Remote Sensing

25 pages, 22618 KB

Open AccessEditor’s ChoiceArticle

MT-InSAR and Dam Modeling for the Comprehensive Monitoring of an Earth-Fill Dam: The Case of the Benínar Dam (Almería, Spain)

by Miguel Marchamalo-Sacristán, Antonio Miguel Ruiz-Armenteros, Francisco Lamas-Fernández, Beatriz González-Rodrigo, Rubén Martínez-Marín, José Manuel Delgado-Blasco, Matus Bakon, Milan Lazecky, Daniele Perissin, Juraj Papco and Joaquim J. Sousa

Remote Sens. 2023, 15(11), 2802; https://doi.org/10.3390/rs15112802 - 28 May 2023

Cited by 13 | Viewed by 4385

Abstract

The Benínar Dam, located in Southeastern Spain, is an earth-fill dam that has experienced filtration issues since its construction in 1985. Despite the installation of various monitoring systems, the data collected are sparse and inadequate for the dam’s lifetime. The present research integrates [...] Read more.

The Benínar Dam, located in Southeastern Spain, is an earth-fill dam that has experienced filtration issues since its construction in 1985. Despite the installation of various monitoring systems, the data collected are sparse and inadequate for the dam’s lifetime. The present research integrates Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) and dam modeling to validate the monitoring of this dam, opening the way to enhanced integrated monitoring systems. MT-InSAR was proved to be a reliable and continuous monitoring system for dam deformation, surpassing previously installed systems in terms of precision. MT-InSAR allowed the almost-continuous monitoring of this dam since 1992, combining ERS, Envisat, and Sentinel-1A/B data. Line-of-sight (LOS) velocities of settlement in the crest of the dam evolved from maximums of −6 mm/year (1992–2000), −4 mm/year (2002–2010), and −2 mm/year (2015–2021) with median values of −2.6 and −3.0 mm/year in the first periods (ERS and Envisat) and −1.3 mm/year in the Sentinel 1-A/B period. These results are consistent with the maximum admissible modeled deformation from construction, confirming that settlement was more intense in the dam’s early stages and decreased over time. MT-InSAR was also used to integrate the monitoring of the dam basin, including critical slopes, quarries, and infrastructures, such as roads, tracks, and spillways. This study allows us to conclude that MT-InSAR and dam modeling are important elements for the integrated monitoring systems of embankment dams. This conclusion supports the complete integration of MT-InSAR and 3D modeling into the monitoring systems of embankment dams, as they are a key complement to traditional geotechnical monitoring and can overcome the main limitations of topographical monitoring. Full article

(This article belongs to the Special Issue Dam Stability Monitoring with Satellite Geodesy)

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17 pages, 12058 KB

Open AccessEditor’s ChoiceArticle

Drone-Based Imaging Polarimetry of Dark Lake Patches from the Viewpoint of Flying Polarotactic Insects with Ecological Implication

by Dénes Száz, Péter Takács, Balázs Bernáth, György Kriska, András Barta, István Pomozi and Gábor Horváth

Remote Sens. 2023, 15(11), 2797; https://doi.org/10.3390/rs15112797 - 27 May 2023

Cited by 9 | Viewed by 2358

Abstract

Aquatic insects detect water by the horizontal polarization of water-reflected light and thus are attracted to such light. Recently, in the Hungarian Lake Balaton we observed dark water patches forming between every autumn and spring because of the inflow of black suspended/dissolved organic [...] Read more.

Aquatic insects detect water by the horizontal polarization of water-reflected light and thus are attracted to such light. Recently, in the Hungarian Lake Balaton we observed dark water patches forming between every autumn and spring because of the inflow of black suspended/dissolved organic matter into the bright lake water. Earlier, the polarization characteristics of such water surfaces were mapped by imaging polarimeters from the ground. In order to measure the reflection-polarization patterns of these dark lake patches from the higher viewpoint of flying polarotactic aquatic insects, we designed a drone-based imaging polarimeter. We found that the dark lake patches reflected light with very high (60% ≤ d ≤ 80%) degrees of horizontal polarization at the Brewster’s angle, while the bright lake water was only weakly (d < 20%) horizontally polarizing. There was a large contrast in both the radiance and degree of polarization between dark lake patches and bright lake water, while there was no such contrast in the angle of polarization. The ecological implication of these findings could be that these dark lake patches attract water-seeking polarotactic insects, which may oviposit more frequently in them than in the brighter lake water. However, it might not matter if they lay their eggs in these dark patches rather than the bright lake water, because this may simply increase the abundance of breeding flying insects in areas where dark patches are common. Full article

(This article belongs to the Topic Drones for Coastal and Coral Reef Environments)

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20 pages, 21214 KB

Open AccessEditor’s ChoiceArticle

Line Scan Hyperspectral Imaging Framework for Open Source Low-Cost Platforms

by Akram Al-Hourani, Sivacarendran Balendhran, Sumeet Walia and Tetiana Hourani

Remote Sens. 2023, 15(11), 2787; https://doi.org/10.3390/rs15112787 - 26 May 2023

Cited by 9 | Viewed by 8845

Abstract

With advancements in computer processing power and deep learning techniques, hyperspectral imaging is continually being explored for improved sensing applications in various fields. However, the high cost associated with such imaging platforms impedes their widespread use in spite of the availability of the [...] Read more.

With advancements in computer processing power and deep learning techniques, hyperspectral imaging is continually being explored for improved sensing applications in various fields. However, the high cost associated with such imaging platforms impedes their widespread use in spite of the availability of the needed processing power. In this paper, we develop a novel theoretical framework required for an open source ultra-low-cost hyperspectral imaging platform based on the line scan method suitable for remote sensing applications. Then, we demonstrate the design and fabrication of an open source platform using consumer-grade commercial off-the-shelf components that are both affordable and easily accessible to researchers and users. At the heart of the optical system is a consumer-grade spectroscope along with a basic galvanometer mirror that is widely used in laser scanning devices. The utilized pushbroom scanning method provides a very high spectral resolution of 2.8 nm, as tested against commercial spectral sensors. Since the resolution is limited by the slit width of the spectroscope, we also provide a deconvolution method for the line scan in order to improve the monochromatic spatial resolution. Finally, we provide a cost-effective testing method for the hyperspectral imaging platform where the results validate both the spectral and spatial performances of the platform. Full article

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24 pages, 7856 KB

Open AccessEditor’s ChoiceArticle

Ionospheric–Thermospheric Responses to Geomagnetic Storms from Multi-Instrument Space Weather Data

by Rasim Shahzad, Munawar Shah, M. Arslan Tariq, Andres Calabia, Angela Melgarejo-Morales, Punyawi Jamjareegulgarn and Libo Liu

Remote Sens. 2023, 15(10), 2687; https://doi.org/10.3390/rs15102687 - 22 May 2023

Cited by 18 | Viewed by 5369

Abstract

We analyze vertical total electron content (vTEC) variations from the Global Navigation Satellite System (GNSS) at different latitudes in different continents of the world during the geomagnetic storms of June 2015, August 2018, and November 2021. The resulting ionospheric perturbations at the low [...] Read more.

We analyze vertical total electron content (vTEC) variations from the Global Navigation Satellite System (GNSS) at different latitudes in different continents of the world during the geomagnetic storms of June 2015, August 2018, and November 2021. The resulting ionospheric perturbations at the low and mid-latitudes are investigated in terms of the prompt penetration electric field (PPEF), the equatorial electrojet (EEJ), and the magnetic H component from INTERMAGNET stations near the equator. East and Southeast Asia, Russia, and Oceania exhibited positive vTEC disturbances, while South American stations showed negative vTEC disturbances during all the storms. We also analyzed the vTEC from the Swarm satellites and found similar results to the retrieved vTEC data during the June 2015 and August 2018 storms. Moreover, we observed that ionospheric plasma tended to increase rapidly during the local afternoon in the main phase of the storms and has the opposite behavior at nighttime. The equatorial ionization anomaly (EIA) crest expansion to higher latitudes is driven by PPEF during daytime at the main and recovery phases of the storms. The magnetic H component exhibits longitudinal behavior along with the EEJ enhancement near the magnetic equator. Full article

(This article belongs to the Special Issue Satellite Observations of the Global Ionosphere and Plasma Dynamics)

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22 pages, 6387 KB

Open AccessEditor’s ChoiceArticle

Source Model of the 2023 Turkey Earthquake Sequence Imaged by Sentinel-1 and GPS Measurements: Implications for Heterogeneous Fault Behavior along the East Anatolian Fault Zone

by Shuiping Li, Xin Wang, Tingye Tao, Yongchao Zhu, Xiaochuan Qu, Zhenxuan Li, Jianwei Huang and Shunyue Song

Remote Sens. 2023, 15(10), 2618; https://doi.org/10.3390/rs15102618 - 18 May 2023

Cited by 56 | Viewed by 9611

Abstract

On 6 February 2023, a devastating doublet of earthquakes with magnitudes of Mw 7.8 and Mw 7.6 successively struck southeastern Turkey near the border of Syria. The earthquake sequence represents the strongest earthquakes in Turkey during the past 80 years and caused an [...] Read more.

