Science of Remote Sensing最新文献

{"title":"Satellite observation reveals wetland-induced local cooling moderated by regional climate gradients","authors":"Xiaohong Gao ,&nbsp;Zhuoran Yan ,&nbsp;Lun Bao ,&nbsp;Xuan Li ,&nbsp;Li Gao ,&nbsp;Lingxue Yu","doi":"10.1016/j.srs.2025.100292","DOIUrl":"10.1016/j.srs.2025.100292","url":null,"abstract":"<div><div>Wetlands influence local land surface temperature (LST) via biogeophysical processes, nevertheless, their temperature regulation under different moisture conditions remain unclear. This study quantified the spatial heterogeneity and drivers of wetland-induced temperature effects in the Amur River Basin using multi-year averaged LST data (2003–2022) within a space for time paired comparison framework. Our findings demonstrate that LST regulation of wetlands showed distinct diurnal asymmetry. During the growing season (May–September), natural wetlands induce substantial daytime cooling (−1.20 ± 0.77 K) and slight nighttime warming (0.05 ± 0.63 K). Artificial paddy wetlands show similar patterns but stronger nighttime warming (0.64 ± 0.41 K), reducing net cooling. Both wetland types absorb more solar radiation than adjacent drylands (natural: −0.67 % ± 0.88 %; artificial: −0.60 % ± 0.65 %) and dissipate energy primarily through enhanced evapotranspiration in early growing season (May–July) (0.13 ± 0.30 mm/d; 0.02 ± 0.22 mm/d). Nighttime heat release from water and soil partially offsets daytime cooling. Natural wetlands maintain superior cooling via stable non-radiative processes, with synchronized nighttime cooling in humid regions and compensatory nighttime warming in arid regions, ensuring consistent temperature reduction across hydrological gradients. Conversely, artificial paddy fields in semi-arid areas achieve strong cooling (−0.82 ± 0.34 K) through dual-phase evapotranspiration (0.128 ± 0.263 mm/d; 0.003 ± 0.236 mm/d). In humid regions, nighttime heat storage and release exceed daytime cooling, causing marginal warming. Thus, the cooling effect of artificial paddy fields is governed by moisture, evapotranspiration, and inundation. These results highlight that the artificial paddies cannot fully replace natural wetlands in climate regulation, underscoring the need to prioritize natural wetland conservation and restoration in land-use and climate strategies.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"12 ","pages":"Article 100292"},"PeriodicalIF":5.2,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265185","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}

{"title":"Fine extraction of planting structure at branch canal scale in the Hetao Irrigation District based on multiple extraction method","authors":"Yi Zhao ,&nbsp;Haibin Shi ,&nbsp;Xianyue Li ,&nbsp;Shuya Yang ,&nbsp;Qingfeng Miao ,&nbsp;Jianwen Yan ,&nbsp;Cong Hou","doi":"10.1016/j.srs.2025.100303","DOIUrl":"10.1016/j.srs.2025.100303","url":null,"abstract":"<div><div>The Hetao Irrigation District in Inner Mongolia represents a significant agricultural irrigation area in China, where the precise extraction of planting structure is crucial for the advancement of precision agriculture and smart irrigation practices in the region. To investigate the trends in crop cultivation area changes at the branch canal scale within the Hetao Irrigation District, a 3-year experiment was conducted in the Zuo Er Branch Canal, which is located downstream of the district. This study employed an innovative multiple extraction method combined with a machine learning model to accurately extract the planting structure. The results showed that from April to September over a 3-year period, the NDVI spectral curves for cultivated land and ditch canal exhibited a trend of increasing first and then decreasing, while wasteland and road displayed relatively stable curves with minimal variation. When extracting land use types, the overall accuracy of the gradient boosting tree model was improved by 1.74 %, and 11.43 % compared with that of the random forest and decision tree, and the accuracy of the detail validation was higher. For planting structure extraction, the gradient boosting tree model has an average 3-year overall accuracy improvement of 3.24 % and 8.35 % over the random forest and decision tree models. The area designated for sunflower planting has increased annually, showing a 27.47 % rise in 2024 compared to 2022. In contrast, the area allocated for maize planting has decreased each year, with a significant 66.18 % reduction in 2024 relative to 2022. This study offers crucial theoretical insights and practical implications for the dynamic analysis of planting structure and the modern management of agriculture within the Hetao Irrigation District.