Remote Sensing Applications-Society and Environment最新文献

{"title":"Enhanced multi-mission remote sensing of inland water surface elevation using Sentinel-3, Sentinel-6, and SWOT satellite altimeters and an environmentally informed LSTM-based neural network","authors":"Mahdis Rezapour ,&nbsp;Alireza Taheri Dehkordi ,&nbsp;Mohammad Javad Valadan Zoej ,&nbsp;Elahe Khesali ,&nbsp;Amir Naghibi ,&nbsp;Hossein Hashemi","doi":"10.1016/j.rsase.2026.102019","DOIUrl":"10.1016/j.rsase.2026.102019","url":null,"abstract":"<div><div>Frequent and accurate measurements of inland Water Surface Elevation (WSE) are essential for effective water resource management. However, single-mission satellite altimetry often lacks the temporal resolution needed to capture detailed WSE changes. While multi-mission integration can improve temporal coverage, it is hindered by inter- and intra-mission biases arising from variations in sensor design, orbital characteristics, atmospheric effects, and environmental conditions. These biases, which have been insufficiently addressed in previous studies, are typically nonlinear, spatiotemporally variable, and require advanced methods for correction. This study proposes EILSTMNet, an Environmentally Informed Long Short-Term Memory (LSTM)-based Neural Network that enables multi-mission synergy of satellite altimetry data (Sentinel-3, Sentinel-6, and SWOT) by correcting altimetric measurements of WSE through the integration of environmental variables such as precipitation, temperature, and evapotranspiration. EILSTMNet employs stacked LSTM layers to capture temporal dependencies in environmental drivers, combined with a fully connected neural network that incorporates static inputs such as altimetric WSE, day of year, and satellite-specific identifiers. The proposed approach is validated over three U.S. lakes, Michigan, Ontario, and Winnebago, using in-situ gauge measurements. Results show that EILSTMNet-based estimates are significantly improved compared to altimeter-derived WSE measurements, reducing the Root Mean Squared Error from 0.31 m to 0.09 m and increasing the Pearson correlation coefficient from 0.69 to 0.93. Furthermore, the model demonstrates strong generalization to unseen time periods, highlighting its temporal transferability. The proposed approach refines multi-mission altimetric measurements, yielding temporally frequent, higher-accuracy WSE observations, thereby enhancing water resource management and advancing the understanding of hydrological processes.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"42 ","pages":"Article 102019"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147710294","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":"Exploring the impact of vertical population migration on vegetation restoration in the mountain areas of Southwest China: A case analysis from Xide County","authors":"Xudong Guan ,&nbsp;Wei Zhao ,&nbsp;Bo Kong","doi":"10.1016/j.rsase.2026.102030","DOIUrl":"10.1016/j.rsase.2026.102030","url":null,"abstract":"<div><div>Over the past two decades (2000-2020), vertical population migration has emerged as a transformative socioeconomic phenomenon across the mountainous regions of Southwest China, influencing the local ecosystems. To assess this impact, this study establishes an innovative analytical framework integrating NDVI time-series (MOD13Q1, 250 m resolution), gridded population density (WorldPop), and digital elevation models (SRTM DEM) to quantify the coupled human-environment interactions and takes Xide County of Liangshan Prefecture as a typical example. Advanced trend analysis techniques including seasonal-trend decomposition (STL) and deep learning approaches were employed to disentangle climate and anthropogenic drivers of vegetation dynamics. Notably, we developed a LSTM-derived detrended climate-normalized NDVI residual series to isolate human-induced vegetation changes. Spatially explicit correlation analyses revealed elevation-dependent patterns: land abandonment hotspots (15.6% of total area) predominantly clustered at high elevations (&gt;2330m ASL), peaking during 2005-2015 (13.7%). Multivariate regression models demonstrated divergent elevation-mediated relationships - population outmigration showed strong positive correlations with abandonment in dispersed settlements, contrasting with urban agglomeration effects. Climate-corrected NDVI trends revealed divergent ecological outcomes: abandoned lands showed marginal degradation (−0.0013 yr⁻¹) versus active farmland recovery (0.0058 yr⁻¹). Crucially, cross-correlation analysis uncovered 3-5 year lagged vegetation responses to demographic shifts. These findings provide mechanistic insights into elevation-specific human-landscape feedbacks, informing sustainable mountain development strategies.