Pub Date : 2026-06-01Epub Date: 2026-01-07DOI: 10.1016/j.srs.2026.100363
Tian Xia, Yanan Zhao, Liguang Jiang
Satellite altimetry has been increasingly used in monitoring inland water bodies. Waveform retracking plays a major role in water level retrieval. However, there remain many challenges to retrieving accurate river water levels, especially for rivers surrounded by various water bodies. In this study, we investigated this problem by diagnosing six retrackers in the Yangtze River, where the environment is very complex. Results show that the official retracker (i.e., OCOG and threshold retrackers) used in Sentinel-3 product exhibits varying performance across 12 virtual stations, with RMSE in the range of 0.55–2.76 m. Surprisingly, no one retracker performs consistently well across all virtual stations. The enhanced multiple waveform persistent peak (MWaPP+) retracker was slightly better than the others. Taking multiple waveforms into consideration is a better strategy than single waveform-based ones. Poor performance is due to irregular waveforms, which are attributed to various water bodies surrounding the river. The number, elevation, and proportion of anomalous water bodies within the footprint are found decisive. In such complex environments, a combination of multiple strategies is needed to improve the accuracy of retrieved water levels. The proposed strategy, by combining FFSAR and MWaPP+, substantially enhanced accuracy and the number of observations. Nevertheless, we call for a round robin exercise to test more retracking strategies to deal with this problem.
{"title":"Altimetry river water level retrieval over complex environments: assessment and diagnosis of different strategies","authors":"Tian Xia, Yanan Zhao, Liguang Jiang","doi":"10.1016/j.srs.2026.100363","DOIUrl":"10.1016/j.srs.2026.100363","url":null,"abstract":"<div><div>Satellite altimetry has been increasingly used in monitoring inland water bodies. Waveform retracking plays a major role in water level retrieval. However, there remain many challenges to retrieving accurate river water levels, especially for rivers surrounded by various water bodies. In this study, we investigated this problem by diagnosing six retrackers in the Yangtze River, where the environment is very complex. Results show that the official retracker (i.e., OCOG and threshold retrackers) used in Sentinel-3 product exhibits varying performance across 12 virtual stations, with RMSE in the range of 0.55–2.76 m. Surprisingly, no one retracker performs consistently well across all virtual stations. The enhanced multiple waveform persistent peak (MWaPP+) retracker was slightly better than the others. Taking multiple waveforms into consideration is a better strategy than single waveform-based ones. Poor performance is due to irregular waveforms, which are attributed to various water bodies surrounding the river. The number, elevation, and proportion of anomalous water bodies within the footprint are found decisive. In such complex environments, a combination of multiple strategies is needed to improve the accuracy of retrieved water levels. The proposed strategy, by combining FFSAR and MWaPP+, substantially enhanced accuracy and the number of observations. Nevertheless, we call for a round robin exercise to test more retracking strategies to deal with this problem.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"13 ","pages":"Article 100363"},"PeriodicalIF":5.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Precise soil moisture estimation is critical for irrigation scheduling and water resource management, especially in semi-arid regions like the Kulfo watershed, Ethiopia, where water availability is highly climate-dependent. However, quantifying the reliability of soil moisture estimates remains a key challenge. Standard satellite products like SMAP provide a measure of aleatoric uncertainty, but this offers limited insight into the confidence of a predictive model. This study develops a deep learning framework to produce a more reliable and better-calibrated measure of epistemic uncertainty, which directly quantifies analytical confidence. Using data from the Soil Moisture Active Passive (SMAP) mission, time-series data were processed in Google Earth Engine (GEE) and structured for deep learning models. Ten different models were tested: one basic long short-term memory (LSTM) model that doesn't consider uncertainty and nine uncertainty-aware model (including five deep ensemble models, Monte Carlo Dropout (MC Dropout), and Quantile Regression models). The Deep Ensemble model achieved the highest accuracy (RMSE = 0.131, R2 = 0.993) and a 94.51 % more reliable uncertainty estimate than the baseline. The MC Dropout model also delivered a much-improved confidence estimate, showing a 41.79 % enhancement. Among the quantile regression models, the 5th percentile model produced a 40.58 % better confidence estimate. Among the quantile regression models, the 5th percentile model produced a 40.58 % better confidence estimate. This study demonstrates that coupling an LSTM-based model with the Deep Ensemble method provides a highly reliable and precise measure of model-specific analytical confidence for soil moisture estimates, offering a more trustworthy approach for decision-making in data-limited regions.
