Remote Sensing Applications-Society and Environment最新文献

{"title":"GNSS-CORS as water vapor sensors for local atmospheric monitoring: Comparing high-end geodetic-grade and low-cost stations in S Spain","authors":"M. Selmira Garrido-Carretero ,&nbsp;M. Clara De Lacy-Pérez de los Cobos ,&nbsp;Elena Giménez-De Ory ,&nbsp;Leire Anne Retegui-Schiettekatte","doi":"10.1016/j.rsase.2026.101880","DOIUrl":"10.1016/j.rsase.2026.101880","url":null,"abstract":"<div><div>In order to study a possible densification of the regional GNSS network in S Spain, a local cost-effective GNSS network has been installed in the province of Jaén: JAENet. This network is the first one installed in Spain to analyze the GNSS-derived Precipitable Water Vapor (GNSS-PWV) and its time variations. JAE1 is the first low-cost GNSS Continuously Operating Reference Station (GNSS-CORS) setup. It is strategically located very close to UJAE, a high-end geodetic-grade GNSS-CORS, in order to investigate the GNSS-PWV at the same geographic location and under the same environmental and atmospheric conditions. This study aims to evaluate the performance of low-cost GNSS devices for atmospheric water vapor monitoring through an experimental design and the first comparison of data coming from low-cost and high-precision devices over a period of more than one year. Eighteen months divided into six seasonal periods is considered. The common GNSS data period for both GNSS-CORS has been processed using the Precise Point Positioning (PPP) method to estimate their coordinates and evaluate the Zenith Tropospheric Delay (ZTD) using open-source GNSS software. The results show a good agreement between JAE1 and UJAE ZTD time series, with differences ranging from −11.59 mm to 10.12 mm, a mean difference value at the 2-mm level and a remarkably high correlation equal to 0.99. The difference between the mean of GNSS-PWV at GNSS-CORS throughout the six periods analyzed is always under the 1-mm level. The results show that low-cost GNSS-CORS are promising as water vapor sensors for local atmospheric monitoring.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101880"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977224","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":"Seasonal patterns and atmospheric modulators of erythemal UV radiation in a sensitive region of the Brazilian Amazon: Implications for environmental health risk assessment","authors":"Pericles Vale Alves ,&nbsp;Vandoir Bourscheidt ,&nbsp;Damaris Kirsch Pinheiro ,&nbsp;Rodrigo Martins Moreira ,&nbsp;Marcos André Braz Vaz ,&nbsp;Mônica Santos ,&nbsp;Marcos Antônio Lima Moura ,&nbsp;Carlos Alexandre Santos Querino ,&nbsp;Paula Regina Humbelino de Melo ,&nbsp;Maria Adriana Moreira","doi":"10.1016/j.rsase.2025.101857","DOIUrl":"10.1016/j.rsase.2025.101857","url":null,"abstract":"<div><div>Ultraviolet (UV) radiation is a critical environmental driver influencing ecological and human health, with its variability shaped by atmospheric factors and climate dynamics. This study examined the seasonal patterns and temporal trends of the erythemal UV radiation and key atmospheric variables in the Brazilian Amazon, using satellite remote sensing data from OMI/Aura and climate reanalysis data from CAMS spanning 2005 to 2022. Temporal trends were assessed using robust statistical approaches, while the relative influence of atmospheric drivers on erythemal UV variability was quantified using SHAP (Shapley Additive Explanations). A Susceptibility Index (SI) for UV-related health risks was developed, integrating biological, behavioral, and socioeconomic dimensions. Results revealed a distinct seasonal erythemal UV cycle, with peaks from January to April and lows from June to August, maintaining predominantly “very high” to “extreme” levels year-round. Statistically significant trends were observed in cloud optical thickness (COT) and total ozone column (TOC), while SHAP analysis indicated that variables such as water vapor (through its association with cloud processes), aerosols, and TOC emerged as primary predictors of surface UV, followed by PM₂.₅ and PM₁₀, thereby reinforcing the model's potential as a tool for environmental health risk assessment. The SI indicated moderate to high susceptibility among most individuals, strongly modulated by social inequalities and sun exposure habits. The empirical validation of the SI through estimated UV dose and Minimal Erythemal Dose (MED) exceedance supports its potential as a tool for environmental health risk monitoring. These findings underscore the importance of integrated strategies that consider atmospheric and social factors to mitigate UV-related health risks in tropical regions under climate change scenarios.