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"12 ","pages":"Article 100303"},"PeriodicalIF":5.2,"publicationDate":"2025-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265186","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}

{"title":"Using low-resolution LIDAR on a ground-based agile robot to estimate height, DBH and crown volume of trees","authors":"Omar A. Lopez,&nbsp;Kasper Johansen,&nbsp;Dario Scilla,&nbsp;Mariana Elías-Lara,&nbsp;Victor Angulo,&nbsp;Samer Almashharawi,&nbsp;Matthew F. McCabe","doi":"10.1016/j.srs.2025.100296","DOIUrl":"10.1016/j.srs.2025.100296","url":null,"abstract":"<div><div>Measurements of tree structural properties, such as trunk diameter at breast height (DBH), tree height, crown diameter, and volume, are crucial for estimating aboveground biomass, carbon stocks, or for managing forestry and silviculture applications. Traditional manual surveys are time-consuming, inaccurate, inconsistent, and subject to observer bias. In this study, we explore the capacity of a ground-based robotic quadruped (“Spot”, from Boston Dynamics), equipped with an Enhanced Autonomy Payload (EAP) module and a Velodyne VLP-16 LIDAR sensor, to measure tree height, DBH, and crown volume. Here, we leverage the Spot EAP’s low-beam LIDAR for efficient data processing and maximizing payload capacity without compromising the EAP’s primary navigation function, leading to lower energy consumption. We developed a scanning method and pre-processing pipeline to generate high-quality point clouds for tree structural analysis. Focusing the study in an urban park with 58 trees (22 <em>Erythrina variegata</em> and 36 <em>Ficus altissima</em>), we collected tree height using a metric staff for reference data and also measurements for DBH. Crown volume reference data were derived by combining height measurements obtained from the metric staff with crown extent measurements captured by a LIDAR system mounted on an unmanned aerial vehicle (UAV). Implementing a multi-pose scanning strategy improved the vertical field of view from ±15 to ±60 degrees and increased the point cloud density by more than 800%, achieving a point cloud registration root mean square error (RMSE) of 2.09 cm. Efficient 2D-assisted segmentation, which combines a simplified delineation step with automated refinement, along with leveling, produced individual tree point clouds suitable for structural estimations. Height estimation based on the ground-based robot achieved an RMSE of 29.27 cm and a relative RMSE (rRMSE) of 5.23%. The algorithm for identifying bifurcated trees at breast height showed 100% accuracy. DBH estimates had an RMSE of 4.5 cm and a rRMSE of 18.9%. Crown volume estimation achieved a coefficient of determination (R<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>) of 0.895, outperforming several existing methods. Overall, the study underscores the potential of agile ground-based robots for efficient and accurate tree structural analysis, with likely future improvements possible through automatic segmentation and parameter tuning.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"12 ","pages":"Article 100296"},"PeriodicalIF":5.2,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265184","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}