{"title":"Deep learning for epistemic uncertainty in SMAP-derived soil moisture estimates over the Kulfo watershed, Ethiopia","authors":"Demiso Daba Dugassa , Aschalew Cherie Workneh , Babur Tesfaye Yersaw , Getachew Enssa Sedeta , Mulusew Bezabih Chane , Sintayehu Yadete Tola , Sufiyan Abdulmenan Ousman , Zelalem Anley Birhan","doi":"10.1016/j.srs.2025.100357","DOIUrl":"10.1016/j.srs.2025.100357","url":null,"abstract":"<div><div>Precise soil moisture estimation is critical for irrigation scheduling and water resource management, especially in semi-arid regions like the Kulfo watershed, Ethiopia, where water availability is highly climate-dependent. However, quantifying the reliability of soil moisture estimates remains a key challenge. Standard satellite products like SMAP provide a measure of aleatoric uncertainty, but this offers limited insight into the confidence of a predictive model. This study develops a deep learning framework to produce a more reliable and better-calibrated measure of epistemic uncertainty, which directly quantifies analytical confidence. Using data from the Soil Moisture Active Passive (SMAP) mission, time-series data were processed in Google Earth Engine (GEE) and structured for deep learning models. Ten different models were tested: one basic long short-term memory (LSTM) model that doesn't consider uncertainty and nine uncertainty-aware model (including five deep ensemble models, Monte Carlo Dropout (MC Dropout), and Quantile Regression models). The Deep Ensemble model achieved the highest accuracy (RMSE = 0.131, R<sup>2</sup> = 0.993) and a 94.51 % more reliable uncertainty estimate than the baseline. The MC Dropout model also delivered a much-improved confidence estimate, showing a 41.79 % enhancement. Among the quantile regression models, the 5th percentile model produced a 40.58 % better confidence estimate. Among the quantile regression models, the 5th percentile model produced a 40.58 % better confidence estimate. This study demonstrates that coupling an LSTM-based model with the Deep Ensemble method provides a highly reliable and precise measure of model-specific analytical confidence for soil moisture estimates, offering a more trustworthy approach for decision-making in data-limited regions.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"13 ","pages":"Article 100357"},"PeriodicalIF":5.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
SAR operating at shorter wavelengths, exhibits shallower penetration depth and interacts primarily with the upper canopy layer. Consequently, its scattering mechanisms differ from those observed at longer wavelengths. Using this as a basis, this study presents a comprehensive evaluation of Soil Moisture retrieval using C-band data from two satellites missions, EOS-04 and Sentinel-1A through the application of Water Cloud Model. The in-situ datasets were acquired over southern India between June 2022 and January 2024. A total of 43 Sentinel-1A and 32 EOS-04 images were analysed alongside in situ measurements of SM and LAI collected over four crops, as well as bare soil. The novelty of this work lies in the comparative assessment of polarization configurations operating at the shorter wavelength, providing new insights into their relative sensitivity, retrieval performance and development of scattering mechanism across diverse canopy structures. The WCM was calibrated using LAI as the vegetation descriptor, the resulting SM estimates achieved RMSE ranging from 6.28 % to 10.15 %. HH polarization exhibited greater sensitivity under dense canopies, such as turmeric, whereas VV yielded slightly higher overall retrieval accuracy for most crop structures. Analysis of the scattering behaviour revealed that vegetation influence becomes dominant in VV at relatively lower biomass at this wavelength. Results also indicated that at higher SM levels, sensitivity and retrieval accuracy decline due to saturation effects. Overall, this study provides insights into polarization-dependent scattering mechanism in shorter wavelength and highlighting the importance of accounting for crop structure for SM retrieval.
{"title":"Assessing the impact of polarization on soil moisture retrieval using C-band SAR data across diverse crop structures","authors":"Vaibhav Gupta , Dharmendra Kumar Pandey , Nicolas Baghdadi , Mehrez Zribi , Sekhar Muddu","doi":"10.1016/j.srs.2026.100377","DOIUrl":"10.1016/j.srs.2026.100377","url":null,"abstract":"<div><div>SAR operating at shorter wavelengths, exhibits shallower penetration depth and interacts primarily with the upper canopy layer. Consequently, its scattering mechanisms differ from those observed at longer wavelengths. Using this as a basis, this study presents a comprehensive evaluation of Soil Moisture retrieval using C-band data from two satellites missions, EOS-04 and Sentinel-1A through the application of Water Cloud Model. The in-situ datasets were acquired over southern India between June 2022 and January 2024. A total of 43 Sentinel-1A and 32 EOS-04 images were analysed alongside in situ measurements of SM and LAI collected over four crops, as well as bare soil. The novelty of this work lies in the comparative assessment of polarization configurations operating at the shorter wavelength, providing new insights into their relative sensitivity, retrieval performance and development of scattering mechanism across diverse canopy structures. The WCM was calibrated using LAI as the vegetation descriptor, the resulting SM estimates achieved RMSE ranging from 6.28 % to 10.15 %. HH polarization exhibited greater sensitivity under dense canopies, such as turmeric, whereas VV yielded slightly higher overall retrieval accuracy for most crop structures. Analysis of the scattering behaviour revealed that vegetation influence becomes dominant in VV at relatively lower biomass at this wavelength. Results also indicated that at higher SM levels, sensitivity and retrieval accuracy decline due to saturation effects. Overall, this study provides insights into polarization-dependent scattering mechanism in shorter wavelength and highlighting the importance of accounting for crop structure for SM retrieval.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"13 ","pages":"Article 100377"},"PeriodicalIF":5.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-06-01Epub Date: 2026-01-08DOI: 10.1016/j.srs.2026.100367