On 6 February 2023, a devastating doublet of earthquakes with magnitudes of Mw 7.8 and Mw 7.6 successively struck southeastern Turkey near the border of Syria. The earthquake sequence represents the strongest earthquakes in Turkey during the past 80 years and caused an extensive loss of life and property. In this study, we processed Sentinel-1 and GPS data to derive the complete surface displacement caused by the earthquake sequence. The surface displacements were adopted to invert for the fault geometry and coseismic slip distribution on the seismogenic faults of the earthquake sequence. The results indicate that the coseismic rupture of the Turkey earthquake sequence was dominated by left-lateral strike slips with a maximum slip of ~10 m on the East Anatolian Fault Zone (EAFZ) and the Sürgü fault (SF). Significant surface ruptures are recognized based on the geodetic inversion, which is consistent with the analysis of post-earthquake satellite images. The cumulative released moment of the two earthquakes reached 9.62 × 10²⁰ Nm, which corresponds to an event of Mw 7.95. Additionally, the interseismic fault slip rates and locking depths along the central and western segments of the EAFZ were estimated using the high-resolution long-term velocity field. The results reveal significant lateral variations of fault slip rates and locking depths along the central and western segments of the EAFZ. Generally, the estimated fault locking zone showed good spatial consistency with the coseismic fault rupture of the Mw 7.8 shock on the EAFZ. The static coulomb failure stress (CFS) change due to the Mw 7.8 earthquakes suggests that the subsequent Mw 7.6 event was certainly promoted by the Mw 7.8 shock. The stress transfers from the fault EAFZ to the fault SF were realized by unclamping the interface of the fault SF, which significantly reduces the effective normal stress on the fault plane. Large CFS increases in the western Puturge segment of the EAFZ, which was not ruptured in the 2020 Mw 6.8 and the 2023 Mw 7.8 earthquakes, highlight the future earthquake risk in this fault segment. Full article

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

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29 pages, 2126 KB

Open AccessFeature PaperEditor’s ChoiceReview

Climate-Change-Driven Droughts and Tree Mortality: Assessing the Potential of UAV-Derived Early Warning Metrics

by Ewane Basil Ewane, Midhun Mohan, Shaurya Bajaj, G. A. Pabodha Galgamuwa, Michael S. Watt, Pavithra Pitumpe Arachchige, Andrew T. Hudak, Gabriella Richardson, Nivedhitha Ajithkumar, Shruthi Srinivasan, Ana Paula Dalla Corte, Daniel J. Johnson, Eben North Broadbent, Sergio de-Miguel, Margherita Bruscolini, Derek J. N. Young, Shahid Shafai, Meshal M. Abdullah, Wan Shafrina Wan Mohd Jaafar, Willie Doaemo, Carlos Alberto Silva and Adrian Cardil Show full author list Hide full author list

Remote Sens. 2023, 15(10), 2627; https://doi.org/10.3390/rs15102627 - 18 May 2023

Cited by 19 | Viewed by 6425

Abstract

Protecting and enhancing forest carbon sinks is considered a natural solution for mitigating climate change. However, the increasing frequency, intensity, and duration of droughts due to climate change can threaten the stability and growth of existing forest carbon sinks. Extreme droughts weaken plant [...] Read more.

Protecting and enhancing forest carbon sinks is considered a natural solution for mitigating climate change. However, the increasing frequency, intensity, and duration of droughts due to climate change can threaten the stability and growth of existing forest carbon sinks. Extreme droughts weaken plant hydraulic systems, can lead to tree mortality events, and may reduce forest diversity, making forests more vulnerable to subsequent forest disturbances, such as forest fires or pest infestations. Although early warning metrics (EWMs) derived using satellite remote sensing data are now being tested for predicting post-drought plant physiological stress and mortality, applications of unmanned aerial vehicles (UAVs) are yet to be explored extensively. Herein, we provide twenty-four prospective approaches classified into five categories: (i) physiological complexities, (ii) site-specific and confounding (abiotic) factors, (iii) interactions with biotic agents, (iv) forest carbon monitoring and optimization, and (v) technological and infrastructural developments, for adoption, future operationalization, and upscaling of UAV-based frameworks for EWM applications. These UAV considerations are paramount as they hold the potential to bridge the gap between field inventory and satellite remote sensing for assessing forest characteristics and their responses to drought conditions, identifying and prioritizing conservation needs of vulnerable and/or high-carbon-efficient tree species for efficient allocation of resources, and optimizing forest carbon management with climate change adaptation and mitigation practices in a timely and cost-effective manner. Full article

(This article belongs to the Collection Feature Paper Special Issue on Forest Remote Sensing)

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20 pages, 4694 KB

Open AccessEditor’s ChoiceArticle

Vegetation Growth Response and Trends after Water Deficit Exposure in the Loess Plateau, China

by Yuanyuan Luo, Wei Liang, Jianwu Yan, Weibin Zhang, Fen Gou, Chengxi Wang and Xiaoru Liang

Remote Sens. 2023, 15(10), 2593; https://doi.org/10.3390/rs15102593 - 16 May 2023

Cited by 9 | Viewed by 4737

Abstract

Understanding the impact of water availability on vegetation growth in the context of climate change is crucial for assessing the resilience of vegetation to environmental shifts. In this study, the relationship between vegetation growth and water availability was studied using a variety of [...] Read more.

Understanding the impact of water availability on vegetation growth in the context of climate change is crucial for assessing the resilience of vegetation to environmental shifts. In this study, the relationship between vegetation growth and water availability was studied using a variety of indicators. The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and Solar-Induced Chlorophyll Fluorescence (SIF) were utilized as vegetation growth indicators, while the standardized precipitation evapotranspiration index (SPEI) and soil moisture indicators served as water use indices. To investigate the vegetation response to water deficit in the Loess Plateau during the growing season from 2000 to 2020, Spearman’s rank correlation coefficients were calculated using a 5-year sliding window approach. The spatial and temporal heterogeneity of vegetation response to water deficit during the growing seasons were also explored. The results showed that: (1) with the improvement of moisture conditions, vegetation growth recovered significantly, and there was no expansion trend for vegetation water deficit. (2) The most sensitive timescale of vegetation to water deficit was 6–8 months; the response degree and sensitivity of vegetation to water surplus and deficit were the highest from June to August; and broadleaved forest was the vegetation type most sensitive to water deficit in the early growing season, while grass was the vegetation type most sensitive to water deficit during the mid and late growing seasons. (3) Soil moisture emerged as the dominant factor influencing vegetation growth in the Loess Plateau, followed by precipitation, albeit to a lesser extent. These findings contribute to understanding the mechanism and characteristics of the response of vegetation to climate fluctuations induced by global climate change. Full article

(This article belongs to the Special Issue Remote Sensing of Arid/Semiarid Lands II)

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10 pages, 2896 KB

Open AccessEditor’s ChoiceCommunication

Enhanced Doppler Resolution and Sidelobe Suppression Performance for Golay Complementary Waveforms

by Jiahua Zhu, Yongping Song, Nan Jiang, Zhuang Xie, Chongyi Fan and Xiaotao Huang

Remote Sens. 2023, 15(9), 2452; https://doi.org/10.3390/rs15092452 - 6 May 2023

Cited by 63 | Viewed by 3003

Abstract

An enhanced Doppler resolution and sidelobe suppression have long been practical issues for moving target detection using Golay complementary waveforms. In this paper, Golay complementary waveform radar returns are combined with a proposed processor, the pointwise thresholding processor (PTP). Compared to the pointwise [...] Read more.

An enhanced Doppler resolution and sidelobe suppression have long been practical issues for moving target detection using Golay complementary waveforms. In this paper, Golay complementary waveform radar returns are combined with a proposed processor, the pointwise thresholding processor (PTP). Compared to the pointwise minimization processor (PMP) illustrated in a previous work, which could only achieve a Doppler resolution comparable to existing methods, this approach essentially increases the Doppler resolution to a very high level in theory. This study also introduced a further filtering process for the delay-Doppler map of the PTP, and simulations verified that the method results in a delay-Doppler map virtually free of range sidelobes. Full article

(This article belongs to the Special Issue Advanced Array Signal Processing for Target Imaging and Detection)

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16 pages, 17072 KB

Open AccessEditor’s ChoiceArticle

A Multi-Pixel Split-Window Approach to Sea Surface Temperature Retrieval from Thermal Imagers with Relatively High Radiometric Noise: Preliminary Studies

by Gian Luigi Liberti, Mattia Sabatini, David S. Wethey and Daniele Ciani

Remote Sens. 2023, 15(9), 2453; https://doi.org/10.3390/rs15092453 - 6 May 2023

Cited by 4 | Viewed by 3210

Abstract

In the following decade(s), a set of satellite missions carrying thermal infrared (TIR) imagers with a relatively high noise equivalent differential temperature (NEdT) are expected, e.g., the high resolution TIR imagers flying on the future Thermal infraRed Imaging Satellite for High-resolution Natural resource [...] Read more.

In the following decade(s), a set of satellite missions carrying thermal infrared (TIR) imagers with a relatively high noise equivalent differential temperature (NEdT) are expected, e.g., the high resolution TIR imagers flying on the future Thermal infraRed Imaging Satellite for High-resolution Natural resource Assessment (TRISHNA), Land Surface Temperature Monitoring (LSTM) and NASA-JPL/ASI Surface Biology and Geology Thermal (SBG) missions or the secondary payload on board the ESA Earth Explorer 10 Harmony. The instruments on board these missions are expected to be characterized by an NEdT of ⪆0.1 K. In order to reduce the impact of radiometric noise on the retrieved sea surface temperature (SST), this study investigates the possibility of applying a multi-pixel atmospheric correction based on the hypotheses that (i) the spatial variability scales of radiatively active atmospheric variables are, on average, larger than those of the SST and (ii) the effect of atmosphere is accounted for via the split window (SW) difference. Based on 32 Sentinel 3 SLSTR case studies selected in oceanic regions where SST features are mainly driven by meso to sub-mesoscale turbulence (e.g., corresponding to major western boundary currents), this study documents that the local spatial variability of the SW difference term on the scale of ≃3 × 3 km² is comparable with the noise associated with the SW difference. Similarly, the power spectra of the SW term are shown to have, for small scales, the behavior of white noise spectra. On this basis, we suggest to average the SW term and to use it for the atmospheric correction procedure to reduce the impact of radiometric noise. In principle, this methodology can be applied on proper scales that can be dynamically defined for each pixel. The applicability of our findings to high-resolution TIR missions is discussed and an example of an application to ECOSTRESS data is reported. Full article

(This article belongs to the Special Issue Atmospheric Correction of Remote Sensing Imagery)

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16 pages, 10668 KB

Open AccessEditor’s ChoiceCommunication

Ionospheric Response to the 6 February 2023 Turkey–Syria Earthquake

by Artem Vesnin, Yury Yasyukevich, Natalia Perevalova and Erman Şentürk

Remote Sens. 2023, 15(9), 2336; https://doi.org/10.3390/rs15092336 - 28 Apr 2023

Cited by 32 | Viewed by 5497

Abstract

Two strong earthquakes occurred in Turkey on 6 February 2023, at 01:17:34 (nighttime, Mw = 7.8) and at 10:24:50 UT (daytime, Mw = 7.5). The seismo-ionospheric impact is an important part of the near-Earth environment state. This paper provides the first results on [...] Read more.

