Remote Sensing of Environment最新文献

{"title":"Heatwave impacts on intertidal seagrass reflectance: from laboratory experiment to satellite mapping of seagrass heat shock index","authors":"Simon Oiry ,&nbsp;Bede Ffinian Rowe Davies ,&nbsp;Philippe Rosa ,&nbsp;Augustin Debly ,&nbsp;Maria Laura Zoffoli ,&nbsp;Anne-Laure Barillé ,&nbsp;Nicolas Harin ,&nbsp;Marta Román ,&nbsp;Jimmy de Fouw ,&nbsp;Pierre Gernez ,&nbsp;Laurent Barillé","doi":"10.1016/j.rse.2026.115248","DOIUrl":"10.1016/j.rse.2026.115248","url":null,"abstract":"<div><div>Seagrasses play a vital role in coastal ecosystems, providing habitat, stabilising sediments, and contributing to carbon sequestration. However, global warming has increased the frequency and intensity of heatwaves, posing a significant threat to seagrass health. This study investigates the effects of marine and atmospheric heatwaves on the spectral reflectance of the intertidal seagrass <em>Zostera noltii</em>. Laboratory experiments were conducted under controlled heatwave conditions, where hyperspectral reflectance measurements were taken to assess the impacts over time. Simulated heatwave conditions caused a substantial decline in seagrass reflectance, particularly in the green and near-infrared regions, corresponding to the darkening of green leaves. Key vegetation indices, including the Normalized Difference Vegetation Index (NDVI) and Green Leaf Index (GLI), showed pronounced reductions under heatwave stress, with NDVI values decreasing by up to 34% and GLI by 57%. A novel metric, the Seagrass Heat Shock Index (SHSI), was developed to quantify the transition of seagrass leaves from green to brown, demonstrating a stronger ability to capture the effects of heatwave exposure on seagrass colouration. Multispectral satellite observations corroborated the laboratory results, revealing widespread darkening of seagrass leaves during marine and atmospheric heatwave events in South Brittany, France. Notably, darkened seagrass patches were observed in intertidal areas exposed to air temperatures exceeding 32 °C for over 13.5 h per day. These findings highlight the potential of spectral reflectance as a tool for detecting early signs of heatwave-induced stress in seagrasses, offering a valuable method for remote sensing-based habitat assessment. The present study underscores the potential of remote sensing to capture rapid environmental changes in intertidal zones, enabling for continuous monitoring of seagrass meadows under the current and future climate regimes.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"335 ","pages":"Article 115248"},"PeriodicalIF":11.4,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146015016","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":"Mapping urban built-up types from 2000 to 2022 at 10-m resolution using super-resolution of Landsat spectral-temporal metrics and center-patch classification","authors":"Vu-Dong Pham ,&nbsp;Franz Schug ,&nbsp;David Frantz ,&nbsp;Sebastian van der Linden","doi":"10.1016/j.rse.2026.115251","DOIUrl":"10.1016/j.rse.2026.115251","url":null,"abstract":"<div><div>Detailed information on urban development types, e.g. residential, industrial, or transportation infrastructure, is essential for assessing urban growth rates, population dynamics, and environmental impacts. Earth observation imagery from Landsat and Sentinel-2 provides valuable data for characterizing urban areas and their development over large spatial extents and long temporal scales. However, mapping large areas over multiple decades poses several challenges. Specifically, the coarse resolution of historical Landsat data (30-m) limits the capacity to capture the spatial detail of diverse built-up types. Furthermore, existing mapping techniques—such as pixel-based, scene-based, and semantic segmentation - often face limitations in capturing spatial context, compromise mapping resolution, or rely on hand-crafted training data. To address these challenges, this study proposes a novel workflow comprising two key components: (1) enhancing historical Landsat spatial resolution to 10-m using a generative super-resolution model, with a focus on synthetic images derived from spectral-temporal metrics, and (2) a “center-patch classification” method, wherein patch images serve as input for the central pixel classification. We applied the proposed methodologies to produce tri-annual maps of urban built-up classes—including <em>Residential buildings, Industrial buildings, Open spaces,</em> and <em>Non built-up</em>—across the Baltic Sea region for two decades (2000−2021). Evaluation of the baseline performance demonstrated that the 10-m maps derived from super-resolution Landsat data achieved higher accuracy than existing Sentinel-2-based products. Furthermore, when applied to earlier years within the study period, the super-resolution Landsat data consistently exhibited improved classification accuracy across all urban classes compared to native-resolution Landsat data.