{"title":"Annual national tree canopy cover mapping: A novel workflow with temporal transferability and improved uncertainty quantification","authors":"Joshua Heyer ,&nbsp;Karen Schleeweis ,&nbsp;Bonnie Ruefenacht ,&nbsp;Ian Housman ,&nbsp;Yang Zhiqiang ,&nbsp;Daniel Ryerson ,&nbsp;Jaclyn Reischmann ,&nbsp;Kevin Megown ,&nbsp;M. Seth Bogle","doi":"10.1016/j.srs.2025.100301","DOIUrl":"10.1016/j.srs.2025.100301","url":null,"abstract":"<div><div>In 2023, a new generation of National Land Cover Database (NLCD) Tree Canopy Cover (TCC) data were produced that include annual 30m gridded time-series products for the Conterminous United States (CONUS). These NLCD TCC v2021.4 products have more frequent time steps, inter-annual coherency, lower product release latency, improved accuracy, uncertainty metrics, and documentation over previous versions. Our methodology leveraged Google Earth Engine (GEE) to generate annual time-series composites from Landsat and Sentinel-2 imagery that were used to predict annual pixel-wise TCC. Next, we used a moving window approach with a 5x5 window of equal-area tiles (480 × 480 km) to calibrate 54 random forest models on 2011 Forest Inventory and Analysis (FIA) reference data. Then we used model temporal transfer to apply each 2011 model to the window's center tile, with annual time-series predictors, to predict per pixel annual TCC and standard error (SE). To quantify model uncertainty, we simulated uncertainty for each TCC model to derive an uncertainty metric, Tau (τ), which enables users to incorporate confidence boundaries into environmental and ecological applications using the NLCD TCC v2021.4 data. An independent statistical assessment of the 2011 TCC map, conducted using 16,607 FIA observations, yielded a weighted Root Mean Square Deviation (RMSD_w) of 12.8 percent TCC and weighted Mean Absolute Error (MAE_w) of 8.0 percent TCC at the CONUS scale. This paper provides a detailed description of the methodology and example use cases of the NLCD TCC and United States Forest Service Science v2021.4 products, paving the way for robust and repeatable future TCC updates.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"12 ","pages":"Article 100301"},"PeriodicalIF":5.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265187","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}

{"title":"Near real-time monitoring reveals extensive recent forest disturbance in Ghana's protected areas","authors":"Luofan Dong ,&nbsp;Xiaojing Tang ,&nbsp;Foster Mensah ,&nbsp;Bashara Ahmed Abubakari ,&nbsp;Kelsee H. Bratley ,&nbsp;Pontus Olofsson ,&nbsp;Curtis E. Woodcock","doi":"10.1016/j.srs.2025.100299","DOIUrl":"10.1016/j.srs.2025.100299","url":null,"abstract":"<div><div>The Protected Areas (PAs) in Ghana play a critical role in preserving the abundant biodiversity of the West Africa Green Belt. But recent changes in policies and regulations have facilitated logging and mining activities, which have accelerated forest disturbances. While there is a consensus that PAs are undergoing destructive change, the extent, rate, and locations of forest disturbances are undocumented. In this study, we applied the fusion near real-time (FNRT) algorithm that utilizes Landsat, Sentinel-1, and Sentinel-2 data and sampling to monitor forests in the PAs of Ghana. The results reveal that 704.74 (±177.24) km² of forest in the PAs were lost in 2023 and 2024, representing 6 % (±1.5 %) of their forest area. Additionally, the forest disturbance rate in 2024 was estimated to be 91 % higher than the rate observed in 2023 (95 % CI: 34 %–172 %). Extensive forest disturbance areas were found in the PAs around Kumasi, such as the Tano Ofin (15.4 % ±3.9 %), Tinte Bepo (42 % ± 10.5 %), and Oda River (18.5 % ± 4.7 %) PAs. Native forests in these PAs are at high risk of further degradation or disappearance in the absence of effective conservation measures. We compared the FNRT results with other alert systems, including RADD and GLAD-L. The comparison demonstrates that multi-sensor fusion provides more timely and accurate detection of forest disturbances in the study area.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"12 ","pages":"Article 100299"},"PeriodicalIF":5.2,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145218955","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}

