Remote Sensing of Environment最新文献

{"title":"Multi-satellite data fusion for improved field-scale evapotranspiration mapping on Google Earth Engine","authors":"Hui Liu ,&nbsp;Yun Yang ,&nbsp;Martha C. Anderson ,&nbsp;Feng Gao ,&nbsp;Christopher R. Hain ,&nbsp;Vikalp Mishra ,&nbsp;John M. Volk ,&nbsp;Yanghui Kang","doi":"10.1016/j.rse.2026.115299","DOIUrl":"10.1016/j.rse.2026.115299","url":null,"abstract":"<div><div>Accurate field-scale evapotranspiration (ET) data with high spatiotemporal resolution is crucial for characterizing surface energy and water balance dynamics and guiding water resource management. OpenET, implemented on Google Earth Engine (GEE), provides field-scale ET estimates and relies mainly on Landsat data. Although Landsat thermal infrared (TIR) observations are effective for field-scale ET mapping, the ∼8-day revisit interval of the combined Landsat 8/9 constellation provides insufficient temporal sampling for short-term ET dynamics. This study presents a framework to improve the spatiotemporal resolution of ET mapping by integrating TIR observations from ECOSTRESS and VIIRS with Harmonized Landsat-Sentinel (HLS) data on GEE. Land surface temperature (LST) data from Landsat, ECOSTRESS and VIIRS were sharpened to 30-m resolution using the Data Mining Sharpener (DMS) algorithm. These sharpened LST data, along with 30-m Leaf Area Index (LAI) and albedo derived from HLS, were used as inputs to the GEE-based Disaggregated Atmosphere-Land Exchange (DisALEXI) model to produce daily 30-m ET estimates. ET estimates were validated against flux tower observations and compared with baseline Landsat-derived ET at six sites with varying land cover and climatic conditions. Results indicated that incorporating ECOSTRESS and VIIRS generally improved ET estimation accuracy, reducing average MAE (mm/day) by 8.64% (1.12 to 1.02, daily), 14.40% (1.00 to 0.85, weekly), 16.37% (0.82 to 0.69, monthly) relative to Landsat-only baselines. This GEE-based framework establishes a prototype workflow for integrating new satellite data sources into the OpenET modeling framework, supporting sustainable agriculture and water resource management.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"336 ","pages":"Article 115299"},"PeriodicalIF":11.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146161757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating the contributions of herbaceous vegetation biomass to soil accretion in Louisiana's coastal deltaic wetlands using airborne imaging spectroscopy","authors":"Daniel Jensen ,&nbsp;Elena Solohin ,&nbsp;Edward Castañeda-Moya ,&nbsp;Marc Simard ,&nbsp;David R. Thompson ,&nbsp;Cathleen E. Jones ,&nbsp;Alexandra Christensen ,&nbsp;Andre Rovai ,&nbsp;Andy Fontenot ,&nbsp;Robert Twilley","doi":"10.1016/j.rse.2026.115301","DOIUrl":"10.1016/j.rse.2026.115301","url":null,"abstract":"<div><div>Understanding the relationship between vegetation productivity and soil accretion is critical for assessing the resilience of deltaic wetlands to relative sea-level rise. This study integrates airborne imaging spectroscopy from NASA's AVIRIS-NG sensor with field measurements from the Delta-X campaign to quantify herbaceous vegetation contributions to vertical accretion processes in the Mississippi River Delta's coastal wetlands. We developed imaging spectroscopy–based products for live above- and belowground carbon (AGC, BGC), and aboveground necromass (AGN; or non-photosynthetic vegetation), the latter being derived using a novel combination of spectral unmixing and Random Forest regression. These vegetation maps were evaluated alongside in situ soil accretion measurements—including surface sediment accretion, soil bulk density, organic matter content, and soil organic carbon density—derived from feldspar marker horizons. These data were sampled concurrently with airborne campaigns conducted in April and August 2021 to coincide with the early- and peak-biomass season conditions. While vegetation metrics explained a limited fraction of variability in total vertical accretion, AGC, AGN, and BGC together accounted for a substantial portion of the variance in soil bulk density and organic carbon density, indicating that vegetation-driven organic matter inputs exert a stronger control on soil mass and carbon storage than on short-term elevation gain. These results demonstrate that airborne imaging spectroscopy can resolve distinct vegetation pools that differentially influence wetland soil properties, providing new insight into the coupled vegetation–soil processes underpinning blue carbon dynamics. This highlights imaging spectroscopy's value for advancing landscape-scale assessments of wetland resilience and informing management strategies in sediment-deprived coastal basins.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"336 ","pages":"Article 115301"},"PeriodicalIF":11.