Remote Sensing of Environment最新文献

{"title":"Deep learning detection and analysis of eddies in the East Greenland marginal ice zone from Sentinel-1 SAR imagery","authors":"Fei Jiang, Xiaofeng Li, Yingjie Liu, Yibin Ren","doi":"10.1016/j.rse.2025.115177","DOIUrl":"https://doi.org/10.1016/j.rse.2025.115177","url":null,"abstract":"","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"55 1","pages":""},"PeriodicalIF":13.5,"publicationDate":"2025-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689855","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":"High-resolution surface and rootzone soil moisture over US cropland: A novel framework assimilating multi-source remote sensing data, machine learning, and the Layered Green and Ampt Infiltration with Redistribution model","authors":"Shuohao Cai ,&nbsp;Yijia Xu ,&nbsp;Zhengwei Yang ,&nbsp;Wade T. Crow ,&nbsp;Zhou Zhang ,&nbsp;Jiali Shang ,&nbsp;Jiangui Liu ,&nbsp;Peter La Follette ,&nbsp;Chris Reberg-Horton ,&nbsp;Harry Schomberg ,&nbsp;Steven Mirsky ,&nbsp;Brian Davis ,&nbsp;Sarah Seehaver ,&nbsp;Alexis Correira ,&nbsp;Andrea Basche ,&nbsp;Ashley Waggoner ,&nbsp;Charles Ellis ,&nbsp;Dara Park ,&nbsp;Danielle D. Treadwell ,&nbsp;David Campbell ,&nbsp;Jingyi Huang","doi":"10.1016/j.rse.2025.115167","DOIUrl":"10.1016/j.rse.2025.115167","url":null,"abstract":"<div><div>Accurate and high spatiotemporal resolution soil moisture (SM) monitoring in cropland is important for water resource management, drought forecasting, and nutrient transport estimation at the field scale for sustainable crop production. Although recent research has applied machine learning (ML) to downscale coarse-resolution satellite SM products, most of this past work has focused only on surface SM estimation, and the performance of rootzone SM products has not been intensively evaluated in cropland. This study introduces a novel framework that integrates multi-source satellite-based ML models with the Layered Green and Ampt Infiltration with Redistribution (LGAR) model to produce high-resolution (100 m, hourly) SM products for both the surface layer (0–5 cm) and rootzone (0–100 cm) across cropland in the contiguous United States (CONUS). First, six ML models were trained using multiple high-resolution remote sensing datasets (Sentinel-1, Sentinel-2, and Landsat) to predict surface and rootzone SM. These ML predictions were then assimilated into the LGAR model using the ensemble Kalman filter (EnKF). The framework was developed and validated using an eight-fold cross-validation scheme with in-situ data from 431 cropland sites across CONUS, sourced from three networks (SCAN, USCRN, and PSA). The 100-m hourly SM data from this framework surpasses existing products (9-km SMAP L4, SMAP-based 1-km thermal hydraulic disaggregation of SM product) in spatial and temporal resolution and captures rootzone SM that is not available in the SMAP-HydroBlocks SM product. It achieves good performance, with median bias-corrected root mean squared error (ubRMSE) of 0.053 m³/m³ and median Kling-Gupta efficiency (KGE) of 0.379 in the surface layer, and median ubRMSE of 0.027 m³/m³ and median KGE of 0.302 in the rootzone. While the framework demonstrates strong performance, its accuracy varies across climatic regimes, with surface SM performing better in non-humid areas (median KGE = 0.375 versus median KGE = 0.416) and rootzone SM in humid regions (median KGE = 0.313 versus median KGE = 0.127). This high-resolution cropland SM product can potentially benefit multiple agricultural applications, such as irrigation management and nutrient leaching estimation, and provide valuable insights to support farmers and land managers in decision-making processes.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115167"},"PeriodicalIF":11.4,"publicationDate":"2025-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689084","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":"Constrained negentropy optimisation (CoNE-opt): Using independent components to merge satellite data products","authors":"Suraj Shah ,&nbsp;Yi Liu ,&nbsp;Seokhyeon Kim ,&nbsp;Ashish Sharma","doi":"10.1016/j.rse.2025.115170","DOIUrl":"10.1016/j.rse.2025.115170","url":null,"abstract":"<div><div>Merging multiple uncertain datasets cancels random errors, resulting in a merged product that is equivalent to or better than the best individual parent product used. Merging proceeds via a weighted average of individual products, with