Remote Sensing Applications-Society and Environment最新文献

{"title":"Comparison between machine learning classification and trajectory-based change detection for identifying eucalyptus areas in Landsat time series","authors":"Debora da Paz Gomes Brandão Ferraz,&nbsp;Raúl Sánchez Vicens","doi":"10.1016/j.rsase.2024.101444","DOIUrl":"10.1016/j.rsase.2024.101444","url":null,"abstract":"<div><div>In forestry, where species management has inter-annual durations, time series longer than one year have been used to map planted areas, estimate biophysical parameters, and determine planting age. Eucalyptus plantations have characteristics that enhance the use of time series in their classification, such as clear-cutting before planting or previous rotation and the rapid growth of large vegetation cover, which remains stable throughout the rotation period. This article compares the performance of two classification methods for mapping Eucalyptus areas: an object-oriented classification (GEOBIA) using products generated by the LandTrendr change detection algorithm based on trajectories and a classification using machine learning (random forest). Using a Landsat time series from 1985 to 2020, tests were conducted in a pilot area in Rio de Janeiro, Brazil. Both methods showed high accuracy in detecting Eucalyptus areas. However, the trajectory-based classification proved slightly superior, achieving a global accuracy of 0.988 and an F-Score of 0.975, while the classification using the random forest algorithm achieved a global accuracy of 0.954 and an F-score of 0.849. Regarding identifying the initial year of planting, both methods proved effective without showing significant differences (p-value = 0.1003). However, detecting the initial year using the LandTrendr algorithm proved more assertive. Both methods revealed periods of increase and stabilization in Eucalyptus planting throughout the time series, proving promising for determining the location and age of each stand and, thus, obtaining information about the time of use of that area for Eucalyptus cultivation.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101444"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143101042","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":"ViT-ChangeFormer: A deep learning approach for cropland abandonment detection in lahore, Pakistan using Landsat-8 and Sentinel-2 data","authors":"Mannan Karim ,&nbsp;Haiyan Guan ,&nbsp;Jiahua Zhang ,&nbsp;Muhammad Ayoub","doi":"10.1016/j.rsase.2025.101468","DOIUrl":"10.1016/j.rsase.2025.101468","url":null,"abstract":"<div><div>Cropland abandonment poses significant environmental, economic, and social challenges globally. As urbanization encroaches on agricultural areas, understanding the dynamics of abandoned croplands and accurately classifying and detecting them are essential for informed sustainable land use and effective policy development. However, traditional methods struggle to identify abandoned croplands due to temporal variability, limited spectral data and challenges in land cover variations. To address these challenges, we introduced an innovative deep learning approach that combines a Vision Transformer (ViT) with ChangeFormer for the classification and change detection of cropland abandonment using Landsat-8 and Sentinel-2 datasets in Lahore, Pakistan. We employed ViT for image classification, enhancing its efficacy through the incorporation of Vegetation Indices (VIs). This integration led to notable improvements in F1 score and Overall Accuracy (OA), elevating them from 86% and 88%to 92% and 95% respectively. Subsequently, ViT-generated classified rasters facilitated in identification of abandoned lands using ChangeFormer model. The direct comparison showcased a significant enhancement in ChangeFormer's performance, with F1 score and OA escalating from 91% and 90% to 97.5% and 96%, respectively. The improvment was particularly evident when testing ChangeFormer with ViT-generated rasters compared to raw imagery for binary change detection. The study identified 32,043 ha of abandoned cropland (14,613 in 2019 and 17,430 in 2024), with 16.35% converted to built-up areas in 2024. Urbanization was the primary driver, followed by conversions to barren land and water bodies. While our approach improves cropland abandonment detection, addressing unavailability of high-resolution imagery, computational costs, and integrating socio-economic and climate factors could enhance its accuracy and effectiveness.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101468"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143101043","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 multi-method ASTER data analysis and geometric average modeling for hydrothermal alteration mapping and mineral prospectivity assessment of copper deposits, Anti-Atlas, Morocco","authors":"Lahsen Achkouch ,&nbsp;Ahmed Attou ,&nbsp;Hafid Mezougane ,&nbsp;Mohammed Ouchchen ,&nbsp;Younesse El Cheikh ,&nbsp;Younes Mamouch ,&nbsp;Abdelhamid Bajadi ,&nbsp;Bouchra Dadi ,&nbsp;Rachid Ahmed ,&nbsp;Behnam Sadeghi","doi":"10.1016/j.rsase.2025.101467","DOIUrl":"10.1016/j.rsase.2025.101467","url":null,"abstract":"<div><div>Satellite imagery is a crucial tool for mineral exploration, as it enables the identification of mineralization systems through hydrothermal alteration mapping. This study focuses on the Tikirt region within the Anti-Atlas belt, well-documented for its copper mineralization, to produce a predictive mineral prospectivity map. Advanced processing of ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) satellite imagery was conducted