Juan Quiros-Vargas , Cosimo Brogi , Alexander Damm , Bastian Siegmann , Patrick Rademske , Vicente Burchard-Levine , Vera Krieger , Marius Schmidt , Jan Hanuš , Mauricio Martello , Lutz Weihermüller , Onno Muller , Uwe Rascher
Restrictions in the soil water availability can strongly impact crop productivity. The increasing frequency and severity of drought events, as a result of global warming, has made the assessment of drought stress effects on vegetation of utmost importance for meeting humanity's agricultural production needs. Recent advances in remote sensing of solar-induced chlorophyll fluorescence (SIF) provide a basis for new approaches to directly assess crop water status, since SIF is closely related to photosynthesis and, thus, to early plant physiological processes triggered by limitations in the water supply. This study provides new insights into the effect of varying levels of plant available water (PAW) in the soil on SIF emissions. We used several SIF datasets acquired with the high-performance airborne imaging spectrometer HyPlant during five subsequent vegetation periods (2018, 2019, 2020, 2021 and 2022), each having a different precipitation regime. We normalized the SIF maps for the underlying effects of canopy structure, calculated SIF emission efficiency (eSIF) and selected various crop fields including sugar beet, wheat and potato. Maps of eSIF were compared with spatial PAW patterns, which were derived from a forward soil infiltration model. Our results show positive correlation between eSIF and PAW in rainfed sugar beet fields at early growing stage, which remained consistent when accounting for variations in the leaf area index (LAI). This suggests that eSIF variations in sugar beet reflect the spatial reduction of photosynthesis caused by reduced PAW. In irrigated potato fields, conversely, no eSIF-PAW correlations were found. This indicates the absence of leaf-level water stress in these well-irrigated fields. In rainfed winter wheat fields that were already in a late developmental stage, the variations in the SIF signal were dominated by locally different ripening, i.e., chlorophyll degradation, and therefore not representative of changing PAW. With this study, we could demonstrate that normalized airborne SIF measurements are related to the functional water stress response in different crops. This study supports future investigations on the development of SIF-based tools for the improvement of water management in agriculture.
{"title":"Solar-induced chlorophyll fluorescence (SIF) tracks variations in the soil-plant available water (PAW): a multiyear analysis on three crops","authors":"Juan Quiros-Vargas , Cosimo Brogi , Alexander Damm , Bastian Siegmann , Patrick Rademske , Vicente Burchard-Levine , Vera Krieger , Marius Schmidt , Jan Hanuš , Mauricio Martello , Lutz Weihermüller , Onno Muller , Uwe Rascher","doi":"10.1016/j.srs.2026.100367","DOIUrl":"10.1016/j.srs.2026.100367","url":null,"abstract":"<div><div>Restrictions in the soil water availability can strongly impact crop productivity. The increasing frequency and severity of drought events, as a result of global warming, has made the assessment of drought stress effects on vegetation of utmost importance for meeting humanity's agricultural production needs. Recent advances in remote sensing of solar-induced chlorophyll fluorescence (SIF) provide a basis for new approaches to directly assess crop water status, since SIF is closely related to photosynthesis and, thus, to early plant physiological processes triggered by limitations in the water supply. This study provides new insights into the effect of varying levels of plant available water (PAW) in the soil on SIF emissions. We used several SIF datasets acquired with the high-performance airborne imaging spectrometer HyPlant during five subsequent vegetation periods (2018, 2019, 2020, 2021 and 2022), each having a different precipitation regime. We normalized the SIF maps for the underlying effects of canopy structure, calculated SIF emission efficiency (eSIF) and selected various crop fields including sugar beet, wheat and potato. Maps of eSIF were compared with spatial PAW patterns, which were derived from a forward soil infiltration model. Our results show positive correlation between eSIF and PAW in rainfed sugar beet fields at early growing stage, which remained consistent when accounting for variations in the leaf area index (LAI). This suggests that eSIF variations in sugar beet reflect the spatial reduction of photosynthesis caused by reduced PAW. In irrigated potato fields, conversely, no eSIF-PAW correlations were found. This indicates the absence of leaf-level water stress in these well-irrigated fields. In rainfed winter wheat fields that were already in a late developmental stage, the variations in the SIF signal were dominated by locally different ripening, i.e., chlorophyll degradation, and therefore not representative of changing PAW. With this study, we could demonstrate that normalized airborne SIF measurements are related to the functional water stress response in different crops. This study supports future investigations on the development of SIF-based tools for the improvement of water management in agriculture.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"13 ","pages":"Article 100367"},"PeriodicalIF":5.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-06-01Epub Date: 2026-01-29DOI: 10.1016/j.srs.2026.100375
