Remote Sensing of Environment最新文献

{"title":"Visualizing the pre-visual: Rice blast infection signals revealed","authors":"Long Tian ,&nbsp;Susan L. Ustin ,&nbsp;Bowen Xue ,&nbsp;Pablo J. Zarco-Tejada ,&nbsp;Yufang Jin ,&nbsp;Xia Yao ,&nbsp;Yan Zhu ,&nbsp;Weixing Cao ,&nbsp;Tao Cheng","doi":"10.1016/j.rse.2025.114905","DOIUrl":"10.1016/j.rse.2025.114905","url":null,"abstract":"<div><div>Disentangling pathogen infection signals in plants is critical for understanding the physiological processes that underlie the complex host-pathogen interactions and predicting impending disease outbreak. The rapid progression of rice blast lesions, caused by the filamentous fungus <em>Magnaporthe oryzae</em>, and its imperceptible disease-related symptoms during the asymptomatic stages render real-time detection and visualization challenging. Efforts to reveal pre-visual disease symptoms are of both broad concern and significant interest but remain challenging, as subtle disease signals are often obscured or diluted by other factors at asymptomatic stage. We introduce an imaging spectroscopy-based purification methodology that isolates the disease signals revealed by spectral unmixing on a pixel basis without considering the complex pathogen-induced physiological variations. With multi-temporal proximal hyperspectral imagery, our method captured the transition of disease lesions from asymptomatic to severely symptomatic stages, and successfully distinguished the subtle pathogen-induced signals with few false alarms as early as three days (two days after inoculation, DAI 2) before visual lesions became apparent (DAI 5). The lesion prediction results were confirmed by extensive in vivo visual inspections. Remarkably, we demonstrated that spatially aggregating the isolated disease signals improved the accuracy of pre-visual RB identification to a remarkable level up to 93 % (F1-score = 0.91), enabling unprecedented visualization of potential lesions in a narrow time window of pathogen infection. Although limitations remain regarding model validation and scalability for broader applications, this method represents a significant advancement in early disease forecasting across spectral and spatial domains, and offers new opportunities for high-throughput screening of susceptible varieties in next-generation plant resilience phenotyping.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114905"},"PeriodicalIF":11.1,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144579244","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":"Vegetation height from L-band SAR backscatter and interferometric temporal coherence measurements","authors":"Chiara Telli ,&nbsp;Marco Lavalle ,&nbsp;Nazzareno Pierdicca","doi":"10.1016/j.rse.2025.114879","DOIUrl":"10.1016/j.rse.2025.114879","url":null,"abstract":"<div><div>This paper presents an algorithm for mapping tree height from L-band spaceborne synthetic aperture radar measurements. The proposed tree height retrieval algorithm integrates dual-polarimetric (HH, HV) backscatter and interferometric coherence time series using a model-based approach. Physics-based models relate radar backscatter and interferometric temporal coherence to forest properties and capture the varying environmental conditions over time. These models are calibrated using lidar measurements, and then jointly inverted to retrieve vegetation height at the pixel level. The algorithm is evaluated using a time series of 6 dual-polarimetric ALOS-2 acquisitions over the Lenoir Landing (AL) site, supported by high-resolution lidar data, weather measurements, and the land cover map from the National Land Cover Database. Over the study area, temporal coherence alone proves reliable for estimating forest height, with varying performance depending on vegetation height and class. Comparison with lidar-derived tree height confirms that integrating multi-temporal interferometric coherence and backscatter significantly improves the estimation performance compared to using backscatter time series alone. In particular, incorporating interferometric coherence extends the dynamic range of the estimated tree heights, overcoming the saturation of backscatter signals for tall vegetation. The algorithm is applicable to repeated radar acquisitions collected within a narrow orbital tube and represents an opportunity to map forest properties with upcoming and future low-frequency radar missions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114879"},"PeriodicalIF":11.1,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144571948","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":"Improve topographic LiDAR point cloud interpolation accuracy with geodesic distance","authors":"Xinqiao Duan ,&nbsp;Yong Ge ,&nbsp;Haiqing He","doi":"10.1016/j.rse.2025.114900","DOIUrl":"10.1016/j.rse.2025.114900","url":null,"abstract":"<div><div>High-precision topographic LiDAR point clouds provide a concrete elevation basis for many environmental and ecological applications, but they suffer from distinctly uneven density with voids of varying sizes. Interpolation tools most commonly serve to resample the density and fill the voids under the designated scale of the application. However, the target spaces in which the topographic point clouds reside are essentially non-Euclidean surfaces; the true distances between sample points are curved geodesic distances, which differ significantly from the conventionally used Euclidean distances, so classical interpolation models should be investigated for potential systematic biases.