Science of Remote Sensing最新文献

{"title":"Weed classification in sugarcane fields in Northeast Thailand from multi-temporal Sentinel-1 and Sentinel-2 data together with random forest algorithm","authors":"Savittri Ratanopad Suwanlee ,&nbsp;Muhammad Hanif ,&nbsp;Kemin Kasa ,&nbsp;Surasak Keawsomsee ,&nbsp;Jaturong Som-ard ,&nbsp;Vorraveerukorn Veerachitt ,&nbsp;Phattamon Heawchaiyaphum ,&nbsp;Akkawat Puntura ,&nbsp;Mohammad D. Hossain ,&nbsp;Sarawut Ninsawat","doi":"10.1016/j.srs.2025.100352","DOIUrl":"10.1016/j.srs.2025.100352","url":null,"abstract":"<div><div>Timely and accurate weed detection is essential for sustainable crop production and management. The integration of multiple satellite data sources with powerful machine learning has transformed precision agriculture by enhancing the accuracy and automation of object classification, enabling large-scale analysis and real-time predictions. However, challenges remain in effectively managing agricultural practices, particularly in weed control. This study employed Sentinel-1 (S1) and Sentinel-2 (S2) satellite data, combined with vegetation indices and random forest (RF) classification algorithm, to map weed presence in sugarcane fields in Northeastern Thailand. The large number of reference data consisting of 744 points was utilized to train and validate weed identification. The combined S1 and S2 dataset significantly enhanced the detection capabilities of the best RF model, achieving an overall classification result of 96 % accuracy and F1 scores exceeding 93 %. While overall weed levels were low, several high-density zones were clearly detected, underscoring the importance of targeted weed management. The combination of S1 and S2 data improved classification performance, addressing challenges posed by mixed pixels in small fields. Stratifying weed density provided deeper insights into field variability over the large scale. Our work presents a scientifically robust and operationally scalable framework for monitoring weed infestations in sugarcane cultivation. The proposed approach demonstrates strong potential for advancing sustainable precision agriculture by facilitating timely and spatially precise interventions.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"13 ","pages":"Article 100352"},"PeriodicalIF":5.2,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145748181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The rayleigh effects on the atmospheric correction of the ultraviolet imager on HY-1C and HY-1D satellites","authors":"Zhihua Mao ,&nbsp;Xianliang Zhang ,&nbsp;Dayi Yin ,&nbsp;Longwei Zhang ,&nbsp;Yiwei Zhang ,&nbsp;Qifei Wang ,&nbsp;Bangyi Tao ,&nbsp;Jianyu Chen ,&nbsp;Zengzhou Hao ,&nbsp;Qiankun Zhu ,&nbsp;Haiqing Huang","doi":"10.1016/j.srs.2025.100349","DOIUrl":"10.1016/j.srs.2025.100349","url":null,"abstract":"<div><div>Ultraviolet (UV) light is a key component of solar radiation, significantly impacting marine ecosystems. It is necessary to measure the ultraviolet light on the ocean which is now available from the Ultraviolet Imager (UVI) on the two ocean color satellites (HY-1C and HY-1D). The atmospheric correction (AC) procedure of the UVI data is based on the Layer Removal Scheme for Atmospheric Correction (LRSAC). Evaluation with Marine Optical Buoy in-situ data shows water-leaving reflectance (Rrs) accuracy with mean relative error (MRE) of 0.84 % (high sensitivity) and 0.13 % (low sensitivity) at Band 1 of HY-1C, and 6.79 % (high sensitivity) and −5.55 % (low sensitivity) at Band 2 of HY-1C, respectively. The mean absolute error (MAE) values are 0.0023 sr⁻¹ (high sensitivity) and 0.0034 sr⁻¹ (low sensitivity) at Band 1 of HY-1C, and 0.0021 sr⁻¹ (high sensitivity) and −0.0028 sr⁻¹ (low sensitivity) at Band 2 of HY-1C, respectively. The MRE values are 13.96 % (high sensitivity) and 20.27 % (low sensitivity) at Band 1 of HY-1D, and 8.09 % (high sensitivity) and 6.99 % (low sensitivity) at Band 2 of HY-1D, respectively. The MAE values are 0.0021 sr⁻¹ (high sensitivity) and 0.0022 sr⁻¹ (low sensitivity) at Band 1 of HY-1D, and 0.0023 sr⁻¹ (high sensitivity) and 0.0022 sr⁻¹ (low sensitivity) at Band 2 of HY-1D, respectively. The global daily and the 8-day composite images of the UVI demonstrate the spatial patterns of Rrs in the ultraviolet region, similar to the Rrs products of the Chinese Ocean Color and Temperature Scanner at blue bands. The accuracy of the Rayleigh can affect the performance of the AC mainly due to the largest part in the satellite-received radiance of the UVI. The selection of different volume scattering phases can cause about 1 % of MRE in the generation of the lookup table of Rayleigh varying with the solar and viewing angles. The ozone concentrations, sea surface winds, and atmospheric pressure of the global daily climatology have been generated and used to estimate the Rayleigh scattering at the ultraviolet bands. The ozone concentrations can cause about −0.6 % of MRE with about −0.8 % for winds and −0.4 % for pressure on the global Rayleigh distributions. The products are now available on the website for the oceanography study.