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101857"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926037","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 framework for marine oil spill detection in SAR imagery fusing edge supervision enhancement and group attention mechanism","authors":"Xinrong Lyu ,&nbsp;Haosha Su ,&nbsp;Christos Grecos ,&nbsp;Peng Ren","doi":"10.1016/j.rsase.2026.101901","DOIUrl":"10.1016/j.rsase.2026.101901","url":null,"abstract":"<div><div>Rapid and accurate detection of marine oil spills is crucial for environmental protection and emergency response. Synthetic Aperture Radar (SAR), a primary tool for sea surface oil spill monitoring, faces persistent challenges such as varying spill scales, blurred boundaries, and confusion with look-alike phenomena. To address these issues, this study proposes OilSeg-SARNet, a novel architecture tailored for SAR oil spill detection. The model incorporates a Group Convolutional Block Attention Module Enhancer to emphasize salient features and suppress background noise, an Atrous Spatial Pyramid Pooling module to capture multi-scale contextual information, and an improved Edge Supervision Enhancement Module to refine boundary representation and facilitate gradient propagation. These components work synergistically to enhance detection precision under complex marine conditions. Experimental results on the public SAR Oil Spill Detection Dataset demonstrate that OilSeg-SARNet achieves class-specific Intersection-over-Unions (IoUs) of 61.33%, 64.86%, and 45.10% for oil spill, look-alike, and ship categories, respectively, outperforming the best prior method by +0.85%, +3.73%, and +9.89%, respectively. The model attains an overall mean IoU (mIoU) of 72.22% and an F<span><math><msub><mrow></mrow><mrow><mn>1</mn></mrow></msub></math></span>-score of 79.33%. The proposed model surpasses existing methods with reduced complexity, offering a reliable and efficient framework for marine oil spill monitoring, thereby enhancing early detection and supporting timely environmental response.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101901"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077859","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":"Crop flood damage assessment integrating Sentinel-2 imagery and in situ data: the 2023 Emilia-Romagna case","authors":"Filippo Bocchino ,&nbsp;Valeria Belloni ,&nbsp;Roberta Ravanelli ,&nbsp;Camillo Zaccarini ,&nbsp;Mattia Crespi ,&nbsp;Roderik Lindenbergh","doi":"10.1016/j.rsase.2025.101852","DOIUrl":"10.1016/j.rsase.2025.101852","url":null,"abstract":"<div><div>Floods are among the most severe consequences of climate change, causing significant damage across several sectors, including agriculture. Nevertheless, the assessment of agricultural flood damage remains limited, particularly in agriculturally intensive regions where timely support is crucial. This work proposes a data-driven approach for assessing crop flood damage through a machine learning classification framework applied to features derived from Earth Observation (EO) data, trained and tested on field-level damage data collected by agronomists. Specifically, we applied a Random Forest model to classify fields into three damage classes by integrating Sentinel-2–derived indices, topographic information, and flood extent maps. The analysis focused on the flood event that struck the Emilia-Romagna region (Italy) in May 2023, one of the costliest floods globally that year. The model was trained and tested on 412 fields, achieving an overall accuracy of 0.74, with precision, recall, and F1 score of 0.75, 0.74, and 0.74, each with a standard deviation of 0.04, indicating stable model performance. The model accurately identified high-damage fields, which were characterized by greater flood exposure, lower elevations, and pronounced declines in vegetation indices. However, it struggled to distinguish between no-damage and medium-damage fields, particularly for permanent crops, where damage often occurs beneath the canopy and flooded areas may be partially occluded. The main novelty of this work lies in the use of in situ crop damage assessments, enabling a data-driven estimation of flood impacts. These results have direct implications for policymakers: the framework relies on free EO data, providing a tool that can support post-event compensation and decision-making in flood-prone regions.