Remote Sensing of Environment最新文献

{"title":"A hybrid physics-informed and data-driven model for estimating ocean internal wave phase speeds from remote sensing imagery","authors":"Guangxi Cui ,&nbsp;Zhongya Cai ,&nbsp;Zhiqiang Liu","doi":"10.1016/j.rse.2026.115247","DOIUrl":"10.1016/j.rse.2026.115247","url":null,"abstract":"<div><div>The propagation speed of internal waves is a fundamental parameter for understanding their physical mechanisms, dynamic behavior, and environmental impact. However, traditional estimation methods are typically based on numerical simulations or sparse <em>in-situ</em> observations, which limit their accuracy and scalability, and results in a significant scarcity of available phase speed datasets. To overcome these challenges, we propose a physics-informed and data-driven model for estimating internal wave phase speed from satellite imagery. The proposed model incorporates three key innovations: (1) the integration of theoretical equations (KdV, BO, and eKdV equations) as physical constraints to ensure consistency with real-world ocean dynamics; (2) the adoption of an adaptive ensemble learning framework that fuses data-driven and physical-informed features to improve model robustness and prediction accuracy; and (3) the introduction of a transfer learning strategy to mitigate discrepancies between theoretical predictions and observational real-world internal wave results. Experimental results demonstrate that the model achieves superior performance across varying water depths, with an average RMSE of 0.04 m/s, MRE of 2.5%, and R² of 98.8% on the testing set. Additionally, the model was applied to the South China Sea, revealing a distinct propagation pattern: average phase speed initially increased (from 2.427 m/s to 2.53 m/s), then decreased (to 1.464 m/s), and subsequently increased again (to 1.703 m/s) as internal waves propagated westward across the Dongsha Islands and Hainan Island. The model was further validated on a global scale, achieving an average percentage error of 4.95%, confirming its scalability and generalization capability. This study presents an efficient and automated approach for accurately retrieving internal wave phase speed.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"335 ","pages":"Article 115247"},"PeriodicalIF":11.4,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072688","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":"TIDA-SR: A time-conditioned deformable attention network for DEM super-resolution in cloud-covered mountainous regions","authors":"Kai Chen ,&nbsp;Fayuan Li ,&nbsp;Sijin Li ,&nbsp;Haoyu Cao ,&nbsp;Wen Dai ,&nbsp;Guoan Tang","doi":"10.1016/j.rse.2026.115254","DOIUrl":"10.1016/j.rse.2026.115254","url":null,"abstract":"<div><div>The complex terrain and variable climatic conditions in mountainous regions often cause cloud and fog occlusions, which hinder the generation of high-resolution Digital Elevation Models (DEMs) (below 10 m) using optical satellite photogrammetry. To address the issues of insufficient accuracy and data gaps in cloud-affected DEM areas, this study proposes a Time-Conditioned Deformable Convolution Attention Super-Resolution Network (TIDA-SR). The network performs super-resolution reconstruction using open-source DEMs and integrates multiple DEM sources through a feathered blending strategy to achieve high-accuracy results. Its architecture incorporates a diffusion process, deformable convolutions, and a Convolutional Block Attention Module (CBAM), combined with a composite loss function, to enhance the recovery of complex terrain details. TIDA-SR network, combined with the cloud-affected 5 m DEMs generated from GF-7 stereo imagery and open-source 30 m DEMs, is employed to reconstruct and fuse cloud-affected regions with 5 m resolution. The experimental results in the Loess Plateau and the Rocky Mountains demonstrate that, compared with traditional interpolation methods and existing deep learning approaches, TIDA-SR reduces RMSE and MAE by approximately 5%–78% on the validation dataset and by 3%–25% on the open-source DEM dataset. Slope accuracy improvements of approximately 3%–42% on the validation dataset and 3%–8% on the open-source DEM dataset are observed. The feathered blending strategy effectively mitigates stitching artifacts between cloud and noncloud areas, enhancing overall spatial continuity. TIDA-SR exhibits superior performance in high-resolution DEM reconstruction for cloud-affected mountainous regions and shows strong potential for practical applications, including surface process simulations, mountain hydrological modeling, geomorphological analysis, and other terrain-driven geoscience tasks.