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":"2026-01-24","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 : 2026-01-23DOI: 10.1016/j.srs.2026.100379
Youngwook Kim , Ji-Hyung Park , Jinyang Du
Land-margin ecosystems surrounding river mouths are hydro-biogeochemical hotspots where water, carbon, and nutrients are exchanged between land and ocean. The land-margin ecosystems have recently experienced significant variations in surface water extent (Fw) due to increasing intensity of climate and environmental changes. The variations of the Fw at the river mouth areas are closely linked with the changes in biogeochemical cycles, including greenhouse gas emissions, ocean chlorophyll production and nutrient exports from land-margin ecosystems. Multi-source environmental remote sensing data records were used to investigate how changes in Fw affect hydroclimates, including precipitation, surface soil moisture, and root-zone soil moisture, and biogeochemical fluxes associated with heterotrophic respiration, atmospheric CH4, and terrigenous dissolved organic matter (tDOM). The study focused on 253 major river mouth sites, identified within the boundary of each river mouth using the MERIT-Hydro map derived from a digital elevation model. The long-term (2003–2022) satellite-derived Fw data showed a strong increasing trend in the mean annual Fw over global land areas and major river mouths. However, the Fw trends varied across aridity zones in response to climate and environmental changes —likely due to the changes in surface dryness and permafrost melting dynamics —with 46 % of river mouths showing a decreasing Fw trend, indicating lower surface wetness conditions. Fw generally showed positive correlations with heterotrophic respiration in the area surrounding river mouths. Its relationship with atmospheric CH4 concentration was also positive in river mouth areas located in semi-arid and sub-humid zones. Particularly, in arid regions, the increasing Fw led to enhance heterotrophic respiration, but significantly reduced atmosphere CH4 concentrations. The deceased flux of tDOM exported from land to water may be linked to the reduced runoffs from river mouth areas as indicated by the Fw decreases. The decreased Fw lowered tDOM exports to coastal waters in 61 % of the studied river mouth areas. The results highlight that long-term satellite-derived Fw observations, alongside multi-source remote sensing data, are critical for monitoring surface wetness in land-margin ecosystems and assessing its impact on hydro-biogeochemical fluxes in near-coastal environments.
{"title":"Satellite remote sensing of hydro-biogeochemical responses to near-coastal water dynamics in global river mouth areas","authors":"Youngwook Kim , Ji-Hyung Park , Jinyang Du","doi":"10.1016/j.srs.2026.100379","DOIUrl":"10.1016/j.srs.2026.100379","url":null,"abstract":"<div><div>Land-margin ecosystems surrounding river mouths are hydro-biogeochemical hotspots where water, carbon, and nutrients are exchanged between land and ocean. The land-margin ecosystems have recently experienced significant variations in surface water extent (Fw) due to increasing intensity of climate and environmental changes. The variations of the Fw at the river mouth areas are closely linked with the changes in biogeochemical cycles, including greenhouse gas emissions, ocean chlorophyll production and nutrient exports from land-margin ecosystems. Multi-source environmental remote sensing data records were used to investigate how changes in Fw affect hydroclimates, including precipitation, surface soil moisture, and root-zone soil moisture, and biogeochemical fluxes associated with heterotrophic respiration, atmospheric CH<sub>4</sub>, and terrigenous dissolved organic matter (tDOM). The study focused on 253 major river mouth sites, identified within the boundary of each river mouth using the MERIT-Hydro map derived from a digital elevation model. The long-term (2003–2022) satellite-derived Fw data showed a strong increasing trend in the mean annual Fw over global land areas and major river mouths. However, the Fw trends varied across aridity zones in response to climate and environmental changes —likely due to the changes in surface dryness and permafrost melting dynamics —with 46 % of river mouths showing a decreasing Fw trend, indicating lower surface wetness conditions. Fw generally showed positive correlations with heterotrophic respiration in the area surrounding river mouths. Its relationship with atmospheric CH<sub>4</sub> concentration was also positive in river mouth areas located in semi-arid and sub-humid zones. Particularly, in arid regions, the increasing Fw led to enhance heterotrophic respiration, but significantly reduced atmosphere CH<sub>4</sub> concentrations. The deceased flux of tDOM exported from land to water may be linked to the reduced runoffs from river mouth areas as indicated by the Fw decreases. The decreased Fw lowered tDOM exports to coastal waters in 61 % of the studied river mouth areas. The results highlight that long-term satellite-derived Fw observations, alongside multi-source remote sensing data, are critical for monitoring surface wetness in land-margin ecosystems and assessing its impact on hydro-biogeochemical fluxes in near-coastal environments.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"13 ","pages":"Article 100379"},"PeriodicalIF":5.2,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078057","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 : 2026-01-23DOI: 10.1016/j.srs.2026.100380