Two strong earthquakes occurred in Turkey on 6 February 2023, at 01:17:34 (nighttime, Mw = 7.8) and at 10:24:50 UT (daytime, Mw = 7.5). The seismo-ionospheric impact is an important part of the near-Earth environment state. This paper provides the first results on the ionospheric effects associated with the aforementioned earthquakes. We used data from global navigation satellite system (GNSS) receivers and ionosondes. We found that both earthquakes generated circle disturbance in the ionosphere, detected by GNSS data. The amplitude of the ionospheric response caused by daytime M7.5 earthquake exceeded by five times that caused by nighttime M7.8 earthquake: 0.5 TECU/min and 0.1 TECU/min, respectively, according to the ROTI data. The velocities of the earthquake-related ionospheric waves were ~2000 m/s, as measured by ROTI, for the M7.5 earthquake. TEC variations with 2–10 min periods showed velocities from 1500 to 900 m/s as disturbances evolved. Ionospheric disturbances occurred around epicenters and propagated to the south by means of 2–10 min TEC variations. ROTI data showed a more symmetric distribution with irregularities observed both to the South and to the North from 10:24:50 UT epicenter. The ionospheric effects were recorded over 750 km from the epicenters. Ionosonde located 420/490 km from the epicenters did not catch ionospheric effects. The results show significant asymmetry in the propagation of coseismic ionospheric disturbances. We observed coseismic ionospheric disturbances associated with Rayleigh mode and acoustic modes, but we did not observe disturbances associated with acoustic gravity mode. Full article

(This article belongs to the Special Issue Recent Advances in Observation and Simulation of the Lithosphere-Atmosphere-Space Coupling)

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19 pages, 3507 KB

Open AccessEditor’s ChoiceArticle

Validation of NASA Sea Surface Temperature Satellite Products Using Saildrone Data

by Kalliopi Koutantou, Philip Brunner and Jorge Vazquez-Cuervo

Remote Sens. 2023, 15(9), 2277; https://doi.org/10.3390/rs15092277 - 25 Apr 2023

Cited by 9 | Viewed by 5294

Abstract

Sea Surface Temperature (SST) is at the core of many processes in the oceans. Various remote sensing platforms have been used to obtain SST products of different scales, but their validation remains a topic of ongoing research. One promising platform is an uncrewed [...] Read more.

Sea Surface Temperature (SST) is at the core of many processes in the oceans. Various remote sensing platforms have been used to obtain SST products of different scales, but their validation remains a topic of ongoing research. One promising platform is an uncrewed surface vehicle called Saildrone. We use the data from eight Saildrone deployments of the USA West Coast 2019 campaign to validate MODIS level-2 and Multi-scale Ultra-high Resolution (MUR) level-4 satellite SST products at 1 km spatial resolution and to assess the robustness of the quality levels of MODIS level-2 products over the California Coast. Pixel-based SST comparisons between Saildrone and the satellite products were performed, as well as thermal gradient comparisons computed both at the pixel-base level and using kriging interpolation. The results generally showed better accuracies for the MUR products. The characterization of the MODIS quality level proved to be valid in areas covered by bad-quality MODIS pixels but less valid in areas covered by lower-quality pixels. The latter implies possible errors in the MODIS quality level characterization and MUR interpolation processes. We have demonstrated the ability of the Saildrones to accurately validate near-shore satellite SST products and provide important information for the quality assessment of satellite products. Full article

(This article belongs to the Special Issue Advances in Retrieval, Operationalization, Monitoring and Application of Sea Surface Temperature II)

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20 pages, 8340 KB

Open AccessEditor’s ChoiceArticle

Analysis of Spatial and Temporal Criteria for Altimeter Collocation of Significant Wave Height and Wind Speed Data in Deep Waters

by Ricardo M. Campos

Remote Sens. 2023, 15(8), 2203; https://doi.org/10.3390/rs15082203 - 21 Apr 2023

Cited by 6 | Viewed by 2605

Abstract

This paper investigates the spatial and temporal variability of significant wave height (Hs) and wind speed (U10) using altimeter data from the Australian Ocean Data Network (AODN) and buoy data from the National Data Buoy Center (NDBC). The main goal is to evaluate [...] Read more.

This paper investigates the spatial and temporal variability of significant wave height (Hs) and wind speed (U10) using altimeter data from the Australian Ocean Data Network (AODN) and buoy data from the National Data Buoy Center (NDBC). The main goal is to evaluate spatial and temporal criteria for collocating altimeter data to fixed-point positions and to provide practical guidance on altimeter collocation in deep waters. The results show that a temporal criterion of 30 min and a spatial criterion between 25 km and 50 km produce the best results for altimeter collocation, in close agreement with buoy data. Applying a 25 km criterion leads to slightly better error metrics but at the cost of fewer matchups, whereas using 50 km augments the resulting collocated dataset while keeping the differences to buoy measurements very low. Furthermore, the study demonstrates that using the single closest altimeter record to the buoy position leads to worse results compared to the collocation method based on temporal and spatial averaging. The final validation of altimeter data against buoy observations shows an RMSD of 0.21 m, scatter index of 0.09, and correlation coefficient of 0.98 for Hs, confirming the optimal choice of temporal and spatial criteria employed and the high quality of the calibrated AODN altimeter dataset. Full article

(This article belongs to the Special Issue Remote Sensing of Wave Fields under Extreme Weather Conditions (in Tropical and Extra-Tropical Cyclones and Polar Lows))

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18 pages, 3883 KB

Open AccessEditor’s ChoiceArticle

Methods of Analyzing the Error and Rectifying the Calibration of a Solar Tracking System for High-Precision Solar Tracking in Orbit

by Yingqiu Shao, Zhanfeng Li, Xiaohu Yang, Yu Huang, Bo Li, Guanyu Lin and Jifeng Li

Remote Sens. 2023, 15(8), 2213; https://doi.org/10.3390/rs15082213 - 21 Apr 2023

Cited by 3 | Viewed by 2032

Abstract

Reliability is the most critical characteristic of space missions, for example in capturing and tracking moving targets. To this end, two methods are designed to track sunlight using solar remote-sensing instruments (SRSIs). The primary method is to use the offset angles of the [...] Read more.

Reliability is the most critical characteristic of space missions, for example in capturing and tracking moving targets. To this end, two methods are designed to track sunlight using solar remote-sensing instruments (SRSIs). The primary method is to use the offset angles of the guide mirror for closed-loop tracking, while the alternative method is to use the sunlight angles, calculated from the satellite attitude, solar vector, and mechanical installation correction parameters, for open-loop tracking. By comprehensively analyzing the error and rectifying the calibration of the solar tracking system, we demonstrate that the absolute value of the azimuth tracking precision is less than 0.0121° and the pitch is less than 0.0037° with the primary method. Correspondingly, they are 0.0992° and 0.0960° with the alternative method. These precisions meet the requirements of SRSIs. In addition, recalibration due to mechanical vibration during the satellite’s launch may invalidate the above methods, leading to the failure of SRSIs. Hence, we propose a dedicated injection parameter strategy to rectify the sunlight angles to capture and track the sunlight successfully. The stable and effective results in the ultraviolet to near-infrared spectrum validate the SRSI’s high-precision sunlight tracking performance. Furthermore, the above methods can also be applied to all orbital inclinations and may provide a solution for capturing and tracking moving targets. Full article

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41 pages, 2368 KB

Open AccessEditor’s ChoiceArticle

Fast, Efficient, and Viable Compressed Sensing, Low-Rank, and Robust Principle Component Analysis Algorithms for Radar Signal Processing

by Reinhard Panhuber

Remote Sens. 2023, 15(8), 2216; https://doi.org/10.3390/rs15082216 - 21 Apr 2023

Cited by 10 | Viewed by 2769

Abstract

Modern radar signal processing techniques make strong use of compressed sensing, affine rank minimization, and robust principle component analysis. The corresponding reconstruction algorithms should fulfill the following desired properties: complex valued, viable in the sense of not requiring parameters that are unknown in [...] Read more.

Modern radar signal processing techniques make strong use of compressed sensing, affine rank minimization, and robust principle component analysis. The corresponding reconstruction algorithms should fulfill the following desired properties: complex valued, viable in the sense of not requiring parameters that are unknown in practice, fast convergence, low computational complexity, and high reconstruction performance. Although a plethora of reconstruction algorithms are available in the literature, these generally do not meet all of the aforementioned desired properties together. In this paper, a set of algorithms fulfilling these conditions is presented. The desired requirements are met by a combination of turbo-message-passing algorithms and smoothed

ℓ_{0}

-refinements. Their performance is evaluated by use of extensive numerical simulations and compared with popular conventional algorithms. Full article

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24 pages, 16057 KB

Open AccessEditor’s ChoiceArticle

KaRIn Noise Reduction Using a Convolutional Neural Network for the SWOT Ocean Products

by Anaëlle Tréboutte, Elisa Carli, Maxime Ballarotta, Benjamin Carpentier, Yannice Faugère and Gérald Dibarboure

Remote Sens. 2023, 15(8), 2183; https://doi.org/10.3390/rs15082183 - 20 Apr 2023

Cited by 23 | Viewed by 3689

Abstract

The SWOT (Surface Water Ocean Topography) mission will provide high-resolution and two-dimensional measurements of sea surface height (SSH). However, despite its unprecedented precision, SWOT’s Ka-band Radar Interferometer (KaRIn) still exhibits a substantial amount of random noise. In turn, the random noise limits the [...] Read more.

The SWOT (Surface Water Ocean Topography) mission will provide high-resolution and two-dimensional measurements of sea surface height (SSH). However, despite its unprecedented precision, SWOT’s Ka-band Radar Interferometer (KaRIn) still exhibits a substantial amount of random noise. In turn, the random noise limits the ability of SWOT to capture the smallest scales of the ocean’s topography and its derivatives. In that context, this paper explores the feasibility, strengths and limits of a noise-reduction algorithm based on a convolutional neural network. The model is based on a U-Net architecture and is trained and tested with simulated data from the North Atlantic. Our results are compared to classical smoothing methods: a median filter, a Lanczos kernel smoother and the SWOT de-noising algorithm developed by Gomez-Navarro et al. Our U-Net model yields better results for all the evaluation metrics: 2 mm root mean square error, sub-millimetric bias, variance reduction by factor of 44 (16 dB) and an accurate power spectral density down to 10–20 km wavelengths. We also tested various scenarios to infer the robustness and the stability of the U-Net. The U-Net always exhibits good performance and can be further improved with retraining if necessary. This robustness in simulation is very encouraging: our findings show that the U-Net architecture is likely one of the best candidates to reduce the noise of flight data from KaRIn. Full article

(This article belongs to the Special Issue Applications of Satellite Altimetry in Ocean Observation)

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14 pages, 4294 KB

Open AccessEditor’s ChoiceArticle

Stratospheric Water Vapor from the Hunga Tonga–Hunga Ha’apai Volcanic Eruption Deduced from COSMIC-2 Radio Occultation

by William J. Randel, Benjamin R. Johnston, John J. Braun, Sergey Sokolovskiy, Holger Vömel, Aurelien Podglajen and Bernard Legras

Remote Sens. 2023, 15(8), 2167; https://doi.org/10.3390/rs15082167 - 20 Apr 2023

Cited by 13 | Viewed by 3506

Abstract

The eruption of the Hunga Tonga–Hunga Ha’apai (HTHH) volcano on 15 January 2022 injected large amounts of water vapor (H₂O) directly into the stratosphere. While normal background levels of stratospheric H₂O are not detectable in radio occultation (RO) measurements, [...] Read more.

