Remote Sensing Applications-Society and Environment最新文献

{"title":"BGR-net: A boundary-guided refinement network for multi-class cloud detection","authors":"Shaowei Bai ,&nbsp;Yan Mo ,&nbsp;Wanting Zhou","doi":"10.1016/j.rsase.2026.102024","DOIUrl":"10.1016/j.rsase.2026.102024","url":null,"abstract":"<div><div>To address the limitations of existing cloud detection methods in handling blurred boundaries, omission of fragmented small-scale clouds, and interference from complex terrestrial backgrounds, this paper proposes a Boundary-Guided Refinement Network (BGR-Net). The network adopts a hierarchical architecture that deeply integrates the local inductive bias of Convolutional Neural Networks (CNNs) with the global long-range dependency modeling capabilities of Transformers. During the encoding stage, a multi-scale downsampling attention mechanism and a convolutional positional enhancement module are employed to dynamically capture rich spatial details and content-aware information across scales. In the decoding stage, a cross-layer modulation mechanism effectively integrates multi-level features from the encoder and decoder, significantly enhancing the discriminative power of cloud-related features while suppressing background noise. Furthermore, by leveraging cloud boundary and positional feature guidance, the network achieves efficient reconstruction of complex cloud edges. Experimental results based on the Himawari-9 satellite remote sensing dataset demonstrate that BGR-Net strikes a superior balance between segmentation accuracy and computational efficiency. Its mIoU (68.37%) and F1-score (81.21%) significantly outperform current state-of-the-art models. With its low computational complexity, BGR-Net fulfills the practical requirements for real-time monitoring in remote sensing applications. Code is available at: <span><span>https://github.com/NotRoots/BGR-Net</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"42 ","pages":"Article 102024"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147710291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An analysis of the regenerative capacity of vegetation using NDVI and NBR after large-scale wildfires (1990-2024) in the Mediterranean province of Castellón, Spain","authors":"Enrique Montón Chiva,&nbsp;José Quereda Sala","doi":"10.1016/j.rsase.2026.102020","DOIUrl":"10.1016/j.rsase.2026.102020","url":null,"abstract":"<div><h3>Purpose</h3><div>This study analyses one of the ways in which Mediterranean vegetation adapts to climate and wildfires: its regenerative capacity following wildfires that occurred in the Spanish province of Castellón. It also evaluates the suitability of satellite indices for assessing this regenerative capacity using field photographs and Google Earth imagery.</div></div><div><h3>Methods</h3><div>The study examines wildfire databases in Spain and Castellón and changes in NDVI (Normalized Difference Vegetation Index) between 1990 and 2024 in the affected areas using Landsat images processed with the Climate Engine application, as well as NBR (Normalized Burn Ratio) values calculated from Landsat imagery in GEE (Google Earth Engine). Both indices are calculated using distinct spectral bands from the Landsat satellite series. This regeneration capacity is quantified by comparing the values recorded from pre-fire and immediately post-fire conditions, in addition to analysing the indices five years after the fire event.</div></div><div><h3>Results</h3><div>The analysis reveals a downward trend in fires in Spain and Castellón, especially in the number of fire events. The NDVI values also indicate the strong regenerative capacity of well-adapted Mediterranean vegetation, with values rising to above the threshold of 75% of the pre-fire NDVI in all cases within five years. Conversely, the NBR percentages are less impressive.</div></div><div><h3>Conclusions</h3><div>The variations observed in both indices only partially reflect the actual situation since, as field photographs and the Google Earth application show, although the vegetation cover has recovered, its structure has not, as this process takes a longer period of time.