Yan Zhang , Feng Han , Juwei Xiang , Jiwu Guan , Song Wang
To address the challenge faced by existing thin-cloud removal methods in balancing global structure reconstruction and local texture restoration under complex cloud conditions, this paper proposes a remote sensing image de-clouding approach based on a Hierarchical Convolutional Variational Vision Transformer (HCV-CVAE). Built upon the conventional CVAE framework, the proposed model introduces an HCV-ViT encoder that integrates the strengths of convolutional networks and Transformers to enhance local texture representation while capturing global semantic dependencies. Furthermore, strategies such as KL-divergence annealing, cross-dimensional weighted mutual information loss, and test-time augmentation are incorporated to improve the stability of the latent space and the robustness of the generation process. The proposed approach exhibits superior performance over existing algorithms on the RICE2 and T-Cloud datasets, with the highest PSNR and SSIM reaching 40.93 dB and 0.9872, respectively. The HCV-CVAE effectively restores fine details and spectral characteristics beneath clouds while maintaining global structural consistency, exhibiting significant advantages in both visual quality and quantitative metrics. All implementation code and pretrained models are publicly available at: https://github.com/Kyperio/HCV-CVAE.
{"title":"HCV-CVAE: A hierarchical convolutional variational transformer for thin cloud removal in remote sensing imagery","authors":"Yan Zhang , Feng Han , Juwei Xiang , Jiwu Guan , Song Wang","doi":"10.1016/j.srs.2026.100380","DOIUrl":"10.1016/j.srs.2026.100380","url":null,"abstract":"<div><div>To address the challenge faced by existing thin-cloud removal methods in balancing global structure reconstruction and local texture restoration under complex cloud conditions, this paper proposes a remote sensing image de-clouding approach based on a Hierarchical Convolutional Variational Vision Transformer (HCV-CVAE). Built upon the conventional CVAE framework, the proposed model introduces an HCV-ViT encoder that integrates the strengths of convolutional networks and Transformers to enhance local texture representation while capturing global semantic dependencies. Furthermore, strategies such as KL-divergence annealing, cross-dimensional weighted mutual information loss, and test-time augmentation are incorporated to improve the stability of the latent space and the robustness of the generation process. The proposed approach exhibits superior performance over existing algorithms on the RICE2 and T-Cloud datasets, with the highest PSNR and SSIM reaching 40.93 dB and 0.9872, respectively. The HCV-CVAE effectively restores fine details and spectral characteristics beneath clouds while maintaining global structural consistency, exhibiting significant advantages in both visual quality and quantitative metrics. All implementation code and pretrained models are publicly available at: <span><span>https://github.com/Kyperio/HCV-CVAE</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"13 ","pages":"Article 100380"},"PeriodicalIF":5.2,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077415","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 : 2026-01-21DOI: 10.1016/j.srs.2026.100378
Henri Bazzi , Nicolas Baghdadi , Cecile Cazals , Sami Najem , Damien Desroches , Frédéric Frappart , Mehrez Zribi , François Charron
While primarily designed for ocean and inland water monitoring through Interferometric SAR (InSAR) technology, the Surface Water and Ocean Topography (SWOT) Ka-band SAR sensor also presents a novel potential for agricultural applications. This study explores the sensitivity of SWOT's Ka-band backscatter to soil moisture variations, focusing on detecting irrigation events using daily observations collected during the calibration/validation (Cal/Val) phase. Daily backscatter variations from the SWOT Level 1B High-Rate Single-look Complex product were examined over an experimental irrigated grassland site, in response to irrigation events and rainfall. The analysis included first evaluating the stability of SWOT Ka-band backscatter signal, the temporal responses to both irrigation and rainfall, and the influence of vegetation density on Ka-band SAR signal penetration. Main findings showed that the Ka-band SAR data was sensitive to soil moisture variation due to irrigation, inducing an increased backscattering by an average of 4.3 dB on the same day of irrigation. For some cases of flooded vegetation