The eruption of the Hunga Tonga–Hunga Ha’apai (HTHH) volcano on 15 January 2022 injected large amounts of water vapor (H₂O) directly into the stratosphere. While normal background levels of stratospheric H₂O are not detectable in radio occultation (RO) measurements, effects of the HTHH eruption are clearly observed as anomalous refractivity profiles from COSMIC-2, suggesting the possibility of detecting the HTHH H₂O signal. To separate temperature and H₂O effects on refractivity, we use co-located temperature observations from the Microwave Limb Sounder (MLS) to constrain a simplified H₂O retrieval. Our results show enhancements of H₂O up to ~2500–3500 ppmv in the stratosphere (~29–33 km) in the days following the HTHH eruption, with propagating patterns that follow the dispersing volcanic plume. The stratospheric H₂O profiles derived from RO are in reasonable agreement with limited radiosonde observations over Australia. The H₂O profiles during the first few days after the eruption show descent of the plume at a rate of ~−1 km/day, likely due to strong radiative cooling (~−10 K/day) induced by high H₂O concentrations; slower descent (~−200 m/day) is observed over the following week as the plume disperses. The total mass of H₂O injected by HTHH is estimated as 110 ± 14 Tg from measurements in the early plumes during 16–18 January, which equates to approximately 8% of the background global mass of stratospheric H₂O. These RO measurements provide novel quantification of the unprecedented H₂O amounts and the plume evolution during the first week after the HTHH eruption. Full article

(This article belongs to the Section Atmospheric Remote Sensing)

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27 pages, 4773 KB

Open AccessEditor’s ChoiceArticle

The InflateSAR Campaign: Developing Refugee Vessel Detection Capabilities with Polarimetric SAR

by Peter Lanz, Armando Marino, Morgan David Simpson, Thomas Brinkhoff, Frank Köster and Matthias Möller

Remote Sens. 2023, 15(8), 2008; https://doi.org/10.3390/rs15082008 - 10 Apr 2023

Cited by 4 | Viewed by 2922

Abstract

In the efforts to mitigate the ongoing humanitarian crisis at the European sea borders, this work builds detection capabilities to help find refugee boats in distress. For this paper, we collected dual-pol and quad-pol synthetic aperture radar (SAR) data over a 12 m [...] Read more.

In the efforts to mitigate the ongoing humanitarian crisis at the European sea borders, this work builds detection capabilities to help find refugee boats in distress. For this paper, we collected dual-pol and quad-pol synthetic aperture radar (SAR) data over a 12 m rubber inflatable in a test-bed lake near Berlin, Germany. To consider a real scenario, we prepared the vessel so that its backscattering emulated that of a vessel fully occupied with people. Further, we collected SAR imagery over the ocean with different sea states, categorized by incidence angle and by polarization. These were used to emulate the conditions for a vessel located in ocean waters. This setup enabled us to test nine well-known vessel-detection systems (VDS), to explore the capabilities of new detection algorithms and to benchmark different combinations of detectors (detector fusion) with respect to different sensor and scene parameters (e.g., the polarization, wind speed, wind direction and boat orientation). This analysis culminated in designing a system that is specifically tailored to accommodate different situations and sea states. Full article

(This article belongs to the Special Issue Remote Sensing for Marine Environmental Disaster Response)

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24 pages, 2950 KB

Open AccessEditor’s ChoiceArticle

Can Plot-Level Photographs Accurately Estimate Tundra Vegetation Cover in Northern Alaska?

by Hana L. Sellers, Sergio A. Vargas Zesati, Sarah C. Elmendorf, Alexandra Locher, Steven F. Oberbauer, Craig E. Tweedie, Chandi Witharana and Robert D. Hollister

Remote Sens. 2023, 15(8), 1972; https://doi.org/10.3390/rs15081972 - 8 Apr 2023

Cited by 5 | Viewed by 2968

Abstract

Plot-level photography is an attractive time-saving alternative to field measurements for vegetation monitoring. However, widespread adoption of this technique relies on efficient workflows for post-processing images and the accuracy of the resulting products. Here, we estimated relative vegetation cover using both traditional field [...] Read more.

Plot-level photography is an attractive time-saving alternative to field measurements for vegetation monitoring. However, widespread adoption of this technique relies on efficient workflows for post-processing images and the accuracy of the resulting products. Here, we estimated relative vegetation cover using both traditional field sampling methods (point frame) and semi-automated classification of photographs (plot-level photography) across thirty 1 m² plots near Utqiaġvik, Alaska, from 2012 to 2021. Geographic object-based image analysis (GEOBIA) was applied to generate objects based on the three spectral bands (red, green, and blue) of the images. Five machine learning algorithms were then applied to classify the objects into vegetation groups, and random forest performed best (60.5% overall accuracy). Objects were reliably classified into the following classes: bryophytes, forbs, graminoids, litter, shadows, and standing dead. Deciduous shrubs and lichens were not reliably classified. Multinomial regression models were used to gauge if the cover estimates from plot-level photography could accurately predict the cover estimates from the point frame across space or time. Plot-level photography yielded useful estimates of vegetation cover for graminoids. However, the predictive performance varied both by vegetation class and whether it was being used to predict cover in new locations or change over time in previously sampled plots. These results suggest that plot-level photography may maximize the efficient use of time, funding, and available technology to monitor vegetation cover in the Arctic, but the accuracy of current semi-automated image analysis is not sufficient to detect small changes in cover. Full article

(This article belongs to the Special Issue Advanced Technologies in Wetland and Vegetation Ecological Monitoring)

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22 pages, 8449 KB

Open AccessEditor’s ChoiceArticle

Accuracy Assessment of High-Resolution Globally Available Open-Source DEMs Using ICESat/GLAS over Mountainous Areas, A Case Study in Yunnan Province, China

by Menghua Li, Xiebing Yin, Bo-Hui Tang and Mengshi Yang

Remote Sens. 2023, 15(7), 1952; https://doi.org/10.3390/rs15071952 - 6 Apr 2023

Cited by 12 | Viewed by 3790

Abstract

The Open-Source Digital Elevation Model (DEM) is fundamental data of the geoscientific community. However, the variation of its accuracy with land cover type and topography has not been thoroughly studied. This study evaluates the accuracy of five globally covered and open-accessed DEM products [...] Read more.

The Open-Source Digital Elevation Model (DEM) is fundamental data of the geoscientific community. However, the variation of its accuracy with land cover type and topography has not been thoroughly studied. This study evaluates the accuracy of five globally covered and open-accessed DEM products (TanDEM-X90 m, SRTEM, NASADEM, ASTER GDEM, and AW3D30) in the mountain area using ICESat/GLAS data as the GCPs. The robust evaluation indicators were utilized to compare the five DEMs’ accuracy and explore the relationship between these errors and slope, aspect, landcover types, and vegetation coverage, thereby revealing the consistency differences in DEM quality under different geographical feature conditions. The Taguchi method is introduced to quantify the impact of these surface characteristics on DEM errors. The results show that the slope is the main factor affecting the accuracy of DEM products, accounting for about 90%, 81%, 85%, 83%, and 65% for TanDEM-X90, SRTM, NASADEM, ASTER GDEM, and AW3D30, respectively. TanDEM-X90 has the highest accuracy in very flat areas (slope < 2°), NASADEM and SRTM have the greatest accuracy in flat areas (2 ≤ slope < 5°), while AW3D30 accuracy is the best in other cases and shows the best consistency on slopes. This study makes a new attempt to quantify the factors affecting the accuracy of DEM, and the results can guide the selection of open-source DEMs in related geoscience research. Full article

(This article belongs to the Special Issue Quantifying Geomorphological Processes Using Remote Sensing Techniques)

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23 pages, 9006 KB

Open AccessEditor’s ChoiceArticle

Terrestrial Laser Scanning for Non-Destructive Estimation of Aboveground Biomass in Short-Rotation Poplar Coppices

by María Menéndez-Miguélez, Guillermo Madrigal, Hortensia Sixto, Nerea Oliveira and Rafael Calama

Remote Sens. 2023, 15(7), 1942; https://doi.org/10.3390/rs15071942 - 5 Apr 2023

Cited by 8 | Viewed by 2854

Abstract

Poplar plantations in high-density and short-rotation coppices (SRC) are a suitable way for the fast production of wood that can be transformed into bioproducts or bioenergy. Optimal management of these coppices requires accurate assessment of the total standing biomass. However, traditional field inventory [...] Read more.

Poplar plantations in high-density and short-rotation coppices (SRC) are a suitable way for the fast production of wood that can be transformed into bioproducts or bioenergy. Optimal management of these coppices requires accurate assessment of the total standing biomass. However, traditional field inventory is a challenging task, given the existence of multiple shoots, the difficulty of identifying terminal shoots, and the extreme high density. As an alternative, in this work, we propose to develop individual stool and plot biomass models using metrics derived from terrestrial laser scanning (TLS) as predictors. To this aim, we used data from a SRC poplar plantation, including nine plots and 154 individual stools. Every plot was scanned from different positions, and individual stools were felled, weighed, and dried to compute aboveground biomass (AGB). Individual stools were segmented from the cloud point, and different TLS metrics at stool and plot level were derived following processes of bounding box, slicing, and voxelization. These metrics were then used, either alone or combined with field-measured metrics, to fit biomass models. Our results indicate that at individual-stool level, the biomass models combining TLS metrics and easy to measure in field metrics (stool diameter) perform similarly to the traditional allometric models based on field inventories, while at plot scales, TLS-derived models show superiority over traditional models. Our proposed methodology permits accurate and non-destructive estimates of the biomass fixed in SRC plantations. Full article

(This article belongs to the Special Issue Remote Sensing for Forest Morphological and Physiological Traits Monitoring)

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19 pages, 2390 KB

Open AccessEditor’s ChoiceTechnical Note

Radiometric Terrain Flattening of Geocoded Stacks of SAR Imagery

by Piyush S. Agram, Michael S. Warren, Scott A. Arko and Matthew T. Calef

Remote Sens. 2023, 15(7), 1932; https://doi.org/10.3390/rs15071932 - 4 Apr 2023

Cited by 6 | Viewed by 3679

Abstract

We have described an efficient approach to radiometrically flatten geocoded stacks of calibrated synthetic aperture radar (SAR) data for terrain-related effects. We have used simulation to demonstrate that, for the Sentinel-1 mission, one static radiometric terrain-flattening factor derived from actual SAR imaging metadata [...] Read more.

