Remote Sensing of Environment最新文献

{"title":"Corrigendum to “Substantial increases in burned area in circumboreal forests from 1983 to 2020 captured by the AVHRR record and a new autoregressive burned area detection algorithm” [Remote Sensing of Environment 325(2025) 114789]","authors":"Connor W. Stephens, Anthony R. Ives, Volker C. Radeloff","doi":"10.1016/j.rse.2025.115069","DOIUrl":"https://doi.org/10.1016/j.rse.2025.115069","url":null,"abstract":"The authors regret that in our original paper, a data summary error resulted in a slight overestimate of our burned area estimates. This error occurred during our conversion of the 0.05-degree pixel burn fraction maps to total annual burned area. During this process, we multiplied subpixel burn fraction (0-1) by pixel size (hectares) to estimate subpixel burned area (hectares per pixel). However, we specified a constant pixel size corresponding to the area of a 0.05-degree cell at the equator and did not adjust to the latitude of the 0.05-degree cell. This resulted in the overestimation of pixel-level burned area and a slight overestimate of the percentage of circumboreal forests experiencing increasing trends in burned area. The error does not alter the overall conclusions of our article that decadal average annual burned area has increased substantially (prior estimate: 53 %, revised estimate: 45 %) and that annual burned area has increased in about a fifth of the area circumboreal forests (prior estimate: 19.6 %, revised estimate: 17.5 %). However, the error affected our results based on direct estimates of burned area. Tables and figures created using these direct estimates of burned area are revised to reflect revised burned area totals as described below.","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"21 1","pages":""},"PeriodicalIF":13.5,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145283352","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":"Assessment of EOS-07 MHS satellite observations and retrieval of specific humidity profiles using a random forest-based algorithm","authors":"Manoj Kumar Mishra,&nbsp;Rishi Kumar Gangwar,&nbsp;Munn Vinayak Shukla,&nbsp;Prashant Kumar,&nbsp;Pradeep Kumar Thapliyal","doi":"10.1016/j.rse.2025.115066","DOIUrl":"10.1016/j.rse.2025.115066","url":null,"abstract":"<div><div>An in-house-developed millimeter-wave humidity sounder onboard EOS-07 (EOS-07 MHS), launched in February 2023, operates at six frequencies around the 183.3 GHz water vapor absorption band. This study presents a preliminary performance assessment of EOS-07 MHS, including brightness temperature validation, humidity profile retrieval methodology and its validation.</div><div>Under clear-sky conditions, the biases in brightness temperature measured by EOS-07 MHS, relative to RTTOV simulations were within ±1 K, except for channels 1 and 6. Similarly, intercomparisons with ATMS observations showed biases within ±1 K and a standard deviation of 2–3 K.</div><div>A random forest-based method was employed to retrieve specific humidity profiles from EOS-07 MHS observations demonstrated agreement with ERA5 reanalysis and radiosonde observations. Compared with radiosonde data, the mean bias and standard deviation of retrieved specific humidity were approximately 0.78 g/kg and 2.3 g/kg, respectively. The mean percentage bias was within ±20 % below the 800 hPa pressure level, and ranged between ±20 % and ± 40 % above the 800 hPa pressure level. Relative to ERA5, the mean bias and root-mean-square deviation (RMSD) were under 30 % and 50 %, respectively. The estimated total precipitable water vapor showed a mean bias of 1.7–3.1 mm and a standard deviation of 5.2–5.7 mm compared to ERA5. Additionally, the EOS-07 MHS data were assimilated into the WRF model, resulting in improved atmospheric analyses and forecasts. A month-long cyclic assimilation experiment demonstrated consistent enhancements in moisture representation across the lower and middle atmosphere.