persisting after irrigation, specular reflection and/or double-bounce scattering mechanisms were observed, causing an extreme increase in the Ka-band backscattering. Following complete infiltration, irrigation events induced an average increase of about 2 dB one day after irrigation which dropped back to previous levels two days later due to natural soil drying. Despite the Ka-band's short wavelength, typically limiting canopy penetration, SWOT's near-vertical incidence angle appears to enhance its ability to penetrate dense vegetation cover reaching the soil surface and detecting soil moisture dynamics. These findings open new perspectives for leveraging the daily CAL/VAL SWOT acquisitions to map irrigated areas and support agricultural water management.
{"title":"Observing irrigation using SWOT SAR Ka-band data from daily calibration and validation acquisitions","authors":"Henri Bazzi , Nicolas Baghdadi , Cecile Cazals , Sami Najem , Damien Desroches , Frédéric Frappart , Mehrez Zribi , François Charron","doi":"10.1016/j.srs.2026.100378","DOIUrl":"10.1016/j.srs.2026.100378","url":null,"abstract":"<div><div>While primarily designed for ocean and inland water monitoring through Interferometric SAR (InSAR) technology, the Surface Water and Ocean Topography (SWOT) Ka-band SAR sensor also presents a novel potential for agricultural applications. This study explores the sensitivity of SWOT's Ka-band backscatter to soil moisture variations, focusing on detecting irrigation events using daily observations collected during the calibration/validation (Cal/Val) phase. Daily backscatter variations from the SWOT Level 1B High-Rate Single-look Complex product were examined over an experimental irrigated grassland site, in response to irrigation events and rainfall. The analysis included first evaluating the stability of SWOT Ka-band backscatter signal, the temporal responses to both irrigation and rainfall, and the influence of vegetation density on Ka-band SAR signal penetration. Main findings showed that the Ka-band SAR data was sensitive to soil moisture variation due to irrigation, inducing an increased backscattering by an average of 4.3 dB on the same day of irrigation. For some cases of flooded vegetation persisting after irrigation, specular reflection and/or double-bounce scattering mechanisms were observed, causing an extreme increase in the Ka-band backscattering. Following complete infiltration, irrigation events induced an average increase of about 2 dB one day after irrigation which dropped back to previous levels two days later due to natural soil drying. Despite the Ka-band's short wavelength, typically limiting canopy penetration, SWOT's near-vertical incidence angle appears to enhance its ability to penetrate dense vegetation cover reaching the soil surface and detecting soil moisture dynamics. These findings open new perspectives for leveraging the daily CAL/VAL SWOT acquisitions to map irrigated areas and support agricultural water management.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"13 ","pages":"Article 100378"},"PeriodicalIF":5.2,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037867","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 : 2026-01-21DOI: 10.1016/j.srs.2026.100373
Mahmoud Abdallah , Songbo Wu , Xiaoli Ding
Interferograms are basic observables of any Interferometric Synthetic Aperture Radar (InSAR) measurements. Interferometric decorrelation, however, often reduces the quality of interferograms, sometimes to an extent where no interferometric measurements can be properly carried out. Techniques such as applying a filter can help in reducing the impact of noise in interferograms but often cannot overcome the problem of decorrelation satisfactorily. This paper presents an approach based on a novel two-stage generative adversarial network (GAN) tailored for reconstructing interferometric phase values in decorrelated areas. The approach comprises an edge mapping stage (EMS) and a phase predicting stage (PPS). During the edge mapping stage, a pre-trained convolutional neural network (CNN) identifies fringe lines, while a GAN reconnects the discontinuous fringes. In the phase predicting stage, a second GAN uses the reconnected fringes as a guide to reconstruct the phase information. The model was trained on simulated datasets, achieving an overall accuracy (OA) of 84 % in fringe reconnection and a structural similarity index (SSIM) of 96 %. We validated the proposed model with real-world case studies, successfully reconstructing the phases of co-seismic deformation interferograms for the Tonopah, Nevada earthquake (M 6.5, May 15, 2020) and the Western Xizang earthquake (M 6.3, July 22, 2020). We also evaluated the adaptability of the proposed model using topographic mapping datasets. The experimental results achieved a cross-correlation range of 0.72–0.87 when reconstructing phase information over the Greater Bay Area (GBA) with fine-tuning, indicating potential applicability of the approach to a broader range of InSAR applications.