We have described an efficient approach to radiometrically flatten geocoded stacks of calibrated synthetic aperture radar (SAR) data for terrain-related effects. We have used simulation to demonstrate that, for the Sentinel-1 mission, one static radiometric terrain-flattening factor derived from actual SAR imaging metadata per imaging geometry is sufficient for flattening interferometrically compliant stacks of SAR data. We have quantified the loss of precision due to the application of static flattening factors, and show that these are well below the stated requirements of change-detection algorithms. Finally, we have discussed the implications of applying radiometric terrain flattening to geocoded SAR data instead of the traditional approach of flattening data provided in the original SAR image geometry. The proposed approach allows for efficient and consistent generation of five different Committee of Earth-Observation Satellites (CEOS) Analysis-Ready Dataset (ARD) families—Geocoded Single-Look Complex (GSLC), Interferometric Radar (InSAR), Normalized Radar Backscatter (NRB), Polarimetric Radar (POL) and Ocean Radar Backscatter (ORB) from SAR missions in a common framework. Full article

(This article belongs to the Section Engineering Remote Sensing)

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30 pages, 19069 KB

Open AccessEditor’s ChoiceArticle

Insights into the Magmatic Feeding System of the 2021 Eruption at Cumbre Vieja (La Palma, Canary Islands) Inferred from Gravity Data Modeling

by Fuensanta G. Montesinos, Sergio Sainz-Maza, David Gómez-Ortiz, José Arnoso, Isabel Blanco-Montenegro, Maite Benavent, Emilio Vélez, Nieves Sánchez and Tomás Martín-Crespo

Remote Sens. 2023, 15(7), 1936; https://doi.org/10.3390/rs15071936 - 4 Apr 2023

Cited by 13 | Viewed by 6033

Abstract

This study used spatiotemporal land gravity data to investigate the 2021 eruption that occurred in the Cumbre Vieja volcano (La Palma, Canary Islands). First, we produced a density model by inverting the local gravity field using data collected in July 2005 and July [...] Read more.

This study used spatiotemporal land gravity data to investigate the 2021 eruption that occurred in the Cumbre Vieja volcano (La Palma, Canary Islands). First, we produced a density model by inverting the local gravity field using data collected in July 2005 and July 2021. This model revealed a low-density body beneath the western flank of the volcano that explains a highly fractured and altered structure related to the active hydrothermal system. Then, we retrieved changes in gravity and GNSS vertical displacements from repeated measurements made in a local network before (July 2021) and after (January 2022) the eruption. After correcting the vertical surface displacements, the gravity changes produced by mass variation during the eruptive process were used to build a forward model of the magmatic feeding system consisting of dikes and sills based on an initial model defined by the paths of the earthquake hypocenters preceding the eruption. Our study provides a final model of the magma plumbing system, which establishes a spatiotemporal framework tracing the path of magma ascent from the crust–mantle boundary to the surface from 11–19 September 2021, where the shallowest magma path was strongly influenced by the low-density body identified in the inversion process. Full article

(This article belongs to the Special Issue Investigation of Volcanic Activity Using Potential and Deformation Fields Retrieved from Proximal and Remote Sensing Techniques)

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16 pages, 6277 KB

Open AccessEditor’s ChoiceArticle

A 3D Interferometer-Type Lightning Mapping Array for Observation of Winter Lightning in Japan

by Junchen Yang, Daohong Wang, Haitao Huang, Ting Wu, Nobuyuki Takagi and Kazuo Yamamoto

Remote Sens. 2023, 15(7), 1923; https://doi.org/10.3390/rs15071923 - 3 Apr 2023

Cited by 8 | Viewed by 3241

Abstract

We have developed and deployed a 3D Interferometer-type Lightning Mapping Array (InLMA) for observing winter lightning in Japan. InLMA consists of three broadband interferometers installed at three stations with a distance from 3 to 5 km. At each interferometer station, three discone antennas [...] Read more.

We have developed and deployed a 3D Interferometer-type Lightning Mapping Array (InLMA) for observing winter lightning in Japan. InLMA consists of three broadband interferometers installed at three stations with a distance from 3 to 5 km. At each interferometer station, three discone antennas were installed, forming a right triangle with a separation of 75 m along their two orthogonal baselines. The output of each InLMA antenna is passed through a 400 MHz low-pass filter and then recorded at 1 GS/s with 16-bit accuracy. A new method has been proposed for finding 3D solutions of a lightning mapping system that consists of multiple interferometers. Using the InLMA, we have succeeded in mapping a positive cloud-to-ground (CG) lightning flash in winter, particularly its preliminary breakdown (PB) process. A study on individual PB pulse processes allows us to infer that each PB pulse process contains many small-scale discharges scattering in a height range of about 150 m. These small-scale discharges in a series of PB pulses appear to be continuous in space, though discontinuous in time. We have also examined the positive return stroke in the CG flash and found a 3D average return stroke speed of 7.5 × 10⁷ m/s. Full article

(This article belongs to the Special Issue Advances in Instrumentation and Algorithms for Atmospheric Electricity Applications)

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20 pages, 4886 KB

Open AccessEditor’s ChoiceArticle

Accuracy Assessment of Surveying Strategies for the Characterization of Microtopographic Features That Influence Surface Water Flooding

by Rakhee Ramachandran, Yadira Bajón Fernández, Ian Truckell, Carlos Constantino, Richard Casselden, Paul Leinster and Mónica Rivas Casado

Remote Sens. 2023, 15(7), 1912; https://doi.org/10.3390/rs15071912 - 2 Apr 2023

Cited by 7 | Viewed by 3553

Abstract

With the increase in rainfall intensity, population, and urbanised areas, surface water flooding (SWF) is an increasing concern impacting properties, businesses, and human lives. Previous studies have shown that microtopography significantly influences flow paths, flow direction, and velocity, impacting flood extent and depth, [...] Read more.

With the increase in rainfall intensity, population, and urbanised areas, surface water flooding (SWF) is an increasing concern impacting properties, businesses, and human lives. Previous studies have shown that microtopography significantly influences flow paths, flow direction, and velocity, impacting flood extent and depth, particularly for the shallow flow associated with urban SWF. This study compares two survey strategies commonly used by flood practitioners, S1 (using Unmanned Aerial Systems-based RGB data) and S2 (using manned aircraft with LiDAR scanners), to develop guidelines on where to use each strategy to better characterise microtopography for a range of flood features. The difference between S1 and S2 in elevation and their accuracies were assessed using both traditional and robust statistical measures. The results showed that the difference in elevation between S1 and S2 varies between 11 cm and 37 cm on different land use and microtopographic flood features. Similarly, the accuracy of S1 ranges between 3 cm and 70 cm, and the accuracy of S2 ranges between 3.8 cm and 30.3 cm on different microtopographic flood features. Thus, this study suggests that the flood features of interest in any given flood study would be key to select the most suitable survey strategy. A decision framework was developed to inform data collection and integration of the two surveying strategies to better characterise microtopographic features. The findings from this study will help improve the microtopographic representation of flood features in flood models and, thus, increase the ability to identify high flood-risk prompt areas accurately. It would also help manage and maintain drainage assets, spatial planning of sustainable drainage systems, and property level flood resilience and insurance to better adapt to the effects of climate change. This study is another step towards standardising flood extent and impact surveying strategies. Full article

(This article belongs to the Special Issue State-of-the-Art Remote Sensing Technologies for Environmental Monitoring)

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14 pages, 2757 KB

Open AccessEditor’s ChoiceCommunication

Underwater 3D Scanning System for Cultural Heritage Documentation

by Christian Bräuer-Burchardt, Christoph Munkelt, Michael Bleier, Matthias Heinze, Ingo Gebhart, Peter Kühmstedt and Gunther Notni

Remote Sens. 2023, 15(7), 1864; https://doi.org/10.3390/rs15071864 - 31 Mar 2023

Cited by 20 | Viewed by 4993

Abstract

Three-dimensional capturing of underwater archeological sites or sunken shipwrecks can support important documentation purposes. In this study, a novel 3D scanning system based on structured illumination is introduced, which supports cultural heritage documentation and measurement tasks in underwater environments. The newly developed system [...] Read more.

Three-dimensional capturing of underwater archeological sites or sunken shipwrecks can support important documentation purposes. In this study, a novel 3D scanning system based on structured illumination is introduced, which supports cultural heritage documentation and measurement tasks in underwater environments. The newly developed system consists of two monochrome measurement cameras, a projection unit that produces aperiodic sinusoidal fringe patterns, two flashlights, a color camera, an inertial measurement unit (IMU), and an electronic control box. The opportunities and limitations of the measurement principles of the 3D scanning system are discussed and compared to other 3D recording methods such as laser scanning, ultrasound, and photogrammetry, in the context of underwater applications. Some possible operational scenarios concerning cultural heritage documentation are introduced and discussed. A report on application activities in water basins and offshore environments including measurement examples and results of the accuracy measurements is given. The study shows that the new 3D scanning system can be used for both the topographic documentation of underwater sites and to generate detailed true-scale 3D models including the texture and color information of objects that must remain under water. Full article

(This article belongs to the Special Issue Documenting and Monitoring Underwater Cultural Heritage Using Remote Sensing)

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19 pages, 1145 KB

Open AccessEditor’s ChoiceArticle

CRSNet: Cloud and Cloud Shadow Refinement Segmentation Networks for Remote Sensing Imagery

by Chao Zhang, Liguo Weng, Li Ding, Min Xia and Haifeng Lin

Remote Sens. 2023, 15(6), 1664; https://doi.org/10.3390/rs15061664 - 20 Mar 2023

Cited by 34 | Viewed by 3941

Abstract

Cloud detection is a critical task in remote sensing image tasks. Due to the influence of ground objects and other noises, the traditional detection methods are prone to miss or false detection and rough edge segmentation in the detection process. To avoid the [...] Read more.

