Remote Sensing Applications-Society and Environment最新文献

{"title":"Analysis of subsidence factors and modeling of susceptibility under coupled geohydrological conditions - A case study of Jiangsu Yangtze River section","authors":"Wen-Jiang Long ,&nbsp;Xue-Xiang Yu ,&nbsp;Ming-Fei Zhu","doi":"10.1016/j.rsase.2025.101491","DOIUrl":"10.1016/j.rsase.2025.101491","url":null,"abstract":"<div><div>Ground subsidence along the riverbanks near the Yangtze River Delta has been accelerating due to human activities and other factors, seriously impacting various aspects of social development. Mapping susceptibility patterns and analyzing subsidence factors are crucial for effective management. This study focused on the Yangtze River riparian perimeter in Jiangsu Province, our study area. We assessed the importance of different factors using the random forest regression (RFR) model and the temporal convolution network (TCN). Additionally, we used GeoDetector to analyze the spatial relationship between sedimentation and potential drivers. Finally, we utilized the RFR and Maxent model to map susceptibility to sedimentation patterns in different risk zones. The study results show that the method effectively depicts the susceptibility to subsidence in each risk zone (44.18% and 32.56% for high and average risk zones, respectively). Anthropogenic factors mainly drive the subsidence-prone areas around the Yangtze River in Jiangsu. Groundwater extraction and soft soil thickness are the primary drivers of subsidence patterns in high-risk areas. In contrast, the main drivers of subsidence in other risk areas vary. These differences reflect the delayed effects of natural and anthropogenic factors on subsidence and the significant differences in how anthropogenic drivers affect the marginal effects of subsidence. Through susceptibility modeling and driver evaluation, this study reveals that establishing risk zones has improved our understanding of the impact of regional variations in environmental variables on subsidence. This understanding will facilitate the development of subsidence management strategies tailored to different regions.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101491"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143428053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the coherency of different El Niño events with vegetation health using time-series remote sensing data and wavelet coherency analysis in part of Southeast Asia","authors":"Sanjiwana Arjasakusuma,&nbsp;Yusri Khoirurrizqi,&nbsp;Tsamarah Huwaida","doi":"10.1016/j.rsase.2025.101460","DOIUrl":"10.1016/j.rsase.2025.101460","url":null,"abstract":"<div><div>El Niño Southern Oscillation (ENSO) is a cyclic period of warm and cold events that affect the seasonal global weather patterns due to the weaker trade winds in Pacific Oceans. During the warm ENSO events, or El Niño, regions including Southeast Asia, are exposed to the reduced precipitation and warmer temperatures, which able to affect the vegetation dynamics. El Niño warm events can occur in different magnitudes and intensities but the impacts of different warm ENSO magnitudes to vegetation health is rarely studied. This study explored the coherency of different El Niño events as indicated by ENSO indices with vegetation health in Indonesia by using wavelet semblance analysis (WSA). Our results suggested that short-duration and strong ENSO events highly affected the vegetation dynamics, and potential coupled impact of ENSO warm event and Indian Ocean Dipole (IOD) such as in 2006 can influence the vegetation health. The 1982, 1997–1998 and 2015–2016 strong ENSO events affected for more than 40 % of vegetation health in Indonesia, while 2006's combined weak-ENSO and IOD events affected up to 44 % of vegetation health. From the total 40–59 % areas that were affected in 1997–1998 ENSO's event, 38–56 % was Evergreen Broadleaf Forests (EBF). This indicates that strong magnitude of El Niño combined with IOD could increase the impact of climate anomalies to the vegetation health.