10 to 20 mg/m3, respectively. Spectral comparisons were also carried out along with some statistics calculations such as RMSE, MAE, or bias in order to validate the obtained results. Full article
(This article belongs to the Special Issue New Advancements in Remote Sensing Image Processing)
►▼ Show Figures

Graphical abstract

19 pages, 5366 KiB  
Article
Corrections of Mesoscale Eddies and Kuroshio Extension Surface Velocities Derived from Satellite Altimeters
by Yuhan Cao, Changming Dong, Zehao Qiu, Brandon J. Bethel, Haiyun Shi, Haibin Lü and Yinhe Cheng
Remote Sens. 2023, 15(1), 184; https://doi.org/10.3390/rs15010184 - 29 Dec 2022
Cited by 4 | Viewed by 2060
Abstract
Oceanic datasets derived from satellite altimeters are of great significance to physical oceanography and ocean dynamics research and the protection of marine environmental resources. Ageostrophic velocity induced by centrifugal force is not considered in altimeter products. This study introduces an iterative method to [...] Read more.
Oceanic datasets derived from satellite altimeters are of great significance to physical oceanography and ocean dynamics research and the protection of marine environmental resources. Ageostrophic velocity induced by centrifugal force is not considered in altimeter products. This study introduces an iterative method to perform cyclogeostrophic corrections of mesoscale eddies’ surface velocities derived from satellite altimeters. The corrected eddy velocity field and geostrophic velocity field were compared by combining eddy detection and mathematical statistics methods. The results show that eddies with small curvature radii, high roundness, or Rossby number larger than 0.1 illustrate that cyclogeostrophic correction is required. The cyclogeostrophic velocity is greater (less) than the geostrophic velocity in anticyclonic (cyclonic) eddies. Additionally, the iterative method is applied to cyclogeostrophic-corrected multi-year (1998–2012) Kuroshio surface velocities. The effect of cyclogeostrophic correction is significant for the Kuroshio Extension region, where the maximum relative difference of velocities with and without correction is about 10% and the eddy kinetic energy is 20%. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Oceanography: Prospects and Challenges)
►▼ Show Figures

Graphical abstract

25 pages, 28288 KiB  
Article
Comparison of Machine Learning Algorithms for Flood Susceptibility Mapping
by Seyd Teymoor Seydi, Yousef Kanani-Sadat, Mahdi Hasanlou, Roya Sahraei, Jocelyn Chanussot and Meisam Amani
Remote Sens. 2023, 15(1), 192; https://doi.org/10.3390/rs15010192 - 29 Dec 2022
Cited by 66 | Viewed by 8021
Abstract
Floods are one of the most destructive natural disasters, causing financial and human losses every year. As a result, reliable Flood Susceptibility Mapping (FSM) is required for effective flood management and reducing its harmful effects. In this study, a new machine learning model [...] Read more.
Floods are one of the most destructive natural disasters, causing financial and human losses every year. As a result, reliable Flood Susceptibility Mapping (FSM) is required for effective flood management and reducing its harmful effects. In this study, a new machine learning model based on the Cascade Forest Model (CFM) was developed for FSM. Satellite imagery, historical reports, and field data were used to determine flood-inundated areas. The database included 21 flood-conditioning factors obtained from different sources. The performance of the proposed CFM was evaluated over two study areas, and the results were compared with those of other six machine learning methods, including Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Deep Neural Network (DNN), Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost). The result showed CFM produced the highest accuracy compared to other models over both study areas. The Overall Accuracy (AC), Kappa Coefficient (KC), and Area Under the Receiver Operating Characteristic Curve (AUC) of the proposed model were more than 95%, 0.8, 0.95, respectively. Most of these models recognized the southwestern part of the Karun basin, northern and northwestern regions of the Gorganrud basin as susceptible areas. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Flood Forecasting and Monitoring)
►▼ Show Figures