Cloud detection is a critical task in remote sensing image tasks. Due to the influence of ground objects and other noises, the traditional detection methods are prone to miss or false detection and rough edge segmentation in the detection process. To avoid the defects of traditional methods, Cloud and Cloud Shadow Refinement Segmentation Networks are proposed in this paper. The network can correctly and efficiently detect smaller clouds and obtain finer edges. The model takes ResNet-18 as the backbone to extract features at different levels, and the Multi-scale Global Attention Module is used to strengthen the channel and spatial information to improve the accuracy of detection. The Strip Pyramid Channel Attention Module is used to learn spatial information at multiple scales to detect small clouds better. Finally, the high-dimensional feature and low-dimensional feature are fused by the Hierarchical Feature Aggregation Module, and the final segmentation effect is obtained by up-sampling layer by layer. The proposed model attains excellent results compared to methods with classic or special cloud segmentation tasks on Cloud and Cloud Shadow Dataset and the public dataset CSWV. Full article

(This article belongs to the Special Issue Advanced Application of Artificial Intelligence and Machine Vision in Remote Sensing II)

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17 pages, 11877 KB

Open AccessEditor’s ChoiceArticle

Absolute Localization of Targets Using a Phase-Measuring Sidescan Sonar in Very Shallow Waters

by Mark Borrelli, Bryan Legare, Bryan McCormack, Pedro Paulo Guy Martins dos Santos and Daniel Solazzo

Remote Sens. 2023, 15(6), 1626; https://doi.org/10.3390/rs15061626 - 17 Mar 2023

Cited by 7 | Viewed by 2821

Abstract

The detection, classification, and localization of targets or features on the seafloor in acoustic data are critical to many disciplines. This is most important in cases where human safety is in jeopardy, such as hazards to navigation, mitigation of mine countermeasures, or unexploded [...] Read more.

The detection, classification, and localization of targets or features on the seafloor in acoustic data are critical to many disciplines. This is most important in cases where human safety is in jeopardy, such as hazards to navigation, mitigation of mine countermeasures, or unexploded ordnance. This study quantifies the absolute localization of targets, in the form of inert unexploded ordnance, in very shallow waters (2–3 m) on two intertidal bottom types in a meso-tidal environment (tide range = ~3.0 m). The two sites, a sandy intertidal flat and a mixed sand and gravel beach with abundant cobble-sized material, were seeded at low tide with targets (wax-filled 60-, 81-, 105- and 155-mm, projectile and mortar shells). An RTK-GPS was used to collect positional data for the targets and an unoccupied aerial system (UAS) survey was conducted on both sites. At the next high-tide, a vessel-based acoustic survey was performed, and at the subsequent low tide, the targets were re-surveyed with RTK-GPS. We focus here on the sidescan backscatter from a phase-measuring sidescan sonar (PMSS) and the sources of uncertainty for absolute localization. A total of 1426 calls of acoustic targets were made within the sidescan backscatter data, yielding an accuracy of 0.41 ± 0.26 m, with 98.9% of all calls <1 m from their absolute location. Distance from nadir was the most significant source of uncertainty, and targets between 3–7 m had the lowest uncertainty (0.32 ± 0.23 m) with increasing values toward and away from nadir. Bathymetry and bathymetry-mode backscatter were less useful for the detection and classification of targets compared to sidescan backscatter, but once detected, the accuracy of absolute localization were similar. This is likely due to target calls from these two datasets that were orders of magnitude less and that focused on the larger sized targets, thus more work is needed to better understand these differences. Lastly, the absolute localization of targets detected on sandy and cobble bottoms for all datasets were statistically similar. These acoustic instruments, their datasets, and methods presented herein can better document the absolute localization within acoustic data for many uses in very shallow waters. Full article

(This article belongs to the Special Issue Remote Sensing for Shallow and Deep Waters Mapping and Monitoring)

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39 pages, 7209 KB

Open AccessEditor’s ChoiceArticle

AVHRR NDVI Compositing Method Comparison and Generation of Multi-Decadal Time Series—A TIMELINE Thematic Processor

by Sarah Asam, Christina Eisfelder, Andreas Hirner, Philipp Reiners, Stefanie Holzwarth and Martin Bachmann

Remote Sens. 2023, 15(6), 1631; https://doi.org/10.3390/rs15061631 - 17 Mar 2023

Cited by 13 | Viewed by 4786

Abstract

Remote sensing image composites are crucial for a wide range of remote sensing applications, such as multi-decadal time series analysis. The Advanced Very High Resolution Radiometer (AVHRR) instrument has provided daily data since the early 1980s at a spatial resolution of 1 km, [...] Read more.

Remote sensing image composites are crucial for a wide range of remote sensing applications, such as multi-decadal time series analysis. The Advanced Very High Resolution Radiometer (AVHRR) instrument has provided daily data since the early 1980s at a spatial resolution of 1 km, allowing analyses of climate change-related environmental processes. For monitoring vegetation conditions, the Normalized Difference Vegetation Index (NDVI) is the most widely used metric. However, to actually enable such analyses, a consistent NDVI time series over the AVHRR time-span needs to be created. In this context, the aim of this study is to thoroughly assess the effect of different compositing procedures on AVHRR NDVI composites, as no standard procedure has been established. Thirteen different compositing methods have been implemented; daily, decadal, and monthly composites over Europe and Northern Africa have been calculated for the year 2007, and the resulting data sets have been thoroughly evaluated according to six criteria. The median approach was selected as the best-performing compositing algorithm considering all the investigated aspects. However, the combination of the NDVI value and viewing and illumination angles as the criteria for the best-pixel selection proved to be a promising approach, too. The generated NDVI time series, currently ranging from 1981–2018, shows a consistent behavior and close agreement to the standard MODIS NDVI product. The conducted analyses demonstrate the strong influence of compositing procedures on the resulting AVHRR NDVI composites. Full article

(This article belongs to the Section Remote Sensing Image Processing)

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26 pages, 16407 KB

Open AccessEditor’s ChoiceArticle

Autonomous Detection of Mouse-Ear Hawkweed Using Drones, Multispectral Imagery and Supervised Machine Learning

by Narmilan Amarasingam, Mark Hamilton, Jane E. Kelly, Lihong Zheng, Juan Sandino, Felipe Gonzalez, Remy L. Dehaan and Hillary Cherry

Remote Sens. 2023, 15(6), 1633; https://doi.org/10.3390/rs15061633 - 17 Mar 2023

Cited by 15 | Viewed by 3447

Abstract

Hawkweeds (Pilosella spp.) have become a severe and rapidly invading weed in pasture lands and forest meadows of New Zealand. Detection of hawkweed infestations is essential for eradication and resource management at private and government levels. This study explores the potential of [...] Read more.

Hawkweeds (Pilosella spp.) have become a severe and rapidly invading weed in pasture lands and forest meadows of New Zealand. Detection of hawkweed infestations is essential for eradication and resource management at private and government levels. This study explores the potential of machine learning (ML) algorithms for detecting mouse-ear hawkweed (Pilosella officinarum) foliage and flowers from Unmanned Aerial Vehicle (UAV)-acquired multispectral (MS) images at various spatial resolutions. The performances of different ML algorithms, namely eXtreme Gradient Boosting (XGB), Support Vector Machine (SVM), Random Forest (RF), and K-nearest neighbours (KNN), were analysed in their capacity to detect hawkweed foliage and flowers using MS imagery. The imagery was obtained at numerous spatial resolutions from a highly infested study site located in the McKenzie Region of the South Island of New Zealand in January 2021. The spatial resolution of 0.65 cm/pixel (acquired at a flying height of 15 m above ground level) produced the highest overall testing and validation accuracy of 100% using the RF, KNN, and XGB models for detecting hawkweed flowers. In hawkweed foliage detection at the same resolution, the RF and XGB models achieved highest testing accuracy of 97%, while other models (KNN and SVM) achieved an overall model testing accuracy of 96% and 72%, respectively. The XGB model achieved the highest overall validation accuracy of 98%, while the other models (RF, KNN, and SVM) produced validation accuracies of 97%, 97%, and 80%, respectively. This proposed methodology may facilitate non-invasive detection efforts of mouse-ear hawkweed flowers and foliage in other naturalised areas, enabling land managers to optimise the use of UAV remote sensing technologies for better resource allocation. Full article

(This article belongs to the Special Issue Machine Learning for Multi-Source Remote Sensing Images Analysis)

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19 pages, 2089 KB

Open AccessEditor’s ChoiceArticle

Mapping Fractional Vegetation Coverage across Wetland Classes of Sub-Arctic Peatlands Using Combined Partial Least Squares Regression and Multiple Endmember Spectral Unmixing

by Heidi Cunnick, Joan M. Ramage, Dawn Magness and Stephen C. Peters

Remote Sens. 2023, 15(5), 1440; https://doi.org/10.3390/rs15051440 - 4 Mar 2023

Cited by 9 | Viewed by 2901

Abstract

Vegetation communities play a key role in governing the atmospheric-terrestrial fluxes of water, carbon, nutrients, and energy. The expanse and heterogeneity of vegetation in sub-arctic peatland systems makes monitoring change at meaningful spatial resolutions and extents challenging. We use a field-collected spectral endmember [...] Read more.

Vegetation communities play a key role in governing the atmospheric-terrestrial fluxes of water, carbon, nutrients, and energy. The expanse and heterogeneity of vegetation in sub-arctic peatland systems makes monitoring change at meaningful spatial resolutions and extents challenging. We use a field-collected spectral endmember reference library to unmix hyperspectral imagery and map vegetation coverage at the level of plant functional type (PFT), across three wetland sites in sub-arctic Alaska. This study explores the optimization and parametrization of multiple endmember spectral mixture analysis (MESMA) models to estimate coverage of PFTs across wetland classes. We use partial least squares regression (PLSR) to identify a parsimonious set of critical bands for unmixing and compare the reference and modeled coverage. Unmixing, using a full set of 110-bands and a smaller set of 4-bands, results in maps that effectively discriminate between PFTs, indicating a small investment in fieldwork results in maps mirroring the true ground cover. Both sets of spectral bands differentiate between PFTs, but the 4-band unmixing library results in more accurate predictive mapping with lower computational cost. Reducing the unmixing reference dataset by constraining the PFT endmembers to those identified in the field-site produces only a small advantage for mapping, suggesting extensive fieldwork may not be necessary for MESMA to have a high explanatory value in these remote environments. Full article

(This article belongs to the Special Issue Application of Remote Sensing for Monitoring of Peatlands)

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25 pages, 4062 KB

Open AccessEditor’s ChoiceArticle

Irrigation Timing Retrieval at the Plot Scale Using Surface Soil Moisture Derived from Sentinel Time Series in Europe

by Michel Le Page, Thang Nguyen, Mehrez Zribi, Aaron Boone, Jacopo Dari, Sara Modanesi, Luca Zappa, Nadia Ouaadi and Lionel Jarlan

Remote Sens. 2023, 15(5), 1449; https://doi.org/10.3390/rs15051449 - 4 Mar 2023

Cited by 9 | Viewed by 3896

Abstract

The difficulty of calculating the daily water budget of irrigated fields is often due to the uncertainty surrounding irrigation amounts and timing. The automated detection of irrigation events has the potential to greatly simplify this process, and the combination of high-resolution SAR (Sentinel-1) [...] Read more.