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"335 ","pages":"Article 115254"},"PeriodicalIF":11.4,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072687","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":"InSAR analysis using both co- and cross-polarized data at Death Valley, California from 2017–2025","authors":"Olivia Paschall,&nbsp;Rowena Benfer Lohman","doi":"10.1016/j.rse.2026.115265","DOIUrl":"10.1016/j.rse.2026.115265","url":null,"abstract":"<div><div>The Sentinel-1 satellite mission has been key to the achievement of interferometric synthetic aperture radar (InSAR)-based displacement rates that approach mm/yr precision, particularly in regions without significant vegetation and where long time series of observations exist. However, for more subtle displacement signals, separating the effects of surface processes from deformation due to deeper sources is still challenging. Here, we present a new method based on combinations of co-polarized (VV) and cross-polarized (VH) InSAR data. Cross-polarized data is typically noisier than the co-polarized data and are not widely used for InSAR. However, comparisons of co- and cross-polarized phase data can allow separation of the contributions from different processes. Signals due to deeper sources, such as slip along faults, should appear the same in both data types, while differences can be due to changes in surface characteristics. We examine full-resolution, unfiltered, VV and VH Sentinel-1 data covering Death Valley, California between January 2017 and March 2025. We find that displacement rates derived from VV and VH data differ by several mm/yr in some areas. We also show that rates based only on the VV imagery differ by a few mm/yr between subsets of pixels where the VV-VH differences are large or small, suggesting that VV-VH combinations can help researchers reliably identify pixels that are the least impacted by surface processes. While our work focuses on Death Valley, similar mm/yr-scale biases could impact endorheic basins around the world and influence analyses of interseismic motion, hazard estimates, and groundwater studies.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"335 ","pages":"Article 115265"},"PeriodicalIF":11.4,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072689","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":"Enhancing two-week live fuel moisture content forecasts through biophysical modelling and remote sensing data assimilation","authors":"Qinglong Jia ,&nbsp;Xingwen Quan ,&nbsp;Víctor Resco de Dios ,&nbsp;Marta Yebra ,&nbsp;Binbin He ,&nbsp;Xing Li ,&nbsp;Zhanmang Liao ,&nbsp;Rodrigo Balaguer-Romano ,&nbsp;Miquel De Cáceres","doi":"10.1016/j.rse.2026.115267","DOIUrl":"10.1016/j.rse.2026.115267","url":null,"abstract":"<div><div>Live Fuel Moisture Content (LFMC) is a critical determinant of wildfire ignition and spread. Accurate forecasting of LFMC dynamics, particularly at a two-week timescale, is essential for early wildfire danger assessment. While satellite remote sensing provides valuable current and historical observations, it lacks the ability to predict future LFMC dynamics. Meanwhile, although weather forecasts are relatively reliable over short timescales (up to two weeks), LFMC models based solely on meteorological inputs often fall short, particularly when predicting conditions at the species level. To address these limitations, this study introduces a species-specific approach that integrates MODIS-derived LFMC data into the biophysical process-based MEDFATE model to optimize LFMC simulations and enable short-term forecasting based on daily weather projections. A global sensitivity analysis was conducted to identify key input parameters for different tree species within MEDFATE. These parameters guided the development of a cost function that quantifies discrepancies between model-simulated and field-measured LFMC, enabling species-specific model calibration. To enhance model optimization, the global optimal DEoptim algorithm was combined with four-dimensional variational data assimilation (4D-Var) to integrate MODIS-derived LFMC estimates into MEDFATE. Using weather projections, the optimized MEDFATE model produced LFMC forecasts at about a two-week timescale. Time-series measurements of LFMC dynamics for <em>Quercus faginea</em>, <em>Quercus ilex</em>, and <em>Pinus halepensis</em> in Spain, <em>Pinus ponderosa</em> in the USA, and <em>Eucalyptus</em> species in Australia demonstrated that model calibration improved daily LFMC estimates (R² increased from 0.22 to 0.31; RMSE reduced from 18.71% to 16.04%). Further incorporation of MODIS-derived LFMC data significantly enhanced accuracy (R² = 0.56; RMSE = 9.75%). Validation across seven wildfire events in Spain, Australia, and the USA confirmed the effectiveness and operational relevance of the approach for early fire warning. These findings underscore the potential of integrating satellite remote sensing and meteorological data into biophysical process-based models to improve tree species-specific LFMC prediction and support proactive fire management.