{"title":"A novel two-stage adversarial joint learning model for reconstructing InSAR phase in decorrelated areas","authors":"Mahmoud Abdallah , Songbo Wu , Xiaoli Ding","doi":"10.1016/j.srs.2026.100373","DOIUrl":"10.1016/j.srs.2026.100373","url":null,"abstract":"<div><div>Interferograms are basic observables of any Interferometric Synthetic Aperture Radar (InSAR) measurements. Interferometric decorrelation, however, often reduces the quality of interferograms, sometimes to an extent where no interferometric measurements can be properly carried out. Techniques such as applying a filter can help in reducing the impact of noise in interferograms but often cannot overcome the problem of decorrelation satisfactorily. This paper presents an approach based on a novel two-stage generative adversarial network (GAN) tailored for reconstructing interferometric phase values in decorrelated areas. The approach comprises an edge mapping stage (EMS) and a phase predicting stage (PPS). During the edge mapping stage, a pre-trained convolutional neural network (CNN) identifies fringe lines, while a GAN reconnects the discontinuous fringes. In the phase predicting stage, a second GAN uses the reconnected fringes as a guide to reconstruct the phase information. The model was trained on simulated datasets, achieving an overall accuracy (OA) of 84 % in fringe reconnection and a structural similarity index (SSIM) of 96 %. We validated the proposed model with real-world case studies, successfully reconstructing the phases of co-seismic deformation interferograms for the Tonopah, Nevada earthquake (M 6.5, May 15, 2020) and the Western Xizang earthquake (M 6.3, July 22, 2020). We also evaluated the adaptability of the proposed model using topographic mapping datasets. The experimental results achieved a cross-correlation range of 0.72–0.87 when reconstructing phase information over the Greater Bay Area (GBA) with fine-tuning, indicating potential applicability of the approach to a broader range of InSAR applications.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"13 ","pages":"Article 100373"},"PeriodicalIF":5.2,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037866","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 : 2026-01-17DOI: 10.1016/j.srs.2026.100371
Zhiqin Gui , Huiqin Ma , Jingcheng Zhang , Wenjiang Huang , Lin Yuan , Kehui Ren
<div><div>Yellow rust (<em>Puccinia striiformis</em> f. sp. <em>Tritici</em>, YR) and Fusarium head blight (<em>Fusarium graminearum</em>, FHB) are two major wheat diseases. These two diseases frequently pose concurrent risks to grain security, particularly in high-yielding wheat regions of eastern China. Accurate regional-scale discrimination of wheat YR and FHB is essential for developing effective green and intelligent disease management strategies. While satellite remote sensing shows potential for regional crop disease monitoring, conventional machine learning modeling approaches widely employed often fail to exploit the spectral-spatial information inherent in imagery. Meanwhile, the scarcity of ground-based disease survey samples limits the application of emerging sample-driven deep learning methods. This study evaluated the effectiveness of 27 sample-feature-algorithm combinatorial modeling strategies for discriminating regional-scale wheat YR and FHB using Sentinel-2 imagery. We augmented disease samples using a stepwise approach that combines marking diseased field vector boundaries with sliding window segmentation (SWS), horizontal-vertical flipping (HVF), and multi-angle rotation (MAR). Recursive feature elimination with cross-validation (RFECV) was employed to optimize spectral and textural features, yielding in two distinct feature sets: disease-sensitive spectral features (SFs) and spectral-textural combined features (STCFs). The original spectral bands (OSBs) served as a third feature set. These sample sets and feature sets were input into several fundamentally distinct algorithms to construct wheat YR and FHB discrimination models. These include three commonly used machine learning (ML) methods, namely, support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost). Additionally, include two deep learning methods, namely, the two-dimensional convolutional neural network (2D-CNN) and the spectral-spatial attention network (SSAN). The results indicated that three ML algorithms exhibited stable performance across all three feature sets under