to detect key alteration minerals, including argillic, phyllic, propylitic, and iron oxide. Spectral analysis techniques such as Spectral Feature Fitting (SFF), Matched Filtering (MF), Least-Squares Fitting (LS-Fit), and Constrained Energy Minimization (CEM) were utilized alongside the references spectra from the USGS library to delineate alteration zones. These were integrated using the geometric average model to generate a comprehensive prospectivity map highlighting areas with high mineralization potential. Field validation and laboratory analyses, confirmed the accuracy of the predictive map, demonstrating a strong alignment between high-potential zones, mapped faults, and mineralization occurrences. Assessment of the accuracy of alteration mapping indicates a satisfactory level of consistency with reference data (overall accuracy = 80%) and a high level of agreement (Kappa coefficient = 73.3%). Notably, the study identified several new areas with significant potential for copper mineralization, particularly associated with Neoproterozoic formations located in the northeast, northwest, south, and southeast of the study area. The results revealed a strong correlation between ASTER image processing and field/laboratory data, underscoring the effectiveness of integrating ASTER imagery with advanced spectral analysis for regional-scale mineral prospectivity. This integrated approach offers significant potential for guiding future exploration projects.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101467"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143101044","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":"Remote sensing and gravity investigations for barite detection in Neoproterozoic rocks in the Ariab area, Red Sea Hills, Sudan","authors":"Abazar M.A. Daoud ,&nbsp;Ali Shebl ,&nbsp;Mohamed M. Abdelkader ,&nbsp;Ali Ahmed Mohieldain ,&nbsp;Árpád Csámer ,&nbsp;Albarra M.N. Satti ,&nbsp;Péter Rózsa","doi":"10.1016/j.rsase.2024.101416","DOIUrl":"10.1016/j.rsase.2024.101416","url":null,"abstract":"<div><div>The increasing global demand for barite, driven by its geological importance and various industrial applications, advises the scientific community to improve attempts to identify and explore its deposits in different geological settings. This boost in interest aims to ensure sustainable supply by locating new sources and better understanding the conditions in which barite forms. This study presents an integrated approach using multispectral (Landsat 8 &amp; 9, Sentinel-2, and ASTER) and hyperspectral (PRISMA) remote sensing data, along with geophysical gravity data, to improve the localization of barite deposits. Several image processing methods, including false colour composites, principal component analysis, band ratios, minimum noise fraction, and spectral analysis, were employed for the discrimination of barite deposits, revealing their association with felsic rocks (referred to as group C). Additionally, lineament extraction was performed using the recent and advanced different filters like Tilt Angle Horizontal Gradient (TAHG) and Enhanced Horizontal Gradient Amplitude (EHGA) on Bouguer anomalies, highlighting the structural control of barite deposits by the D3 deformation phase. Field investigations were conducted to validate our findings. Based on these field observations, the integrated methodology successfully mapped the distribution of barite and its host rocks, resulting in an updated geological map for barite distribution that can be used in further exploration phases. We strongly recommend the adopted approach and the newly proposed image combinations for preliminary explorations of barite in similar arid terrains.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101416"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092290","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":"Automated rice mapping using multitemporal Sentinel-1 SAR imagery using dynamic threshold and slope-based index methods","authors":"Aishwarya Hegde A. ,&nbsp;Pruthviraj Umesh ,&nbsp;Mohit P. Tahiliani","doi":"10.1016/j.rsase.2024.101410","DOIUrl":"10.1016/j.rsase.2024.101410","url":null,"abstract":"<div><div>Rice cultivation plays a crucial role in food security and economic development, particularly in regions like India, due to its vast population and position as the top rice producer globally. This work introduces a novel framework, the Rice Mapping Method (RMM), which leverages Multitemporal Sentinel-1 Synthetic Aperture Radar (SAR) imagery for automated rice mapping. Contrary to the traditional approaches, RMM combines the Dynamic Threshold Method (DTM) for robust rice field identification and a slope-based index for classifying single and double cropping practices. By analyzing VH backscatter patterns and employing specific thresholds, DTM separates rice pixels from the other background pixels. The DTM, which relies on VH backscatter values during the growing season, has been tested across various rice cultivation landscapes, demonstrating high accuracy up to 0.95. DTM is also tested on different rice-growing areas such as the hilly Kodagu district, with an F1 Score of 0.96, and in the flooded delta region of Kuttanad, achieving an F1 Score of 0.93. The Slope-based Index <span><math><msub><mrow><mi>I</mi></mrow><mrow><mrow><mo>(</mo><mi>r</mi><mo>,</mo><mi>c</mi><mo>)</mo></mrow></mrow></msub></math></span> is introduced to differentiate the single and double cropping pixels by calculating the index for the second season of cropping and gives F1 Score of 0.81. The DTM’s effectiveness in rice field identification is evaluated by comparing it to the classification of the Bi-directional Gated Recurrent Unit (Bi-GRU) network. Similarly, the Slope-based Index is compared with other established automated rice mapping methods to assess its accuracy in distinguishing cropping patterns. RMM was successfully applied in mapping rice-growing areas in the Udupi district for 2021, estimating Kharif and Rabi season areas, the estimated rice area is compared to official statistics by the Directorate of Economics and Statistics, Karnataka State. The proposed RMM approach offers a robust solution for mapping rice fields, particularly in regions with complex cropping landscapes, and enhances agricultural monitoring and decision-making processes contributing to sustainable rice production and food security initiatives.