weights derived using second-order error statistics (variance/covariance), which cannot fully capture skewed or heavy-tailed error structures, and often restrictive assumptions (e.g., zero error cross-correlation, ECC), which are frequently violated when products share retrieval algorithms or calibration data. We propose here a novel alternative for merging geophysical data, Constrained Negentropy Optimisation (CoNE-opt), which assumes the true dataset exhibits the greatest departure from Gaussianity, an assumption commonly invoked in performing Independent Component Analysis (ICA) to derive source signals. CoNE-opt maximises negentropy, which is a measure of non-Gaussianity, and uses it as the objective function while incorporating a linear error model constraint. This formulation allows joint estimation of the full error spectrum and the merging weights. The method is validated through synthetic experiments followed by merger of three global satellite-derived surface soil moisture products, SMAP, SMOS, and SMOS-IC. Comparison against reference datasets demonstrate that CoNE-opt outperforms existing merging alternatives that rely on second order statistics instead. Notably, CoNE-opt improves error magnitudes in the presence of high ECC and outliers, where most Ordinary Least Squares (OLS)-based merging methods struggle. Superior performance was observed in soil moisture merging, where the overall normalised RMSE equals 0.15 (vs. 0.34 for the best OLS-based alternative), consistently surpassing input products and existing methods. These findings support the potential of CoNE-opt as a robust framework for generating global soil moisture datasets, thereby enhancing land surface and hydrological modelling in data-sparse regions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115170"},"PeriodicalIF":11.4,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689083","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":"RayExtract: A fast, scalable method for tree volume reconstruction from terrestrial laser scanning","authors":"Timothy Devereux ,&nbsp;Thomas Lowe ,&nbsp;Joshua Rivory ,&nbsp;Rafael Bohn Reckziegel ,&nbsp;Kim Calders ,&nbsp;Raja Ram Aryal ,&nbsp;Glen Eaton ,&nbsp;Zane Cooper ,&nbsp;Shaun Levick ,&nbsp;Stuart Phinn ,&nbsp;William Woodgate","doi":"10.1016/j.rse.2025.115162","DOIUrl":"10.1016/j.rse.2025.115162","url":null,"abstract":"<div><div>Accurate tree volume and structure are crucial for forest biomass estimation and ecosystem investigations. While terrestrial laser scanning (TLS) offers non-destructive pathways for the detailed three-dimensional tree reconstruction, current methods overestimate small branch volumes and often require tree segmentation and leaf–wood separation as a priori. This study introduces and validates RayExtract, a novel method for reconstructing woody volume from TLS data, utilising tools from the <em>RayCloudTools</em> library, to automate the extraction of tree structural metrics from point clouds. Our method incorporates two key morphological rules — Self-Similarity and Leonardo’s Rule — to aid branch radius and taper calculations. Likewise, it enables rapid and automated plot-scale reconstruction by integrating tree segmentation and woody structure modelling without requiring leaf point classification. In this study, RayExtract demonstrated high accuracy across four high-quality destructive harvest reference sets with concordance correlation coefficient (CCC) values ranging from 0.82 to 0.97 (n=124). To explore algorithm behaviours under different leaf conditions and point densities, we implement a framework using TLS simulation of highly realistic synthetic trees. Results from the simulation framework show consistent high accuracy of total woody volume, with CCC ranging from 0.97 to 0.98 (n=18) across four distinct scanning configurations. Fine-scale volumetric analysis revealed that incorporating simple morphological rules can effectively inform branch taper and reduce woody volume overestimation, particularly in smaller components. Furthermore, it identifies a limitation in volumetric accuracy in trees exhibiting significant taper in the lower stem. Analysis of RayExtract’s computational efficiency demonstrates that runtime and memory usage scale predictably with input data size, primarily driven by point count and the associated structural complexity within the point cloud, positioning the algorithm as well suited for large-scale applications. RayExtract represents a significant advancement in forest reconstruction, biomass estimation, and vegetation structural analysis. The method’s efficiency, accuracy, and robustness across varied forest conditions mark a substantial improvement in forest structural assessment techniques using laser scanning and have broad implications for improving regional biomass estimations, and contributing to the calibration and validation of broad-scale remote sensing observations.