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101460"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143101041","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":"Integration of PRISMA hyperspectral satellite data with ground based geological investigation for mapping alteration minerals associated with the Neem-ka-Thana Cu belt in Rajasthan, India","authors":"Angana Saikia ,&nbsp;Ajanta Goswami ,&nbsp;Bijan Jyoti Barman ,&nbsp;Kanishka Hans Sugotra ,&nbsp;Hrishikesh Kumar","doi":"10.1016/j.rsase.2024.101421","DOIUrl":"10.1016/j.rsase.2024.101421","url":null,"abstract":"<div><div>Hydrothermal deposits are commonly associated with specific alteration minerals that serve as key indicators for mineral exploration. The Neem Ka Thana Cu Belt, situated southeast of the Khetri Cu deposit within the Alwar-Ajabgarh sub-basin of the North Delhi Fold Belt, is notable for its Bornite-rich Cu-S mineralization. Despite its geological significance, detailed spectral mapping to delineate the alteration minerals associated with base metal mineralization remained limited. This study addresses this gap by utilizing the “PRecursore IperSpettrale della Missione Applicativa” (PRISMA) hyperspectral sensor to detect and map alteration minerals associated with Cu-S mineralization.</div><div>To achieve this, we applied Relative Band Depth (RBD) indices on targeted spectral subsets of PRISMA data to identify Fe-oxides/hydroxides and Al-OH-bearing minerals. We detected key alteration minerals, including muscovite, illite, chlorite, montmorillonite and Fe-oxide and hydroxides such as goethite, hematite, and limonite, by targeting their diagnostic absorption features. The resulting spectral maps highlighting the spatial distribution of the targeted mineral groups were validated with field investigations and laboratory assessments. The study demonstrates that the integration of hyperspectral analysis with conventional geological techniques can help to understand the mineral distribution and associated alteration processes. The use of PRISMA hyperspectral data provides a powerful, non-invasive means for reconnaissance mapping of exposed lithologies, delivering targeted information that is crucial for optimizing subsequent field investigations and drilling operations. The present work highlights the potential of PRISMA data in advancing the methodologies of mineral exploration and lithological mapping, contributing valuable insights for the geoscientific community.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101421"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092308","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 examination of GNSS positioning under dense conifer forest canopy in the Pacific Northwest, USA","authors":"Jacob L. Strunk ,&nbsp;Stephen E. Reutebuch ,&nbsp;Robert J. McGaughey ,&nbsp;Hans-Erik Andersen","doi":"10.1016/j.rsase.2024.101428","DOIUrl":"10.1016/j.rsase.2024.101428","url":null,"abstract":"<div><div>Accurate positioning in the forest (e.g., less than 1–2 m horizontal error) is needed to leverage the potential of high-resolution auxiliary data sources such as airborne or satellite imagery, lidar, and photogrammetric heights used in forest monitoring. Unfortunately, typical short duration occupations in the forest with budget Global Navigation Satellite System (GNSS; GPS is the American constellation) receivers are generally inaccurate (horizontal errors &gt;5–20 m). This study demonstrates that accurate GNSS positioning is feasible beneath 40 to 60 m-tall closed-canopy conifer forests of western Washington state, USA by using survey-grade receivers with at least 15-min occupations. We also demonstrate the effects of receiver height, occupation duration, base-station distance, and differential post-processing modes (e.g., autonomous, code, fixed-integer, and floating-point) on horizontal positioning accuracies in the forest.</div><div>A geodetic survey was our benchmark for accuracy estimation but is difficult to replicate by most other GNSS users in the forest. The difficulty in setting up a geodetic survey has led to common usage of naïve accuracy estimators based on within-occupation coordinate variation (e.g., the “accuracy” reported on the face of a handheld GNSS device). In this study we demonstrate the efficacy of two simple alternatives that outperform the naïve estimator; the naïve esimator was shown to perform poorly.</div><div>The findings in this study on GNSS performance and positioning accuracy estimation supports more effective use of GNSS technology in applications that require high-performance GNSS positioning in the forest.