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic inheritance-enhanced TomoSAR imaging with dual-task deep learning for urban applications","authors":"Haoxuan Duan ,&nbsp;Hong Zhang ,&nbsp;Peifeng Ma ,&nbsp;Yixian Tang ,&nbsp;Zihuan Guo ,&nbsp;Yukun Fan ,&nbsp;Chao Wang","doi":"10.1016/j.rse.2026.115303","DOIUrl":"10.1016/j.rse.2026.115303","url":null,"abstract":"<div><div>Long-term monitoring of urban infrastructure deformation is critical for city safety. Tomographic SAR (TomoSAR) enables precise layover scatterer separation via elevation-based imaging, serving as a key tool for urban monitoring. However, traditional TomoSAR methods are inefficient for new time periods due to repetitive computations and complex inversion algorithms, failing to meet rapid monitoring demands. This study proposes a dynamic inheritance-enhanced TomoSAR Imaging method using dual-task deep learning model. First, a dynamic scatterer sample set is constructed for new periods by efficiently screening and inheriting historical scatterer classes and elevation parameters. Next, the DT-TomoSARNet network is developed to simultaneously perform scatterer classification and elevation regression through a dual-task collaborative mechanism, optimized by a hybrid loss function that adaptively adjusts task weights. Finally, the Historical-constrained-BF algorithm leverages historical data constraints to rapidly update large-scale linear deformation velocities and thermal amplitudes. Experiments using 2015–2024 COSMO-SkyMed long time-series data from Shenzhen City demonstrate that the proposed method effectively inherits historical parameters, achieving a maximum weighted F1-score of 0.97 and a minimum average absolute error of 1.438 m in scatterer classification and elevation regression tasks. Furthermore, the global processing time is reduced to just one-sixth of that required by the traditional BF method, while maintaining high accuracy in physical parameter estimation. This method significantly enhances the applicability of TomoSAR for long-term dynamic urban deformation monitoring.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"336 ","pages":"Article 115303"},"PeriodicalIF":11.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved prediction of winter wheat yield at regional scale with limited ground samples by unmanned aerial vehicle and satellite synergy","authors":"Yuan Xiong,&nbsp;Gaoxiang Yang,&nbsp;Lei Zhang,&nbsp;Weiguo Yu,&nbsp;Yapeng Wu,&nbsp;Jun Lu,&nbsp;Chongya Jiang,&nbsp;Xia Yao,&nbsp;Yan Zhu,&nbsp;Weixing Cao,&nbsp;Tao Cheng","doi":"10.1016/j.rse.2026.115271","DOIUrl":"10.1016/j.rse.2026.115271","url":null,"abstract":"<div><div>Rapid, accurate, and large-scale in-season prediction of winter wheat yield is essential for enhancing food security and guiding agricultural policies. Traditional data-driven methods with satellite imagery face challenges in large-scale prediction of winter wheat yield because of the limited ground sampling data available for model training. Although unmanned aerial vehicle (UAV) images have been integrated with satellite imagery for generating reference data in monitoring vegetation dynamics, the UAV and satellite synergy has not yet been investigated for cross-scale sample augmentation and information fusion in large-scale prediction of winter wheat yield. To address these issues, this study proposed a novel framework integrating ground, UAV, and satellite data with data-driven algorithms to improve regional-scale yield prediction without the need of adding field measured yield samples. The potential contributions of UAV data to yield sample augmentation were examined for compensating the lack of ground samples and improving regional-scale wheat yield prediction. Subsequently, an optimal yield prediction strategy was developed through augmented sample quality and spatial variability analysis with cross-scale information fusion. The proposed framework was evaluated with extensive field-level yield measurements over three consecutive seasons of winter wheat across Jiangsu Province, China.</div><div>The results demonstrated that synthesizing UAV and satellite data achieved superior performance across four data-driven algorithms as compared to using satellite data alone, with the ground-UAV-satellite Deep Neural Networks (DNN) model showing the most significant improvement (<em>R</em>^<em>2</em>: 0.39 vs 0.85, <em>RMSE</em>: 1.05 vs 0.43 t/ha). Additionally, optimizing UAV-derived upscaled samples with the spatial variability indicator (Entropy for the anthesis-filling stage, <em>Entropy_F</em>) proved more effective for yield prediction than the conventional Winter Wheat Vegetation Fraction (WVF). The optimal strategy combination further enhanced the ground-UAV-satellite model, which resulted in the highest accuracy (<em>R</em>^<em>2</em> = 0.90, <em>RMSE</em> = 0.34 t/ha) across six counties. When the optimal ground-UAV-satellite model was transferred to the province, it exhibited strong transferability across seasons (2021–2022: <em>R</em>^<em>2</em> = 0.52, <em>RMSE =</em> 0.94 t/ha; 2022–2023: <em>R</em>^<em>2</em> = 0.62, <em>RMSE =</em> 0.90 t/ha; 2023–2024: <em>R</em>^<em>2</em> <em>=</em> 0.45, <em>RMSE =</em> 0.96 t/ha). These findings suggest that the proposed cross-scale sample augmentation and information fusion approach is highly valuable for enhancing large-scale crop yield prediction accuracy, particularly in smallholder farming systems with limited ground samples.