SWS-based sample augmentation. SVM yielded the best overall accuracy, but texture features provided only limited improvement over the SVM model compared with RF and XGBoost. The OSBs outperformed SFs and STCFs in 2D-CNN and SSAN modeling, achieving an overall accuracy (OA) comparable to that of SVM under SWS + HVF + MAR-based sample augmentation. Specifically, the SWS + HVF + MAR-OSBs-SSAN model demonstrated superior performance metrics. This model achieved an average accuracy of 81.8 %, a Kappa coefficient of 0.704, a G-means of 0.892, and an F1-score of 81.1 %. These accuracy results surpassed those of the SWS-STCFS-SVM model, even though the latter achieved the highest OA of 82.8 %. Sample augmentation yielded limited gains in modeling for the 2D-CNN but demonstrated more significant gains for the SSAN. Overall, the STCFs-based SVM modeling strategy remains preferab
{"title":"Discriminating winter wheat yellow rust and Fusarium head blight using Sentinel-2 imagery at a regional scale","authors":"Zhiqin Gui , Huiqin Ma , Jingcheng Zhang , Wenjiang Huang , Lin Yuan , Kehui Ren","doi":"10.1016/j.srs.2026.100371","DOIUrl":"10.1016/j.srs.2026.100371","url":null,"abstract":"<div><div>Yellow rust (<em>Puccinia striiformis</em> f. sp. <em>Tritici</em>, YR) and Fusarium head blight (<em>Fusarium graminearum</em>, FHB) are two major wheat diseases. These two diseases frequently pose concurrent risks to grain security, particularly in high-yielding wheat regions of eastern China. Accurate regional-scale discrimination of wheat YR and FHB is essential for developing effective green and intelligent disease management strategies. While satellite remote sensing shows potential for regional crop disease monitoring, conventional machine learning modeling approaches widely employed often fail to exploit the spectral-spatial information inherent in imagery. Meanwhile, the scarcity of ground-based disease survey samples limits the application of emerging sample-driven deep learning methods. This study evaluated the effectiveness of 27 sample-feature-algorithm combinatorial modeling strategies for discriminating regional-scale wheat YR and FHB using Sentinel-2 imagery. We augmented disease samples using a stepwise approach that combines marking diseased field vector boundaries with sliding window segmentation (SWS), horizontal-vertical flipping (HVF), and multi-angle rotation (MAR). Recursive feature elimination with cross-validation (RFECV) was employed to optimize spectral and textural features, yielding in two distinct feature sets: disease-sensitive spectral features (SFs) and spectral-textural combined features (STCFs). The original spectral bands (OSBs) served as a third feature set. These sample sets and feature sets were input into several fundamentally distinct algorithms to construct wheat YR and FHB discrimination models. These include three commonly used machine learning (ML) methods, namely, support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost). Additionally, include two deep learning methods, namely, the two-dimensional convolutional neural network (2D-CNN) and the spectral-spatial attention network (SSAN). The results indicated that three ML algorithms exhibited stable performance across all three feature sets under SWS-based sample augmentation. SVM yielded the best overall accuracy, but texture features provided only limited improvement over the SVM model compared with RF and XGBoost. The OSBs outperformed SFs and STCFs in 2D-CNN and SSAN modeling, achieving an overall accuracy (OA) comparable to that of SVM under SWS + HVF + MAR-based sample augmentation. Specifically, the SWS + HVF + MAR-OSBs-SSAN model demonstrated superior performance metrics. This model achieved an average accuracy of 81.8 %, a Kappa coefficient of 0.704, a G-means of 0.892, and an F1-score of 81.1 %. These accuracy results surpassed those of the SWS-STCFS-SVM model, even though the latter achieved the highest OA of 82.8 %. Sample augmentation yielded limited gains in modeling for the 2D-CNN but demonstrated more significant gains for the SSAN. Overall, the STCFs-based SVM modeling strategy remains preferab","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"13 ","pages":"Article 100371"},"PeriodicalIF":5.2,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037869","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 : 2026-01-16DOI: 10.1016/j.srs.2026.100376