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101428"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092429","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":"MultiGO: An unsupervised approach based on multi-objective growth optimizer for hyperspectral image band selection","authors":"Mohammed Abdulmajeed Moharram,&nbsp;Divya Meena Sundaram","doi":"10.1016/j.rsase.2024.101424","DOIUrl":"10.1016/j.rsase.2024.101424","url":null,"abstract":"<div><div>Hyperspectral imaging (HSI) plays a crucial role in extracting discriminative spectral-spatial features for accurate land cover classification. However, HSI datasets often suffer from the presence of irrelevant and redundant spectral bands, leading to the Hughes phenomenon and increased computational complexity. To address this challenge, this paper proposes an unsupervised approach based on the multi-objective growth optimizer for hyperspectral image dimensionality reduction. The proposed method leverages the learning phase and reflection phase of the growth optimizer to balance exploration and exploitation strategies. By incorporating information richness, reducing redundancy, and considering spatial features, the growth optimizer selects the most informative and significant spectral bands. The approach simultaneously optimizes three objective functions using the growth optimizer, creating trade-offs among them. Extensive results demonstrate the effectiveness and superiority of the proposed method in achieving dimensionality reduction and preserving the essential information in hyperspectral images. Ultimately, four machine learning classifiers, namely support vector machine, random forest, K-Nearest Neighbors, and decision tree, are applied at the pixel level for hyperspectral image classification. Moreover, the proposed method shows a significant improvement compared with five state-of-the-art techniques (bat algorithm, archimedes optimization algorithm, particle swarm optimization, harmony search, and genetic algorithm), with overall accuracy equal to 80.95 %, 92.63 %, and 90.30 % on three benchmark hyperspectral datasets namely Indian Pines, Pavia University, and Botswana, respectively.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101424"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092432","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":"Temporal trends and future projections: Analysing land surface temperature in the Kumaun Himalayas using spatial time series analysis","authors":"Zainab Khan ,&nbsp;Sk Ajim Ali ,&nbsp;Ateeque Ahmad ,&nbsp;Syed Kausar Shamim","doi":"10.1016/j.rsase.2024.101426","DOIUrl":"10.1016/j.rsase.2024.101426","url":null,"abstract":"<div><div>In this ground-breaking study, we introduced a novel approach for projecting Land Surface Temperature (LST) in the Kumaun Himalayas, an area critical for understanding regional impacts of global warming. The novelty of this study lies in the utilization of spatial time series analysis, a method that not previously applied for future LST prediction. In this study we examined LST trends from 1990 to 2020 and predicted LST for the year 2030 using satellite-based remote sensing data for LST estimation, advanced statistical techniques such as the Simple Moving Average (SMA), Sen's Slope, and z-statistics with excellent statistical power. The application of z-statistics provides a robust framework for assessing temperature changes, with significant findings such as a z-statistics value of −15.04 for spring, indicating a marked shift in temperature patterns. Similarly, for autumn, the z-statistics value of −21.41 underscores a drastic deviation from historical norms i.e., from 1990 to 2020. Pearson's correlation and the coefficient of determination were used to validate the accuracy of satellite-based LST estimates and SMA. A correlation of 0.93 and R² of 0.87 were found between observed and estimated LSTs, while the SMA-based LST showed a correlation of 0.92 with estimated LST with R² of 0.85. The results highlight a future that is significantly warmer than the present, bringing into sharp focus the urgency of climate change mitigation and adaptation strategies in this ecologically sensitive region. The study also suggested differential rate of seasonal warming. The study is not only pivotal for local climate policy but also contribute significantly to the broader understanding of climate dynamics in mountainous terrains is seasonal variation in warming rates. Despite challenges like rugged terrain and variable cloud cover affecting data accuracy, our approach offered a scalable model for similar climatic studies in other regions, marking a significant advancement in the field of climate change.