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"42 ","pages":"Article 102030"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147750204","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":"Assessment and methodological proposal for the restoration of Brazilian Atlantic forest ecosystems","authors":"Vinícius de Amorim Silva ,&nbsp;Hercules da Silva Carvalho ,&nbsp;Paulo Sérgio Vila Nova Souza ,&nbsp;Júlio Gonçalves da Silva Júnior ,&nbsp;Ioná Gonçalves Santos Silva","doi":"10.1016/j.rsase.2026.102023","DOIUrl":"10.1016/j.rsase.2026.102023","url":null,"abstract":"<div><div>This research proposes a methodology based on fuzzy logic and spectral indices to identify priority areas for forest recovery in the Environmental Protection Area (EPA) of Lagoa Encantada and Rio Almada (Bahia-Brazil), integrating temporal analyses from 2000 to 2023 of environmental and anthropogenic variables. Time series of Landsat 7 images processed in Google Earth Engine were used, with analyses in RStudio and QGIS. Vegetation and water indices were calculated, correlated with land cover and land use data and forest loss. Fuzzy modeling is considered favorable soil. The correlation revealed a strong negative relationship between forest formation and pastures, evidencing the conversion of natural areas to agricultural use. The NDWI, with values between −0.60 and −0.30, indicated a progressive reduction in soil moisture, associated with variables, such as SAVI and Permanent Preservation Areas, and unfavorable variables, such as pastures and urban infrastructure, to map forest recovery potential. The results indicated an annual reduction of 0.73 km² in forest formation, accompanied by the advance of pastures (+1 km²/year) and urbanization. The NDVI remained stable (0.60–0.72), but the SAVI showed a downward trend, identifying an increase in exposed the decline in water bodies (0.021 km²/year). In addition, 18.42 km² with moderate potential for recovery were identified, concentrated mainly in the western zone of the EPA. It is estimated that there are areas with compacted soil that require specific interventions, such as decompaction and nucleation. The methodology proved effective in prioritizing areas for restoration and as an accessible tool for sustainable environmental management.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"42 ","pages":"Article 102023"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147803462","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":"Deep learning for remote sensing mapping of roads, sidewalks, and bicycle lanes in Bogotá","authors":"Yang Shao ,&nbsp;Justyn Kase Huckleberry ,&nbsp;María Fernanda Ramírez-Bernal ,&nbsp;Camilo Urbano-Castro ,&nbsp;Christian Andrey Rincón-Acosta ,&nbsp;Juan Camilo Aguirre-Salamanca ,&nbsp;Yujie Wu ,&nbsp;Gregory N. Taff ,&nbsp;Jyot Chadha","doi":"10.1016/j.rsase.2026.101993","DOIUrl":"10.1016/j.rsase.2026.101993","url":null,"abstract":"<div><div>Accurate mapping of urban transportation infrastructure is essential for promoting sustainable mobility and resilient city planning. While major road networks are well documented in global datasets, high-resolution information on sidewalks and bicycle lanes remains scarce, particularly in cities without advanced geodata infrastructures. Remote sensing imagery combined with deep learning offers a promising alternative, yet few studies have evaluated these methods at a full metropolitan scale. This study assesses three widely used deep learning models, U-Net, DeepLabV3+, and SegFormer, for semantic segmentation of roads, sidewalks, and bicycle lanes from 30 cm aerial orthophotos across Bogotá, Colombia. Sixteen 1 km² training grids representing diverse urban contexts were used to generate citywide predictions, which were validated against authoritative reference GIS layers. SegFormer achieved the highest accuracy (F1 = 0.864 for roads, 0.703 for sidewalks, 0.600 for bicycle lanes), outperforming DeepLabV3+ and U-Net. Roads were consistently mapped with high accuracy, while sidewalks and bicycle lanes proved more difficult due to narrow geometries and occlusions. Performance was strongly context-dependent: SegFormer performed particularly well when road density exceeded ∼10%, with accuracy saturating at ∼15–20%. Additionally, area distributions of predicted transportation modes closely matched reference GIS totals, suggesting the practical value of deep learning for estimating infrastructure coverage. These results demonstrate the potential of transformer-based models for reliable city-scale mapping of transportation networks and provide insights for extending such methods to data-poor urban regions worldwide.