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"13 ","pages":"Article 100349"},"PeriodicalIF":5.2,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145750389","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":"Satellite remote sensing and field analyses of megaripples and dunes in the Skeleton Coast National Park, Namibia","authors":"A.L. Cohen-Zada ,&nbsp;D.A. Vaz ,&nbsp;I. Katra ,&nbsp;L. Berger ,&nbsp;L. Saban ,&nbsp;S. Silvestro ,&nbsp;H. Yizhaq","doi":"10.1016/j.srs.2025.100347","DOIUrl":"10.1016/j.srs.2025.100347","url":null,"abstract":"<div><div>The morphology of aeolian bedforms is controlled by the boundary conditions during their formation and evolution, playing a crucial role in reconstructing current and past environmental settings. This study investigates the morphology and dynamics of multiscale aeolian bedforms, specifically megaripples and barchan dunes, in Namibia's Skeleton Coast National Park using satellite remote sensing, reanalysis wind data, and field observations. Newly mapped megaripples show average wavelengths between 3.3 and 4.4 m, with crest orientations aligned predominantly with regional southerly winds. Remote sensing data reveal a dune migration rate of ∼30 m yr⁻¹ and an average sand flux of ∼140 m³ m⁻¹ yr⁻¹, while field-based storm observations yield a peak flux of 256 m³ m⁻¹ yr⁻¹. A novel correlation between armoring layer thickness and grain-size median (D₅₀) suggests a feedback loop where larger grains promote ripple growth. Seasonal wind shifts inferred from dune patterns and wind streaks are corroborated by ERA5 reanalysis, indicating a dynamic yet low-energy aeolian system. The results provide insights into sediment transport mechanisms across scales, the evolution of ripple morphometry, and grain-size feedbacks influencing megaripple formation, offering valuable analogs for similar processes on Mars. This multiscale assessment supports improved modeling of terrestrial and planetary aeolian systems.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"13 ","pages":"Article 100347"},"PeriodicalIF":5.2,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694601","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 frequency-based approach to improve the geometric accuracy of FY4B/AGRI geostationary satellite observations","authors":"Zhenduo Deng ,&nbsp;Xuanlong Ma","doi":"10.1016/j.srs.2025.100348","DOIUrl":"10.1016/j.srs.2025.100348","url":null,"abstract":"<div><div>The Advanced Geostationary Radiation Imager (AGRI) onboard the FengYun-4B (FY4B) satellite—a new-generation geostationary (GEO) platform—offers spatial and radiometric resolutions comparable to those of polar-orbiting satellites such as EOS-MODIS, but with substantially higher temporal resolution. This enhanced temporal capability expands the potential of GEO observations beyond meteorology into terrestrial sciences. Precise geometric accuracy is essential for quantitative remote sensing, as the reliability of any downstream retrieval algorithm depends on accurate geolocation. Operational correction of geometric errors is challenging due to the scarcity of ground control points and large data volumes. Here, we evaluated the geolocation accuracy of FY4B/AGRI imagery using a full year of data and developed an integrated geometric correction workflow combining the Phase-Only Correlation method based on Fast Fourier Transform (FFT-POC) with a ray-tracing orthorectification process. In the original imagery, significant geometric instabilities were observed: east-west offsets (COFF) frequently fluctuated between ±5 and ± 10 pixels (reaching ±15 pixels) due to diurnal thermal deformation and operational maneuvers, whereas north-south offsets (LOFF) remained comparatively stable within ±5 pixels. These systematic errors were fully corrected by the FFT-POC step, while the subsequent orthorectification effectively eliminated terrain-induced parallax distortions exceeding 3 pixels in high-altitude regions. The corrected FY4B/AGRI data offers accurate geolocation to support operational hyper-temporal applications such as disaster monitoring and carbon cycle sciences.