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101426"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092530","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 radar vegetation water content integration for SMAP soil moisture retrieval","authors":"Jyoti Sharma ,&nbsp;Rajendra Prasad ,&nbsp;Prashant K. Srivastava ,&nbsp;Shubham K. Singh ,&nbsp;Suraj A. Yadav ,&nbsp;Dharmendra K. Pandey","doi":"10.1016/j.rsase.2024.101443","DOIUrl":"10.1016/j.rsase.2024.101443","url":null,"abstract":"<div><div>The Vegetation Water Content (VWC) serves as a crucial parameter within the framework of the Soil Moisture Active Passive (SMAP) satellite mission, particularly in its utilization for vegetation optical depth estimation in the Single Channel Algorithm (SCA) to determine soil moisture content. This study attempts to enhance the soil moisture estimation by estimating microwave VWC utilizing the Single Look Complex (SLC) format of dual-polarized Sentinel-1 data. This approach aims to refine the efficacy of the Single Channel Algorithm (SCA), thereby elevating the precision and reliability of soil moisture estimations. The Sentinel-1 datasets have been utilized to compute radar indices, particularly the Dual Polarimetric Radar Vegetation Index (DPRVI), Radar Vegetation Index (RVI), and Cross- and Co-Polarized Ratio (CCR). DPRVI reflects vegetation's growth and moisture properties, while RVI and CCR indicate vegetation water content and health status. The radar indices were employed within regression approaches such as random forest (RF), support vector regression (SVR), adaptive neuro-fuzzy inference system (ANFIS), and linear regression to estimate VWC. The performance of DPRVI was found better to capture aspects of vegetation dynamics and effectively estimates VWC values with a high correlation (R2) of 0.59. Furthermore, the DPRVI-estimated VWC values are integrated into the SCA, a renowned method for soil moisture retrieval. The results of SCA are compared to the ground-measured soil moisture along with the already available SMAP L2-enhanced passive soil moisture product. The soil moisture estimation via SCA integrated with the DPRVI-estimated VWC enhances the soil moisture estimations with an accuracy of (RMSE = 0.042 m³/m³ and ubRMSE = 0.039 m³/m³) compared to the SMAP L2 soil moisture. This integration allows for a more comprehensive understanding of soil-vegetation-atmosphere interactions and improves the accuracy of soil moisture assessments, critical for hydrological modeling, agricultural management, and environmental monitoring efforts.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101443"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143091896","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":"New inventory and dynamics of glacial lakes in Alaknanda basin, Uttarakhand, India from 1990 to 2020: A multi-temporal landsat analysis","authors":"Rekha Sahu ,&nbsp;Parvendra Kumar ,&nbsp;Rajnandini Gupta ,&nbsp;Santram Ahirwar ,&nbsp;Vikram Sharma","doi":"10.1016/j.rsase.2025.101470","DOIUrl":"10.1016/j.rsase.2025.101470","url":null,"abstract":"<div><div>Glacial lakes are critical components of high-altitude mountainous regions in the Himalayas. In recent years, glaciers have rapidly receded due to climate change, resulting in the formation of glacial lakes with substantial risks for downstream communities and infrastructure. The present study uses Landsat satellite data to create a comprehensive glacial lake inventory in the Alaknanda Basin, focusing on spatiotemporal changes between 1990 and 2020. The study has recorded 73 glacial lakes (≥0.003 km²) with a total surface area of 2.538 ± 0.037 km² in 2020. The mean depth and volume of glacial lakes were assessed as 7.17 m and 0.432 x 10⁶m³, respectively. During 1990–2020, the total glacial lake area has increased from 0.748 ± 0.020 km² to 2.538 ± 0.037 km² with a growth of ∼1.790 km² (239%; 7.97% a⁻¹). Additionally, 15 common glacial lakes have shown significant growth rates of 91.24% (3.04% a^-¹). Among all the glacial lakes, tiny lakes (&lt;0.02 km²) have shown the maximum growth in both numbers (+33) and area (477.92%; 15.93% a⁻¹). Moraine-dammed lakes have expanded more rapidly in terms of number (+27), while supraglacial lakes have exhibited a higher rate of areal (1771.71%; 59.06% a⁻¹) expansion. Based on the current inventory, flood hazard studies in the Alaknanda Basin can be carried out for a better understanding of glacial-climate related dynamics.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101470"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387341","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":"Ecological condition indicators for dry forest: Forest structure variables estimation with NDVI texture metrics and SAR variables","authors":"María Paula Alvarez ,&nbsp;Laura Marisa Bellis ,&nbsp;Julieta Rocío Arcamone ,&nbsp;Luna Emilce Silvetti ,&nbsp;Gregorio Gavier-Pizarro","doi":"10.1016/j.rsase.2025.101485","DOIUrl":"10.1016/j.rsase.2025.101485","url":null,"abstract":"<div><div>The ecological condition of forest ecosystems is degraded. Limited prior research in vegetation has focused on monitoring ecological condition levels in dry forest at fine scale. We proposed a novel approach to obtain