Graphical abstract

26 pages, 5111 KiB  
Article
Evaluation of Sentinel-6 Altimetry Data over Ocean
by Maofei Jiang, Ke Xu and Jiaming Wang
Remote Sens. 2023, 15(1), 12; https://doi.org/10.3390/rs15010012 - 21 Dec 2022
Cited by 12 | Viewed by 3221
Abstract
The Sentinel-6 Michael Freilich (S6-MF) satellite was launched on 21st November 2020. Poseidon-4, the main payload onboard S6-MF, is the first synthetic aperture radar (SAR) altimeter operating in an interleaved open burst mode. In this study, the sea surface height (SSH), [...] Read more.
The Sentinel-6 Michael Freilich (S6-MF) satellite was launched on 21st November 2020. Poseidon-4, the main payload onboard S6-MF, is the first synthetic aperture radar (SAR) altimeter operating in an interleaved open burst mode. In this study, the sea surface height (SSH), significant wave height (SWH) and wind speed observations from the Poseidon-4 Level 2 altimetry products from November 2021 to October 2022 are assessed. The assessment contains synthetic aperture radar mode (SARM) as well as low-resolution mode (LRM) data. The SSH assessment is conducted using range noise, sea level anomaly (SLA) spectral analysis and crossover analysis, whereas the SWH and wind speed assessments are performed against NDBC buoy data and other satellite altimetry missions. The performance of the Sentinel-6 altimetry data is compared to those of Sentinel-3A/B and Jason-3 altimetry data. The 20 Hz range noise is 3.07 cm for SARM and 6.40 cm for LRM when SWH is 2 m. The standard deviation (STD) of SSH differences at crossovers is 3.76 cm for SARM and 4.27 cm for LRM. Compared against the NDBC measurements, the Sentinel-6 SWH measurements have a root-mean-square error (RMSE) of 0.361 m for SARM and an RMSE of 0.225 m for LRM. The Sentinel-6 wind speed measurements show an RMSE of 1.216 m/s for SARM and an RMSE of 1.323 m/s for LRM. We also present the impacts of ocean waves on parameter retrievals from Sentinel-6 SARM data. The Sentinel-6 SARM data are sensitive to wave period and direction as well as vertical velocity. It should be paid attention to in the future. Full article
(This article belongs to the Special Issue Advances in Satellite Altimetry)
►▼ Show Figures

Figure 1

18 pages, 4264 KiB  
Article
Sentinel-1 Backscatter Time Series for Characterization of Evapotranspiration Dynamics over Temperate Coniferous Forests
by Marlin M. Mueller, Clémence Dubois, Thomas Jagdhuber, Florian M. Hellwig, Carsten Pathe, Christiane Schmullius and Susan Steele-Dunne
Remote Sens. 2022, 14(24), 6384; https://doi.org/10.3390/rs14246384 - 16 Dec 2022
Cited by 6 | Viewed by 3347
Abstract
Forests’ ecosystems are an essential part of the global carbon cycle with vast carbon storage potential. These systems are currently under external pressures showing increasing change due to climate change. A better understanding of the biophysical properties of forests is, therefore, of paramount [...] Read more.
Forests’ ecosystems are an essential part of the global carbon cycle with vast carbon storage potential. These systems are currently under external pressures showing increasing change due to climate change. A better understanding of the biophysical properties of forests is, therefore, of paramount importance for research and monitoring purposes. While there are many biophysical properties, the focus of this study is on the in-depth analysis of the connection between the C-band Copernicus Sentinel-1 SAR backscatter and evapotranspiration (ET) estimates based on in situ meteorological data and the FAO-based Penman–Monteith equation as well as the well-established global terrestrial ET product from the Terra and Aqua MODIS sensors. The analysis was performed in the Free State of Thuringia, central Germany, over coniferous forests within an area of 2452 km2, considering a 5-year time series (June 2016–July 2021) of 6- to 12-day Sentinel-1 backscatter acquisitions/observations, daily in situ meteorological measurements of four weather stations as well as an 8-day composite of ET products of the MODIS sensors. Correlation analyses of the three datasets were implemented independently for each of the microwave sensor’s acquisition parameters, ascending and descending overpass direction and co- or cross-polarization, investigating different time series seasonality filters. The Sentinel-1 backscatter and both ET time series datasets show a similar multiannual seasonally fluctuating behavior with increasing values in the spring, peaks in the summer, decreases in the autumn and troughs in the winter months. The backscatter difference between summer and winter reaches over 1.5 dB, while the evapotranspiration difference reaches 8 mm/day for the in situ measurements and 300 kg/m2/8-day for the MODIS product. The best correlation between the Sentinel-1 backscatter and both ET products is achieved in the ascending overpass direction, with datasets acquired in the late afternoon, and reaches an R2-value of over 0.8. The correlation for the descending overpass direction reaches values of up to 0.6. These results suggest that the SAR backscatter signal of coniferous forests is sensitive to the biophysical property evapotranspiration under some scenarios. Full article
►▼ Show Figures