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"332 ","pages":"Article 115066"},"PeriodicalIF":11.4,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145261002","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":"Winter habitat indices from Landsat 8 and Sentinel-2 imagery for the contiguous US","authors":"David Gudex-Cross,&nbsp;Eduarda M.O. Silveira,&nbsp;Benjamin Zuckerberg,&nbsp;Volker C. Radeloff","doi":"10.1016/j.rse.2025.115064","DOIUrl":"10.1016/j.rse.2025.115064","url":null,"abstract":"<div><div>In seasonally cold ecosystems, ecological processes and biotic communities are strongly influenced by winter conditions. Climate change is affecting these conditions, particularly in the Northern Hemisphere, as temperatures during the cold season continue to warm and alter patterns of frozen ground and snow cover. Yet, the current understanding of the ecological impacts of these changes is limited. A fundamental first step in addressing this knowledge gap is to quantify winter conditions with ecologically meaningful indices across large areas and at spatiotemporal resolutions relevant to on-the-ground management. Here, our goal was to combine Landsat 8 and Sentinel-2 (L8S2) data to derive three 30-m indices of winter conditions (winter habitat indices or WHIs): snow season length, percentage of days of frozen ground without snow, and snow cover variability, for the contiguous United States. We assessed the accuracy of the L8S2 WHIs using a nationwide network of meteorological stations and examined their error rates by land cover type, elevation, and the number of cloud-free observations available for each index calculation. Last, we compared the spatial patterns and errors in the L8S2 WHIs with those in WHIs derived from coarse-resolution MODIS (Moderate Resolution Imaging Spectroradiometer) imagery (500 m). We found that all three L8S2 WHIs accurately characterized winter conditions on the ground. They also had very similar accuracy and spatial patterns as the MODIS WHIs, despite having a lower imaging frequency and the lack of extensively validated snow cover products akin to those from MODIS. The accuracies of the WHIs were generally highest in mountainous areas of the western US and in vegetated areas, and they were lowest in parts of the midwestern and eastern US where cloud-free observations were less frequent, and in developed and barren areas. Having more cloud-free observations improved the accuracy of the WHIs, especially snow season length. Owing to their higher resolution, the L8S2 WHIs detected far more spatial detail than those from MODIS, particularly in topographically complex regions where winter conditions are highly heterogeneous over short distances. Our results demonstrate that 30-m WHIs derived from L8S2 data accurately capture winter conditions across the contiguous US, with accuracies that rival those from MODIS. Thus, the L8S2 WHIs offer exciting opportunities for ecological applications at a 30-m resolution across large spatial scales.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"332 ","pages":"Article 115064"},"PeriodicalIF":11.4,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145247296","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":"An enhanced feature fusion method for urban functional zone mapping with SDGSAT-1 day-night imagery and multi-dimensional geospatial data","authors":"Huiping Jiang ,&nbsp;Mingxing Chen ,&nbsp;Xiangchao Meng ,&nbsp;Hangfeng Qiao ,&nbsp;Dashan Lang ,&nbsp;Zhenhua Zhang","doi":"10.1016/j.rse.2025.115050","DOIUrl":"10.1016/j.rse.2025.115050","url":null,"abstract":"<div><div>Urban functional zones (UFZs) are well-planned spatial units characterized by distinct socioeconomic activities and composite land uses, such as residential areas, industrial zones, and blue-green spaces. Fine-grained UFZ mapping has played an increasingly crucial role in supporting targeted urban renewal and transformation of development mode in megacities, facilitating spatial structure optimization to enhance urban livability and sustainability. Prior UFZ mapping methods that focus on two-dimensional (2D) features of point of interest and multi-spectral imagery, pay little attention to three-dimensional (3D) features of building height and digital surface model, mostly with the absence or underutilization of emerging nighttime light imagery. Given the availability of high-quality day-night spectral signatures provided by the Sustainable Development Science Satellite 1 (SDGSAT-1) in a single sensor observing mode, it has become possible to effectively perform UFZ mapping with day-night feature enhancement. In this study, we proposed a progressive and cross-scale deep fusion architecture for generating UFZ maps at the block scale, enhancing spectral and spatial information through sequential refinement—from feature representation and relationship extraction to context modeling. To verify the effectiveness and generalizability