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"13 ","pages":"Article 100348"},"PeriodicalIF":5.2,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145750391","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 maize yield mapping across Africa using earth observation and machine learning, deep learning, and foundation model","authors":"Krishnagopal Halder ,&nbsp;Frank Ewert ,&nbsp;Anitabha Ghosh ,&nbsp;Kaushik Muduchuru ,&nbsp;Lily-belle Sweet ,&nbsp;Radwa Elshawi ,&nbsp;Jan Timko ,&nbsp;Wenhi Zheng ,&nbsp;Karam Alsafadi ,&nbsp;Gang Zhao ,&nbsp;Michael Maerker ,&nbsp;Manmeet Singh ,&nbsp;Lei Guoging ,&nbsp;Thomas Gaiser ,&nbsp;Dominik Behrend ,&nbsp;Yue Shi ,&nbsp;Liangxiu Han ,&nbsp;Masahiro Ryo ,&nbsp;Amit Kumar Srivastava","doi":"10.1016/j.srs.2025.100344","DOIUrl":"10.1016/j.srs.2025.100344","url":null,"abstract":"<div><div>Africa's food security is increasingly threatened by climate change and population growth, creating an urgent need for high-resolution crop yield maps to support precision agriculture and climate adaptation; however, much of the continent remains poorly monitored due to limited ground observations. This study introduces a novel 250-m spatial resolution maize yield prediction framework for 42 African countries. Our methodology utilizes Net Primary Production (NPP) data to spatially disaggregate national FAO yield statistics, generating fine-scale training data for supervised learning. A comprehensive feature set of 296 variables was constructed by integrating multi-source Earth observation, climate, and soil data. We evaluated multiple benchmark models for tabular data, including XGBoost, LightGBM, a Hybrid Deep Neural Network (HDNN), and, for the first time in this context, a tabular foundation model, the Tabular Prior data Fitted Network (TabPFN). Using an expanding-window temporal cross-validation strategy, XGBoost achieved the highest temporal R² (0.78), while TabPFN demonstrated superior spatial generalization and the lowest mean absolute percentage error (MAPE ≈ 25 %). Causal inference and ablation analyses highlighted the predictive importance of vegetation indices (e.g., NDVI, NDWI), drought metrics, and soil properties. Model outputs closely matched national yields (R² &gt; 0.75; MAPE ≈ 26–28 %). This study provides a scalable framework for yield monitoring in data-scarce regions and represents the first successful application of tabular foundation models in continental-scale agricultural prediction.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"13 ","pages":"Article 100344"},"PeriodicalIF":5.2,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694602","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 hybrid algorithm for land surface temperature retrieval from Chinese GF-5B satellite","authors":"Zhengyu Shen ,&nbsp;Honglian Huang ,&nbsp;Xiao Liu ,&nbsp;Rufang Ti ,&nbsp;Xiaobing Sun ,&nbsp;Qidong Chen","doi":"10.1016/j.srs.2025.100343","DOIUrl":"10.1016/j.srs.2025.100343","url":null,"abstract":"<div><div>Land surface temperature (LST) is a key parameter in understanding surface–atmosphere energy exchanges. Conventional temperature and emissivity separation (TES) and split-window (SW) algorithms often suffer from limited accuracy when applied to surfaces with strong spectral contrast or complex atmospheric conditions. To overcome these issues, this study proposes a Maximum Emissivity Approximation OSTES (MEAOSTES) algorithm that integrates the generalized radiance-based split-window (GRSW) model with the optimized smoothing for temperature and emissivity separation (OSTES) framework. The MEAOSTES introduces a maximum-emissivity adjustment and iterative rollback mechanism to enhance the coupling between LST and emissivity retrievals. Furthermore, in this paper, the SW-TES algorithm is extended to the SW-OSTES algorithm, and the retrieval accuracy of different combination schemes of the SW and OSTES algorithms is discussed. Using data from the GF-5B satellite, which provides four thermal infrared channels at 40 m spatial resolution, the algorithms’ performance were evaluated against simulated datasets, in-situ measurements, and the MODIS MOD21 LST product. The results show that the MEAOSTES achieves the best overall accuracy, root-mean-square errors (RMSEs) of 0.92 K and 1.77 K for simulation and in-situ validation, outperforming both the OSTES and SW-OSTES approaches. The proposed algorithm effectively reduces sensitivity to atmospheric parameters and maintains consistent performance across surfaces with varying spectral contrast. These improvements demonstrate the robustness of MEAOSTES for high-resolution LST retrieval and provide insights for future hybrid algorithm development.