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"42 ","pages":"Article 102020"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147710293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved U-Net with enhanced spatiotemporal features for Chlorophyll-A concentration prediction in the Zhoushan fishery","authors":"Yanling Han,&nbsp;Yizhuo Cai,&nbsp;Ping Ma,&nbsp;Ge Song,&nbsp;Jing Wang,&nbsp;Yun Zhang,&nbsp;Shuhu Yang,&nbsp;Zhonghua Hong","doi":"10.1016/j.rsase.2026.101986","DOIUrl":"10.1016/j.rsase.2026.101986","url":null,"abstract":"<div><div>Chlorophyll-a (Chl-a) is a key indicator of marine ecological conditions, and its abrupt increase often signals the occurrence of harmful algal blooms (HABs). As one of China's major fishing grounds, the Zhoushan fishing ground is highly susceptible to HAB events. However, existing Chl-a prediction methods, which commonly rely on single-source or limited fixed-site observations, often suffer from insufficient integration of spatiotemporal information, inadequate exploration of intrinsic feature correlations, and limited capability in capturing the spatiotemporal dependencies and variability of Chl-a distributions. These limitations reduce their robustness, particularly under extreme meteorological disturbances. To address these challenges, this study proposes a Spatiotemporal Enhanced U-Net with Convolutional Long Short-Term Memory (STE-U-ConvLSTM) for daily Chl-a prediction in the Zhoushan fishing ground based on satellite remote sensing data. Specifically, an enhanced spatiotemporal feature representation strategy is developed by incorporating temporal and geographic encodings, Chl-a concentration gradients, and directional angles to better characterize Chl-a dynamics. Built upon the U-Net architecture, the proposed model embeds a Convolutional Block Attention Module (CBAM) into the encoder to adaptively emphasize informative spatiotemporal features. In addition, the bottleneck layer integrates dilated convolutions with ConvLSTM to capture multiscale spatiotemporal patterns more effectively. Experimental results show that STE-U-ConvLSTM outperforms baseline models, increasing R² by up to 0.139 and reducing RMSE by up to 0.25 μg/L relative to baseline models. The proposed model also demonstrates strong generalization across different seasons and subregions, with particularly notable performance in complex summer and winter conditions as well as dynamic nearshore waters, where external forcing plays a dominant role. Furthermore, the model proves effective for the early warning of actual HAB events, providing a reliable tool for marine environmental monitoring, bloom forecasting, and sustainable fishery management.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"42 ","pages":"Article 101986"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147613004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel index for mapping plastic-mulched farmland using multi-temporal Landsat-8 and Sentinel-2 imagery","authors":"Junyi Chang ,&nbsp;Qian Yang ,&nbsp;Feng Tao ,&nbsp;Huanjun Liu ,&nbsp;Jia Du ,&nbsp;Chong Luo ,&nbsp;Yuan Chai ,&nbsp;Liwen Chen ,&nbsp;Xue Li","doi":"10.1016/j.rsase.2026.101992","DOIUrl":"10.1016/j.rsase.2026.101992","url":null,"abstract":"<div><div>Plastic mulching is a prevalent technology in protected cultivation, and remote sensing offers a robust tool for monitoring the spatiotemporal dynamics of plastic-mulched farmland (PMF). However, extracting PMF information using remote sensing presents several challenges. These challenges include the similarity in spectral characteristics between agricultural films and other land covers, as well as the difficulty in achieving accurate classification over large areas with varying conditions. Traditional indices (such as NDVI, NDWI, NDBI, EVI) are not suitable for detecting PMF because they lack sensitivity to the unique spectral signatures of plastic mulch. Therefore, we proposed the Normalized Agricultural Film Index (NAFI), which has been applied to Landsat 8 OLI and Sentinel 2 MSI images acquired in April and May through Google Earth Engine (GEE). This index has demonstrated superior performance in PMF identification. We designed five different feature combinations, selected the optimal combination based on classification accuracy and performance metrics, and performed the Random Forest (RF) to map PMF. The overall classification accuracy of RF ranges from 92.04% to 99.27%, with kappa coefficients between 0.87 and 0.98, highlighting the effectiveness of the proposed approach in large-scale agricultural monitoring. This study monitors the spatial and temporal changes of agricultural film from 2015 to 2023 in Da’ an, Jilin Province, Northeast China, which witness a northwest expansion.