</div><div>First, we introduce geodesic distance to topographic point clouds as a dimensionality reduction constraint to project the point cloud. This ensures the positive definiteness of the autocorrelation matrix and the revision of conventional interpolation algorithms. Then, we carried out a benchmark interpolation with deterministic and geostatistical models on a relatively “regular” topographic point cloud. Product point cloud resampling was subsequently examined with the optimal method. In response to the computational challenge, we devise a feature-points embedded domain decomposition calculation and propose a cross-validation-based point-to-point distance for better evaluation of the interpolation accuracy.</div><div>The experimental results with different scenarios show substantial improvement in interpolation accuracy with the introduction of geodesic distance, which is of universal significance in prompting the precise utilization of topographic models.</div><div>The related data and code will be open-sourced in the community.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114900"},"PeriodicalIF":11.1,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144572408","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 spatiotemporal coverage of satellite-derived high-resolution NO2 data with a super-resolution model","authors":"Maolin Zhang ,&nbsp;Siwei Li ,&nbsp;Jia Xing ,&nbsp;Ge Song ,&nbsp;Shuangliang Li ,&nbsp;Jiaxin Dong ,&nbsp;Shuxin Zheng ,&nbsp;Ge Han ,&nbsp;Jie Yang","doi":"10.1016/j.rse.2025.114897","DOIUrl":"10.1016/j.rse.2025.114897","url":null,"abstract":"<div><div>Recent advancements in satellites, such as the TROPOspheric Monitoring Instrument (TROPOMI) have improved the spatial resolution of atmospheric chemical products, enabling more accurate estimation of atmospheric chemical concentrations. However, these satellites still face coverage limitations, posing challenges for monitoring atmospheric chemistry over large spatiotemporal scales. We propose a novel deep-learning model, GM-ESR-ResNet, which uses advanced super-resolution reconstruction for the coarse-resolution OMI data, supported by geo-meteorological covariates, to enhance the spatiotemporal coverage of high-resolution NO₂ columns from the original TROPOMI product. The results indicate that GM-ESR-ResNet significantly outperforms interpolation methods and traditional machine-learning models (which did not incorporate OMI NO₂ columns in prior studies) for spatiotemporal imputation of TROPOMI NO₂ data. The model enhances spatial coverage (imputation ratio = 30 %, correlation <em>r</em> = 0.82 vs. <em>r</em> = 0.42) with superior temporal transferability (MAPE of historical application 0.28 vs. 0.29). Additionally, the enhanced high-resolution NO₂ columns lead to more accurate surface NO₂ estimates than TROPOMI-based results, showing an increase in R² (0.856 vs. 0.871) and a 6 % reduction in RMSE. Furthermore, GM-ESR-ResNet, using OMI satellite data, successfully generates high-resolution NO₂ columns from 2015 to 2022, even in the absence of TROPOMI data. This enhancement improves the capture of strong spatial gradients in surface NO₂ concentrations and effectively addresses grid-to-point mismatch issues that previously led to substantial overestimations (by 11–30 %) in downwind and rural areas in the original coarse-resolution retrievals. Our study demonstrates the significant potential of super-resolution models in satellite retrievals, which is essential for aligning high-resolution column data with ground-based measurements.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114897"},"PeriodicalIF":11.1,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144571778","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":"Monitoring fine-scale natural and logging-related tropical forest degradation using Sentinel-1","authors":"Anne-Juul Welsink ,&nbsp;Chloé Dupuis ,&nbsp;Laura Cue La Rosa ,&nbsp;Monne Weghorst ,&nbsp;Jens van der Zee ,&nbsp;Sietse van der Woude ,&nbsp;Marielos Peña-Claros ,&nbsp;Martin Herold ,&nbsp;Kurt Fesenmyer ,&nbsp;Johannes Reiche","doi":"10.1016/j.rse.2025.114878","DOIUrl":"10.1016/j.rse.2025.114878","url":null,"abstract":"<div><div>Tropical forest degradation results in severe biomass loss and biodiversity decline. However, fine-scale