John R. Dymond , James D. Shepherd , Richard Law , Brent Martin , Jan Schindler , Stella Belliss
Timely and accurate national-scale land-cover mapping is essential for resource management. However, achieving this with limited resources is a challenge, particularly in mountainous and ecologically diverse regions with frequent cloud cover like New Zealand. We present a cost-effective, scalable methodology for land-cover classification that integrates Sentinel-2 imagery, spectral decision rules, temporal NDVI analysis, and deep learning (U-Net) within a unified, reproducible workflow. Our approach generates land-cover maps at a spatial resolution of 10 m. National classification was generated in less than 12 h of computing time. Validation against 4500 samples stratified by map class yielded an overall classification accuracy of 96 %, outperforming leading global products. This method balances automation with expert-informed logic, enabling accurate differentiation of challenging classes such as exotic forest, indigenous forest, and croplands. Although developed for New Zealand, the workflow should be adaptable to other countries seeking low-cost, high-frequency land-cover mapping. These land-cover maps can support a range of environmental applications, including carbon accounting, biodiversity assessment, erosion modelling, and detection of land-use change.
{"title":"A cost-effective method for mapping land cover at national scale","authors":"John R. Dymond , James D. Shepherd , Richard Law , Brent Martin , Jan Schindler , Stella Belliss","doi":"10.1016/j.srs.2026.100376","DOIUrl":"10.1016/j.srs.2026.100376","url":null,"abstract":"<div><div>Timely and accurate national-scale land-cover mapping is essential for resource management. However, achieving this with limited resources is a challenge, particularly in mountainous and ecologically diverse regions with frequent cloud cover like New Zealand. We present a cost-effective, scalable methodology for land-cover classification that integrates Sentinel-2 imagery, spectral decision rules, temporal NDVI analysis, and deep learning (U-Net) within a unified, reproducible workflow. Our approach generates land-cover maps at a spatial resolution of 10 m. National classification was generated in less than 12 h of computing time. Validation against 4500 samples stratified by map class yielded an overall classification accuracy of 96 %, outperforming leading global products. This method balances automation with expert-informed logic, enabling accurate differentiation of challenging classes such as exotic forest, indigenous forest, and croplands. Although developed for New Zealand, the workflow should be adaptable to other countries seeking low-cost, high-frequency land-cover mapping. These land-cover maps can support a range of environmental applications, including carbon accounting, biodiversity assessment, erosion modelling, and detection of land-use change.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"13 ","pages":"Article 100376"},"PeriodicalIF":5.2,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037871","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 : 2026-01-16DOI: 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":"2026-01-16","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 : 2026-01-14DOI: 10.1016/j.srs.2026.100372
Zexian Huang , Mashnoon Islam , Brian Armstrong , Billy Bell , Kourosh Khoshelham , Martin Tomko
Historic dry-stone walls hold significant cultural and environmental importance, serving as historical markers and contributing to ecosystem preservation and wildfire management during dry seasons in Australia. However, many of these stone structures in remote or vegetated landscapes remain undocumented due to limited accessibility and the high cost of manual mapping. Deep learning–based segmentation offers a scalable approach for automated mapping of such features, but challenges remain: 1. the visual occlusion of low-lying dry-stone walls by dense vegetation and 2. the scarcity of labeled training data. This study presents DINO-CV, a self-supervised cross-view pre-training framework based on knowledge distillation, designed for accurate and data-efficient mapping of dry-stone walls using Digital Elevation Models (DEMs) derived from high-resolution airborne LiDAR. By learning invariant geometric and geomorphic features across DEM-derived views, (i.e., Multi-directional Hillshade and Visualization for Archaeological Topography), DINO-CV addresses the