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"42 ","pages":"Article 101993"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147613007","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 multi-criteria analysis of urban characteristics for surface urban heat island mitigation based on remote sensing data","authors":"Aynaz Eyni,&nbsp;Maedeh Pourfathollah,&nbsp;Ardalan Aflaki","doi":"10.1016/j.rsase.2026.101974","DOIUrl":"10.1016/j.rsase.2026.101974","url":null,"abstract":"<div><div>The Surface Urban Heat Island (SUHI) effect reduces thermal comfort and increases energy consumption in urban areas, especially in hot and humid climates. In this study, SUHI is examined as an intra-urban phenomenon, where relative land surface temperature differences are evaluated within a morphologically homogeneous residential complex rather than between urban and rural areas. This study investigates the influence of canyon orientation, height-to-width (H/W) ratio, and sky view factor (SVF) on intra-urban land surface temperature (LST) patterns at the micro-scale within a case study area with uniform materials and limited vegetation. LST data derived from 58 Landsat 8 images (2020 and 2024) were used to map persistent thermal patterns, while SVF and H/W calculations were conducted using field observations and modeling software. An Analytical Hierarchy Process (AHP) was applied to reliably evaluate canyon orientations and normalization techniques facilitated comparison of the factors. Spearman rank correlation (n = 10) quantified statistical relationships between LST and morphological parameters. Unlike most previous studies conducted at city or district scales, this research investigates intra-urban surface temperature variability at the level of individual urban canyons within a single residential complex. The results indicate that canyon orientation exhibited the strongest and statistically significant influence on LST (ρ = 0.68, p &lt; 0.05), whereas H/W ratio and SVF showed weaker and non-significant associations under early morning conditions. Comparative analysis between heat and cool points suggests that a 6% reduction in H/W ratio combined with a 3% increase in SVF may correspond to a mean temperature reduction of up to 1.15 °C. When these changes combine with a NW-SE canyon orientation aligned with prevailing cool winds, this reduction can reach by up to 1.5 °C. This study provides a replicable micro-scale analytical framework integrating remote sensing, morphological assessment, and multi-criteria evaluation to support climate-responsive urban design in compact residential contexts.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"42 ","pages":"Article 101974"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147613008","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 analysis of seagrass biophysical properties mapping using multi-resolution satellite imagery and machine learning in the shallow waters of Teluk Pandan, Lampung, Indonesia","authors":"Setiawan Djody Harahap ,&nbsp;Muhammad Wahyu Ramadhan ,&nbsp;Satrio Jati Kinantyo Widhi ,&nbsp;Galih Perkasa ,&nbsp;Annisa Arni Dewi ,&nbsp;Desi Permata Sari ,&nbsp;Pramaditya Wicaksono ,&nbsp;Nurul Khakhim","doi":"10.1016/j.rsase.2026.102002","DOIUrl":"10.1016/j.rsase.2026.102002","url":null,"abstract":"<div><div>This study has evaluated the actual classes that are effectively detectable by a sensor or, in other words, the classes that are effectively suitable from the 'eye' of the sensor across varying spatial and spectral resolutions for seagrass biophysical mapping in Teluk Pandan. Two key analyses were conducted: (1) assess the capability of different spectral resolution images within the same spatial resolution and (2) assess the capability of different spatial resolution images within the same spectral resolution. Results demonstrate that higher spatial resolution image improves the mapping detail, while higher spectral resolution image enhances object differentiation. The PlanetScope (8 bands) image identified up to seven benthic habitat composition classes, surpassing the Sentinel-2 (3 bands) and PlanetScope (3 bands), which identified only five classes. Comparative performance analysis of Random Forest (RF) and XGBoost algorithms in mapping the seagrass biophysical properties, including benthic habitat composition, seagrass percent cover (PCv), and above-ground seagrass carbon (AGC) maps, across various sensors with different spatial and spectral resolutions reveals consistently high mapping accuracy and comparable results. The RF and XGBoost algorithms yield comparable mapping results when the dataset volume is not too large, and the study area exhibits relatively homogenous object variations. Although the performance gap remains insignificant, XGBoost offering slightly improved regression performance. These findings highlight the importance of evaluating machine learning models for optimal algorithm selection, advancing seagrass monitoring techniques, and supporting more precise conservation strategies through enhanced mapping accuracy and detail.