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"335 ","pages":"Article 115267"},"PeriodicalIF":11.4,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071635","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":"Characterizing diurnal variability in power plant carbon emissions in Asia: A top-down estimation approach constrained by geostationary NO2 and OCO-3 CO2 observations","authors":"Tianyi Xu ,&nbsp;Chengxin Zhang ,&nbsp;Cheng Liu","doi":"10.1016/j.rse.2026.115261","DOIUrl":"10.1016/j.rse.2026.115261","url":null,"abstract":"<div><div>Accurate quantification of carbon dioxide emissions is crucial for addressing climate change. However, traditional top-down CO₂ estimates are limited by sparse satellite observations and coarse temporal resolution. Although NO₂ data from polar-orbiting satellites can help constrain CO₂ emissions, temporal mismatch with OCO-3 measurements introduce additional uncertainty. To address this, we estimate hourly CO₂ emissions by combining OCO-3 XCO₂ observations with high temporal resolution NO₂ data from the GEMS instrument onboard the GEO-KOMPSAT-2B satellite. We developed an algorithm based on a Gaussian plume model and wind rotation techniques to estimate CO₂ emissions and NO_x/CO₂ emission ratios from near-synchronous NO₂/CO₂ observations. Hourly CO₂ emissions were further derived using GEMS-based NO_x emissions estimated via the flux divergence method. A total of 59 power plant cases across six Asian countries were identified. For these cases, the estimated CO₂ emissions exhibit distinct diurnal, seasonal, and interannual emission variability, primarily driven by heating demand, decarbonization measures, and pandemic-related industrial slowdowns. These top-down estimates, constrained by GEMS NO₂ data, show strong consistency with bottom-up inventories (<em>R</em> = 0.89), supporting the validity of our optimization approach. Furthermore, comparisons with daytime mean estimates suggest that CO₂ emission estimates constrained by polar-orbiting satellite observations can exhibit biases of approximately 60% relative to GEO-based approaches, underscoring the importance for high-temporal-resolution measurements. This study highlights the value of integrating geostationary NO₂ and CO₂ observations to capture the diurnal dynamics of power plant emissions and improve the accuracy of top-down CO₂ emission monitoring.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"335 ","pages":"Article 115261"},"PeriodicalIF":11.4,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146071830","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":"2026-01-24","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":"Daily 4D landslide movements monitoring via InSAR: A fusion framework integrating physics-based and data-driven models","authors":"Wanji Zheng ,&nbsp;Min Zhao ,&nbsp;Bo Huang ,&nbsp;Aoqing Guo ,&nbsp;Jun Hu","doi":"10.1016/j.rse.2026.115263","DOIUrl":"10.1016/j.rse.2026.115263","url":null,"abstract":"<div><div>Continuous monitoring of deep-seated slow-moving landslides is a critical measure to mitigate risks posed by catastrophic events, especially under increasing extreme weather conditions. As a non-contact, high-resolution measurement technique, space-borne interferometric synthetic aperture radar (InSAR) has been widely applied in landslide monitoring. However, when used for continuous monitoring, challenges remain, including incomplete monitoring dimensions, slow update efficiency, and insufficient temporal resolution. To address these, this paper proposes a fusion framework of physics-based and data-driven models, built on a Kalman Filter, to rapidly obtain daily 4D (spatial 3D with time) landslide movements based on movement characteristics and dependence on hydrometeorological factors. The method's performance was evaluated using both synthetic and real datasets. On synthetic data, RMSEs in the east, north, and vertical directions were 9.6 mm, 3.8 mm, and 1.4 mm, respectively. For real data, the daily 4D movements were projected onto the Line-of-Sight (LOS) directions of Sentinel-1 Track 11 and ALOS2 PALSAR2 for comparison, achieving sub-centimeter Root Mean Square Errors (RMSEs). These results confirm the accuracy of the estimated movements and demonstrate enhanced update efficiency enabled by the Kalman Filter, which allows rapid assimilation of new data without reprocessing the full historical archive. Additionally, by incorporating geophysical and geodynamic methods, we leveraged daily 4D movements to derive various landslide parameters to analyze the kinematics of the Chuwangjing landslide during 2016–2024. The findings indicate that daily 4D movements not only enhance InSAR's performance in continuous landslide monitoring but also provide additional derivative products, deepening the understanding of deep-seated landslide kinematics.