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101852"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977181","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":"Improving forest loss mapping in Nepal using LandTrendr time-series and machine learning","authors":"Sakar Dhakal ,&nbsp;Kamal Raj Aryal ,&nbsp;Uttam Babu Shrestha ,&nbsp;Hari Adhikari","doi":"10.1016/j.rsase.2025.101864","DOIUrl":"10.1016/j.rsase.2025.101864","url":null,"abstract":"<div><div>Understanding forest disturbances is essential for effective conservation strategies. Given Nepal's complex geography and forest ecology, change detection using Remote Sensing remains challenging, with limited time-series studies. This study introduces an enhanced LandTrendr (LT) workflow to improve forest loss mapping using medium-resolution imagery and machine learning. The approach includes: a) a Vision Transformers model (LiteForest-ViT) for semi-automated forest cover mask using Landsat 5, b) masking terrain shadows, c) ensemble of 7 spectral indices: NBR (Normalized Burn Ratio), NDVI (Normalized Difference Vegetation Index), TCA (Tasseled Cap Angle), TCB (Tasseled Cap Brightness), TCG (Tasseled Cap Greenness), EVI (Enhanced Vegetation Index), TCW (Tasseled Cap Wetness) with 6 LT-derived metrics for Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) classification, d) expert-weighted district-level model selection tailored to regional heterogeneity, e) integration of multiple platforms for seamless processing, and f) MODIS-derived snow uncertainty loss estimation. The study spans (1995–2024) across Karnali, Bagmati, and Darchula. Results indicate RF edged XGBoost in the High Mountains and Himalayas, while XGBoost did better in the Siwalik and Middle Mountains. NBR was the most influential index regardless of model classifier and region. The algorithm achieved 0.90 overall accuracy, 0.74 kappa statistics, and 0.93 F1-score, exceeding GFC (Global Forest Change) and REDD + AI (CTrees) benchmarks. Overall, 7870 ha of forest loss were detected, where ∼165 ha accounted for snow-impacted uncertain loss. While loss has decreased, continued disturbance underscores the significance of our findings to support REDD+ (Reducing Emissions from Deforestation and Forest Degradation) in the region.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101864"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977876","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":"In-season winter crop harvest status monitoring in Ukraine for 2022 using unsupervised change detection","authors":"Shabarinath S. Nair ,&nbsp;Josef Wagner ,&nbsp;Sergii Skakun ,&nbsp;Yuval Sadeh ,&nbsp;Manav Gupta ,&nbsp;Thomas Lampert ,&nbsp;Mehdi Hosseini ,&nbsp;Saeed Khabbazan ,&nbsp;Sheila Baber ,&nbsp;Blake Munshell ,&nbsp;Fangjie Li ,&nbsp;Abhishek Kotcharlakota ,&nbsp;Oleksandra Oliinyk ,&nbsp;Danylo Poliakov ,&nbsp;Erik Duncan ,&nbsp;Inbal Becker-Reshef","doi":"10.1016/j.rsase.2026.101877","DOIUrl":"10.1016/j.rsase.2026.101877","url":null,"abstract":"<div><div>The full-scale invasion of Ukraine on 24 February 2022 resulted in widespread disruption to its agricultural system. As winter crops were already planted in late 2021, this led to uncertainty regarding whether all the planted fields would be harvested. Monitoring the harvest status was therefore essential for reliable production estimates. As ground-based assessments were no longer feasible in conflict-affected areas, we relied on remote sensing techniques. We developed a method to monitor crop harvest status in-season with the capability to detect fields that were not-harvested.</div><div>We monitored harvest from 13 June 2022 until 19 September 2022 and found that 94.1% and 87.5% of planted winter crops were harvested in government controlled and temporarily occupied regions, respectively. The highest intensity of not-harvested fields was observed along the occupation boundary. Validation using visually interpreted high-temporal-frequency Planet imagery yielded an overall accuracy of 85%, with an F1-score of 90% for the harvested class and 73% for the not-harvested class.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101877"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146022716","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":"Cost-effective statewide wetland inventory update using weakly supervised deep learning: A case study in Minnesota, USA","authors":"Victor Igwe ,&nbsp;Bahram Salehi ,&nbsp;Mohammad Marjani ,&nbsp;Nima Farhadi ,&nbsp;Masoud Mahdianpari","doi":"10.1016/j.rsase.2026.101871","DOIUrl":"10.1016/j.rsase.2026.101871","url":null,"abstract":"<div><div>Wetlands provide important ecosystem services, including water purification, flood regulation, carbon storage, and habitat for diverse species. Despite their importance, North America continues to see significant loss of wetlands due to development, agriculture, and climate-related pressures. These ongoing declines threaten ecological integrity and reduce the capacity of wetlands to provide essential services. As a result, regular updates and monitoring are essential to protect these