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"42 ","pages":"Article 102002"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147613387","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":"Urban morphology and the heat island effect in African cities: Evidence from Ghana, Togo, and Tanzania","authors":"Stephan Schmidt ,&nbsp;Yucheng Zhang ,&nbsp;Wenzheng Li ,&nbsp;Houpu Li","doi":"10.1016/j.rsase.2026.102005","DOIUrl":"10.1016/j.rsase.2026.102005","url":null,"abstract":"<div><div>Urban heat islands (UHI) are an increasingly urgent concern in rapidly urbanizing regions, yet empirical evidence from Sub-Saharan Africa remains limited. This pilot study examines how urban morphology influences UHI intensity across 388 urban settlements in Ghana, Togo, and Tanzania, adapting conventional approaches to data-poor environments. We integrate MODIS land surface temperature with high-resolution land cover and Africapolis settlement boundaries, introducing an adaptive rural baseline that accounts for elevation and cropland exclusions to isolate urban–rural thermal contrasts. Using class-based and landscape-level metrics, we evaluate the role of land use composition, fragmentation, and settlement form in shaping daytime UHI through ordinary least squares regressions. Similar to studies elsewhere, we show that contiguous urban development intensifies UHI, while fragmented urban fabrics help mitigate heat. However, distinctive patterns also emerge. Peri-urban agricultural cohesion significantly reduces UHI, and irregular settlement shapes, often reflecting ribbon-like development along roads, are associated with stronger UHI effects. These findings diverge from results elsewhere, underscoring the importance of context-specific analysis. Methodologically, the study demonstrates that UHI metrics can be adapted to African cities. The results highlight how preserving peri-urban agriculture and maintaining heterogeneous settlement structures can help reduce heat stress in resource-constrained urban environments.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"42 ","pages":"Article 102005"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147657812","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 type classification in smallholder agriculture of central and South Asia using Sentinel-1/2 data fusion","authors":"Christoph Raab ,&nbsp;Viet Duc Nguyen ,&nbsp;Brian Barrett ,&nbsp;Aksana Zakirova ,&nbsp;Mehwish Zuberi ,&nbsp;Henryk Alff ,&nbsp;Michael Spies","doi":"10.1016/j.rsase.2026.102007","DOIUrl":"10.1016/j.rsase.2026.102007","url":null,"abstract":"<div><div>Accurate crop monitoring in smallholder-dominated regions is challenging due to fragmented fields, high crop diversity, and often limited ground truth data. This study presents a systematic and transferable framework for crop-type classification in smallholder systems by jointly evaluating multi-sensor data fusion, temporal feature aggregation, feature selection, and model applicability. This study evaluates crop-type classification accuracy across smallholder agricultural landscapes in Central and South Asia (Kazakhstan, Tajikistan, Pakistan), leveraging Sentinel-1 radar and Sentinel-2 optical data separately and combined. Employing Random Forest models, we systematically compare temporal aggregation approaches (monthly, bi-monthly, quarterly) and evaluate the impact of feature selection on model performance. Across all study regions, combined Sentinel-1 and Sentinel-2 data achieved overall classification accuracies of approximately 80–96%, with substantial performance gains relative to single-sensor models, particularly in regions where individual sensors showed limited discrimination capability. Depending on region and sensor, accuracy improvements ranged from a few percentage points to more pronounced gains, reflecting strong benefits of data fusion in heterogeneous smallholder systems. Finer temporal aggregation schemes, including monthly aggregation, yielded additional accuracy gains of approximately 1–3 percentage points compared to coarser aggregations, while feature selection further improved model performance by roughly 2–5 percentage points. Sentinel-1 proved particularly effective for structurally distinct crops such as cotton, while Sentinel-2 substantially improved classification of more diverse crop classes. Application of the Area of Applicability concept enabled spatially explicit identification of well-supported and extrapolated predictions, providing a quantitative basis for uncertainty assessment and future sampling strategies. Together, these results demonstrate the value of an integrated and transferable methodological framework for robust crop-type classification in smallholder agricultural systems using freely available Sentinel data.