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115162"},"PeriodicalIF":11.4,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689085","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":"Annotation-free cloud masking for PlanetScope images in the Arctic via cross-platform ability transfer using deep learning and foundation models","authors":"Zhili Li ,&nbsp;Yiqun Xie ,&nbsp;Sergii Skakun ,&nbsp;Xiaowei Jia ,&nbsp;Gengchen Mai ,&nbsp;William Lu ,&nbsp;Matthew Tong ,&nbsp;Zhihao Wang","doi":"10.1016/j.rse.2025.115138","DOIUrl":"10.1016/j.rse.2025.115138","url":null,"abstract":"<div><div>Cloud masking is an essential task for satellite-based Earth monitoring, and the quality of cloud masks can directly impact the solutions of the downstream Earth monitoring tasks. While significant progress has been made especially for data with desired bands (e.g., thermal bands in Landsat-8), the masking quality on small satellites with higher resolution but fewer spectral bands is still unreliable at high latitudes, where confusion with snow and ice makes the task significantly more challenging. We propose a novel learning-enabled cross-platform ability transfer paradigm that offers a scalable and effective solution to tackle this challenge through a case study using PlanetScope images in the Arctic. A unique characteristic of the new paradigm is that it does not require manual annotations to be collected for PlanetScope images, which is often the bottleneck and the most time-consuming part of machine learning-based cloud masking, especially given the similarity between clouds and snow/ice. To realize this, our approach first designs and creates a new training dataset, Co-Clouds, which contains around 45,000 coincident pairs of PlanetScope and Landsat-8 image patches collected within a nearly simultaneous temporal window. This coincident dataset offers a way to generate large volumes of training data and builds a bridge to transfer Landsat-8’s stronger cloud masking skills in the Arctic to PlanetScope images via data-driven learning. We also show the feasibility of the ability transfer from spectral signatures (e.g., thermal bands) to spatial signatures (e.g., textures). Using our Co-Clouds dataset, we train several deep learning models including both regular-size deep learning models and large foundation models. To validate the quality of the masks, we further create a manually labeled cloud mask dataset for PlanetScope images in the Arctic. Both the quantitative and qualitative results show significant improvements over the current operational cloud masks by PlanetScope. For example, the large foundation models such as SegFormer achieve approximately 20 % higher overall accuracy and 28 % higher producer’s accuracy than the operational cloud masks, while maintaining comparable or better user’s accuracy exceeding 90 %. The new approach is also very easy to implement and extend to other platforms, opening new opportunities for broadcasting advanced skills from one platform to others.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115138"},"PeriodicalIF":11.4,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689116","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":"Flood pulse monitoring in wetlands with multi-temporal Sentinel-1 interferometric coherence data: Application to the Okavango Delta (Botswana)","authors":"Louis Gaudaré ,&nbsp;Samuel Corgne ,&nbsp;Marc Jolivet ,&nbsp;Olivier Dauteuil ,&nbsp;Cécile Doubre ,&nbsp;Piotr Wolski ,&nbsp;Raphaël Grandin ,&nbsp;Marie-Pierre Doin ,&nbsp;Philippe Durand ,&nbsp;FLATSIM Working Group","doi":"10.1016/j.rse.2025.115173","DOIUrl":"10.1016/j.rse.2025.115173","url":null,"abstract":"<div><div>Flood-pulsed wetlands are characterized by significant seasonal water fluctuations, which play a critical role in the dynamics of these sensitive ecosystems. Among the growing number of existing remote sensing products, we explore the potential of interferometric (InSAR) coherence time