occlusion by vegetation and data scarcity challenges. Applied to the Budj Bim Cultural Landscape at Victoria, Australia, a UNESCO World Heritage site, the approach achieves a mean Intersection over Union (mIoU) of 68.6% on test areas and maintains 63.8% mIoU when fine-tuned with only 10% labeled data. These results demonstrate the potential of self-supervised learning on high-resolution DEM derivatives for large-scale, automated mapping of cultural heritage features in complex and vegetated environments. Beyond archaeology, this approach offers a scalable solution for environmental monitoring and heritage preservation across inaccessible or environmentally sensitive regions.
{"title":"Mapping hidden heritage: Self-supervised pre-training on high-resolution LiDAR DEM derivatives for archaeological stone wall detection","authors":"Zexian Huang , Mashnoon Islam , Brian Armstrong , Billy Bell , Kourosh Khoshelham , Martin Tomko","doi":"10.1016/j.srs.2026.100372","DOIUrl":"10.1016/j.srs.2026.100372","url":null,"abstract":"<div><div>Historic dry-stone walls hold significant cultural and environmental importance, serving as historical markers and contributing to ecosystem preservation and wildfire management during dry seasons in Australia. However, many of these stone structures in remote or vegetated landscapes remain undocumented due to limited accessibility and the high cost of manual mapping. Deep learning–based segmentation offers a scalable approach for automated mapping of such features, but challenges remain: 1. the visual occlusion of low-lying dry-stone walls by dense vegetation and 2. the scarcity of labeled training data. This study presents <strong>DINO-CV</strong>, a self-supervised cross-view pre-training framework based on knowledge distillation, designed for accurate and data-efficient mapping of dry-stone walls using <strong>Digital Elevation Models (DEMs)</strong> derived from high-resolution airborne LiDAR. By learning invariant geometric and geomorphic features across DEM-derived views, (i.e., Multi-directional Hillshade and Visualization for Archaeological Topography), DINO-CV addresses the occlusion by vegetation and data scarcity challenges. Applied to the <strong>Budj Bim Cultural Landscape</strong> at Victoria, Australia, a UNESCO World Heritage site, the approach achieves a mean Intersection over Union (<em>mIoU</em>) of <em>68.6%</em> on test areas and maintains <em>63.8%</em> mIoU when fine-tuned with only 10% labeled data. These results demonstrate the potential of self-supervised learning on high-resolution DEM derivatives for large-scale, automated mapping of cultural heritage features in complex and vegetated environments. Beyond archaeology, this approach offers a scalable solution for environmental monitoring and heritage preservation across inaccessible or environmentally sensitive regions.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"13 ","pages":"Article 100372"},"PeriodicalIF":5.2,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977578","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 : 2026-01-13DOI: 10.1016/j.srs.2026.100370
Johannes Löw , Christopher Conrad , Steven Hill , Michael Thiel , Tobias Ullmann , Insa Otte
This study presents a novel framework for quantifying uncertainties and variabilities related to the monitoring of crop phenology via Synthetic Aperture Radar (SAR) time series at the field scale. Therefore, the study investigated multi-orbit, multi-feature time series derived from Sentinel-1 (S1) VV/VH polarizations. This multi-feature approach encompasses backscatter intensity, interferometric coherence and alpha/entropy decomposition features. Crop phenology tracking is crucial for assessing agricultural resilience under climate change, yet existing approaches face challenges due to uncertainties and variability in SAR signal interpretation as well as in situ data. Building on previous landscape-level analyses, this work introduces the concept of trackability, defined as the temporal range during which SAR-derived time-series metrics (TSM), such as breakpoints in backscatter intensity or interferometric coherence, align with key phenological stages (e.g., stem elongation in winter wheat). A growing degree day (GDD)-based normalization contextualizes field-specific deviations relative to landscape averages, enabling quantification of uncertainties inherent in both SAR signals and ground observations. The framework captures