of the proposed method, experiments were conducted in two Chinese megacities with distinct UFZ landscapes. Results demonstrated that the medium-resolution SDGSAT-1 imagery could be used as a reliable data source for deriving day-night features, enabling the generation of fine-grained UFZ maps when combined with 2D—3D features from other geospatial big data. Cross-method comparisons also showed that this approach could significantly improve both semantic segmentation and topological interpretation across different UFZ types. Notably, our method could not only achieve acceptable levels of mapping performance (overall accuracy &gt; 0.91 and average F1-score &gt; 0.91), but also realize the accurate extraction of purer UFZ blocks with a small sample size (training-testing ratio = 1:4), further indicating considerable potential in large-scale UFZ mapping. The source codes are available at: <span><span>https://github.com/Sustainable-City-Lab/UFZ-data-fusion</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"332 ","pages":"Article 115050"},"PeriodicalIF":11.4,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145255311","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":"Three decades evolution of land subsidence driven by anthropogenic activities in the Yellow River Delta (YRD) from continuous SAR interferometry","authors":"Wenbin Xu ,&nbsp;Jinheng Liu ,&nbsp;Lei Xie ,&nbsp;Mimi Peng ,&nbsp;Hao Wang","doi":"10.1016/j.rse.2025.115053","DOIUrl":"10.1016/j.rse.2025.115053","url":null,"abstract":"<div><div>The Yellow River Delta (YRD) has experienced severe subsidence due to anthropogenic activities. However, no study has resolved continuous deformation and investigate its mechanism across a three-decade timeframe from InSAR perspective since the early 1990s. This study proposes a Trend-Adaptive Functional Modeling and Connection method (TAFMC) for multi-sensor InSAR time series integration, with features of adaptability, robustness, and efficiency. It enables the identification of continuous deformation trends previously obscured by data gaps, thereby directly supporting the discovery of decadal deformation evolution. Second, six InSAR sensors were integrated to retrospect to the ground deformation in YRD from 1992 to 2024. The connected InSAR time series indicates three decades of subsidence, caused by deep groundwater extraction, reached up to 220 cm in Guangrao County. But the subsidence has been effectively controlled from &gt;10 cm/yr during 1992–2021 to ∼5 cm/yr after 2021. Since 2015, an inland migration of a coastal subsidence funnel occurred due to the brine industry. Multiple small-scale subsidence funnels with rates exceeding 15 cm/yr emerged, linked to shrimp aquaculture around 2023. These findings provide comprehensive insights for the interaction between anthropogenic activities and the YRD subsidence on a decadal scale, and offer a methodological framework applicable to InSAR multi-decadal analysis in other delta regions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"332 ","pages":"Article 115053"},"PeriodicalIF":11.4,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145247297","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":"Estimation of 1 km all-sky erythemal ultraviolet radiation and daily dose based on MODIS data and ancillary information: algorithm development, global product generation, and accuracy assessment","authors":"Tao He ,&nbsp;Jiaxuan Han ,&nbsp;Shunlin Liang ,&nbsp;Yichuan Ma ,&nbsp;Xiaotong Zhang ,&nbsp;Xiang Zhao ,&nbsp;Longping Si","doi":"10.1016/j.rse.2025.115021","DOIUrl":"10.1016/j.rse.2025.115021","url":null,"abstract":"<div><div>Ultraviolet (UV) radiation plays a vital role in maintaining ecosystem balance and can have both beneficial and harmful effects on human health. Erythemal UV radiation (UVER), which is a weighted sum of UVA and UVB radiation, is directly linked to skin cancer in humans. Although satellite observations offer a useful tool to monitor UV radiation globally, current global satellite products have limitations such as coarse spatial/temporal resolutions and poor spatial/temporal continuity. To address this issue, we proposed a novel method to estimate all-sky UVER by establishing a practical parametric model between UVER and downward shortwave radiation (DSR), total ozone column (TOC), solar zenith angle (SZA), and elevation. A multiple scattering correction algorithm was also developed to improve the accuracy of