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"42 ","pages":"Article 101992"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147613378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Smart integration of sliding window and vote-based fusion: Advancing UAV-based instance segmentation with YOLOv8 for high-resolution vegetation mapping","authors":"Ilias Jennaoui ,&nbsp;El Mostafa Bachaoui ,&nbsp;Mohamed Biniz ,&nbsp;Abderrazak El Harti ,&nbsp;Abdrrahmane El Ghmari","doi":"10.1016/j.rsase.2026.101994","DOIUrl":"10.1016/j.rsase.2026.101994","url":null,"abstract":"<div><div>Vegetation segmentation from high-resolution drone imagery faces fundamental challenges due to scale mismatches between training and inference phases. This issue becomes particularly pronounced when limited high-resolution images are available for model training, necessitating patch-based approaches that create significant scale inconsistencies. The present study addresses these challenges by introducing a spatial consensus voting methodology with confidence-weighted fusion for high-resolution vegetation mapping in the El Ksiba region of Morocco. Unlike previous multi-model ensemble approaches, the proposed method employs a single model generating 10-12 overlapping predictions per pixel, achieving 8.2 × performance recovery from scale mismatch while requiring 3 × less computational cost. Comprehensive evaluation across multiple architectures demonstrates that DeepLabV3+ provides superior precision (0.882) and boundary coherence, while YOLOv8 delivers high recall (0.923) suitable for comprehensive coverage. Contrary to conventional expectations, class imbalance methods consistently degrade deployment performance despite training improvements. This research reveals a focal loss paradox wherein +14.1% training improvement reverses to −1.7% deployment degradation through complex interactions with deployment parameters. Furthermore, minority species benefit substantially more from scale-consistent inference (+55.9% for <em>Lentisque</em>) than from training-time balancing approaches. Experimental results demonstrate effectiveness with mean Intersection over Union of 0.721 and F1-score of 0.826, providing both comprehensive vegetation detection and reliable classification. By resolving scale mismatch problems inherent in training on resized images, this study presents a scalable solution for vegetation segmentation in remote sensing applications, with potential extensions to multimodal sensor integration for enhanced species-specific classification.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"42 ","pages":"Article 101994"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147613481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The investigation of variations in chlorophyll-a in the southern coast of Sulawesi: a case study from 1998 to 2023","authors":"Yusuf Jati Wijaya ,&nbsp;Ulung Jantama Wisha ,&nbsp;Bayu Munandar ,&nbsp;Lilik Maslukah ,&nbsp;Seto Windarto ,&nbsp;Muhammad Zainuri","doi":"10.1016/j.rsase.2026.102016","DOIUrl":"10.1016/j.rsase.2026.102016","url":null,"abstract":"<div><div>The southern coast of Sulawesi has the potential to produce yearly upwelling events, exhibiting significant seasonal variations in chlorophyll-a (Chl-a) concentration. However, the mechanism responsible for the seasonal fluctuations of Chl-a has not been entirely revealed. Utilization of the latest long-term data to ascertain annual variability patterns also remains infrequent. Therefore, this study employed a combination of Chl-a satellite data and reanalysis data of oceanographic parameter to explore the factors influencing seasonal and annual fluctuations in upwelling event. We found that the seasonal rise in Chl-a was most pronounced during the peak intensity of the southeasterly monsoon (SEM), specifically in June, July, and August (JJA). In the year 2004, Chl-a concentrations reached their peak during the period of 1998-2023, especially in August, averaging 1.6 mg/m³. This occurred concurrently with the coldest water temperature in that year, which fell at 26.3 °C, in comparison to other years. Despite the fact that 2004 exhibited weak El Niño events and a neutral Indian Ocean Dipole (IOD). In 2015, during an extreme El Niño event, and in 2019, characterized by a strong positive IOD, the aquatic conditions exhibited warmer temperatures (26.8° and 26.5 °C) and lower Chl-a concentrations (1.4 and 1.0 mg/m³). We discovered that the significant increase in Chl-a concentration and coldest temperature in 2004 was affected by subsurface ocean current off the western coast and westward flowing surface currents along the southern coast of Sulawesi. The interplay of these two ocean currents significantly influences the variability and spatial distribution of Chl-a and sea surface temperature.