{"title":"A perspective on the interpretability of poverty maps derived from Earth Observation","authors":"Gary R. Watmough ,&nbsp;Dan Brockington ,&nbsp;Charlotte L.J. Marcinko ,&nbsp;Ola Hall ,&nbsp;Rose Pritchard ,&nbsp;Tristan Berchoux ,&nbsp;Lesley Gibson ,&nbsp;Enrique Delamonica ,&nbsp;Doreen Boyd ,&nbsp;Reason Mlambo ,&nbsp;Seán Ó Héir ,&nbsp;Sohan Seth","doi":"10.1016/j.srs.2025.100298","DOIUrl":"10.1016/j.srs.2025.100298","url":null,"abstract":"<div><div>The use of Earth Observation Data and Machine Learning models to generate gridded micro-level poverty maps has increased in recent years, with several high-profile publications. producing some compelling results. Poverty alleviation remains one of the most critical global challenges. Earth Observation (EO) technologies represent a promising avenue to enhance our ability to address poverty through improved data availability. However, global poverty maps generated by these technologies tend to oversimplify the complex and nuanced nature of poverty preventing progression from proof-of-concept studies to technology that can be deployed in decision making. We provide a perspective on the EO4Poverty field with a focus on areas that need attention. To increase the awareness of what is possible with this technology and reduce the discomfort with model-based estimates, we argue that the EO4Poverty models could and should focus on explainability and operationalizability alongside accuracy and robustness. The use of raw imagery in black-box models results in predictions that appear highly accurate but that are often flawed when investigated in specific local contexts. These models will benefit for incorporating interpretable geospatial features that are directly linked to local context. The use of domain expertise from local end users could make model predictions accessible and more transferable to hard-to-reach areas with little training data.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"12 ","pages":"Article 100298"},"PeriodicalIF":5.2,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219496","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}

{"title":"Mapping of anomalous C-band backscatter signals caused by subsurface scattering and their correlations with land surface characteristics over the Tibetan Plateau","authors":"Xiaojing Bai ,&nbsp;Donghai Zheng ,&nbsp;Xiangzhuo Liu","doi":"10.1016/j.srs.2025.100295","DOIUrl":"10.1016/j.srs.2025.100295","url":null,"abstract":"<div><div>Subsurface scattering is a rarely recognized phenomenon, which may cause abnormal backscatter (σ^o) signal and deteriorate soil moisture (SM) retrieval. Although this phenomenon has been recently detected by Wagner et al. (2024a) over the global scale, the cold regions such as the Tibetan Plateau (TP) widely covered by frozen soil were excluded for analysis. This study aims to fill the research gap to detect and map the σ^o anomalies and subsurface scattering on the TP, discuss their impacts on SM retrievals and explore their correlations with land surface characteristics in the warm seasons when surface soil is thawed. Pearson correlation coefficients (R) obtained between ASCAT or Sentinel-1A σ^o observations and in-situ or ERA5-Land SM data at local scale show that anomalous C-band σ^o signals are detected in the arid area of TP as indicated by negative R values. This directly leads to poorer performance of ASCAT SM retrievals over barren areas than over grasslands in the humid and semi-humid regions. By fitting two backscatter models without and with subsurface scattering term to above collocated σ^o and SM data pairs, the detected anomalous σ^o signals are found to be dominated by subsurface scattering. The results obtained with three indicators proposed to characterize the subsurface scattering further confirm above findings, which show that notable σ^o anomalies and strong subsurface scattering are predominately detected in the western part of TP and Qaidam Basin. Along with the dryness of SM, high level of soil pH index and sparse vegetation cover especially bare land are important factors that favor their occurrences. Moreover, the maps of above indicators with certain thresholds show great potential for masking the regions with high probability of anomalous σ^o signals caused by subsurface scattering and thus inaccurate ASCAT SM retrievals. These results demonstrate for the first time the potential of detecting σ^o anomalies and subsurface scattering and alleviate their effects on SM retrievals in cold and arid regions.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"12 ","pages":"Article 100295"},"PeriodicalIF":5.2,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145218956","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}