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"335 ","pages":"Article 115251"},"PeriodicalIF":11.4,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145996475","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":"Global analysis of nitrogen dioxide and formaldehyde column densities from the Pandora global network: Variability and implications for satellite validation","authors":"Jong-Uk Park ,&nbsp;Subin Lim ,&nbsp;Thomas F. Hanisco ,&nbsp;Nader Abuhassan ,&nbsp;Bryan K. Place ,&nbsp;Apoorva Pandey ,&nbsp;Alexander Cede ,&nbsp;Martin Tiefengraber ,&nbsp;Manuel Gebetsberger ,&nbsp;Jinsoo Park ,&nbsp;Jinsoo Choi ,&nbsp;James H. Crawford ,&nbsp;Chang-Keun Song ,&nbsp;Sang-Woo Kim","doi":"10.1016/j.rse.2026.115249","DOIUrl":"10.1016/j.rse.2026.115249","url":null,"abstract":"<div><div>This study harnesses quality-assured global Pandora observations (2019–2023) from the Pandonia Global Network (PGN) to investigate diurnal and seasonal variations of NO₂ and HCHO—key proxies for tropospheric O₃—and to evaluate TROPOMI satellite observations. NO₂ vertical column densities (VCDs) at Polluted Urban stations peak in winter and gradually increase throughout the day, but show a decrease in afternoons in summer due to photochemical loss. Conversely, Rural/Background stations exhibit summer maxima with monotonic daytime increases across seasons, driven by stratospheric NO₂ variability. HCHO VCDs are higher in summer at most sites, with a morning increase followed by elevated concentrations throughout the afternoon. The spatial representativeness mismatch between satellite and Pandora observations results in negative biases in TROPOMI NO₂ VCDs at Polluted Urban stations and a valley station, while overestimations are found at high-altitude stations. Considerable random uncertainties in TROPOMI HCHO VCDs lead to low correlations (r² = 0.08–0.11) and high random errors (0.27–0.33 DU) across environments. Averaging collocated data points prior to intercomparison effectively reduces random biases, whereas increasing the spatial collocation range introduces biases due to spatial averaging effects. Tropospheric HCHO-to-NO₂ ratios (FNR_trop) retrieved from Pandora observations indicate that Polluted Urban (0.82 ± 0.08) and Rural/Background (1.64 ± 0.07) stations are generally under VOC-limited and NO_x-limited O₃ production regimes, respectively, while summertime increases in FNR_trop put Polluted Urban stations in a transitional range, yielding higher O₃ production efficiencies. TROPOMI-derived FNR_trop shows good agreement with Pandora in Polluted Urban stations (ΔFNR_median = 0.18), whereas random error increases in rural areas with lower tropospheric NO₂.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"335 ","pages":"Article 115249"},"PeriodicalIF":11.4,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146015018","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":"A three-stage framework for stand-level automated stem volume estimation in temperate forests using Mobile laser scanning","authors":"Jinyuan Shao ,&nbsp;Dennis Heejoon Choi ,&nbsp;Jidong Liu ,&nbsp;Xiangxi Tian ,&nbsp;Bina Thapa ,&nbsp;Seunghyeon Lee ,&nbsp;Ayman Habib ,&nbsp;Songlin Fei","doi":"10.1016/j.rse.2026.115246","DOIUrl":"10.1016/j.rse.2026.115246","url":null,"abstract":"<div><div>Accurate stem-level volume estimation at large scale is highly desired in temperate natural forests due to their economic and ecological significance. Mobile Laser Scanning (MLS) systems (e.g., handheld or backpack) offer the ability to efficiently capture high-density point clouds over large areas, creating opportunities for automated, large-scale individual stem volume estimation. However, effective algorithms that can automatically and accurately analyze the dense and complex MLS point clouds of temperate natural forests are lacking. To address this issue, we propose a novel three-stage method to automatically detect, segment, and reconstruct individual stems, enabling direct volume estimations from MLS point clouds of temperate natural forests. First, a deep learning model is employed to separate understory vegetation from overstory trees, reducing point cloud complexity. Next, we introduce a Bidirectional Section Growing (BSG) method for individual stem detection and segmentation, specifically for the segmentation of merchantable logs and multi-stem scenarios, using a novel Least Squares with Similarity Optimization (LeSSO) algorithm. Finally, the Sector Median Points (SMP) method is developed to reconstruct stem shapes for precise volume estimation. Our method is evaluated on four datasets collected in temperate natural forests across the U.S. and Europe. Experimental results demonstrate its superior performance compared to state-of-the-art algorithms, achieving 89.2% Intersection over Union (IoU) for understory removal, 99.4% F-score for stem detection, 91.5% IoU for stem segmentation, and reconstruction accuracy with a Point-to-Mesh distance of 0.0004 m² and a Chamfer distance of 0.05 m. Moreover, we record 42 stem locations in the field for one of the U.S. datasets and conduct destructive measurements of section-wise diameters for each of them to serve as independent reference data to evaluate stem detection and volume estimation. Our method is able to detect all 42 trees from the point cloud, and reconstructed stem models yield the most accurate section-wise diameter estimates with a Root Mean Square Error (RMSE) of 2.27 cm and R² of 0.96, and best volume estimation with RMSE of 0.18 m³ and R² of 0.97. Our method paves the way for automated and accurate estimation of merchantable stem volume from MLS point clouds collected in complex temperate natural forests.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"335 ","pages":"Article 115246"},"PeriodicalIF":11.4,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146015015","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":"2026-03-15","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}