Hejar Shahabi , Saeid Homayouni , Omid Ghorbanzadeh , Didier Perret , Bernard Giroux , Jimmy Poulin
This study assesses three self-supervised learning (SSL) models including SimCLR, SwAV, and DINOv2 for landslide segmentation using Sentinel-2 and Landsat 8 imagery. The evaluation leverages the Landslide4Sense dataset and a custom Canadian dataset encompassing Yukon, the Northwest Territories, British Columbia, and Northern Quebec. Embedding analysis showed SSL models effectively distinguished landslide features, with DINOv2 yielding high similarity (0.56–0.842) for landslide images and low/negative scores (<0.015, −0.175 to −0.181) for dissimilar land covers/noise. Pretrained on unlabeled multispectral data and fine-tuned with 1 % and 10 % labeled data, DINOv2 outperformed SimCLR, SwAV, and a supervised U-Net baseline, achieving F1-scores of 0.87 (1 % data) and 0.94 (10 % data). SimCLR and SwAV scored 0.77 and 0.83 (1 % data), improving to 0.83 and ∼0.88–0.90 (10 % data), while supervised U-Net reached 0.84. In Canadian regions, DINOv2 excelled with F1-scores of 0.72–0.91 across diverse landslide types, followed by SwAV (0.64–0.90), with Sentinel-2 generally outperforming Landsat 8, except for permafrost landslides where Landsat 8 achieved 0.79 vs. 0.72. Compared to prior studies, DINOv2 surpassed supervised baseline and other SSL models, driven by its transformer-based architecture and strategic band selection. Despite limitations with 128 × 128 patches and dataset imbalances, SSL models prioritized high recall, ensuring robust detection. These results enable near-real-time landslide mapping in data-scarce regions using freely available Sentinel-2/Landsat imagery, reducing dependency on expensive manual labeling, and supporting rapid post-event assessment, early warning integration, and resource allocation in disaster response workflows.
{"title":"Unsupervised deep learning for environmental risk monitoring: Landslide detection from multi-resolution remote sensing imagery","authors":"Hejar Shahabi , Saeid Homayouni , Omid Ghorbanzadeh , Didier Perret , Bernard Giroux , Jimmy Poulin","doi":"10.1016/j.srs.2026.100375","DOIUrl":"10.1016/j.srs.2026.100375","url":null,"abstract":"<div><div>This study assesses three self-supervised learning (SSL) models including SimCLR, SwAV, and DINOv2 for landslide segmentation using Sentinel-2 and Landsat 8 imagery. The evaluation leverages the Landslide4Sense dataset and a custom Canadian dataset encompassing Yukon, the Northwest Territories, British Columbia, and Northern Quebec. Embedding analysis showed SSL models effectively distinguished landslide features, with DINOv2 yielding high similarity (0.56–0.842) for landslide images and low/negative scores (<0.015, −0.175 to −0.181) for dissimilar land covers/noise. Pretrained on unlabeled multispectral data and fine-tuned with 1 % and 10 % labeled data, DINOv2 outperformed SimCLR, SwAV, and a supervised U-Net baseline, achieving F1-scores of 0.87 (1 % data) and 0.94 (10 % data). SimCLR and SwAV scored 0.77 and 0.83 (1 % data), improving to 0.83 and ∼0.88–0.90 (10 % data), while supervised U-Net reached 0.84. In Canadian regions, DINOv2 excelled with F1-scores of 0.72–0.91 across diverse landslide types, followed by SwAV (0.64–0.90), with Sentinel-2 generally outperforming Landsat 8, except for permafrost landslides where Landsat 8 achieved 0.79 vs. 0.72. Compared to prior studies, DINOv2 surpassed supervised baseline and other SSL models, driven by its transformer-based architecture and strategic band selection. Despite limitations with 128 × 128 patches and dataset imbalances, SSL models prioritized high recall, ensuring robust detection. These results enable near-real-time landslide mapping in data-scarce regions using freely available Sentinel-2/Landsat imagery, reducing dependency on expensive manual labeling, and supporting rapid post-event assessment, early warning integration, and resource allocation in disaster response workflows.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"13 ","pages":"Article 100375"},"PeriodicalIF":5.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-06-01Epub Date: 2026-01-16DOI: 10.1016/j.srs.2026.100374
Anwar Sagar , Johannes Pohjala , Jesse Muhojoki , Anubhav Dhital , Harri Kaartinen , Kalle Kärhä , Kalervo Järvelin , Reza Ghabcheloo , Juha Hyyppä , Ville Kankare
Forestry is entering a new era where precision and innovation converge through advanced mobile laser scanning (MLS) technologies. Traditional methods of assessing harvesting quality, often manual, time-consuming, and prone to human error, are being replaced by objective, data-driven approaches. In this study, we conducted high-resolution point cloud scanning across four forest stands (11 ha) in Central Finland using the handheld GeoSLAM ZEB Horizon LiDAR system. We aimed to evaluate the capacity of MLS to measure harvesting attributes related to stand density, tree dimensions, and strip road characteristics, to assess the impact of the Ponsse Plc Thinning Density Assistant (TDA), and to detect defective tree stems. Within a 5-ha subset, 11 potentially anomalous trees were identified. A spatially precise tree map was created using QGIS and a separate map application, enabling comparison between manual field measurements and digital measurements. The findings indicate a strong concordance between automated and traditional assessments. With few exceptions, the results were consistent with established Best Practices for Sustainable Forest Management. Preliminary tests of a novel algorithm for curved stem detection further suggest the potential of MLS for automated defect recognition. A strip road width model was also developed to estimate the average strip road width within the forest stand. These findings underscore MLS as a powerful tool for enhancing accuracy, efficiency, and objectivity in modern forest management.