{"title":"AQUAVis: Landsat-sentinel virtual constellation of remote sensing reflectance (Rrs) product for coastal and inland waters","authors":"Thainara M.A. Lima ,&nbsp;Vitor S. Martins ,&nbsp;Rejane S. Paulino ,&nbsp;Cassia B. Caballero ,&nbsp;Claudio C.F. Barbosa ,&nbsp;Akash Ashapure","doi":"10.1016/j.srs.2025.100294","DOIUrl":"10.1016/j.srs.2025.100294","url":null,"abstract":"<div><div>Operational Land Imager (OLI) onboard Landsat-8 and -9 (L89) and the MultiSpectral Instrument (MSI) onboard Sentinel-2A and -2B (S2A-B), as well as the recently launched Sentinel-2C, have been increasingly used for inland and coastal water monitoring. Integrating both data sensors into a single virtual constellation presents opportunities to enhance the revisit rate and capture the temporal variability of optically active water constituents. Since the NASA Harmonized Landsat-Sentinel-2 product is primarily focused on land applications, a new water-focused framework is needed for consistent integration of both OLI and MSI Remote Sensing Reflectance (<span><math><mrow><msub><mi>R</mi><mrow><mi>r</mi><mi>s</mi></mrow></msub></mrow></math></span>) products for aquatic studies. This study proposes a new globally validated framework, called AQUAVis (Aquatic Virtual Constellation for Landsat-Sentinel <span><math><mrow><msub><mi>R</mi><mrow><mi>r</mi><mi>s</mi></mrow></msub></mrow></math></span> Observations), designed to generate a harmonized L89-S2A/B dataset for aquatic applications. The AQUAVis workflow includes atmospheric, adjacency effect, and sunglint corrections, as well as spectral bandpass adjustment derived from a global dataset of more than 4000 water bodies to ensure seamless <span><math><mrow><msub><mi>R</mi><mrow><mi>r</mi><mi>s</mi></mrow></msub></mrow></math></span> integration. We acquired a total of 3568 L89 and 2986 S2A/B images (6.6 TB) between 2015 and 2023, and AQUAVis <span><math><mrow><msub><mi>R</mi><mrow><mi>r</mi><mi>s</mi></mrow></msub></mrow></math></span> retrievals were assessed using 2664 matchup observations with the AErosol RObotic NETwork-Ocean Color across 23 sites worldwide. Results demonstrated high agreement between AQUAVis-derived and <em>in situ</em> <span><math><mrow><msub><mi>R</mi><mrow><mi>r</mi><mi>s</mi></mrow></msub></mrow></math></span> retrievals, with spectral differences ranging from 0.39 % in the blue band to 21 % in the near-infrared band. The AQUAVis <span><math><mrow><msub><mi>R</mi><mrow><mi>r</mi><mi>s</mi></mrow></msub></mrow></math></span> product was further assessed for Chlorophyll-a estimation using the XGBoost model, which showed high spatial agreement between AQUAVis OLI and MSI matchup results in a wide range of Chl-a concentrations. These findings highlight the potential of AQUAVis to provide consistent <span><math><mrow><msub><mi>R</mi><mrow><mi>r</mi><mi>s</mi></mrow></msub></mrow></math></span> products for large-scale water quality monitoring. By facilitating global aquatic data integration, AQUAVis supports applications in aquatic remote sensing, contributing to a broader understanding of water quality dynamics and trends.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"12 ","pages":"Article 100294"},"PeriodicalIF":5.2,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219495","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}

{"title":"Comparative evaluation of thirteen satellite-derived surface solar radiation products over China","authors":"Junmei He ,&nbsp;Liang Hong ,&nbsp;Bing Hu ,&nbsp;Wenjun Tang","doi":"10.1016/j.srs.2025.100291","DOIUrl":"10.1016/j.srs.2025.100291","url":null,"abstract":"<div><div>High-quality surface solar radiation (SSR) data are essential for assessing climate change impacts and quantifying solar energy potential. Satellite remote sensing is the primary method to obtain SSR data globally and regionally, especially in regions with sparse ground observation networks. This study validated thirteen satellite-derived SSR products over China using in