accurate indicators of the ecological condition of the Chaco Serrano forest (Córdoba, Argentina) by estimating forest structure variables (canopy cover (<span><math><mrow><mi>C</mi><mi>C</mi></mrow></math></span>), diameter breast height (<span><math><mrow><mi>D</mi><mi>B</mi><mi>H</mi><mtext>_</mtext><mi>s</mi><mi>u</mi><mi>m</mi></mrow></math></span>), number of woody individuals (<span><math><mrow><mi>N</mi><mi>W</mi></mrow></math></span>) and two first axes of a principal component analysis (<span><math><mrow><mi>P</mi><mi>C</mi><mn>1</mn></mrow></math></span> and <span><math><mrow><mi>P</mi><mi>C</mi><mn>2</mn></mrow></math></span>)) as a measure of forest degradation. To achieve this, first the correlation with two complementary groups of remote sensing derived data (texture metrics over Normalised difference vegetation index and SAR-derived data) was explored. Then, General linear models (GLM) were constructed using the most correlated remote sensing derived variables with forest structure variables as predictor variables. The best estimation was obtained to <span><math><mrow><mi>C</mi><mi>C</mi></mrow></math></span> (<span><math><msup><mrow><mi>r</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>=0.58, rmse=14,5%), followed by <span><math><mrow><mi>D</mi><mi>B</mi><msub><mrow><mi>H</mi></mrow><mrow><mi>s</mi><mi>u</mi><mi>m</mi></mrow></msub></mrow></math></span> (<span><math><msup><mrow><mi>r</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>=0.37, rmse=156.6) and <span><math><mrow><mi>N</mi><mi>W</mi></mrow></math></span> (<span><math><msup><mrow><mi>r</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>=0.22, rmse=14.6), with an spatial arrangement consistent with field observations. Moreover, <span><math><mrow><mi>C</mi><mi>C</mi></mrow></math></span> estimation was more accurate than those at regional and global scale, and highlights the importance of developing local models in areas that exhibit high ecological, geological, and human heterogeneity. In addition, other forest variables could also be evaluated, like floristic composition or others associated with functioning. Results offer valuable insights for developing management strategies suitable for each condition, and for future studies regarding the relationship of the mentioned condition and associated natural and anthropic factors.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101485"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421166","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":"High-resolution maximum air temperature estimation over India from MODIS data using machine learning","authors":"Amal Joy,&nbsp;K. Satheesan,&nbsp;Avinash Paul","doi":"10.1016/j.rsase.2025.101463","DOIUrl":"10.1016/j.rsase.2025.101463","url":null,"abstract":"<div><div>Maximum air temperature is one of the crucial parameters required for climate change research, public health, agriculture, energy consumption, and urban planning. Observations of maximum temperature are limited spatially and temporally, and available observations are discontinuous due to operational constraints. With the advent of the satellite era, the land surface temperature is available continuously across the globe over space and time. This study explores the potential of using land surface temperature data from MODIS satellites to estimate maximum air temperature across India, particularly in areas with low cloud cover. The research employs advanced machine learning techniques to estimate the maximum temperature over India from MODIS land surface temperature and other inputs like NDVI, elevation, land use, geographical location, and the Julian day. We have assessed the capability of three machine learning techniques: XGBoost, Neural network, Generalized additive model and multiple linear regression in estimating maximum temperature over India using MODIS and Insitu data spanning from 2010 to 2022. Results indicate that XGBoost outperforms the other techniques, achieving the lowest RMSE and R² values of 1.79 °C and 0.90, respectively. Our findings reveal that land surface temperature is the most influential predictor of maximum air temperature, followed by Julian day, elevation, latitude, distance to coast, NDVI, and land cover type, in order of importance. This research demonstrates the potential of satellite-derived data and machine learning in addressing gaps in maxiumum temperature observations, which could significantly benefit various sectors reliant on accurate data.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101463"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143101040","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}