The difficulty of calculating the daily water budget of irrigated fields is often due to the uncertainty surrounding irrigation amounts and timing. The automated detection of irrigation events has the potential to greatly simplify this process, and the combination of high-resolution SAR (Sentinel-1) and optical satellite observations (Sentinel-2) makes the detection of irrigation events feasible through the use of a surface soil moisture (SSM) product. The motivation behind this study is to utilize a large irrigation dataset (collected during the ESA Irrigation + project over five sites in three countries over three years) to analyze the performance of an established algorithm and to test potential improvements. The study’s main findings are (1) the scores decrease with SSM observation frequency; (2) scores decrease as irrigation frequency increases, which was supported by better scores in France (more sprinkler irrigation) than in Germany (more localized irrigation); (3) replacing the original SSM model with the force-restore model resulted in an improvement of about 6% in the F-score and narrowed the error on cumulative seasonal irrigation; (4) the Sentinel-1 configuration (incidence angle, trajectory) did not show a significant impact on the retrieval of irrigation, which supposes that the SSM is not affected by these changes. Other aspects did not allow a definitive conclusion on the irrigation retrieval algorithm: (1) the lower scores obtained with small NDVI compared to large NDVI were counter-intuitive but may have been due to the larger number of irrigation events during high vegetation periods; (2) merging different runs and interpolating all SSM data for one run produced comparable F-scores, but the estimated cumulative sum of irrigation was around −20% lower compared to the reference dataset in the best cases. Full article

(This article belongs to the Special Issue Irrigation Estimates and Management from EO Data)

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30 pages, 18928 KB

Open AccessEditor’s ChoiceReview

A Bibliometric and Visualized Analysis of Remote Sensing Methods for Glacier Mass Balance Research

by Aijie Yu, Hongling Shi, Yifan Wang, Jin Yang, Chunchun Gao and Yang Lu

Remote Sens. 2023, 15(5), 1425; https://doi.org/10.3390/rs15051425 - 3 Mar 2023

Cited by 7 | Viewed by 5711

Abstract

In recent decades, climate change has led to global warming, glacier melting, glacial lake outbursts, sea level rising, and more extreme weather, and has seriously affected human life. Remote sensing technology has advanced quickly, and it offers effective observation techniques for studying and [...] Read more.

In recent decades, climate change has led to global warming, glacier melting, glacial lake outbursts, sea level rising, and more extreme weather, and has seriously affected human life. Remote sensing technology has advanced quickly, and it offers effective observation techniques for studying and monitoring glaciers. In order to clarify the stage of research development, research hotspots, research frontiers, and limitations and challenges in glacier mass balance based on remote sensing technology, we used the tools of bibliometrics and data visualization to analyze 4817 works of literature related to glacier mass balance based on remote sensing technology from 1990 to 2021 in the Web of Science database. The results showed that (1) China and the United States are the major countries in the study of glacier mass balance based on remote sensing technology. (2) The Chinese Academy of Sciences is the most productive research institution. (3) Current research hotspots focus on “Climate change”, “Inventory”, “Dynamics”, “Model”, “Retreat”, “Glacier mass balance”, “Sea level”, “Radar”, “Volume change”, “Surface velocity”, “Glacier mapping”, “Hazard”, and other keywords. (4) The current research frontiers include water storage change, artificial intelligence, High Mountain Asia (HMA), photogrammetry, debris cover, geodetic method, area change, glacier volume, classification, satellite gravimetry, grounding line retreat, risk assessment, lake outburst flood, glacier elevation change, digital elevation model, geodetic mass balance, (DEM) generation, etc. According to the results of the visual analysis of the literature, we introduced the three commonly used methods of glacier mass balance based on remote sensing observation and summarized the research status and shortcomings of different methods in glacier mass balance. We considered that the future research trend is to improve the spatial and temporal resolution of data and combine a variety of methods and data to achieve high precision and long-term monitoring of glacier mass changes and improve the consistency of results. This research summarizes the study of glacier mass balance using remote sensing, which will provide valuable information for future research across this field. Full article

(This article belongs to the Special Issue Applications of Satellite Geodesy in Hydrology, Glaciology, and Oceanography)

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34 pages, 7094 KB

Open AccessEditor’s ChoiceArticle

An Ensemble Approach of Feature Selection and Machine Learning Models for Regional Landslide Susceptibility Mapping in the Arid Mountainous Terrain of Southern Peru

by Chandan Kumar, Gabriel Walton, Paul Santi and Carlos Luza

Remote Sens. 2023, 15(5), 1376; https://doi.org/10.3390/rs15051376 - 28 Feb 2023

Cited by 39 | Viewed by 4919

Abstract

This study evaluates the utility of the ensemble framework of feature selection and machine learning (ML) models for regional landslide susceptibility mapping (LSM) in the arid climatic condition of southern Peru. A historical landslide inventory and 24 different landslide influencing factors (LIFs) were [...] Read more.

This study evaluates the utility of the ensemble framework of feature selection and machine learning (ML) models for regional landslide susceptibility mapping (LSM) in the arid climatic condition of southern Peru. A historical landslide inventory and 24 different landslide influencing factors (LIFs) were prepared using remotely sensed and auxiliary datasets. The LIFs were evaluated using multi-collinearity statistics and their relative importance was measured to select the most discriminative LIFs using the ensemble feature selection method, which was developed using Chi-square, gain ratio, and relief-F methods. We evaluated the performance of ten different ML algorithms (linear discriminant analysis, mixture discriminant analysis, bagged cart, boosted logistic regression, k-nearest neighbors, artificial neural network, support vector machine, random forest, rotation forest, and C5.0) using different accuracy statistics (sensitivity, specificity, area under curve (AUC), and overall accuracy (OA)). We used suitable combinations of individual ML models to develop different ensemble ML models and evaluated their performance in LSM. We assessed the impact of LIFs on ML performance. Among all individual ML models, the k-nearest neighbors (sensitivity = 0.72, specificity = 0.82, AUC = 0.86, OA = 78%) and artificial neural network (sensitivity = 0.71, specificity = 0.85, AUC = 0.87, OA = 79%) algorithms showed the best performance using the top five LIFs, while random forest, rotation forest, and C5.0 (sensitivity = 0.76–0.81, specificity = 0.87, AUC = 0.90–0.93, OA = 82–84%) outperformed other models when developed using all twenty-four LIFs. Among ensemble models, the ensemble of k-nearest neighbors and rotation forest, k-nearest neighbors and artificial neural network, and artificial neural network and rotation forest outperformed other models (sensitivity = 0.72–0.73, specificity = 0.83–0.84, AUC = 0.86, OA = 79%) using the top five LIFs. The landslide susceptibility maps derived using these models indicate that ~2–3% and ~10–12% of the total study area fall within the “very high” and “high” susceptibility. The obtained susceptibility maps can be efficiently used to prioritize landslide mitigation activities. Full article

(This article belongs to the Special Issue Advancement of Remote Sensing in Landslide Susceptibility Assessment)

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21 pages, 8362 KB

Open AccessEditor’s ChoiceArticle

AI-TFNet: Active Inference Transfer Convolutional Fusion Network for Hyperspectral Image Classification

by Jianing Wang, Linhao Li, Yichen Liu, Jinyu Hu, Xiao Xiao and Bo Liu

Remote Sens. 2023, 15(5), 1292; https://doi.org/10.3390/rs15051292 - 26 Feb 2023

Cited by 5 | Viewed by 2519

Abstract

The realization of efficient classification with limited labeled samples is a critical task in hyperspectral image classification (HSIC). Convolutional neural networks (CNNs) have achieved remarkable advances while considering spectral–spatial features simultaneously, while conventional patch-wise-based CNNs usually lead to redundant computations. Therefore, in this [...] Read more.

The realization of efficient classification with limited labeled samples is a critical task in hyperspectral image classification (HSIC). Convolutional neural networks (CNNs) have achieved remarkable advances while considering spectral–spatial features simultaneously, while conventional patch-wise-based CNNs usually lead to redundant computations. Therefore, in this paper, we established a novel active inference transfer convolutional fusion network (AI-TFNet) for HSI classification. First, in order to reveal and merge the local low-level and global high-level spectral–spatial contextual features at different stages of extraction, an end-to-end fully hybrid multi-stage transfer fusion network (TFNet) was designed to improve classification performance and efficiency. Meanwhile, an active inference (AI) pseudo-label propagation algorithm for spatially homogeneous samples was constructed using the homogeneous pre-segmentation of the proposed TFNet. In addition, a confidence-augmented pseudo-label loss (CapLoss) was proposed in order to define the confidence of a pseudo-label with an adaptive threshold in homogeneous regions for acquiring pseudo-label samples; this can adaptively infer a pseudo-label by actively augmenting the homogeneous training samples based on their spatial homogeneity and spectral continuity. Experiments on three real HSI datasets proved that the proposed method had competitive performance and efficiency compared to several related state-of-the-art methods. Full article

(This article belongs to the Special Issue Active Learning Methods for Remote Sensing Data Processing)

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20 pages, 12427 KB

Open AccessEditor’s ChoiceArticle

TChange: A Hybrid Transformer-CNN Change Detection Network

by Yupeng Deng, Yu Meng, Jingbo Chen, Anzhi Yue, Diyou Liu and Jing Chen

Remote Sens. 2023, 15(5), 1219; https://doi.org/10.3390/rs15051219 - 22 Feb 2023

Cited by 25 | Viewed by 6433

Abstract

Change detection is employed to identify regions of change between two different time phases. Presently, the CNN-based change detection algorithm is the mainstream direction of change detection. However, there are two challenges in current change detection methods: (1) the intrascale problem: CNN-based change [...] Read more.