Figure 1

22 pages, 8592 KiB  
Article
Improving Spatial Resolution of Satellite Imagery Using Generative Adversarial Networks and Window Functions
by Kinga Karwowska and Damian Wierzbicki
Remote Sens. 2022, 14(24), 6285; https://doi.org/10.3390/rs14246285 - 12 Dec 2022
Cited by 12 | Viewed by 5551
Abstract
Dynamic technological progress has contributed to the development of systems imaging of the Earth’s surface as well as data mining methods. One such example is super-resolution (SR) techniques that allow for the improvement of the spatial resolution of satellite imagery on the basis [...] Read more.
Dynamic technological progress has contributed to the development of systems imaging of the Earth’s surface as well as data mining methods. One such example is super-resolution (SR) techniques that allow for the improvement of the spatial resolution of satellite imagery on the basis of a low-resolution image (LR) and an algorithm using deep neural networks. The limitation of these solutions is the input size parameter, which defines the image size that is adopted by a given neural network. Unfortunately, the value of this parameter is often much smaller than the size of the images obtained by Earth Observation satellites. In this article, we presented a new methodology for improving the resolution of an entire satellite image, using a window function. In addition, we conducted research to improve the resolution of satellite images acquired with the World View 2 satellite using the ESRGAN network, we determined the number of buffer pixels that will make it possible to obtain the best image quality. The best reconstruction of the entire satellite imagery using generative neural networks was obtained using a Triangular window (for 10% coverage). The Hann-Poisson window worked best when more overlap between images was used. Full article
(This article belongs to the Special Issue Deep Learning in Optical Satellite Images)
►▼ Show Figures

Figure 1

37 pages, 24105 KiB  
Article
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 10 | Viewed by 3278
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
►▼ Show Figures

Graphical abstract

22 pages, 54888 KiB  
Article
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 10 | Viewed by 3046
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)
►▼ Show Figures

Figure 1

20 pages, 4905 KiB  
Article
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 13 | Viewed by 4831
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)
►▼ Show Figures

Figure 1

29 pages, 8926 KiB  
Article
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 29 | Viewed by 5138
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)
►▼ Show Figures

Graphical abstract

22 pages, 12227 KiB  
Article
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 4382
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)
►▼ Show Figures

Figure 1

22 pages, 1732 KiB  
Review
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 42 | Viewed by 9332
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
►▼ Show Figures

Figure 1

16 pages, 8305 KiB  
Article
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 2219
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)
►▼ Show Figures

Figure 1

20 pages, 2714 KiB  
Article
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 11 | Viewed by 4759
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)
►▼ Show Figures

Graphical abstract

28 pages, 37926 KiB  
Article
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 3681
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)
►▼ Show Figures

Figure 1

11 pages, 2257 KiB  
Technical 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 7 | Viewed by 2812
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)
►▼ Show Figures