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"42 ","pages":"Article 102016"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147710208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative assessment of vegetation cover and management factor in soil loss modelling using Sentinel-2 MSI time-series imagery: A case study from the Romanian Carpathian-Subcarpathian region","authors":"Marina Vîrghileanu ,&nbsp;Ionuț Șandric ,&nbsp;Bogdan-Andrei Mihai ,&nbsp;Ionuț Săvulescu ,&nbsp;Carmen-Gabriela Bizdadea","doi":"10.1016/j.rsase.2026.102013","DOIUrl":"10.1016/j.rsase.2026.102013","url":null,"abstract":"<div><div>Vegetation cover and management (C-factor) is a key component of the RUSLE model, which is widely used for predicting soil erosion. The C-factor is the most dynamic variable, being influenced by plant phenology which introduces substantial uncertainties in erosion modelling. Achieving accurate C-factor mapping is crucial for reliable soil erosion estimation across different scales. The aim of our paper is to assess the performance of complementary approaches for C-factor estimation and analysing of its spatio-temporal variability, using spectral and biophysical indices derived from time-series Sentinel-2 MSI imagery. The study focuses on a test area within the Carpathian-Subcarpathian region of Romania, with potential for the results to be up-scaled to regional or national levels. The workflow involves four key stages: (1) processing Sentinel-2 MSI time-series from 2016 to 2024 to derive spectral and biophysical indices, (2) estimating the C-factor through different remote sensing approaches proposed in literature, (3) analysing the spatio-temporal variability of the C-factor, as well as inter-comparison with empirical values from literature, and (4) estimating seasonal and annual soil loss rates using the RUSLE equation. Our results indicate that the integration of multitemporal imagery from the same sensor enhances the objectivity and the significance of the C-factor estimation at both local and regional levels. The importance of the comparative approach is of practical value in improving the soil loss models from their static to a dynamic framework. The study provides foundational insights for sustainable land-use management scenarios, supporting policy-making and land-use planning.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"42 ","pages":"Article 102013"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147710292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial divergence in urbanization–ecological environment coordination in the coastal city belt of the Yangtze River Delta: Evidence from pixel-level time-series remote sensing","authors":"Xingru Shen ,&nbsp;Chao Sun ,&nbsp;Yun Lin ,&nbsp;Jialin Li ,&nbsp;Zhenjie Yang ,&nbsp;Kai Tan","doi":"10.1016/j.rsase.2026.102049","DOIUrl":"10.1016/j.rsase.2026.102049","url":null,"abstract":"<div><div>Achieving sustainable coastal development requires a quantitative understanding of the coordination between urban growth and ecological conditions. In this study, we developed a pixel-level remote sensing framework to assess long-term urbanization–environment coupling in the coastal city belt of the Yangtze River Delta (YRD), China, across three decades (1992-2021). Urbanization intensity was characterized using a Comprehensive Nighttime Light Index (CNLI) derived from calibrated annual nighttime light data, while ecological conditions were represented by a time-series Remote Sensing Ecological Index (ts-RSEI) constructed from annual Landsat composites. These indicators were integrated using four-quadrant analysis and a Coupling Coordination Degree (CCD) model to evaluate spatiotemporal dynamics and sustainability trajectories. The main results were as follows: (1) The urbanization levels increased rapidly, with the mean CNLI rising by 0.39. Shanghai consistently represented the peak of urbanization development (ΔCNLI = 0.502), exerting a notable radiative driving effect on surrounding cities. (2) The environment experienced degradation amid fluctuations, with the mean ts-RSEI decreasing by 0.033. This degradation exhibited a bimodal spatial distribution centered around Shanghai (Δts-RSEI = −0.153) and Zhoushan (Δts-RSEI = −0.051). (3) The development in the coupling coordination relationship between urbanization and the environment shifted from Quadrant II to Quadrant IV, with the CCD increasing from 0.356 to 0.684 before declining to 0.432, showing an inverted U-shaped trend. Spatially, metropolitan core areas shifted from relatively coordinated to comparatively unbalanced states, whereas northern, southern, and suburban coastal zones showed gradual improvement. This spatial divergence reflects differentiated coastal development pathways shaped by land–sea resource endowment, ecological constraints, and policy-driven infrastructure optimization. By providing spatially explicit evidence of sustainability transitions in a rapidly urbanizing coastal megaregion, this study offers practical insights for integrated coastal zone management, ecological redline delineation, and adaptive coastal governance.