{"title":"Utilising mobile laser scanning point clouds to assess harvesting quality in thinning stands","authors":"Anwar Sagar , Johannes Pohjala , Jesse Muhojoki , Anubhav Dhital , Harri Kaartinen , Kalle Kärhä , Kalervo Järvelin , Reza Ghabcheloo , Juha Hyyppä , Ville Kankare","doi":"10.1016/j.srs.2026.100374","DOIUrl":"10.1016/j.srs.2026.100374","url":null,"abstract":"<div><div>Forestry is entering a new era where precision and innovation converge through advanced mobile laser scanning (MLS) technologies. Traditional methods of assessing harvesting quality, often manual, time-consuming, and prone to human error, are being replaced by objective, data-driven approaches. In this study, we conducted high-resolution point cloud scanning across four forest stands (11 ha) in Central Finland using the handheld GeoSLAM ZEB Horizon LiDAR system. We aimed to evaluate the capacity of MLS to measure harvesting attributes related to stand density, tree dimensions, and strip road characteristics, to assess the impact of the Ponsse Plc Thinning Density Assistant (TDA), and to detect defective tree stems. Within a 5-ha subset, 11 potentially anomalous trees were identified. A spatially precise tree map was created using QGIS and a separate map application, enabling comparison between manual field measurements and digital measurements. The findings indicate a strong concordance between automated and traditional assessments. With few exceptions, the results were consistent with established Best Practices for Sustainable Forest Management. Preliminary tests of a novel algorithm for curved stem detection further suggest the potential of MLS for automated defect recognition. A strip road width model was also developed to estimate the average strip road width within the forest stand. These findings underscore MLS as a powerful tool for enhancing accuracy, efficiency, and objectivity in modern forest management.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"13 ","pages":"Article 100374"},"PeriodicalIF":5.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-06-01Epub Date: 2026-01-07DOI: 10.1016/j.srs.2026.100369
Aiping Jiang , Dongsheng Wang , Tiantian Jin , Peng Li , Tao Xu , Di Zhang , Boran Zhu , Junqiang Lin , Qidong Peng
Reservoir construction has led to frequent algal blooms in tributaries of backwater areas, threatening water quality, aquatic ecological safety, and public health. The mechanisms governing algal blooms in reservoir bays are complex, leading to spatiotemporal occurrence patterns that are difficult to accurately identify. In this study, Sentinel-2 imagery from 2019 to 2023 were used to characterize the spatiotemporal distribution of algal blooms in Xiangxi Bay (XXB) near the dam of the Three Gorges Reservoir (TGR). The Google Earth Engine (GEE) platform was used for remote sensing image selection, preprocessing, and spectral index calculation, while random forest (RF) model and polynomial regression were employed to develop chlorophyll-a (Chl-a) retrieval model. Box plots and the Mann–Whitney U test were used to compare bloom and non-bloom conditions across nine hydrometeorological variables from the perspective of external influences, while the internal bloom mechanisms were analyzed by integrating the critical depth theory of algal growth with the hydrodynamic mixing characteristics and bloom occurrence patterns in XXB. The results indicated that: (1) Sentinel-2 imagery effectively captures bloom dynamics in narrow tributary embayments, and the Normalized Difference Chlorophyll Index (NDCI) accurately retrieves Chl-a, with the retrieval model achieving an R2 of 0.76; (2) In recent years, algal blooms in XXB have been more likely to occur in March, July, August, and September, particularly in August, with approximately 18 % of the XXB experiencing blooms, predominantly at level II intensity. High-frequency bloom areas are mainly distributed in the mid-to upper reaches of the backwater zone; (3) During the flood season, mid-layer intrusion from the Yangtze River and upstream tributary inflow promote stable stratification in XXB, especially under low-rainfall conditions, thereby favoring algal bloom formation in the mid-to upper reaches. Enhancing hydrodynamic disturbance through physical mixing or reservoir inflow and water-level regulation, particularly under low 10-day rainfall (<35 mm), can effectively suppress bloom development.