UVER estimation over highly reflective surfaces. The UVER estimation was validated against 49 ground stations worldwide and showed high accuracy with R² = 0.86, RMSE = 607.92 J/m²∙day, relative RMSE = 23.97 %, MBE = -81.32 J/m²∙day, relative MBE = -3.76 %. Based on the proposed method, a seamless global land high-resolution (1 km) all-sky daily dose of UVER (EDD) product during 2005–2022 was developed as a new member of the Global Land Surface Satellites (GLASS) product suite. Additional independent validation of this product was conducted with 8 sites located in Norway, showing a high accuracy with R² = 0.93, RMSE = 281.50 J/m²∙day, relative RMSE = 19.47 %, MBE = 37.91 J/m²∙day, relative MBE = 3.84 %. The product was compared with multiple global UVER products and showed comparable accuracy and spatiotemporal distributions at 1° × 1° resolution. The 1 km resolution GLASS EDD product is spatially and temporally continuous and freely available to the public, and thus suitable for climate and human health related applications that require high spatial resolution and accuracy.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"332 ","pages":"Article 115021"},"PeriodicalIF":11.4,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145229762","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 novel bathymetric mapping framework integrating indirect inversion of ICESat-2 and multi-source remote sensing data","authors":"Zijia Wang ,&nbsp;Sheng Nie ,&nbsp;Cheng Wang ,&nbsp;Jian Zuo ,&nbsp;Xiaohuan Xi ,&nbsp;Xiaolin Bian ,&nbsp;Xiaoxiao Zhu ,&nbsp;Bisheng Yang","doi":"10.1016/j.rse.2025.115054","DOIUrl":"10.1016/j.rse.2025.115054","url":null,"abstract":"<div><div>Satellite-derived bathymetry (SDB) plays a critical role in coastal zone management, navigation safety, and marine environmental monitoring. However, conventional SDB methods are constrained by limited depth penetration and reduced accuracy, largely driven by water optical properties, environmental variability, and sensor limitations. To address these challenges, this study proposes a novel bathymetric mapping framework that integrates wave-based indirect depth inversion from photon-counting LiDAR data with multi-source remote sensing data. Specifically, a novel Progressive Adaptive Window for Local Period (PAWLP) algorithm is developed to derive water depth from ICESat-2 surface waves. By dynamically adjusting the analysis window to local wave variations, PAWLP enhances inversion robustness based on linear wave theory. In addition, we construct a multi-source SDB random forest inversion model by fusing multispectral imagery, synthetic aperture radar (SAR), tidal height, and tidal velocity. To further improve model generalizability and reduce scene-specific noise, a temporal sample transfer strategy is applied. In this study, the proposed methods are validated using in situ bathymetry data from the U.S. Virgin Islands (clear waters) and from Bar Harbor (turbid waters). Results show that PAWLP adaptively captures local wave characteristics to retrieve water depth, achieving an average root mean square error (RMSE) of 1.56 m and weighted mean absolute percentage error (WMAPE) of 10.01 %, with reductions of approximately 17.89 % in RMSE and 19.46 % in WMAPE compared to the fixed-period method. The proposed multi-source bathymetric inversion framework further improves prediction accuracy, achieving RMSEs of 1.64 m in clear waters and 2.32 m in turbid areas, outperforming traditional methods across diverse conditions. The integration of SAR data and tidal features substantially enhances prediction stability, particularly under optically complex waters. Overall, this study highlights the potential of wave-based indirect depth inversion to extend the effective depth range for SDB. By integrating ICESat-2 bathymetric measurements with multi-source remote sensing data and temporal sample transfer strategy, our method enhances mapping accuracy and spatial coverage, mitigates optical saturation effects, and provides a scalable solution for reliable bathymetric mapping across diverse coastal environments.