natural and logging-related forest disturbances remain difficult to trace, both from the ground as well as remotely. Comprehensive, landscape scale characterization of anthropogenic forest degradation requires accurate accounting of baseline canopy disturbance rates and patterns. This paper has evaluated the feasibility of radar data for detecting canopy gaps created by natural and anthropogenic mechanisms at large spatial scale by assessing the extent to which the Sentinel-1 C-band radar signal can be used to map fine-scale disturbances in both naturally disturbed and logged landscapes. Our physical-based method detects disturbances based on changes in backscatter resulting from radar shadow and/or layover. We apply various detection thresholds to explore the trade-off between detection and false detection and validate our method in study areas for which spatially exhaustive drone-based canopy gap maps are available for validation, namely Barro Colorado Island nature reserve (median gap area: 39 m²) and five logging concessions in the Congo Basin (median gap area: 237 m²). With a moderate threshold (2.5 dB backscatter reduction), we reach detection rates above 65 percent for disturbances above 200 m² in both naturally disturbed and logged areas. Detection rates were primarily driven by gap area; gap depth had a smaller, yet significant, influence. These results significantly improve on operational forest disturbance products and previous studies on fine-scale disturbance detection using Sentinel-1 radar. Moreover, the improved insight in detection accuracies of anthropogenic disturbances fosters a move towards monitoring forest dynamics across large scales at which we cannot be certain whether the disturbance driver is anthropogenic or natural.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114878"},"PeriodicalIF":11.1,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144572410","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":"Seasonal dynamics of a coupled hillslope — river system in the Arctic revealed by semi-automated satellite image analysis","authors":"Christian M. Erikson ,&nbsp;Evan N. Dethier ,&nbsp;Carl E. Renshaw","doi":"10.1016/j.rse.2025.114883","DOIUrl":"10.1016/j.rse.2025.114883","url":null,"abstract":"<div><div>Proliferation of retrogressive thaw slumps has the potential to dominate the sediment yield in arctic watersheds through an influx of hillslope-derived sediment to rivers. We develop a suite of algorithms to semi-automate measurements of thaw slump growth and river suspended sediment concentrations from satellite imagery with the intent of linking the production of sediments on a hillslope to the presence of sediment in an adjacent stream at the seasonal scale. We apply our semi-automated methods to quantify seasonal changes in scar zone area, headwall retreat, and suspended sediment concentration at a thaw slump in the Canadian Northwest Territories, which has doubled in size during the observation period. The semi-automated approaches record up to 30 cm/day of headwall retreat, strongly correlated with warm season temperature, and an approximate doubling in suspended sediment concentration downstream of the thaw slump relative to an upstream control reach. The semi-automated results of the slump growth compare well with previous observations of retrogressive thaw slumps and with manual measurements from satellite imagery. Seasonal patterns in the suspended sediment concentration and thaw slump erosion identify the signal of thaw slump sediments in the river as most prominent during the late summer, when sediment stored in the scar zone can be mobilized by precipitation. The ability of the semi-automated methods to capture the seasonal dynamics of the reach demonstrates the possibility of extending their use to other thaw slumps across the Arctic.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114883"},"PeriodicalIF":11.1,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144568910","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":"Optimized processing of SWOT fast-sampling data for marine gravity recovery: lessons for the 21-day science phase","authors":"Daocheng Yu ,&nbsp;Cheinway Hwang","doi":"10.1016/j.rse.2025.114903","DOIUrl":"10.1016/j.rse.2025.114903","url":null,"abstract":"<div><div>The new Surface Water and Ocean Topography (SWOT) altimeter mission, launched on December 16, 2022, marked a significant leap in ocean topography measurement by transitioning from traditional nadir-looking altimetry to wide-swath interferometric radar altimetry. During its 3-month fast-sampling phase from March 29 to July 9, 2023, SWOT operated on a 1-day repeat orbit focused on calibration and validation. Currently, SWOT is operating in its science phase on a 21-day repeat orbit. This study uses the Version C Level 2 Ka-band Radar Interferometer data in the South China Sea, collected during the SWOT fast-sampling phase, to