series, derived from Sentinel-1 synthetic-aperture radar images, to characterize the hydrological dynamics of the Okavango Delta, a vast flood-pulsed wetland. Interferometric coherence reflects changes in surface conditions, making it a powerful tool for detecting flood propagation. By fitting harmonic functions, we produce parameters that quantify the seasonality of coherence time series with short isotemporal baselines (12 days). In particular, we developed a normalized seasonal index based on the ratio between the seasonal amplitude and the root-mean-square error of the fitted harmonic function, to map the seasonality of the coherence time series. A multi-annual analysis of coherence time series reveals a strong relationship between their seasonality, land cover, and flood frequency. Unsupervised clustering applied to statistical and seasonal metrics of coherence time series yields consistent classifications that map the variability of flood frequencies across wetland areas and clearly distinguish wetlands from dry zones. Similarly, thresholds applied to normalized seasonal indices delineate the year-to-year extent of flood pulses with accuracy around 79 %. We show that coherence time series in never flooded areas exhibit a pronounced seasonal pattern driven by rainfall cycle, whereas this seasonality is disrupted by flood pulses in wetlands. Building on this, we developed a change-detection approach to map the floods by identifying the date when coherence time series diverge from their seasonal pattern. The resulting flood arrival dates achieve 74–83 % accuracy compared to a reference dataset derived from optical data. Our results highlight the potential of coherence time series as a robust indicator of seasonal variations in inundation extent in flood-pulsed wetlands.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115173"},"PeriodicalIF":11.4,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689079","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":"The role of cross-polarization in producing high-resolution pan-Arctic sea ice motion from the RADARSAT Constellation Mission over several years","authors":"Alexander S. Komarov ,&nbsp;Mathieu Plante ,&nbsp;Jean-François Lemieux ,&nbsp;Stephen E.L. Howell ,&nbsp;Mike Brady","doi":"10.1016/j.rse.2025.115175","DOIUrl":"10.1016/j.rse.2025.115175","url":null,"abstract":"<div><div>We introduce a new pan-Arctic Environment and Climate Change Canada (ECCC) HIgh-Resolution sea Ice Tracking System (HIRITS) operating with the RADARSAT Constellation Mission (RCM) HH and HV synthetic aperture radar (SAR) images resampled at a resolution of 80 m. The spacing between neighbour output vectors was often around 1 km. When combining HH and HV, the resulting ice displacements (over 2 h to 3.5 days) derived for June 1, 2022 – May 31, 2025 were in an excellent agreement with International Arctic Buoy Programme data with the overall root-mean square error (RMSE) of 1.48 km, and correlation of 0.996 for the <span><math><mi>x</mi></math></span> and <span><math><mi>y</mi></math></span> components. Ice motion vectors provided by HV had consistently greater tracking cross-correlation coefficients (with the average value of 0.56) than those derived from HH (average value of 0.45), implying a higher confidence. The RCM HH + HV ice motion agreed well with the existing passive microwave products from the National Snow and Ice Data Center (NSIDC) and Ocean and Sea Ice Satellite Application Facility (OSI SAF) with RMSEs of 4.19 km/d and 5.03 km/d respectively. We introduced an aggregated sea ice motion pan-Arctic gridded product at 2 km resolution that combines individual RCM ice motion products (HH and HV) derived over 3- and 7-day rolling time windows. A greater number of vectors was derived from HV compared to HH across the pan-Arctic domain, except for the situations where the HV signal is low, such as over smooth land fast ice. The new RCM HH + HV products generated since mid-May 2022 will substantially benefit various applications that require sea ice motion at high spatial resolution including accurate computation of sea ice deformation.