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"335 ","pages":"Article 115271"},"PeriodicalIF":11.4,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146075017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Microvibration detection and compensation for SDGSAT-1 based on line-by-line bundle adjustment","authors":"Shu Xu ,&nbsp;Jinshan Cao ,&nbsp;Peng Huang ,&nbsp;Yining Yuan ,&nbsp;Yi Fang ,&nbsp;Huijun Chen ,&nbsp;Mengchao Wu ,&nbsp;Tengfei Long","doi":"10.1016/j.rse.2026.115245","DOIUrl":"10.1016/j.rse.2026.115245","url":null,"abstract":"<div><div>Microvibrations degrade the geometric quality of optical Earth observation satellite imagery by introducing intra-scene spatial distortions, necessitating robust detection and compensation strategies. Like many optical Earth observation satellites, the Sustainable Development Goals Science Satellite 1 (SDGSAT-1) faces similar challenges. To address this, this paper presented a novel microvibration detection and compensation framework based on line-by-line bundle adjustment for SDGSAT-1. By minimizing directionally weighted residuals of a modified rigorous imaging model—constructed from tie points (TPs) and virtual ground control points—the framework enabled high-temporal-resolution estimation of microvibrations, parameterized through the discrete attitude microvibration model, via least-squares optimization. A new sensitivity indicator was also proposed to evaluate the adequacy of the weighting scheme for microvibration detection during optimization and to guide the dynamic adjustment of TP weights. Applied to SDGSAT-1 data, the method successfully characterized microvibrations at 1.0 Hz in the along-track direction and 0.4 Hz in the cross-track direction for the first time. Experimental results demonstrated that the proposed framework effectively suppressed microvibration-induced geometric distortions, consistently outperforming both raw imagery and classical approaches: it achieved a 33.16% reduction in RMSE compared to uncorrected data and improved cross-track precision by 27.82% over the conventional method. The impact of charge-coupled devices operating at heterogeneous imaging speeds was evaluated, with results showing no significant degradation in detection performance. These results validated the framework's effectiveness in enhancing geometric accuracy through robust microvibration modeling and compensation.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"335 ","pages":"Article 115245"},"PeriodicalIF":11.4,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146075018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging wide snapshot XCO2 pre-training to estimate urban fossil fuel CO2 emissions from space","authors":"Zeyu Wang ,&nbsp;Feng Zhang ,&nbsp;Jieyi Wang ,&nbsp;Long Cao","doi":"10.1016/j.rse.2026.115260","DOIUrl":"10.1016/j.rse.2026.115260","url":null,"abstract":"<div><div>Recent and upcoming carbon satellites, such as the Orbiting Carbon Observatory-3 (OCO-3) and the Copernicus Anthropogenic Carbon Dioxide Monitoring Mission (CO2M), offer unprecedented opportunities for top-down estimation of urban <span><math><mrow><msub><mi>CO</mi><mn>2</mn></msub></mrow></math></span> emissions. Their observations, i.e., <span><math><mn>80</mn><mo>×</mo><mn>80</mn></math></span> <span><math><mrow><msup><mi>km</mi><mn>2</mn></msup></mrow></math></span> Snapshot Area Map (SAM) for OCO-3 and 250 km wide swath for CO2M, enable the detection of urban emissions in a single pass. However, accurately identifying urban plumes remains challenging due to their broad spatial extent, low signal-to-noise ratio, and substantial data gaps in quality-filtered <span><math><mrow><msub><mi>XCO</mi><mn>2</mn></msub></mrow></math></span> snapshots. To address these challenges, we propose a Transformer-based deep learning (DL) model for <span><math><mrow><msub><mi>XCO</mi><mn>2</mn></msub></mrow></math></span> interpolation and plume detection. Our approach uses masked pre-training on synthetic CO2M data to learn spatial dependencies and emission-related structures of <span><math><mrow><msub><mi>XCO</mi><mn>2</mn></msub></mrow></math></span> values before fine-tuning for plume detection tasks. Experimental results on synthetic datasets show that the model reconstructs <span><math><mrow><msub><mi>XCO</mi><mn>2</mn></msub></mrow></math></span> with mean absolute errors below the instrumental noise and achieves stable plume detection performance across noise levels. It improves <span><math><mrow><msub><mi>XCO</mi><mn>2</mn></msub></mrow></math></span> gap-filling accuracy especially under regional and swath-missing conditions and significantly outperforms test- and wind-based methods in plume region segmentation