validate SWOT’s performance in recovering marine gravity fields. The 95 cycles of fast-sampling phase data are equivalent to about 5.5 years of data from the science phase, offering sufficient repeat data to evaluate the potential of SWOT for deriving long-term, stacked marine gravity fields. We outline optimal processing strategies for refining SWOT sea surface heights (SSHs) to derive marine gravity anomalies. These strategies include rejecting outliers, mitigating ocean variability by removing sea level anomaly, correcting tilts across the entire SWOT swath segment using a three-dimensional plane, and averaging geoid gradients to reduce random errors. This study introduces a new method, the least-squares adjustment (LSA), for estimating the north and east components of the geoid gradient from SWOT SSH data. The gravity anomalies derived from the gradient components determined using the LSA method show comparable accuracy to those using least-squares collocation (LSC), with values of 2.32 mgal versus 2.28 mgal, respectively. Moreover, the LSA method is more computationally efficient than LSC. SWOT demonstrates the capability to derive north and east gradient components and marine gravity fields at finer scales compared to conventional altimeters, particularly over rough seafloor areas where SWOT can observe high-wavenumber SSHs. SWOT reveals continental shelf margins more clearly than conventional altimeters. Marine gravity anomalies from 3 months of SWOT data have an accuracy of 2.28 mGal, which is 12% better than the accuracy achieved with 14 years of data from conventional nadir altimeters. The gravity recovery procedures developed using SWOT fast-sampling phase data can be extended to derive optimal local and global marine gravity fields during the mission’s science phase.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114903"},"PeriodicalIF":11.1,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144572407","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 carbon stock and growth of individual street trees using LiDAR-camera fusion-based mobile mapping system","authors":"Tackang Yang ,&nbsp;Youngryel Ryu ,&nbsp;Ryoungseob Kwon ,&nbsp;Changhyun Choi ,&nbsp;Zilong Zhong ,&nbsp;Yunsoo Nam ,&nbsp;Seongwoo Jo","doi":"10.1016/j.rse.2025.114895","DOIUrl":"10.1016/j.rse.2025.114895","url":null,"abstract":"<div><div>Urban street trees account for a significant fraction of trees in urban areas, yet the amount and changes of their carbon stocks remain largely unexamined. This study introduces a framework utilizing a Light Detection and Ranging (LiDAR)-camera fusion-based Mobile Mapping System (MMS) to estimate carbon stocks in individual street trees regularly. This system allows repetitive and simultaneous collection of species information and structural parameters on a city-wide scale, enabling the estimation of carbon stock and its change. The framework comprises two principal components: the detection of individual street trees and the estimation of their respective carbon stocks. To detect individual street trees, we initially employed image-based deep learning model to diminish the effort needed in constructing point cloud training data and designing a universal rule applicable to complex and diverse urban streetscapes. In the carbon stock estimation phase, we used species-specific allometric equations based on species information derived from YOLOv3 and Diameter at Breast Height (DBH) measurements from trunk point cloud circle fitting. The proposed individual street tree detection method achieved an F1-score of 81.9 %, precision of 86.3 %, and recall of 78.5 % in city-scale experiments. Additionally, the Root Mean Square Error for the estimates of DBH and tree height (H) was 3.2 cm (11.4 %) and 1.8 m (18.3 %), respectively. Repeated acquisitions between two years revealed the median change of H, DBH, and carbon stock as 0.4 m yr⁻¹, 1.4 cm yr⁻¹, and 27.1 kgC yr⁻¹, respectively. Applying our method in most vehicle accessible streets in Suwon, Republic of Korea, we mapped 34,124 street trees, revealing a total carbon stock of 6.18 GgC. These results underscore the accuracy and scalability of the framework, highlighting its potential to facilitate efficient urban carbon management.