{"title":"Remote sensing facilitates the exploration of algal bloom dynamics and its hydrometeorological drivers in tributary bays of the Three Gorges Reservoir","authors":"Aiping Jiang , Dongsheng Wang , Tiantian Jin , Peng Li , Tao Xu , Di Zhang , Boran Zhu , Junqiang Lin , Qidong Peng","doi":"10.1016/j.srs.2026.100369","DOIUrl":"10.1016/j.srs.2026.100369","url":null,"abstract":"<div><div>Reservoir construction has led to frequent algal blooms in tributaries of backwater areas, threatening water quality, aquatic ecological safety, and public health. The mechanisms governing algal blooms in reservoir bays are complex, leading to spatiotemporal occurrence patterns that are difficult to accurately identify. In this study, Sentinel-2 imagery from 2019 to 2023 were used to characterize the spatiotemporal distribution of algal blooms in Xiangxi Bay (XXB) near the dam of the Three Gorges Reservoir (TGR). The Google Earth Engine (GEE) platform was used for remote sensing image selection, preprocessing, and spectral index calculation, while random forest (RF) model and polynomial regression were employed to develop chlorophyll-a (Chl-a) retrieval model. Box plots and the Mann–Whitney <em>U</em> test were used to compare bloom and non-bloom conditions across nine hydrometeorological variables from the perspective of external influences, while the internal bloom mechanisms were analyzed by integrating the critical depth theory of algal growth with the hydrodynamic mixing characteristics and bloom occurrence patterns in XXB. The results indicated that: (1) Sentinel-2 imagery effectively captures bloom dynamics in narrow tributary embayments, and the Normalized Difference Chlorophyll Index (NDCI) accurately retrieves Chl-a, with the retrieval model achieving an R<sup>2</sup> of 0.76; (2) In recent years, algal blooms in XXB have been more likely to occur in March, July, August, and September, particularly in August, with approximately 18 % of the XXB experiencing blooms, predominantly at level II intensity. High-frequency bloom areas are mainly distributed in the mid-to upper reaches of the backwater zone; (3) During the flood season, mid-layer intrusion from the Yangtze River and upstream tributary inflow promote stable stratification in XXB, especially under low-rainfall conditions, thereby favoring algal bloom formation in the mid-to upper reaches. Enhancing hydrodynamic disturbance through physical mixing or reservoir inflow and water-level regulation, particularly under low 10-day rainfall (<35 mm), can effectively suppress bloom development.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"13 ","pages":"Article 100369"},"PeriodicalIF":5.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-06-01Epub Date: 2026-02-05DOI: 10.1016/j.srs.2026.100388
Steven K. Filippelli , Jody C. Vogeler , Francisco Mauro , Corli Coetsee , Patrick A. Fekety , Melissa McHale , David Bunn
Adaptive management of savanna ecosystems requires frequent monitoring of woody vegetation structure, and although vegetation structure and changes may be captured with repeat airborne lidar, it is spatially and temporally limited across African savannas. As an alternative, this study evaluates the extension of spaceborne waveform lidar canopy metrics (RH98, Cover, Foliage Height Diversity) from the Global Ecosystem Dynamics Investigation (GEDI) across the Greater Kruger region in South Africa using moderate resolution optical sensors (Landsat and Harmonized Landsat Sentinel-2 [HLS]), L-band Synthetic Aperture Radar (PALSAR-1 and -2), and topographic and soil covariates. We compared the performance of 12 predictor sets incorporating different sensor combinations and temporal processing methods (LandTrendr and CCDC) in random forest models using temporal cross-validation to assess extrapolation accuracy. The most parsimonious fusion model (LandTrendr + SAR + topography/soils) achieved RMSEs of 3.04 m for RH98, 13.38% for Cover and 0.34 for FHD, which was comparable to more complex models using HLS and CCDC. All models demonstrated good temporal transferability with minimal bias but tended to overestimate low values and underestimate high values, which muted the estimated magnitude of change. Annual canopy structure maps derived from the best model captured expected spatial patterns and were used in model-based estimators to quantify changes in areas impacted by elephants, timber harvesting, fuelwood extraction, and woody encroachment. Extending GEDI metrics with moderate-resolution sensors thus offers a viable approach for large-scale savanna monitoring and detecting change in high impact areas.