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"42 ","pages":"Article 102007"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147710209","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":"Agronomic variables outperform multispectral indices for individual plant yield prediction in Andean quinoa","authors":"Samuel Pizarro ,&nbsp;Erika Garcia ,&nbsp;Esthefany Gavino ,&nbsp;Edilson Requena-Rojas ,&nbsp;Kevin Ortega ,&nbsp;Dennis Ccopi","doi":"10.1016/j.rsase.2026.102027","DOIUrl":"10.1016/j.rsase.2026.102027","url":null,"abstract":"<div><div>Accurate pre-harvest yield estimation is essential for decision-making in high-altitude agriculture. This study evaluated agronomic and multispectral UAV variables for near-harvest prediction of individual quinoa grain weight, with data collected across six phenological stages to identify when predictors achieve reliable performance, under Andean conditions. A total of 374 plants were monitored across six phenological stages at Santa Ana Experimental Station (Huancayo, Peru, 3280 m a.s.l.) during 2024. OLS, Random Forest, Support Vector Machine, and Neural Network models were trained using agronomic-only (AGRO), spectral-only (IND), and combined (COMP) predictor sets, evaluated through 5-fold cross-validation reporting mean ± standard deviation. Agronomic and combined models achieved moderate performance (R² = 0.22–0.25, RPD = 1.10–1.15), suitable for relative plant ranking in breeding programs, while spectral-only models failed across all algorithms (R² ≤ 0.044, CCC ≤0.080), constrained by saturation, phenological decoupling, and canopy heterogeneity. Variable importance analysis confirmed that late-season structural traits dominated predictions, while spectral indices contributed marginally despite including red-edge bands. These results challenge spectral-only approaches for individual plant phenotyping in heterogeneous canopies, demonstrating that integrating simple ground measurements with UAV spectral data is essential for reliable quinoa yield estimation.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"42 ","pages":"Article 102027"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147710287","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":"Assessment of daytime and nighttime land surface temperature behavior in an arid region: evidence of thermal trends asymmetry","authors":"Lizbeth María Flores-González ,&nbsp;Jesús Gabriel Rangel-Peraza ,&nbsp;Sergio Alberto Monjardin-Armenta ,&nbsp;Wenseslado Plata-Rocha ,&nbsp;Antonio Jesus Sanhouse-García ,&nbsp;Zuriel Dathan Mora-Felix","doi":"10.1016/j.rsase.2026.102021","DOIUrl":"10.1016/j.rsase.2026.102021","url":null,"abstract":"<div><div>In-situ data scarcity has limited the mechanistic understanding of climate trends in arid Northwestern Mexico. This study provides the first systematic quantification and attribution of diurnal Land Surface Temperature (LST) asymmetry in this region from 2000 to 2024. Using a non-parametric analysis of MODIS data coupled with high-resolution national land cover dynamics, the results reveal a severe and well-defined thermal asymmetry: a statistically significant nighttime warming trend (+0.71 °C/decade) contrasted with a net cooling trend during the day (−0.25 °C/decade). This study demonstrates that the most acute manifestations of this nocturnal warming are spatially coupled with anthropogenic land surface modifications, specifically agricultural intensification and urbanization. The loss of nocturnal cooling alters the surface energy balance, acting as a systemic risk multiplier for regional water resources and agricultural viability. These findings establish that analyzing diurnal and nocturnal phases independently, and linking them to land-use dynamics, is essential for identifying climate risks in data-scarce arid ecosystems.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"42 ","pages":"Article 102021"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147710290","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}