the spatio-temporally variable nature of crop development by estimating the first and last phenologically relevant TSM occurrence within a defined uncertainty window, thus providing relational and relative indicators of phenological tracking. This approach reduces dependencies of extensive in situ data and enhances comparability across studies with differing SAR processing methods and their acquisition geometries. Results reproduce known feature-stage relationships (e.g., tracking for stem elongation by interferometric coherence) and reveal inter-seasonal variability influenced by weather conditions and acquisition parameters. On average relevant TSM occurrences were found at approximately 90 % of GDD progression of in situ reported phenological stages, while systematic differences of around 5 % by relative orbit were discovered. The study highlights the potential of integrating multiple S1 features and orbits without optimization-induced information loss, producing quality masks that identify optimal tracking performance at the field level. This framework advances SAR-based phenology monitoring by offering scalable, transferable insights for precision agriculture, while practical implementation still requires detailed field boundaries and early-season crop management information.
{"title":"A novel approach to assessing the tracking accuracy of crop phenology for multi-orbit and multi-feature Sentinel-1 time series","authors":"Johannes Löw , Christopher Conrad , Steven Hill , Michael Thiel , Tobias Ullmann , Insa Otte","doi":"10.1016/j.srs.2026.100370","DOIUrl":"10.1016/j.srs.2026.100370","url":null,"abstract":"<div><div>This study presents a novel framework for quantifying uncertainties and variabilities related to the monitoring of crop phenology via Synthetic Aperture Radar (SAR) time series at the field scale. Therefore, the study investigated multi-orbit, multi-feature time series derived from Sentinel-1 (S1) VV/VH polarizations. This multi-feature approach encompasses backscatter intensity, interferometric coherence and alpha/entropy decomposition features. Crop phenology tracking is crucial for assessing agricultural resilience under climate change, yet existing approaches face challenges due to uncertainties and variability in SAR signal interpretation as well as in situ data. Building on previous landscape-level analyses, this work introduces the concept of trackability, defined as the temporal range during which SAR-derived time-series metrics (TSM), such as breakpoints in backscatter intensity or interferometric coherence, align with key phenological stages (e.g., stem elongation in winter wheat). A growing degree day (GDD)-based normalization contextualizes field-specific deviations relative to landscape averages, enabling quantification of uncertainties inherent in both SAR signals and ground observations. The framework captures the spatio-temporally variable nature of crop development by estimating the first and last phenologically relevant TSM occurrence within a defined uncertainty window, thus providing relational and relative indicators of phenological tracking. This approach reduces dependencies of extensive in situ data and enhances comparability across studies with differing SAR processing methods and their acquisition geometries. Results reproduce known feature-stage relationships (e.g., tracking for stem elongation by interferometric coherence) and reveal inter-seasonal variability influenced by weather conditions and acquisition parameters. On average relevant TSM occurrences were found at approximately 90 % of GDD progression of in situ reported phenological stages, while systematic differences of around 5 % by relative orbit were discovered. The study highlights the potential of integrating multiple S1 features and orbits without optimization-induced information loss, producing quality masks that identify optimal tracking performance at the field level. This framework advances SAR-based phenology monitoring by offering scalable, transferable insights for precision agriculture, while practical implementation still requires detailed field boundaries and early-season crop management information.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"13 ","pages":"Article 100370"},"PeriodicalIF":5.2,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977579","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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