热带稀树草原生态系统的适应性管理需要经常监测木本植被结构，尽管可以通过机载激光雷达重复捕捉到植被结构和变化，但在非洲稀树草原上，这在空间和时间上是有限的。作为替代方案，本研究利用中分辨率光学传感器（Landsat和Harmonized Landsat Sentinel-2 [HLS]）、l波段合成孔径雷达（PALSAR-1和-2）以及地形和土壤共变量，评估了来自全球生态系统动力学调查（GEDI）的星载波形激光雷达冠层指标（RH98、覆盖度、叶高多样性）在南非大克鲁格地区的扩展。我们比较了随机森林模型中包含不同传感器组合和时间处理方法（LandTrendr和CCDC）的12个预测集的性能，使用时间交叉验证来评估外推精度。最简洁的融合模型（LandTrendr + SAR +地形/土壤）RH98的rmse为3.04 m， Cover的rmse为13.38%，FHD的rmse为0.34 m，与使用HLS和CCDC的更复杂模型相当。所有模型均表现出良好的时间可转移性，偏差最小，但往往高估低值，低估高值，这减弱了估计的变化幅度。从最佳模型中获得的年度冠层结构图捕获了预期的空间格局，并用于基于模型的估算器中，以量化受大象、木材采伐、薪柴提取和木材侵占影响的区域的变化。因此，用中等分辨率传感器扩展GEDI指标为在高影响地区进行大规模稀树草原监测和检测变化提供了一种可行的方法。
{"title":"Tracking savanna vegetation structure in South Africa by extension of GEDI canopy metrics with Landsat, Sentinel-2, and PALSAR","authors":"Steven K. Filippelli , Jody C. Vogeler , Francisco Mauro , Corli Coetsee , Patrick A. Fekety , Melissa McHale , David Bunn","doi":"10.1016/j.srs.2026.100388","DOIUrl":"10.1016/j.srs.2026.100388","url":null,"abstract":"<div><div>Adaptive management of savanna ecosystems requires frequent monitoring of woody vegetation structure, and although vegetation structure and changes may be captured with repeat airborne lidar, it is spatially and temporally limited across African savannas. As an alternative, this study evaluates the extension of spaceborne waveform lidar canopy metrics (RH98, Cover, Foliage Height Diversity) from the Global Ecosystem Dynamics Investigation (GEDI) across the Greater Kruger region in South Africa using moderate resolution optical sensors (Landsat and Harmonized Landsat Sentinel-2 [HLS]), L-band Synthetic Aperture Radar (PALSAR-1 and -2), and topographic and soil covariates. We compared the performance of 12 predictor sets incorporating different sensor combinations and temporal processing methods (LandTrendr and CCDC) in random forest models using temporal cross-validation to assess extrapolation accuracy. The most parsimonious fusion model (LandTrendr + SAR + topography/soils) achieved RMSEs of 3.04 m for RH98, 13.38% for Cover and 0.34 for FHD, which was comparable to more complex models using HLS and CCDC. All models demonstrated good temporal transferability with minimal bias but tended to overestimate low values and underestimate high values, which muted the estimated magnitude of change. Annual canopy structure maps derived from the best model captured expected spatial patterns and were used in model-based estimators to quantify changes in areas impacted by elephants, timber harvesting, fuelwood extraction, and woody encroachment. Extending GEDI metrics with moderate-resolution sensors thus offers a viable approach for large-scale savanna monitoring and detecting change in high impact areas.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"13 ","pages":"Article 100388"},"PeriodicalIF":5.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-09-17DOI: 10.1016/j.srs.2025.100288
Nurul Syafiqah Tan , Rohan Gautam , Fangyi Tan , Gina M. Sarkawi , Jędrzej M. Majewski , Junki Komori , Shi Jun Wee , Khai Ken Leoh , Lucas D. Koh , Adam D. Switzer , Aron J. Meltzner
Coral microatolls, geological proxies commonly used for reconstructing relative sea-level (RSL) in low-latitude regions, are valued for their precision and ability to continuously track RSL changes through the elevation of successive concentric surface rings. The brief low-tide window prevents rigorous methods for replicating field observations, limiting opportunities for reinterpretation of coral morphology. Additionally, while the extraction of a physical coral slab remains the preferred method for RSL reconstruction, logistical constraints can render it non-viable. When slabbing is possible, the reliability of the reconstructed RSL might be questionable. This study introduces three-dimensional models created using structure-from-motion photogrammetry and iPhone LiDAR scans to facilitate rigorous analysis of coral microatolls. These methods result in accurate and high-resolution documentation of the coral surface, enabling comprehensive and simultaneous analysis of ring structures of multiple microatolls while ensuring results are representative and replicable. Where slabbing is feasible, this method guides the selection of optimal corals that contain the most complete record of RSL change and validates slabbing results. Where slabbing is not viable, this approach provides an alternative means to obtaining RSL histories. Integrating this model-based approach into conventional fieldwork enables extensive data interpretation off-site. Furthermore, the user-friendly nature of these methods enhances accessibility for researchers with limited resources. The benefits and limitations of each technique are also discussed. While photogrammetry-derived point clouds are denser, they necessitate additional georeferencing steps to ensure accurate scale and orientation. Conversely, iPhone-derived models possess inherent scale, though they require additional processing steps, carrying a potential risk of data loss.