important ecosystems to support evidence-based management and meet the needs of evolving conservation policies. One cost-effective method for monitoring wetlands is the segmentation of satellite images. Automating the segmentation of remote sensing images to update land cover maps enhances the frequency of map production, enabling more timely and efficient monitoring. Deep learning models such as Convolutional Neural Networks (CNNs) have performed well for segmentation. Still, the need for large, densely annotated datasets has limited their adoption in remote sensing. Meeting this requirement poses substantial challenges for regular map updates because of the extensive number of labels, the complexity of annotations, and the significant time and financial resources required for field data collection campaigns. Therefore, this paper targets Minnesota's state-wide wetland monitoring by training deep CNNs with weak labels extracted from existing thematic products. Our approach obtains training samples from existing land-cover maps by applying change detection to identify stable pixels and then refining labels with objects produced by the Simple Non-Iterative Clustering (SNIC) algorithm. The resulting weakly labeled samples are used to train and evaluate U-Net++ and DeepLabV3+ architectures. The proposed method achieved robust performance in the study area, with an average F1-score of 91.3 % for U-Net++ across seven analyzed classes, compared to 90.6 % for DeepLabV3+ and 88.3 % for Random Forest (RF). Notably, U-Net++ outperformed the other models, indicating that dense skip connections effectively classify complex remote-sensing scenes such as wetlands. These results demonstrate the effectiveness of the proposed weak-label-driven deep learning workflow for large-scale wetland-inventory mapping in Minnesota, while remaining generalizable to other land-cover classification problems.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101871"},"PeriodicalIF":4.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146022718","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":"CLIP the landscape: Automated tagging of crowdsourced landscape images","authors":"Ilya Ilyankou ,&nbsp;Natchapon Jongwiriyanurak ,&nbsp;Tao Cheng,&nbsp;James Haworth","doi":"10.1016/j.rsase.2025.101824","DOIUrl":"10.1016/j.rsase.2025.101824","url":null,"abstract":"<div><div>We present a CLIP-based, multi-modal, multi-label classifier for predicting geographical context tags from landscape photos in the Geograph dataset—a crowdsourced image archive spanning the British Isles, including remote regions lacking POIs and street-level imagery. Our approach addresses a Kaggle competition task based on a subset of Geograph’s 8M images, with strict evaluation: exact match accuracy is required across 49 possible tags. We show that combining location and title embeddings with image features improves accuracy over using image embeddings alone. We release an efficient pipeline<span><span>²</span></span> that trains on a modest laptop, using pre-trained CLIP image and text embeddings and a simple classification head. Predicted tags can support downstream tasks such as building location embedders for GeoAI applications, enriching spatial understanding in data-sparse regions.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101824"},"PeriodicalIF":4.5,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840889","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":"Spatiotemporal characterisation of vegetation activity in the seasonal wetlands of the Cuvelai-Etosha Basin using earth observation","authors":"Eliakim Hamunyela,&nbsp;Martin Hipondoka","doi":"10.1016/j.rsase.2025.101853","DOIUrl":"10.1016/j.rsase.2025.101853","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Wetlands with photosynthetically active vegetation are vital grazing areas for wild/domestic herbivores and serve as important breeding sites for many bird species. Yet, like other wetlands, they are increasingly being lost and degraded through anthropogenic activities and climatic change. Urgent interventions to safeguard important ecosystem services provided by wetlands with photosynthetically active vegetation are needed, but spatially-explicit information on vegetation activity of wetlands is lacking for many wetlands across the globe, including the Cuvelai-Etosha Basin (CEB). The CEB is a densely populated transboundary endorheic basin in southern Africa, covering southern Angola (upstream) and northern Namibia (downstream), with livestock-crop subsistence agriculture, human settlement and wildlife conservation as dominant land-uses. Here, we relied on satellite remote sensing to map and characterise vegetation activity in the seasonal wetlands (∼1,066,810 