{"title":"Global assessment of landslide monitoring applicability with the Harmony mission","authors":"Shaokun Guo ,&nbsp;Jie Dong ,&nbsp;Mingsheng Liao","doi":"10.1016/j.rse.2026.115236","DOIUrl":"10.1016/j.rse.2026.115236","url":null,"abstract":"<div><div>The Harmony mission, comprising two passive companion satellites that will operate in a bistatic configuration with Sentinel-1D after its planned launch, is designed to enhance Earth observation through improved diversity of observation geometry. The benefits are substantial, particularly for landslide monitoring, where three-dimensional deformation can be more effectively resolved. However, a quantitative global-scale assessment of its anticipated performance is still lacking. This study addresses this gap through a systematic geometric analysis. We modify the conventional applicability framework to support a bistatic configuration, leveraging multiple mission-related datasets to produce global landslide applicability products for the three stations, including Sentinel-1D and the two Harmony satellites. The products are accessible through a cloud platform. Findings reveal that, under the ascending-descending combined scheme, invalid regions rise by 9% globally, surpassing 25% in certain rugged areas. This is accompanied by notable improvements: the global sensitivity index increases by 0.11 (from 0.61 to 0.72), with low-sensitivity (&lt;0.3) regions declining from 10.4% to 2.1%. Theoretical experiments reveal a northward/southward sensitivity improvement exceeding 0.15, while the direct 3D inversion capability, a key benefit of the additional observation directions, is confirmed under moderate noise. Overall, Harmony provides a robust and effective approach to 3D landslide monitoring, markedly enhancing reliable landslide detection globally.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"335 ","pages":"Article 115236"},"PeriodicalIF":11.4,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146015021","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":"Reconstructing all-weather remotely sensed air temperature via a kernel-based temporal filling and bias correction (KTF-BC) framework","authors":"Yan Xin ,&nbsp;Yongming Xu ,&nbsp;Xudong Tong ,&nbsp;Meng Ji ,&nbsp;Yaping Mo ,&nbsp;Yonghong Liu ,&nbsp;Shanyou Zhu","doi":"10.1016/j.rse.2026.115253","DOIUrl":"10.1016/j.rse.2026.115253","url":null,"abstract":"<div><div>Thermal infrared (TIR) remote sensing provides an effective means of mapping near-surface air temperature (<em>T</em>_<em>a</em>) at large scales. However, cloud coverage introduces substantial data gaps, posing a considerable challenge for producing all-weather <em>T</em>_<em>a</em> datasets. This study proposed a two-step framework, termed Kernel-based Temporal Filling and Bias Correction (KTF-BC), to reconstruct all-weather remotely sensed <em>T</em>_<em>a</em>. In the first stage, a kernel-based temporal filling method was developed to estimate the theoretical clear-sky <em>T</em>_<em>a</em> for cloud-covered pixels. In the second stage, a bias correction model was constructed to adjust these theoretical estimates toward the actual <em>T</em>_<em>a</em> under cloudy conditions. The proposed framework was applied across China from 2019 to 2023 to generate spatially complete daily mean <em>T</em>_<em>a</em> at 1-km resolution. Validation against meteorological stations under cloudy conditions demonstrated consistently high accuracy, with R² values up to 0.99, mean absolute errors (MAEs) ranging from 0.91 to 0.95 °C, root mean square errors (RMSEs) ranging from 1.21 to 1.26 °C, and biases close to 0 °C. The method effectively captured fine-scale thermal heterogeneity and demonstrated robust performance across varying cloud conditions and surface environments. This study provides a practical and reliable solution for reconstructing all-weather <em>T</em>_<em>a</em> from satellite observations.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"335 ","pages":"Article 115253"},"PeriodicalIF":11.4,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145996477","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":"Automated rice mapping under diverse cropping patterns and establishment methods by integrating phenological knowledge and synergy of optical and SAR imagery","authors":"Xingrong Li ,&nbsp;Gaoxiang Yang ,&nbsp;Meng He ,&nbsp;Yuan Xiong ,&nbsp;Leilei Liu ,&nbsp;Hifza Mariam ,&nbsp;Iftikhar Ali ,&nbsp;Chongya Jiang ,&nbsp;Xia Yao ,&nbsp;Yan Zhu ,&nbsp;Weixing Cao ,&nbsp;Tao Cheng","doi":"10.1016/j.rse.2026.115255","DOIUrl":"10.1016/j.rse.2026.115255","url":null,"abstract":"<div><div>Accurate and timely information on rice cultivation areas and cropping intensity is essential for precision crop management, food security and environmental sustainability. However, the generation of high-quality rice products is often hindered by the lack of ground truth