Figure 1

24 pages, 8065 KiB  
Article
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 6 | Viewed by 2841
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)
►▼ Show Figures

Figure 1

20 pages, 13336 KiB  
Article
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 1869
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)
►▼ Show Figures

Figure 1

16 pages, 958 KiB  
Article
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 6 | Viewed by 4300
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)
►▼ Show Figures

Figure 1

27 pages, 13124 KiB  
Article
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 2433
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)
►▼ Show Figures

Figure 1

18 pages, 2776 KiB  
Article
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 3 | Viewed by 3771
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
►▼ Show Figures

Figure 1

20 pages, 5605 KiB  
Article
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 28 | Viewed by 4347
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)
►▼ Show Figures

Figure 1

19 pages, 4180 KiB  
Article
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 46 | Viewed by 8723
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)
►▼ Show Figures

Figure 1

25 pages, 715 KiB  
Review
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 65 | Viewed by 16689
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)
►▼ Show Figures

Graphical abstract

20 pages, 3616 KiB  
Article
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 12 | Viewed by 3534
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)
►▼ Show Figures

Graphical abstract

23 pages, 20371 KiB  
Article
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 4594
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
►▼ Show Figures

Figure 1

24 pages, 9479 KiB  
Article
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 15 | Viewed by 3264
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
►▼ Show Figures

Figure 1

20 pages, 5059 KiB  
Article
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 76 | Viewed by 10336
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
►▼ Show Figures

Figure 1

33 pages, 9288 KiB  
Review
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 46 | Viewed by 8775
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)
►▼ Show Figures

Figure 1

18 pages, 4730 KiB  
Article
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 66 | Viewed by 8599
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)
►▼ Show Figures

Figure 1

30 pages, 83951 KiB  
Article
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 68 | Viewed by 19572
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)
►▼ Show Figures

Graphical abstract

19 pages, 15925 KiB  
Article
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 12 | Viewed by 3858
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)
►▼ Show Figures

Figure 1

17 pages, 5462 KiB  
Article
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 5475
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
►▼ Show Figures

Figure 1

14 pages, 17655 KiB  
Article
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 Hriscuadd Show full author list remove Hide full author list
Remote Sens. 2022, 14(16), 3845; https://doi.org/10.3390/rs14163845 - 9 Aug 2022
Cited by 5 | Viewed by 5085
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)
►▼ Show Figures

Figure 1

22 pages, 18108 KiB  
Article
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 8 | Viewed by 3871
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 θp: (1) under deflection configuration (θp>0°), the alongshore sediment transport was trapped at the updrift side of the headland, promoting sand accretion. (2) Under shadowed configuration (θp<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 (θp=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
►▼ Show Figures

Graphical abstract

31 pages, 10136 KiB  
Article
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 21 | Viewed by 7415
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)
►▼ Show Figures

Figure 1

32 pages, 12352 KiB  
Article
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 206 | Viewed by 26896
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)
►▼ Show Figures

Graphical abstract

32 pages, 10402 KiB  
Article
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 6 | Viewed by 5117
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)
►▼ Show Figures

Figure 1

28 pages, 22500 KiB  
Article
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 5 | Viewed by 4544
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)
►▼ Show Figures

Graphical abstract

27 pages, 28374 KiB  
Article
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 42 | Viewed by 5929
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)
►▼ Show Figures

Graphical abstract

26 pages, 7602 KiB  
Article
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 8 | Viewed by 4086
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)
►▼ Show Figures

Figure 1

15 pages, 2158 KiB  
Article
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 2055
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)
►▼ Show Figures

Graphical abstract

27 pages, 11106 KiB  
Article
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 2754
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)
►▼ Show Figures

Figure 1

19 pages, 2235 KiB  
Article
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 5 | Viewed by 2708
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
►▼ Show Figures

Figure 1

110 pages, 11090 KiB  
Review
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 135 | Viewed by 42673
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)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying article 151-200 on page 4 of 13.
Back to TopTop