{"title":"Three-dimensional models of coral microatolls using structure-from-motion photogrammetry and iPhone LiDAR scanning: A fast, reproducible method for collecting relative sea-level data in the field","authors":"Nurul Syafiqah Tan , Rohan Gautam , Fangyi Tan , Gina M. Sarkawi , Jędrzej M. Majewski , Junki Komori , Shi Jun Wee , Khai Ken Leoh , Lucas D. Koh , Adam D. Switzer , Aron J. Meltzner","doi":"10.1016/j.srs.2025.100288","DOIUrl":"10.1016/j.srs.2025.100288","url":null,"abstract":"<div><div>Coral microatolls, geological proxies commonly used for reconstructing relative sea-level (RSL) in low-latitude regions, are valued for their precision and ability to continuously track RSL changes through the elevation of successive concentric surface rings. The brief low-tide window prevents rigorous methods for replicating field observations, limiting opportunities for reinterpretation of coral morphology. Additionally, while the extraction of a physical coral slab remains the preferred method for RSL reconstruction, logistical constraints can render it non-viable. When slabbing is possible, the reliability of the reconstructed RSL might be questionable. This study introduces three-dimensional models created using structure-from-motion photogrammetry and iPhone LiDAR scans to facilitate rigorous analysis of coral microatolls. These methods result in accurate and high-resolution documentation of the coral surface, enabling comprehensive and simultaneous analysis of ring structures of multiple microatolls while ensuring results are representative and replicable. Where slabbing is feasible, this method guides the selection of optimal corals that contain the most complete record of RSL change and validates slabbing results. Where slabbing is not viable, this approach provides an alternative means to obtaining RSL histories. Integrating this model-based approach into conventional fieldwork enables extensive data interpretation off-site. Furthermore, the user-friendly nature of these methods enhances accessibility for researchers with limited resources. The benefits and limitations of each technique are also discussed. While photogrammetry-derived point clouds are denser, they necessitate additional georeferencing steps to ensure accurate scale and orientation. Conversely, iPhone-derived models possess inherent scale, though they require additional processing steps, carrying a potential risk of data loss.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"12 ","pages":"Article 100288"},"PeriodicalIF":5.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145121183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-08-23DOI: 10.1016/j.srs.2025.100275
Nima Dorji , Joseph L. Awange , Ayalsew Zerihun
The impacts of global warming are pronounced in mountainous regions, yet a scarcity of long-term climate data hinders robust documentation. Reanalysis (ERA5, ERA5-Land, MERRA2), gridded observational (CRU TS), and satellite-derived (MODIS LST) datasets serve as alternatives, but their reliability for local-scale impact studies remains uncertain without rigorous evaluation. Here, we present the first comprehensive assessment of these datasets across Bhutan's complex topography, comparing them to in-situ observations (1996–2023) using systemic statistical metrics, which is a critical prerequisite for their applications. Results reveal that pre-corrected datasets contain severe systematic cold bias increasing with elevation at 3.1–4.2 °C/km, culminating to bias up to −19 °C in the high-altitude areas. The post-correction analysis reveals that elevation-corrected reanalyses data reduces mean bias by a maximum of 31 %. However, enhancement of spatial representativeness of temperature through dynamically estimated lapse rate on in-situ temperature markedly reduces mean bias across all datasets including MODIS-derived air temperature. The altitudinal bias gradient, depending on reanalyses data, is reduced to 0.1°C–0.8 °C/km. Despite these notable improvements in accuracy, MODIS LST and reanalyses/CRU datasets continue to exhibit over- and underestimation, respectively. These findings suggest that limitations of accuracy stem not only from model assimilation or interpolation, but also from limited spatial representativeness of station observations. Our findings underscore that the use of these datasets directly in climate impact studies is impractical without prior corrections. This work provides a framework for evaluating temperature products in mountainous regions, ensuring their utility for adaptation planning in Bhutan and analogous terrains globally.
{"title":"Reliability of satellite, reanalysis and observation-based gridded temperature datasets for climate change impact studies in Bhutan","authors":"Nima Dorji , Joseph L. Awange , Ayalsew Zerihun","doi":"10.1016/j.srs.2025.100275","DOIUrl":"10.1016/j.srs.2025.100275","url":null,"abstract":"<div><div>The impacts of global warming are pronounced in mountainous regions, yet a scarcity of long-term climate data hinders robust documentation. Reanalysis (ERA5, ERA5-Land, MERRA2), gridded observational (CRU TS), and satellite-derived (MODIS LST) datasets serve as alternatives, but their reliability for local-scale impact studies remains uncertain without rigorous evaluation. Here, we present the first comprehensive assessment of these datasets across Bhutan's complex topography, comparing them to <em>in-situ</em> observations (1996–2023) using systemic statistical metrics, which is a critical prerequisite for their applications. Results reveal that pre-corrected datasets contain severe systematic cold bias increasing with elevation at 3.1–4.2 °C/km, culminating to bias up to −19 °C in the high-altitude areas. The post-correction analysis reveals that elevation-corrected reanalyses data reduces mean bias by a maximum of 31 %. However, enhancement of spatial representativeness of temperature through dynamically estimated lapse rate on <em>in-situ</em> temperature markedly reduces mean bias across all datasets including MODIS-derived air temperature. The altitudinal bias gradient, depending on reanalyses data, is reduced to 0.1°C–0.8 °C/km. Despite these notable improvements in accuracy, MODIS LST and reanalyses/CRU datasets continue to exhibit over- and underestimation, respectively. These findings suggest that limitations of accuracy stem not only from model assimilation or interpolation, but also from limited spatial representativeness of station observations. Our findings underscore that the use of these datasets directly in climate impact studies is impractical without prior corrections. This work provides a framework for evaluating temperature products in mountainous regions, ensuring their utility for adaptation planning in Bhutan and analogous terrains globally.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"12 ","pages":"Article 100275"},"PeriodicalIF":5.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144904576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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