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101410"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092313","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":"Seasonality and post fire recovery in a wetland dominated region: Insights from satellite data analysis in northern Argentina","authors":"Griselda Isabel Saucedo ,&nbsp;Ditmar Bernardo Kurtz","doi":"10.1016/j.rsase.2025.101480","DOIUrl":"10.1016/j.rsase.2025.101480","url":null,"abstract":"<div><div>Scientific literature indicates that climate change is driving an increase in wildfires globally. This study was done on a wetland dominated area in Northern Argentina and aims to, i) analyze the monthly and annual variability of burned areas between 2001 and 2022; ii) identify the fire frequency considering inter annual variability; iii) characterize the frequency of fires by season and the affected vegetation cover; and iv) evaluate the ecosystems recovery following the mega fire events of 2022. We found that 80,728 km² burned during the study period, with a seasonal concentration of patchy fires at the end of winter. However, larger burned areas were observed in summer, following dry periods. The highest concentration of burned areas was recorded in the central-east and northwest of the province. 71% of the burned areas experienced at least one fire, while 29% showed increased recurrence. Differences in fire activity based on vegetation cover and seasonal changes revealed that grasslands and wetlands are particularly prone to burning during the summer and winter. The atypical fires of 2022, which coincided with the peak of the growing season, caused phenological shifts of the typical vegetation pattern. Likewise, an analogous pattern was observed in unburned vegetation, attributable to the prevailing climatic conditions. Post-fire precipitation spurred on vegetation recovery depending on the prevailing land cover as follows, grasslands, wetlands, and native forests showed exponential post-disturbance recovery, characterized by an initial rapid recovery phase. In contrast, cultivated forests exhibited very low recovery. As climate change trends intensify in the future, anthropogenic and natural wildfires may exhibit varying impacts on different types of land cover. This research provides novel insights into the spatial and temporal variability of fires and recovery dynamics for the region.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101480"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143091983","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":"Hyperspectral classification of ancient cultural remains using machine learning","authors":"Rongji Luo ,&nbsp;Peng Lu ,&nbsp;Panpan Chen ,&nbsp;Hongtao Wang ,&nbsp;Xiaohu Zhang ,&nbsp;Shugang Yang ,&nbsp;Qingli Wei ,&nbsp;Tao Wang ,&nbsp;Yongqiang Li ,&nbsp;Tao Liu ,&nbsp;Deyang Jiang ,&nbsp;Jun Du ,&nbsp;Yan Tian ,&nbsp;Zhen Wang ,&nbsp;Hui Wang ,&nbsp;Duowen Mo","doi":"10.1016/j.rsase.2025.101457","DOIUrl":"10.1016/j.rsase.2025.101457","url":null,"abstract":"<div><div>The application of remote sensing in archaeology has recently gained widespread recognition, leading to the discovery of numerous significant cultural remains. However, the lack of theoretical data on spectral classification severely constrains the practicability of remote sensing archaeological investigations. In this study, we have collected a comprehensive dataset comprising over 15,000 spectral curves acquired from eight distinct categories of typical archaeological remains in Central China. Machine learning is utilized to conduct an in-depth analysis and classification of the hyperspectral attributes of cultural remains. The feature spectra are preprocessed using the Standard Normal Variable Transform (SNV) and Principal Component Analysis (PCA). A spectral classification model is proposed to improve the accuracy of typical archaeological remains using Support Vector Machines (SVM). The evaluation demonstrated that the SVM exhibited the highest classification accuracy of 99.82%. It was ultimately determined that the most distinguishable bands from ancient cultural remains were in the ranges of 524–553 nm, 663–686 nm, 974–1000 nm, 1092–1114 nm, and 2161–2185 nm. The research provides an important theoretical basis and a scientific method for remote sensing archaeology investigations, which is of great significance in understanding the past and facilitating present sustainable development.