Change detection is employed to identify regions of change between two different time phases. Presently, the CNN-based change detection algorithm is the mainstream direction of change detection. However, there are two challenges in current change detection methods: (1) the intrascale problem: CNN-based change detection algorithms, due to the local receptive field limitation, can only fuse pairwise characteristics in a local range within a single scale, causing incomplete detection of large-scale targets. (2) The interscale problem: Current algorithms generally fuse layer by layer for interscale communication, with one-way flow of information and long propagation links, which are prone to information loss, making it difficult to take into account both large targets and small targets. To address the above issues, a hybrid transformer–CNN change detection network (TChange) for very-high-spatial-resolution (VHR) remote sensing images is proposed. (1) Change multihead self-attention (Change MSA) is built for global intrascale information exchange of spatial features and channel characteristics. (2) An interscale transformer module (ISTM) is proposed to perform direct interscale information exchange. To address the problem that the transformer tends to lose high-frequency features, the use of deep edge supervision is proposed to replace the commonly utilized depth supervision. TChange achieves state-of-the-art scores on the WUH-CD and LEVIR-CD open-source datasets. Furthermore, to validate the effectiveness of Change MSA and the ISTM proposed by TChange, we construct a change detection dataset, TZ-CD, that covers an area of 900

{km}^{2}

and contains numerous large targets and weak change targets. Full article

(This article belongs to the Special Issue Image Change Detection Research in Remote Sensing)

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25 pages, 9423 KB

Open AccessEditor’s ChoiceArticle

Assessing the Performance of a Handheld Laser Scanning System for Individual Tree Mapping—A Mixed Forests Showcase in Spain

by Frederico Tupinambá-Simões, Adrián Pascual, Juan Guerra-Hernández, Cristóbal Ordóñez, Tiago de Conto and Felipe Bravo

Remote Sens. 2023, 15(5), 1169; https://doi.org/10.3390/rs15051169 - 21 Feb 2023

Cited by 27 | Viewed by 5540

Abstract

The use of mobile laser scanning to survey forest ecosystems is a promising, scalable technology to describe the 3D structure of forests at a high resolution. We use a structurally complex, mixed-species Mediterranean forest to test the performance of a mobile Handheld Laser [...] Read more.

The use of mobile laser scanning to survey forest ecosystems is a promising, scalable technology to describe the 3D structure of forests at a high resolution. We use a structurally complex, mixed-species Mediterranean forest to test the performance of a mobile Handheld Laser Scanning (HLS) system to estimate tree attributes within a forest patch in central Spain. We describe the different stages of the HLS approach: field position, ground data collection, scanning path design, point cloud processing, alignment between detected trees and measured reference trees, and finally, the assessment of main tree structural attributes diameter at breast height (DBH) and tree height considering species and tree size as control factors. We surveyed 418 reference trees to account for omission and commission error rates over a 1 ha plot divided into 16 sections and scanned using two different scanning paths. The HLS-based approach reached a high of 88 and 92% tree detection rate for the best combination of scanning path and point cloud processing modes for the HLS system. The root mean squared errors for DBH estimates varied between species: errors for Pinus pinaster were below 2 cm for Scan 02. Quercus pyrenaica, and Alnus glutinosa showed higher error rates. We observed good agreement between ALS and HLS estimates for tree height, highlighting differences to field measurements. Despite the complexity of the mixed forest area surveyed, our results show that HLS is highly efficient at detecting tree locations, estimating DBH, and supporting tree height measurements as confirmed with airborne laser data used for validation. This study is one of the first HLS-based studies conducted in the Mediterranean mixed forest region, where variability in tree allometries and spacing and the presence of natural regeneration pose challenges for the HLS approach. HLS is a feasible, time-efficient, scalable technology for tree mapping in mixed forests with potential to support forest monitoring programmes such as national forest inventories lacking three-dimensional, remote sensing data to support field measurements. Full article

(This article belongs to the Special Issue Advances in Vegetation Structure Modelling to Support the Sustainable Development Goals Acquisition through Forest Management)

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21 pages, 8080 KB

Open AccessEditor’s ChoiceArticle

Remote Sensing Monitoring of the Pietrafitta Earth Flows in Southern Italy: An Integrated Approach Based on Multi-Sensor Data

by Davide Mazza, Antonio Cosentino, Saverio Romeo, Paolo Mazzanti, Francesco M. Guadagno and Paola Revellino

Remote Sens. 2023, 15(4), 1138; https://doi.org/10.3390/rs15041138 - 19 Feb 2023

Cited by 10 | Viewed by 3197

Abstract

Earth flows are complex gravitational events characterised by a heterogeneous displacement pattern in terms of scale, style, and orientation. As a result, their monitoring, for both knowledge and emergency purposes, represents a relevant challenge in the field of engineering geology. This paper aims [...] Read more.

Earth flows are complex gravitational events characterised by a heterogeneous displacement pattern in terms of scale, style, and orientation. As a result, their monitoring, for both knowledge and emergency purposes, represents a relevant challenge in the field of engineering geology. This paper aims to assess the capabilities, peculiarities, and limitations of different remote sensing monitoring techniques through their application to the Pietrafitta earth flow (Southern Italy). The research compared and combined data collected during the main landslide reactivations by different ground-based remote sensors such as Robotic Total Station (R-TS), Terrestrial Synthetic Aperture Radar Interferometry (T-InSAR), and Terrestrial Laser Scanner (TLS), with data being derived by satellite-based Digital Image Correlation (DIC) analysis. The comparison between R-TS and T-InSAR measurements showed that, despite their different spatial and temporal resolutions, the observed deformation trends remain approximately coherent. On the other hand, DIC analysis was able to detect a kinematic process, such as the expansion of the landslide channel, which was not detected by the other techniques used. The results suggest that, when faced with complex events, the use of a single monitoring technique may not be enough to fully observe and understand the processes taking place. Therefore, the limitations of each different technique alone can be solved by a multi-sensor monitoring approach. Full article

(This article belongs to the Special Issue Ground and Structural Deformations Monitoring Systems Integrating Remote Sensing and Ground-Based Data)

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29 pages, 9416 KB

Open AccessEditor’s ChoiceArticle

Evaluation of Airborne HySpex and Spaceborne PRISMA Hyperspectral Remote Sensing Data for Soil Organic Matter and Carbonates Estimation

by Theodora Angelopoulou, Sabine Chabrillat, Stefano Pignatti, Robert Milewski, Konstantinos Karyotis, Maximilian Brell, Thomas Ruhtz, Dionysis Bochtis and George Zalidis

Remote Sens. 2023, 15(4), 1106; https://doi.org/10.3390/rs15041106 - 17 Feb 2023

Cited by 37 | Viewed by 5087

Abstract

Remote sensing and soil spectroscopy applications are valuable techniques for soil property estimation. Soil organic matter (SOM) and calcium carbonate are important factors in soil quality, and although organic matter is well studied, calcium carbonates require more investigation. In this study, we validated [...] Read more.

Remote sensing and soil spectroscopy applications are valuable techniques for soil property estimation. Soil organic matter (SOM) and calcium carbonate are important factors in soil quality, and although organic matter is well studied, calcium carbonates require more investigation. In this study, we validated the performance of laboratory soil spectroscopy for estimating the aforementioned properties with referenced in situ data. We also examined the performance of imaging spectroscopy sensors, such as the airborne HySpex and the spaceborne PRISMA. For this purpose, we applied four commonly used machine learning algorithms and six preprocessing methods for the evaluation of the best fitting algorithm.. The study took place over crop areas of Amyntaio in Northern Greece, where extensive soil sampling was conducted. This is an area with a very variable mineralogical environment (from lignite mine to mountainous area). The SOM results were very good at the laboratory scale and for both remote sensing sensors with R² = 0.79 for HySpex and R² = 0.76 for PRISMA. Regarding the calcium carbonate estimations, the remote sensing accuracy was R² = 0.82 for HySpex and R² = 0.36 for PRISMA. PRISMA was still in the commissioning phase at the time of the study, and therefore, the acquired image did not cover the whole study area. Accuracies for calcium carbonates may be lower due to the smaller sample size used for the modeling procedure. The results show the potential for using quantitative predictions of SOM and the carbonate content based on soil and imaging spectroscopy at the air and spaceborne scales and for future applications using larger datasets. Full article

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

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19 pages, 10071 KB

Open AccessEditor’s ChoiceArticle

Extension of Scattering Power Decomposition to Dual-Polarization Data for Tropical Forest Monitoring

by Ryu Sugimoto, Ryosuke Nakamura, Chiaki Tsutsumi and Yoshio Yamaguchi

Remote Sens. 2023, 15(3), 839; https://doi.org/10.3390/rs15030839 - 2 Feb 2023

Cited by 6 | Viewed by 3444

Abstract

A new scattering power decomposition method is developed for accurate tropical forest monitoring that utilizes data in dual-polarization mode instead of quad-polarization (POLSAR) data. This improves the forest classification accuracy and helps to realize rapid deforestation detection because dual-polarization data are more frequently [...] Read more.

A new scattering power decomposition method is developed for accurate tropical forest monitoring that utilizes data in dual-polarization mode instead of quad-polarization (POLSAR) data. This improves the forest classification accuracy and helps to realize rapid deforestation detection because dual-polarization data are more frequently acquired than POLSAR data. The proposed method involves constructing scattering power models for dual-polarization data considering the radar scattering scenario of tropical forests (i.e., ground scattering, volume scattering, and helix scattering). Then, a covariance matrix is created for dual-polarization data and is decomposed to obtain three scattering powers. We evaluated the proposed method by using simulated dual-polarization data for the Amazon, Southeast Asia, and Africa. The proposed method showed an excellent forest classification performance with both user’s accuracy and producer’s accuracy at >98% for window sizes greater than 7 × 14 pixels, regardless of the transmission polarization. It also showed a comparable deforestation detection performance to that obtained by POLSAR data analysis. Moreover, the proposed method showed better classification performance than vegetation indices and was found to be robust regardless of the transmission polarization. When applied to actual dual-polarization data from the Amazon, it provided accurate forest map and deforestation detection. The proposed method will serve tropical forest monitoring very effectively not only for future dual-polarization data but also for accumulated data that have not been fully utilized. Full article

(This article belongs to the Special Issue SAR, Interferometry and Polarimetry Applications in Geoscience)

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19 pages, 5440 KB

Open AccessEditor’s ChoiceArticle

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 13 | Viewed by 2975

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)

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27 pages, 6203 KB

Open AccessEditor’s ChoiceArticle

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 35 | Viewed by 7024

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)

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20 pages, 4461 KB

Open AccessEditor’s ChoiceArticle

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 36 | Viewed by 4798

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)

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20 pages, 6714 KB

Open AccessEditor’s ChoiceArticle

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 2334

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)

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20 pages, 6547 KB

Open AccessEditor’s ChoiceArticle

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 30 | Viewed by 7572

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)

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17 pages, 9291 KB

Open AccessEditor’s ChoiceArticle

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 29 | Viewed by 10217

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)

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19 pages, 5366 KB

Open AccessEditor’s ChoiceArticle

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 2316

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)

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25 pages, 28288 KB

Open AccessEditor’s ChoiceArticle

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 83 | Viewed by 9418

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)

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26 pages, 5111 KB

Open AccessEditor’s ChoiceArticle

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 3743

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)

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18 pages, 4264 KB

Open AccessEditor’s ChoiceArticle

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 7 | Viewed by 3669

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

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22 pages, 8592 KB

Open AccessEditor’s ChoiceArticle

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 17 | Viewed by 6172

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)

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