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"332 ","pages":"Article 115054"},"PeriodicalIF":11.4,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145241169","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":"Rice yield prediction in unseen years at field level with high-resolution gross primary productivity derived from Sentinel-2 imagery","authors":"Weiguo Yu ,&nbsp;Yuan Xiong ,&nbsp;Xingrong Li ,&nbsp;Hengbiao Zheng ,&nbsp;Chongya Jiang ,&nbsp;Xia Yao ,&nbsp;Yan Zhu ,&nbsp;Weixing Cao ,&nbsp;Lin Qiu ,&nbsp;Lijuan Song ,&nbsp;Tao Cheng","doi":"10.1016/j.rse.2025.115061","DOIUrl":"10.1016/j.rse.2025.115061","url":null,"abstract":"<div><div>Accurate field-level rice yield prediction for an unseen year is valuable for optimizing precision farming practices and strengthening national food security frameworks. Although many studies have use<u>d</u> vegetation indices or gross primary productivity (GPP) to predict crop yield, few have systematically evaluated their differences in predictive performance and stability using time series satellite imagery across the entire growing season. Simultaneously, little research has focused on field-level prediction for unseen years over large regions. To address these issues, we conducted an in-depth comparison between the Sentinel-2-derived normalized difference red edge index (NDRE) and high-resolution GPP generated via a modified two-leaf light use efficiency model in their correlations with rice yield. The optimal time window for yield prediction was identified using original and harmonic fitted GPP data at 10-day intervals. Additionally, cross-year GPP correction (CGC) was proposed as an efficient approach for model transfer to unseen years and compared with that of the adversarial discriminative domain adaptation (ADDA), an emerging data-driven domain transfer learning algorithm. Specifically, these methods were assessed with an extensive field-level rice yield dataset from eastern and northeastern China spanning 2019–2022.</div><div>We found that GPP outperformed NDRE in predicting rice yield (individual monthly: <em>∆r</em>^<em>2</em> = 0.04–0.29, cumulative monthly: <em>∆r</em>^<em>2</em> = 0.22–0.41), with greater stability and reliability. Furthermore, the harmonic fitted GPP could improve the yield prediction accuracy. Additionally, the CGC method improved interannual prediction accuracy (<em>R</em>^<em>2</em> = 0.55–0.73) for the two regions, showing better predictive performance than the ADDA model (<em>R</em>^<em>2</em> = 0.54–0.62). The proposed method relied only on a limited amount of ground-truth yield samples and exhibited robust performance in years characterized by pronounced interannual yield variability (2019) or extreme weather conditions (2022). This research has great potential for implementing rice yield prediction over large regions with publicly available imagery and limited ground-truth yield data, particularly for smallholder farming systems in the context of precision crop management and food security assessment.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"332 ","pages":"Article 115061"},"PeriodicalIF":11.4,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145229761","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":"Satellite observations reveal ecosystem resistance and resilience to short-term water stress driven by dominant vegetation along a rainfall gradient in Australia","authors":"Huanhuan Wang ,&nbsp;Qiaoyun Xie ,&nbsp;Sally E. Thompson ,&nbsp;Caitlin E. Moore ,&nbsp;David L. Miller ,&nbsp;Erik J. Veneklaas ,&nbsp;Richard P. Silberstein ,&nbsp;Xing Li ,&nbsp;Jingfeng Xiao ,&nbsp;Belinda E. Medlyn ,&nbsp;William K. Smith","doi":"10.1016/j.rse.2025.115046","DOIUrl":"10.1016/j.rse.2025.115046","url":null,"abstract":"<div><div>Climate change is projected to intensify water stress in many ecosystems and poses threats to their stability, which can be quantified through ecosystem resistance and resilience. Relevant studies mostly focused on multi-year or annual droughts, and in spatially homogeneous or species-specific ecosystems. However, resilience and resistance within complex ecosystems, where different plants exhibit different adaptations and recovery behaviours, are less understood. Using productivity data from satellite-derived GOSIF (Global Orbiting Carbon Observatory-2 Solar-Induced Fluorescence) and flux towers, we examined vegetation responses to short-term (&lt;1 year) water stress events from 2000 to 2018 along the North Australia Tropical Transect, which spans a 1600 mm rainfall gradient and transitions from seasonal mesic to non-seasonal arid ecosystems. We define resistance as productivity maintained during stress relative to a multi-year average baseline, and resilience as the extent to which productivity recovered one year after