situ data from the China Meteorological Administration (CMA). Results revealed that Himawari-8-based estimates (H8-ITP, H8-AIR, and GeoNEX) outperformed other products. CERES-SYN, GLASS, ISCCP-ITP, and CLARA-A3 showed moderately inferior performance, whereas BESS, the four MCD18 variants (MCD18A1.V61, MCD18A1.V62, MCD18C1.V61, MCD18C1.V62), and DSCOVR/EPIC demonstrated relatively poor performance in China. Across all stations, the mean bias error (MBE) for these products ranged from −16.9 (BESS) to 26.1 (DSCOVR/EPIC) W m⁻², and the root mean square error (RMSE) from 27.8 (H8-ITP) to 50.0 (DSCOVR/EPIC) W m⁻² at the daily scale. It should be noted that systematic biases (exceeding ±10 W m⁻²) were observed in H8-AIR, GeoNEX, BESS, and DSCOVR/EPIC. H8-AIR and GeoNEX displayed apparent overestimation with MBE values of 14.8 W m⁻² and 12.3 W m⁻², respectively. H8-ITP demonstrated the best overall performance with a minimum RMSE of 27.8 W m⁻², a smaller MBE of 3.9 W m⁻², and a higher R of 0.95 compared to other products. All products exhibit a pronounced accuracy decline in August, with DSCOVR/EPIC showing the sharpest summer deterioration, whereas H8-ITP and GeoNEX remain the most stable. Regionally, all products exhibited generally lower accuracy in the rugged southwestern plateau region and the cloudy southern part of China. These results would provide a valuable reference for selecting the most appropriate SSR product for specific needs, whether for solar energy assessments, climate studies, or surface radiative process analysis.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"12 ","pages":"Article 100291"},"PeriodicalIF":5.2,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145218958","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}

{"title":"Improving soil moisture estimation in wet soils using L-band Synthetic Aperture Radar (SAR) through polarization and filtering optimization","authors":"Naoto Sato ,&nbsp;Shinsuke Aoki ,&nbsp;Daiki Kobayashi ,&nbsp;Yuichi Maruo ,&nbsp;Shunsuke Kodaira ,&nbsp;Kosuke Noborio","doi":"10.1016/j.srs.2025.100290","DOIUrl":"10.1016/j.srs.2025.100290","url":null,"abstract":"<div><div>Accurately mapping soil water distribution is crucial for effective irrigation management and landslide risk assessment. Microwave remote sensing is an effective method for assessing soil water content over extensive areas; however, its efficacy tends to diminish in regions with high soil water content. This study aimed to evaluate the sensitivity of soil moisture estimation in areas with volumetric water content exceeding 0.3 m³/m³, focusing on methodologies of optimizing polarization, despeckling filter application, and ground truth data determination. Among the polarizations of electromagnetic wave analyzed—vertical and horizontal polarization (VH), vertical and vertical polarization (VV), horizontal and horizontal polarization (HH), and horizontal and vertical polarization (HV)—HH and VH polarization exhibited the favorable performance. We evaluated the effectiveness of various despeckling filters—Boxcar, Lee, Frost, Gamma-Map, Refined Lee, and Lee Sigma—on the accuracy of soil moisture estimation. The combination of HH polarization with the Frost filter yielded high correlation coefficients, low root mean square error, and satisfactory sensitivity. While the smallest window size (3 × 3) provided sufficient noise reduction, larger window sizes degraded estimation accuracy. A notable correlation was observed when the ground-truth data were derived from averaging soil water content across an area matching the despeckling filter window size. This suggests that SAR image pixel values effectively represent the mean soil water content within an equivalent spatial extent of the despeckling filter.These findings highlight that accurate soil moisture estimation using SAR backscatter requires careful consideration of polarization, filter type, window size, and the spatial scale of in-situ measurements.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"12 ","pages":"Article 100290"},"PeriodicalIF":5.2,"publicationDate":"2025-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145218957","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}