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101457"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143091976","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":"Analyzing Urban Heat Islands in Pokhara Metropolitan City-Nepal through Remote Sensing Techniques","authors":"Utsav Jamarkattel ,&nbsp;Badri Raj Lamichhane ,&nbsp;Saurav Gautam ,&nbsp;Niraj K.C. ,&nbsp;Bikash Sherchan ,&nbsp;Teerayut Horanont","doi":"10.1016/j.rsase.2025.101479","DOIUrl":"10.1016/j.rsase.2025.101479","url":null,"abstract":"<div><div>This study provides a comprehensive analysis of the temporal and spatial dynamics of Surface Urban Heat Islands (SUHI) in Pokhara Metropolitan City, Nepal, over the period from 2013 to 2022. Utilizing advanced satellite data and various indices to quantify Land Surface Temperature (LST) variations, this research uniquely focuses on a rapidly urbanizing region in the context of a developing country facing the consequences of climate change. The results reveal significant temperature disparities between urban and suburban areas, with urban zones exhibiting markedly higher maximum (39.13 °C), mean (33.23 °C), and minimum (28.48 °C) LST values compared to their suburban counterparts (34.43 °C, 29.49 °C, and 25.90 °C, respectively). Temporal assessments indicate a consistent increase in LST and an expansion of thermal hotspots, particularly during warmer months, underscoring the intensifying SUHI effect. Correlation analyses further elucidate a moderate negative relationship between the Normalized Difference Vegetation Index (NDVI) and LST (r = -0.58), highlighting the cooling influence of vegetation, while a strong positive correlation with the Normalized Difference Built-up Index (NDBI) (r = 0.82) emphasizes the impact of urbanization on rising temperatures. These findings underscore an urgent need for sustainable urban planning that integrates green spaces and adaptive design strategies to mitigate SUHI effects, reduce thermal stress on residents, and enhance urban resilience against climate change impacts, thereby advocating for increased vegetation cover, sustainable construction practices, and innovative cooling solutions to improve overall urban living conditions.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101479"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376572","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":"Utility of Earth Observation data in mapping post-disaster impact: A case of Hurricane Dorian in the Bahamas","authors":"Mohammed Ozigis ,&nbsp;Oluropo Ogundipe ,&nbsp;Samuel J. Valman ,&nbsp;Jessica L. Decker Sparks ,&nbsp;Helen McCabe ,&nbsp;Rebekah Yore ,&nbsp;Bethany Jackson","doi":"10.1016/j.rsase.2025.101466","DOIUrl":"10.1016/j.rsase.2025.101466","url":null,"abstract":"","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101466"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143396087","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":"Tillage direction analysis in agricultural fields from Digital Orthophotos and Sentinel-2 imagery","authors":"Sebastian Goihl","doi":"10.1016/j.rsase.2025.101486","DOIUrl":"10.1016/j.rsase.2025.101486","url":null,"abstract":"<div><div>For questions of soil and water protection, knowledge about agricultural management is relevant, especially in hilly and mountainous areas. In sloping areas, an area-wide knowledge of whether farming is done with or across the contour line would be very valuable for use in regional soil conservation management. In order to ascertain the prevalence of farming practices conducted with or against the slope in a given region, it is necessary to obtain data on the direction in which fields are cultivated. This information can be derived from remote sensing data through the application of geographic information system (GIS) methods. While previous studies have attempted to provide knowledge primarily through the use of small-scale but high-resolution Unmanned Aerial Vehicle (UAV) imagery, this study used medium-resolution imagery from satellite imagery (Sentinel-2 at 10 m × 10 m) and high resolution imagery (0.2 m × 0.2 m) Digital Orthophotos (DOP) from aircraft flights.</div><div>The use of medium-resolution satellite images (such as Sentinel-2) has yet to be explored in the context of addressing this research question, and this study represents their preliminary application in this domain. For this purpose, two GIS-based methods of analysis were proposed, which mainly made use of high-pass filtering, reclassification, vectorization, and compass orientation calculation. The results are promising, as in the best cases the correlation, between processing and ground truth orientation of the field tillage direction, for the DOP is R² of 0.867 for 170 fields and 2.687 ha. For the Sentinel-2 evaluation, an R² of 0.833 was obtained for 141 fields with 2.611 ha. Despite the different spatial resolution of both systems, the results are very comparable in terms of spatial coverage and accuracy of validation. However, for these two cases, this also meant that less than 50% of the total agricultural area and less than 20% of all fields in the study area could be covered. The data obtained from the DOP and Sentinel-2 sensors were collected at different times, resulting in the identification of distinct preferences for specific crop types. These preferences were observed to yield both accurate and less accurate evaluations, respectively. For instance, wheat exhibited favorable outcomes. Overall, the proposed approach demonstrated the capacity to derive area-wide information on farming direction with satisfactory results. Especially the temporarily high data availability of Sentinel-2 should be used to generate an overall picture using crop rotation and different phenological stages of arable crops in the long term.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101486"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421164","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}