ha) of the CEB to improve our knowledge on spatiotemporal distribution of photosynthetically active wetlands within the basin. We quantified vegetation activity using a 30m spatial resolution Normalised Difference Vegetation Index (NDVI) derived from 34-years of Landsat data (1991–2024). Our results show that only 55% of the wetland areas (∼579,579 ha) in the CEB have photosynthetically active vegetation. Of these wetland areas, 74% have very low vegetation activity. We found that photosynthetically inactive wetlands were mostly downstream of the basin, which explains why vegetation activity is much lower on the Namibian side than on the Angolan side (upstream). Temporal trend analysis of annual vegetation activity show that wetland areas with downward trend were mostly downstream of the basin, suggesting an increased erosion of photosynthetically active vegetation in the wetland areas on the Namibian side than on the Angolan side. Wetland areas not exposed to communal grazing were generally more photosynthetically active than communally grazed areas, suggesting that the vegetation activity of some wetlands has been eroded by overgrazing. We also found that wetland areas with lower inundation frequency had more photosynthetically active vegetation than those with high inundation frequency, except those with surface water every year. Essentially, wetland areas with high capacity to accumulate surface water had less vegetation activity. We found a weak to moderate correlation between metrics of vegetation activity and precipitation, suggesting an existence of a complex interplay between the amount of precipitation received in the basin and the vegetation activity of the wetlands. Despite high salinity in Etosha Pan, some parts of the pan had photosynthetically active vegetation, a phenomenon which warrants further research. Overall, this study produced spatially-explicit information on vegetation activity in the wetlands of the CEB to inform sustainable wetland management in the ba","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101853"},"PeriodicalIF":4.5,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840890","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":"Multivariate statistical analysis of rainfall variability in Brazil: Assessing climatic and environmental drivers of precipitation","authors":"Arthur Amaral e Silva ,&nbsp;Leonardo Campos de Assis ,&nbsp;Laura Coelho de Andrade ,&nbsp;Juliana Ferreira Lorentz ,&nbsp;Júlio César de Oliveira ,&nbsp;Maria Lucia Calijuri ,&nbsp;Italo Oliveira Ferreira","doi":"10.1016/j.rsase.2025.101849","DOIUrl":"10.1016/j.rsase.2025.101849","url":null,"abstract":"<div><div>This study develops a remote sensing-based, multivariate analytical framework integrating Principal Component Analysis (PCA) and CLARA clustering to investigate rainfall variability and its climatic and environmental drivers across Brazil. High-resolution datasets from over 900 rainfall stations were processed to link precipitation patterns with vegetation dynamics (NDVI), evapotranspiration, temperature, and topography. Methodologically, PCA reduced data dimensionality, isolating dominant factors controlling rainfall seasonality, while CLARA clustering classified stations into environmentally and climatically coherent groups. PCA results show that atmospheric moisture transport systems, the Flying Rivers and the South Atlantic Convergence Zone (ZCAS), dominate wet-season precipitation, explaining over 40 % of variance in January and February. NDVI and evapotranspiration contributed up to 30 % of variance in the secondary component, reflecting vegetation–climate feedbacks. During the dry season, temperature became the leading driver, negatively correlated with rainfall and intensifying drought risk. CLARA clustering identified distinct seasonal regimes, with humid zones linked to high NDVI and evapotranspiration, arid regions characterized by low rainfall (&lt;75 mm) and high temperatures (&gt;28 °C), and transitional areas sensitive to land-use change. Orographic effects further enhanced precipitation in elevated landscapes, while deforestation in the Amazon disrupted atmospheric moisture fluxes, reducing rainfall connectivity across southeastern Brazil. By integrating dimensionality reduction with spatiotemporal clustering, this research offers a scalable, data-driven framework for understanding rainfall dynamics, supporting climate adaptation, hydrological modeling, and sustainable land-use strategies in tropical regions under environmental pressure.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"41 ","pages":"Article 101849"},"PeriodicalIF":4.5,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840887","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}