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115175"},"PeriodicalIF":11.4,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145689427","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 additional VIIRS information to improve wildfire tracking in the western US","authors":"Shane R. Coffield ,&nbsp;Tempest D. McCabe ,&nbsp;Wilfrid Schroeder ,&nbsp;Yang Chen ,&nbsp;Rebecca C. Scholten ,&nbsp;Elijah Orland ,&nbsp;Tianjia Liu ,&nbsp;Elizabeth Wiggins ,&nbsp;James T. Randerson ,&nbsp;Melanie Follette-Cook ,&nbsp;Douglas C. Morton","doi":"10.1016/j.rse.2025.115156","DOIUrl":"10.1016/j.rse.2025.115156","url":null,"abstract":"<div><div>Recent record-breaking fire activity in the western US poses clear threats to humans, ecosystems, and climate. Larger and faster fires increase the challenges for fire managers and further motivate the need for improved tracking of extreme fire behavior. There are also known limitations to our current ability to monitor fires from space. These include infrequent coverage from moderate resolution (<span><math><mo>≤</mo></math></span> 1 km) sensors, smoke and cloud obscuration, omission of small or low-intensity fires, and atmospheric attenuation of fire radiative power (FRP). These effects diminish our ability to quantify fire behavior and emissions, including persistent burning behind the flaming fire front, particularly in ecosystems with high fuel loads. In this study, we examined the Visible Infrared Imaging Radiometer Suite (VIIRS) imagery and data products to assess the utility of candidate fire pixels in addition to the low/nominal/high confidence 375-m fire detections already included in the active fire product. We found that these candidate pixels added 45% more daytime detections and 12% more nighttime detections for large fires in the western US 2020 fire season. Candidate fires were highly consistent with areas of flaming and smoldering fire activity identified by near-coincident airborne data as well as patterns of known active or candidate fires in sequential VIIRS overpasses, without significantly increasing false detections (commission errors). The candidate fire detections helped fill data gaps due to cloud obscuration during large fires that generated pyrocumulonimbus (pyroCb) clouds. Including this additional information also impacted estimates of fire activity, increasing fire persistence by 20% and FRP by 7% across our sample. Although the contribution from candidate fire detections to total FRP was relatively small, including these additional pixels could provide a more consistent estimate of fire emissions for smoke models and air quality forecasts by filling gaps in active fire information and improving the representation of smoldering fire activity. These results demonstrate the potential to augment the standard VIIRS product with candidate fire information for known large fire events to improve fire tracking and downstream products. Such approaches to leverage additional VIIRS information may be suitable for other biomass burning regions where global fire detection algorithms provide incomplete information for specific fire types and observing conditions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115156"},"PeriodicalIF":11.4,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145652002","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":"Significant uncertainties from overlooking aerosol-cloud coexistence in surface solar radiation estimates using passive satellite observations","authors":"Qin Lang ,&nbsp;Ming Zhang ,&nbsp;Qiuhua He ,&nbsp;Shikuan Jin ,&nbsp;Wenmin Qin ,&nbsp;Lingxia Luo ,&nbsp;Lunche Wang","doi":"10.1016/j.rse.2025.115168","DOIUrl":"10.1016/j.rse.2025.115168","url":null,"abstract":"<div><div>Passive satellite products are a primary source for estimating surface total solar radiation (TSR) and its components—direct (DIR) and diffuse (DIF). However, existing satellite-based methods typically treat aerosol and cloud effects separately. Overlooking aerosol-cloud coexistence may introduce substantial uncertainties that have received little attention. Therefore, this study combines radiative transfer simulations, multi-source satellite data, and ground-based validation to systematically evaluate the impact of overlooking coexistence on DIR, DIF, and TSR estimates. Simulations show that as cloud optical thickness increases from 0.2 to 5, the mean DIR overestimation due to ignoring aerosols drops from 261.58 W m⁻² to 1.02 W m⁻², and DIF shifts from an underestimation of 165.75 W m⁻² to an overestimation of 102.41 W m⁻². Ignoring clouds in coexistence cases yields similar patterns but greater biases. Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation indicate that about 15 % of global samples exhibit aerosol-cloud coexistence, with hotspots concentrated in central Africa. Ground validation shows that ignoring coexistence increases the relative root mean square error (rRMSE) of AQUA-based DIR, DIF, and TSR estimates by 19.19 %, 26.22 %, and 1.95 %, respectively, and Himawari-8-based estimates by 31.21 %, 30.09 %, and 3.60 %. To mitigate these uncertainties, two strategies are tested. Filling missing aerosol or cloud properties using MERRA-2 or MYD08M3 data reduces DIF rRMSE by 7.17 %–10.45 %; incorporating aerosol-cloud coexistence samples into the lookup table reduces it by 3.30 %. Both strategies have minimal impact on DIR and TSR. These findings highlight significant errors from overlooking aerosol-cloud coexistence in passive satellite-based solar radiation estimates and emphasize the need for greater awareness and methodological refinement.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"333 ","pages":"Article 115168"},"PeriodicalIF":11.4,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145657974","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":"Groundwater volume loss and land subsidence in the North China plain investigated using wide-area InSAR surveying and mechanical modeling","authors":"Xing Zhang ,&nbsp;Jun Hu ,&nbsp;Mahdi Motagh ,&nbsp;Mingjia Li ,&nbsp;Yuedong Wang ,&nbsp;Qiuhong Yang ,&nbsp;Guangli Su ,&nbsp;Haigang Wang","doi":"10.1016/j.rse.2025.115164","DOIUrl":"10.1016/j.rse.2025.115164","url":null,"abstract":"<div><div>The long-term over-extraction of groundwater in the North China Plain (NCP) has resulted in permanent loss of groundwater storage, leading to geo-disasters, including surface subsidence, ground fissures, and seawater intrusion. Through integrating wide-area InSAR survey data with a mechanical model, this study reveals the detailed spatiotemporal dynamics of aquifer system deformation and groundwater storage loss (GWSL) across the entire plain. We used a Sentinel-1 A SAR dataset from 2017 to 2023, covering two ascending orbits (T40 and T142) with eight frames, along with time-series InSAR technology and a wide-area multi-track network adjustment model that incorporates spatial constraints. This allowed us to obtain seamless, millimeter-level accuracy time-series deformation sequences for the NCP. Three severe subsidence areas were identified, with the maximum vertical velocity exceeding −150 mm/year and the cumulative subsidence reaching up to 1 m. Thereafter, the time-series InSAR results and groundwater level data were modeled using cross-wavelet analysis to quantify the elastic recovery capacity of the Quaternary aquifer system in the NCP (with the elastic skeletal storage coefficient <span><math><mi>Sₖₑ</mi></math></span> ranging from 0.0002 to 0.021). This revealed significant differences in the aquifer response between the piedmont plain (mostly elastic deformation) and the flood plain (mostly plastic deformation). There was no elastic recovery in the subsidence funnel areas of the flood plain. Subsequently, InSAR observations were integrated with the volume strain model (InSAR-VSM) to construct a novel groundwater loss inversion model, yielding the first 2-km resolution dataset of GWSL for the NCP. The average annual groundwater loss from 2017 to 2023 was estimated to be −4.346 × 10⁸ to −8.692 × 10⁸ m³/yr. Finally, we systematically investigated the spatiotemporal variation patterns of land subsidence over the past 63 years in the NCP, under the combined influence of human activities (such as over-extraction of groundwater, the South-to-North Water Diversion Project (SNWDP), and policies prohibiting groundwater extraction) and natural factors (such as extreme rainfall events and geological structures). The deformation shows a spatial migration characteristic from urban areas to agricultural land, with different deformation trends in the NCP. While urban deformation has stopped or reversed, agricultural areas continue to experience intensifying subsidence. This study characterizes the stress evolution features of the aquifer system at an ultra-high spatial resolution, providing crucial scientific support for adaptive groundwater management and the development of subsidence mitigation strategies in water-scarce sedimentary basins.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"333 ","pages":"Article 115164"},"PeriodicalIF":11.4,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145658131","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}