stress relative to the same baseline. Our results show that ecosystem resistance to water stress was lowest in semi-arid regions but higher in both arid and mesic regions, while ecosystem resilience showed the opposite pattern. These spatial patterns occurred regardless of seasonality and were mainly associated with dominant vegetation type. Woody savanna-dominated mesic regions exhibited highest resistance (0.82 ± 0.13, <em>p</em> &lt; 0.001) and lowest resilience (0.26 ± 0.19, <em>p</em> &lt; 0.001), shrublands in arid areas had intermediate values of both resistance (0.81 ± 0.14, <em>p</em> &lt; 0.001) and resilience (0.27 ± 0.22, <em>p</em> &lt; 0.001), while the grasslands in semi-arid regions had low resistance (0.78 ± 0.15, <em>p</em> &lt; 0.001) and high resilience (0.38 ± 0.24, <em>p</em> &lt; 0.001). The highest likelihood (&gt;75.0 %) of full recovery (i.e., exceeding baseline after one year) occurred during the wet season in mesic regions, likely due to energy limitation, while arid regions showed a lower likelihood (57.0 %). This study provides a remote sensing framework for quantifying ecosystem resistance and resilience under water stress.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"332 ","pages":"Article 115046"},"PeriodicalIF":11.4,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145241170","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":"Mitigating the phenological influence on spectroscopic quantification of rice blast disease severity with extended PROSAIL simulations","authors":"Bowen Xue ,&nbsp;Yuanyuan Kong ,&nbsp;Pablo J. Zarco-Tejada ,&nbsp;Long Tian ,&nbsp;Tomas Poblete ,&nbsp;Xue Wang ,&nbsp;Hengbiao Zheng ,&nbsp;Chongya Jiang ,&nbsp;Xia Yao ,&nbsp;Yan Zhu ,&nbsp;Weixing Cao ,&nbsp;Tao Cheng","doi":"10.1016/j.rse.2025.115063","DOIUrl":"10.1016/j.rse.2025.115063","url":null,"abstract":"<div><div>Rice blast (RB), a devastating fungal disease, causes severe yield losses worldwide and demands accurate severity quantification for effective management. Remote sensing has been demonstrated useful in disease monitoring and offers a scalable solution, but the phenology challenges the robustness of the model built for spectroscopic severity quantification. Since the variations induced by phenology are closely confounded with the infection progression, it is crucial to identify the specific plant traits that explain the inconsistency in disease severity (DS) estimation while mitigating the phenological influence. To address this issue, this study proposed a novel approach by extending the PROSPECT+SAIL model to account for the optical effects induced in RB-infected rice plants. By introducing DS into PROSPECT simulations based on spectral mixture analysis and lesion optical measurements, the use of RB-extended PROSPECT decreased the leaf simulation errors by up to 36.3 % in the crucial spectral regions for RB monitoring. Subsequently, such an extension enabled the generation of synthetic datasets for disentangling phenological versus RB-induced physiological effects. The sensitivity and disentanglement analysis revealed that leaf chlorophyll content was the primary factor that compromises the relationship between DS and the rice blast index (RIBI_nir), which was designed for RB severity quantification. After correcting for these effects by normalizing RIBI_nir with an optimized chlorophyll-sensitive vegetation index (nRIBI_nir), estimation accuracies significantly improved with an increment of R² from 0.67 to 0.79, and rRMSE decreased by 9 %, particularly for vegetative samples with mild infection (R² increased by 0.51). Consequently, the proposed nRIBI_nir overcame the underestimation of severe infection areas in both severity quantification and spatial mapping. The adapted nRIBI_nir for drone and satellite sensors also exhibited great performance in DS estimation. Our findings suggest that RB-extended PROSAIL simulations facilitate mitigating the phenological influence with reliable validations and mechanistic interpretation. Moreover, the adaptation flexibility and robustness of nRIBI_nir ensured its potential in practical applications including resistance breeding, disease tracking, and precision fungicide management at various scales.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"332 ","pages":"Article 115063"},"PeriodicalIF":11.4,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145229763","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}