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114895"},"PeriodicalIF":11.1,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144556772","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":"Recent significant subseasonal fluctuations of supraglacial lakes on Greenland monitored by passive optical satellites","authors":"Jiahui Qiu ,&nbsp;Jiangjun Ran ,&nbsp;Natthachet Tangdamrongsub ,&nbsp;Xavier Fettweis ,&nbsp;Shoaib Ali ,&nbsp;Wei Feng ,&nbsp;Xiaoyun Wan","doi":"10.1016/j.rse.2025.114896","DOIUrl":"10.1016/j.rse.2025.114896","url":null,"abstract":"<div><div>Temporarily impounded liquid water on the surface of the Greenland Ice Sheet (GrIS), prominently represented as supraglacial lakes (SGLs), may enhance ice flow and modulate surface meltwater runoff, serving as a dynamic indicator of the cryohydrologic cycle. Despite their importance in understanding glacier mass balance and regional climate change, a detailed description of SGLs and their intra-annual fluctuations across the entire GrIS remains understudied. Here, we present a deep learning-based approach to automatically map SGLs from passive optical satellite imagery across the entire GrIS during the melt seasons of 2017–2022. Approximately 150,000 Sentinel-2 and Landsat 8/9 images were utilized, each representing a 5-day average composite at a 10 km × 10 km grid resolution, with the Landsat images used as possible supplements. SGL predictions by the proposed method demonstrate high performance, achieving an F1-score of up to 0.959 compared to the independent test dataset. This high accuracy enables a detailed analysis of the key role SGLs play in enhancing surface ablation by absorbing solar radiation and delivering meltwater. The SGL-driven ablation effect was most pronounced in the South-West basin of the GrIS, where the peak lake area in July accounted for 44.9 % of the total GrIS-wide lake area. In contrast, the lowest magnitude (4.2 %) was observed in the South-East basin, despite similarly strong ablation in this region. Among all the generated SGL occurrence grids, peak SGL areas in certain grids (∼14 % of the total) were observed in May or September, rather than exclusively during the typical high-ablation months of June to August, reflecting regional and elevation-dependent variations. Grids further from the ice sheet margin generally showed peak SGL areas later in the melt season, which is evident in the western part of the GrIS. Monthly SGL peak areas shift dramatically from 253.18 ± 123.94 km² to 5084.90 ± 1043.26 km², with the lowest in May 2018 and the highest in August 2021. An extraordinary area spike occurred in September 2022 and was particularly monitored in the South-West basin, where abnormally intense rainfall and runoff simulated by the Modèle Atmosphérique Régional (MAR) model were recorded. Our study highlights the significance of examining SGL area changes at short temporal intervals to understand the dynamics of cryospheric hydrology under future climate scenarios.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114896"},"PeriodicalIF":11.1,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144556773","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 GRACE-based framework for tracking flash drought hotspots and spatiotemporal evolution","authors":"Hongbing Bai ,&nbsp;Yulong Zhong ,&nbsp;Min Zhong ,&nbsp;Wei Feng","doi":"10.1016/j.rse.2025.114893","DOIUrl":"10.1016/j.rse.2025.114893","url":null,"abstract":"<div><div>Flash droughts cause serious damage to ecosystems and human societies due to their rapid onset and intensification. Their fast changes pose significant challenge to existing drought warning and monitoring systems. Although previous studies have focused on meteorological, ecological and soil moisture indicators for assessing flash droughts, an effective quantitative indicator capturing the direct manifestation of flash droughts—specifically, the rapid decline in terrestrial water storage (TWS)—remains unavailable. To address this gap, we propose a pentad-scale hydrological flash drought identification framework based on a daily-scale reconstructed TWS anomaly dataset derived from Gravity Recovery and Climate Experiment (GRACE) observations. We further analyze global flash drought hotspot regions, their spatiotemporal evolution, and key drivers from 1979 to 2018. Our findings reveal that, on a spatial scale, flash drought hotspots are primarily concentrated in humid and semi-humid climate zones. On a temporal scale, the impact of flash droughts has intensified in regions such as Northern Europe, Northern Asia, Southeast Asia, and South Asia, whereas the Amazon, East Africa, and West Africa exhibit a decreasing trend. In other study regions, no significant changes in flash drought conditions are observed. At the global scale, energy-related factors (including temperature and solar radiation) contribute significantly more to flash drought development than water-related factors (such as precipitation). Additionally, approximately 48 % of flash droughts worldwide evolve into long-term droughts, with this transition occurring primarily during the vegetation growing season in humid regions. The hydrological flash drought identification framework proposed in this study effectively addresses gaps in existing monitoring systems, providing a crucial scientific basis for drought early warning and disaster mitigation.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"328 ","pages":"Article 114893"},"PeriodicalIF":11.1,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144548756","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}