accuracy. We further validated the model using 110 SAMs from 39 cities observed by OCO-3, integrating it into a lightweight inversion workflow. The resulting top-down emission estimates show improved consistency with bottom-up inventories compared to baselines (R² = 0.61, total relative deviation = −0.10), and the city-level aggregation reproduces the bottom-up emission rankings with a Pearson’s r of 0.90. These results confirm the transferability and practical utility of our approach across global cities. This study presents a promising approach for reconstructing and detecting urban emission signals from <span><math><mrow><msub><mi>XCO</mi><mn>2</mn></msub></mrow></math></span> snapshots, demonstrating clear potential to support the next-generation carbon monitoring satellites.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"335 ","pages":"Article 115260"},"PeriodicalIF":11.4,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146075415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Airborne and spaceborne imaging spectroscopy capture belowground microbial communities and physicochemical characteristics in invaded grasslands","authors":"M. Ny Aina Rakotoarivony ,&nbsp;Kianoosh Hassani ,&nbsp;Samuel Fuhlendorf ,&nbsp;Benedicte Bachelot ,&nbsp;Robert Hamilton ,&nbsp;Hamed Gholizadeh","doi":"10.1016/j.rse.2026.115250","DOIUrl":"10.1016/j.rse.2026.115250","url":null,"abstract":"<div><div>Belowground properties, including belowground microbial communities and physicochemical characteristics, play a crucial role in ecosystem functioning. Developing scalable approaches to map these properties across large spatial domains is essential for advancing our understanding of ecosystem functioning. However, large-scale approaches for mapping belowground properties, particularly in vegetated ecosystems, have yet to be developed. In this study, we aimed to develop approaches to map belowground microbial communities (bacterial and fungal) and physicochemical characteristics in an extensive grassland ecosystem affected by invasive plants using airborne and spaceborne imaging spectroscopy (hyperspectral remote sensing). We focused on <em>Lespedeza cuneata</em> (<em>L. cuneata</em>), an invasive plant threatening grasslands of the U.S. Southern Great Plains. We developed structural equation models to determine aboveground-belowground linkages. We used airborne hyperspectral data to estimate aboveground characteristics from partial least squares regression and then mapped belowground properties using aboveground characteristics through generalized joint attribute models. We also assessed the capability of spaceborne data in mapping the spatial distribution of belowground properties through fusing coarse spatial resolution DLR's DESIS hyperspectral data with fine spatial resolution PlanetScope multispectral data. Our findings showed that there are linkages between percent cover of <em>L. cuneata</em>, aboveground characteristics, and belowground properties. Large-scale analysis using airborne hyperspectral data showed that belowground properties varied across increasing percent cover of <em>L. cuneata</em>. Similar results were observed when using fused spaceborne data. Our findings indicated that (1) spectral information can reveal belowground properties and (2) fusing spaceborne data can be an effective approach to mapping belowground properties in grassland ecosystems.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"335 ","pages":"Article 115250"},"PeriodicalIF":11.4,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Active and passive co-observations from a spaceborne lidar: Retrieving surface reflectance and aerosol optical thickness using ICESat-2 signal and noise data","authors":"Jian Yang ,&nbsp;Huiying Zheng ,&nbsp;Yue Ma ,&nbsp;Xinyuan Liu ,&nbsp;Song Li ,&nbsp;Xiao Hua Wang ,&nbsp;Wei Gong","doi":"10.1016/j.rse.2026.115264","DOIUrl":"10.1016/j.rse.2026.115264","url":null,"abstract":"<div><div>The ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) carries a revolutionary photon-counting lidar and diverse studies have demonstrated its great observational capabilities on Earth observations. However, it is still difficult to obtain high quality land surface reflectance in that a single measurement (e.g., the laser signal derived apparent surface reflectance) is characterized by two main unknowns, i.e., the SR (surface reflectance) and atmospheric transmission. As the radiative properties of ICESat-2 laser signal and solar noise are simultaneously obtained, we combine signal and noise information to achieve the co-observations, which show how two measurements help to decouple the reflectivity of atmosphere from surfaces and thus resolve this inherent ill-posed problem. Specifically, we propose the theoretical laser signal and solar noise models for spaceborne lidars that link the two measurements (the signal count and background rate) to the two unknowns. Then, a method and workflow applied for ICESat-2 is designed to retrieve the AOTs (aerosol optical thickness) and SRs. The performance is validated against the MODIS (Moderate-resolution Imaging Spectroradiometer) and AERONET (Aerosol Robotic NETwork) product with the average MAPE (mean absolute percentage errors) of less than 30% for AOTs and the average MAPE of less than 15% for SRs in different land cover types. In addition, the ability of this method to identify snow-covered or cloud-covered areas is explored and validated. This study provides a reference for the active and passive co-observations. In the future, satellites carrying both lidar and multispectral cameras could enable higher quality Earth observations, with the lidar enabling more accurate isolation of atmospheric and surface contributions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"335 ","pages":"Article 115264"},"PeriodicalIF":11.4,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146036819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Validation of high-resolution surface soil moisture time series retrieved by means of SAR interferometry","authors":"Francesco De Zan ,&nbsp;Paolo Filippucci ,&nbsp;Luca Brocca","doi":"10.1016/j.rse.2026.115266","DOIUrl":"10.1016/j.rse.2026.115266","url":null,"abstract":"<div><div>This paper presents a novel algorithm for high-resolution soil moisture retrieval based on Synthetic Aperture Radar (SAR) interferometry and closure phases. The proposed method efficiently processes long SAR time series with minimal computational cost, generating a soil moisture measurement for each acquisition.</div><div>Soil moisture data were derived from Sentinel-1 SAR imagery and validated across seven different test sites. Retrieval results were compared with modeled soil moisture data from land surface models, alternative remote-sensing products, and in situ measurements.</div><div>The algorithm demonstrates strong correlations with modeled soil moisture, particularly in areas characterized by high interferometric coherence. However, performance was expectedly limited in regions with low interferometric coherence due to factors such as vegetation cover or snow cover.</div><div>Looking ahead, this study identifies some relevant directions for future research, including the integration of backscatter information alongside phase data and the adaptation of the algorithm for SAR missions operating at different frequencies (e.g., L-band) or with very dense acquisition schedules (e.g., geosynchronous platforms). These advancements would further enhance the applicability and accuracy of soil moisture retrieval using SAR-based techniques.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"335 ","pages":"Article 115266"},"PeriodicalIF":11.4,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transferring soil moisture estimation skills to evapotranspiration and streamflow modeling through remote sensing data assimilation","authors":"Huihui Feng ,&nbsp;Jianhong Zhou ,&nbsp;Zhiyong Wu ,&nbsp;Jianzhi Dong ,&nbsp;Long Zhao ,&nbsp;Luca Brocca ,&nbsp;Hai He","doi":"10.1016/j.rse.2026.115274","DOIUrl":"10.1016/j.rse.2026.115274","url":null,"abstract":"<div><div>Remote sensing (RS) soil moisture (SM) retrievals are frequently assimilated into land surface models (LSMs) to enhance their overall performance. However, uncertainty in LSM parameterization limits the capacity of current models to accurately capture the coupling strengths between SM and hydrological fluxes. This limitation reduces the effectiveness of SM data assimilation (DA) in improving estimates of key fluxes such as evapotranspiration (ET) and streamflow. Here, we introduce an improved SM DA framework with the optimization of LSM coupling strengths between SM and fluxes. Specifically, the SM DA framework is developed based on the Variable Infiltration Capacity (VIC) model. The model first calibrates the SM-ET and SM-runoff coupling strengths using RS data to enhance its physical consistency and representation of land surface processes. Subsequently, RS SM retrievals are assimilated into the calibrated VIC model using the Ensemble Kalman Filter to improve ET and streamflow simulations. Results indicate that the developed SM DA framework enhances DA efficiency, with SM correlation increasing from 0.45 to 0.49. It also enhances hydrological flux simulations, increasing ET correlation from 0.77 to 0.80 and improving the Nash-Sutcliffe efficiency for streamflow from 0.21 to 0.71, relative to the default VIC scheme. These improvements are especially evident in (sub-)humid regions, where the VIC model's runoff generation mechanism – based on saturation-excess processes – is well suited to representing local hydrological processes. Overall, the calibration of coupling strengths within LSMs offers a promising pathway to enhance hydrological fluxes simulation through land DA.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"335 ","pages":"Article 115274"},"PeriodicalIF":11.4,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146075016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}