samples, particularly in the regions with complex cropping patterns. While most rice mapping methods rely on the use of remotely sensed flooding signal from the transplanting period to distinguish rice from other crops, they face significant challenges from the increasingly prevalent direct-seeded rice for which the flooding signal at the beginning of season is weak due to the unique management measures. To address these issues and achieve the comprehensive extraction of rice under diverse cropping patterns and establishment methods, this study proposed the phenological knowledge-guided automatic rice mapping approach using optical and synthetic aperture radar (SAR) data (PHAROS). This method first determined the cropping intensity of rice and its concurrent crops using harmonized multi-resource Normalized Difference Vegetation Index (NDVI) time series and phenological knowledge. Subsequently, the Double Phase SAR Index (DPSI) was constructed to extract candidate rice samples by synthesizing the early and peak growth phases retrieved from NDVI time series and the corresponding SAR polarization features. Consequently, training data were generated automatically and fed into a machine learning classifier for rice mapping. The effectiveness of the PHAROS was evaluated over the Middle and Lower Reaches of the Yangtze River (MLRYR) of China and four other major rice production regions in Asia. Furthermore, the earliest timing of early, middle and late rice in the MLRYR was also quantified via the PHAROS and cross-year model transfer.</div><div>The results demonstrated that the PHAROS could identify rice of diverse cropping pattern with an overall accuracy (OA) from 0.965 to 0.976 in the MLRYR from 2019 to 2023. The classification maps exhibited well-delineated rice parcels and clear separation between single- and double-cropping rice. The PHAROS also yielded OA values of 0.902–0.979 and similar distribution pattern with the reference rice products in Suihua of China, Sataka of Japan, An Giang of Vietnam and Punjab of Pakistan, which represent diverse climatic conditions and cropping patterns across Asia. Compared to its counterpart methods, PHAROS demonstrated significant improvements by 0.022–0.235 in OA in rice planting area extraction and cropping intensity detection. The early, middle and late rice could be identified as early as at tillering, tillering-jointing and sowing/transplanting stages, respectively. This study reveals the necessity of handling the overlooked weak flooding signal from direct-seeded rice and offers a viable solution for large-scale rice cropping intensity detection and mapping under diverse establishment methods.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"335 ","pages":"Article 115255"},"PeriodicalIF":11.4,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146015019","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":"Amplified deviation flood index (ADFI) for fast non-prior flood detection","authors":"Hui Zhang ,&nbsp;Ming Luo ,&nbsp;Zhixin Qi ,&nbsp;Xing Li ,&nbsp;Yongquan Zhao","doi":"10.1016/j.rse.2026.115258","DOIUrl":"10.1016/j.rse.2026.115258","url":null,"abstract":"<div><div>Climate change causes widespread increases in the frequency, magnitude, and extent of flood events, which pose increasing threats to societal and natural systems and highlight the urgency for timely and accurate flood mapping. However, previous flood mapping methods often require prior knowledge (such as the timing and location) of flood events that is usually incomplete or even unavailable when studying historical floods. Here we propose a new amplified deviation flood index (ADFI) using the time-series anomaly statistics from the Synthetic Aperture Radar (SAR) data for mapping fully flooded areas without relying on prior knowledge of flood events. ADFI is constructed by considering two fundamentals of flood events: a decrease in backscatter intensity when ground objects are fully flooded and an increase in the variance of backscatter intensity owing to infrequently sudden occurrence of flood events, thus enabling a fast non-prior detection of flood events and extents. The performance of ADFI is assessed in four study areas across different climate zones of the globe, and the assessment shows that the overall accuracies of ADFI in all study areas exceed 93%, with precision &gt;95% and recall &gt;94%. Further comparison with two existing flood indices suggests that our proposed ADFI-based mapping method can improve the overall accuracy by 12.11%–3.97%, precision by 12.59%–10.17%, and recall by 54.32%–6.37%. A time-series flood mapping based on ADFI demonstrates that our proposed method enables a non-prior, precise, and fast detection of flood events and allows prompt monitoring of flood disasters. Our proposed approach enhances the efficiency and scalability of flood monitoring, providing a valuable tool for rapid disaster response and the reconstruction of long-term flood histories across diverse environments and climates.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"335 ","pages":"Article 115258"},"PeriodicalIF":11.4,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145996476","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":"2026-03-15","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}