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"335 ","pages":"Article 115263"},"PeriodicalIF":11.4,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146036820","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":"Centimeter-resolution 4D dynamics of retrogressive thaw slumps from repeat UAV photogrammetry on the Tibetan Plateau","authors":"Siru Gao ,&nbsp;Yushuo Liu ,&nbsp;Xinyu Men ,&nbsp;Hongting Zhao ,&nbsp;Luyang Wang ,&nbsp;Ziteng Fu ,&nbsp;Zhongqiong Zhang ,&nbsp;Yuzhong Yang ,&nbsp;Guanli Jiang ,&nbsp;Qingbai Wu","doi":"10.1016/j.rse.2026.115262","DOIUrl":"10.1016/j.rse.2026.115262","url":null,"abstract":"<div><div>Retrogressive thaw slumps (RTSs) are critical indicators of permafrost degradation, with significant implications for ecosystems, infrastructure, and carbon cycling. However, their evolutionary processes remain poorly understood due to limited high-resolution observations. Here, centimeter-scale UAV surveys were conducted from 2019 to 2024 to track eight RTSs on the Tibetan Plateau, with site-specific monitoring frequencies ranging from one to six surveys per year. These RTSs displayed tongue-, trumpet-, or funnel-shaped elongated morphologies with an average aspect ratio of 3.0 and were predominantly north-facing. Their areas, volumes, and headwall heights ranged from 1688.4 to 38,991.9 m², 594.1 to 51,961.1 m³, and 0.9 to 4.6 m, respectively. Most RTSs featured a three-part structure—rear-edge slumping, mid-slope sliding, and front-edge accumulation—each with distinct deformation characteristics. Two evolutionary pathways of RTS expansion, immediate-triggered and threshold-delayed, were identified. RTS activity was observed from June to October, with the most intense activity occurred between September and late October. Annual and monthly expansion averaged 1182.9 m² yr⁻¹ and 505.7 m² mo⁻¹, while headwall retreat reached 14.5 m yr⁻¹ and 6.2 m mo⁻¹. Surface elevation exhibited rear-edge subsidence (mean 2.4 m), central uplift (mean 1.4 m), and stable front-edge, with net volume loss mainly due to subsidence (mean 81%). Surface movement decreased from the rear-edge to the front-edge, with maximum values at the rear-edge slumping zone. This study quantified the evolution of small-scale RTSs—including changes in area, headwall retreat, surface movement, elevation, and volume—particularly focusing on monthly dynamics, thereby enhancing understanding and impact assessment of RTS.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"335 ","pages":"Article 115262"},"PeriodicalIF":11.4,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033599","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":"2026-01-23","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":"Physics-guided deep learning for geostationary satellite-based estimation of dead fuel moisture content in Southwest China","authors":"Chunquan Fan ,&nbsp;Yiru Zhang ,&nbsp;Rui Chen ,&nbsp;Hongguo Zhang ,&nbsp;Jianpeng Yin ,&nbsp;Yanxi Li ,&nbsp;Binbin He ,&nbsp;Xingwen Quan","doi":"10.1016/j.rse.2026.115259","DOIUrl":"10.1016/j.rse.2026.115259","url":null,"abstract":"<div><div>Accurate large-scale estimation of forest surface Dead Fuel Moisture Content (DFMC) is critical for wildfire risk warning and scientific decision-making. While existing process-based and empirical models leveraging satellite data show utility at local scales, they exhibit inherent limitations: process-based models suffer from physical simplifications in numerical simulations, while empirical approaches lack mechanistic integration due to shallow learning architectures. This persistent gap necessitates—yet lacks—integrative frameworks that synergize physical realism with deep learning flexibility. To address this challenge, we propose a physics-guided deep learning framework that synergistically integrates geostationary meteorological satellite data and reanalysis data for regional-scale forest surface DFMC estimation. Our methodology fuses Long Short-Term Memory (LSTM) neural network features with physical features derived from the process-based Fuel Stick Moisture Model (FSMM). Critically, the physical feature fuel surface relative humidity (RH_surf) is incorporated into the loss function to constrain model weights, yielding our final Physics-guided LSTM (PyLSTM) model. Validation using single-site 3079 h DFMC data from Chengdu, Sichuan, China, demonstrated PyLSTM's superior temporal performance (R² = 0.70, RMSE = 10.60%). Spatial validation across 241 sites in Xizang, Yunnan, Guizhou, and Sichuan provinces confirmed its robust spatial accuracy (R² = 0.71, RMSE = 16.96%), outperforming both standalone FSMM and LSTM models. PyLSTM successfully captured the declining DFMC trend preceding the Yajiang fire event, with significantly lower estimated DFMC in the burned area compared to surrounding pixels in Yajiang County. These results demonstrate PyLSTM's capability to enhance wildfire risk early warning and identify high-risk areas. Therefore, this study serves as a foundational step toward estimating hourly regional-scale DFMC dynamics—a vital factor in assessing fire danger and behavior.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"335 ","pages":"Article 115259"},"PeriodicalIF":11.4,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033611","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}