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"42 ","pages":"Article 102049"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147956587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"YOLO-FR: An enhanced object detector for small object detection in complex scenes","authors":"Rui Fan ,&nbsp;Renhao Jiao ,&nbsp;Weigui Nan ,&nbsp;Jin Dang ,&nbsp;Zhixue Wang ,&nbsp;Xuefeng Ge ,&nbsp;Baiying Dong ,&nbsp;Xiaoli Luo","doi":"10.1016/j.rsase.2026.102059","DOIUrl":"10.1016/j.rsase.2026.102059","url":null,"abstract":"<div><div>As a key technology, small object detection has shown broad application prospects in the fields of UAV aerial images, remote sensing images, medical diagnosis, intelligent transportation and industrial quality inspection. However, due to the small size of the small object itself, it is susceptible to factors such as low resolution, limited semantic information, and complex background interference, resulting in its detection performance still facing severe challenges. To this end, we propose an improved object detection model YOLO-FR, which aims to improve the detection accuracy and robustness of small objects in complex scenes. Firstly, the C3k2_WT module is designed to expand the receptive field layer by layer and capture the context information by using the cascaded frequency decomposition characteristics of wavelet transform. At the same time, a C3k2_ECA module integrated with Efficient Channel Attention is designed to effectively reduce the number of parameters and enhance the selective response of channel features. Secondly, a small object detection head is introduced to improve the model's support for small object detection. Additionally, the Quad Fusion Downsample downsampling module is proposed to reduce information loss and improve downsampling efficiency. Finally, we compare a variety of emerging Mamba modules to determine the most suitable module for the task of this article, and optimize it on this basis. Our model improves mAP@50:95 by 6.6% and 2.2% on the public benchmark datasets VisDrone2019 and AI-TOD, respectively. At the same time, the parameters of the model are reduced by 36.5%. We also evaluate the segmentation performance of the model on the UAV instance segmentation dataset UAVid, which demonstrates the advancement and applicability of the proposed model in the face of different tasks. The source code will be available at <span><span>https://github.com/unscfr/YOLO-FR</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"42 ","pages":"Article 102059"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147956592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A remote sensing framework for mapping agricultural water priority areas and hot spots in northeast Italy","authors":"Massimiliano N. Lippa,&nbsp;Paolo Tarolli","doi":"10.1016/j.rsase.2026.102022","DOIUrl":"10.1016/j.rsase.2026.102022","url":null,"abstract":"<div><div>The exposure to drought in the agricultural regions of northeast Italy highlights the need for improved water management strategies and identification of regional hot spots. Water authorities often have various levels of technical capacity, creating a need for simple and operational tools to support decision-making. This study presents a threshold-based remote sensing framework that integrates land surface temperature (LST), normalized difference vegetation index (NDVI), and surface soil moisture (SSM) to identify monthly agricultural water priority areas and persistent hot spots at a 1 km resolution. The framework is designed for operational use by water authorities and provides a standardized approach for priority area identification without requiring advanced analytical expertise. Using open-access datasets from May to September during a wet (2019), normal (2020), and dry (2022) year, we map spatial priority patterns and assess hot spot persistence across contrasting hydrological conditions. Results show strong seasonal and interannual variability, with the 2022 drought producing extensive high-priority areas, particularly in midsummer. Persistent priority zones emerge across hydrological regimes and warrant long-term management attention. This research proposes a simple, reproducible, and accessible framework that can support Italian water authorities in identifying critical zones of need to improve water resource allocation and enhance resilience to climate change driven droughts.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"42 ","pages":"Article 102022"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147858906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}