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"13 ","pages":"Article 100343"},"PeriodicalIF":5.2,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145658617","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":"Combining single-date mobile and multitemporal airborne laser scanning for retrospective estimation of individual tree growth over a 10-year period in boreal forests","authors":"Daniella Tavi ,&nbsp;Jesse Muhojoki ,&nbsp;Valtteri Soininen ,&nbsp;Eric Hyyppä ,&nbsp;Teemu Hakala ,&nbsp;Ville Luoma ,&nbsp;Antero Kukko ,&nbsp;Xiaowei Yu ,&nbsp;Mikko Vastaranta ,&nbsp;Juha Hyyppä","doi":"10.1016/j.srs.2025.100345","DOIUrl":"10.1016/j.srs.2025.100345","url":null,"abstract":"<div><div>Accurate estimation of individual tree growth is essential for forest inventories and carbon stock assessments, yet traditional manual methods remain labor-intensive and poorly scalable. Laser scanning offers promising alternatives, but slow tree growth rates, sensor limitations, and limited temporal availability of accurate stem-level data challenge growth estimation. This study presents a novel framework combining a single-date mobile laser scanning (MLS) dataset from 2024 with airborne laser scanning (ALS) datasets from 2014 and 2023 to estimate 10-year growth (2014–2024) in diameter at breast height (DBH) and stem volume at the individual tree level. MLS was used for detecting trees and modeling their stem curves, enabling DBH estimation in 2024. These stem curves, alongside ALS-derived heights, were used for volume estimation in 2024. A linear scaling approach, based on ALS-derived height growth factors, was used to model past DBH and stem curves to obtain 2014 attributes, eliminating the need for historical MLS data. Across eight boreal forest plots, growth and one-time attribute estimation accuracy were evaluated against manual DBH measurements and ALS-based reference heights, with analyses across forest complexities, tree species, and size classes. Volume change estimation achieved <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> values of 0.6–0.8 compared to 0.3–0.4 for DBH change estimation. Root mean square errors (RMSEs) were 0.9–1.7 cm (30%–64%) for DBH change and 0.04–0.10 m³ (25%–65%) for volume change. Growth estimation was most accurate for pines, medium-sized trees (DBH 20–35 cm), and in sparse stands. Although accuracy varied by environment, the proposed method offers a scalable approach for retrospective growth estimation, with potential to enhance the efficiency and cost-effectiveness of forest monitoring.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"13 ","pages":"Article 100345"},"PeriodicalIF":5.2,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694691","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":"Corrigendum to “Transferability of country-wide airborne laser scanning-based models for individual-tree attributes” [Sci. Rem. Sens. 12 (2025) 100310]","authors":"Valtteri Soininen,&nbsp;Xiaowei Yu,&nbsp;Matti Hyyppä,&nbsp;Juha Hyyppä","doi":"10.1016/j.srs.2025.100329","DOIUrl":"10.1016/j.srs.2025.100329","url":null,"abstract":"","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"12 ","pages":"Article 100329"},"PeriodicalIF":5.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145750164","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":"Response of the Sentinel-1 radar backscattering to an extreme wildfire event: surface soil moisture and vegetation cover implications","authors":"Giuseppe Esposito ,&nbsp;Massimo Melillo ,&nbsp;Davide Notti ,&nbsp;Maria Teresa Brunetti ,&nbsp;Silvia Peruccacci ,&nbsp;Luca Pisano ,&nbsp;Luca Brocca ,&nbsp;Rosa Maria Cavalli","doi":"10.1016/j.srs.2025.100339","DOIUrl":"10.1016/j.srs.2025.100339","url":null,"abstract":"<div><div>Extreme wildfire events affecting rugged landscapes may pose necessary conditions for the initiation of cascading rainfall-triggered hazards, such as soil erosion, landslides, debris flows and floods. In particular, severe vegetation damage and modification of soil hydraulic properties are among the primary effects of wildfires influencing the post-fire hazards, as documented in many settings worldwide. In this work, we have analyzed the Sentinel-1 SAR backscattering changes before and after an extreme wildfire occurred in Italy, in areas covered by low-lying vegetation, to investigate how VH and VV coefficients relate with vegetation and surface soil moisture behaviors. For this purpose, SAR data have been coupled with NDVI and NDWI indices retrieved from Sentinel-2 imagery, as well as with time series of rainfall and surface soil moisture estimated at plot scale. The most significant findings reveal that the selected burned areas exhibit higher VV backscattering values after the first post-fire intense rainfall, compared to pre-fire conditions. This anomaly depends on the exceptional availability of water in the topsoil, suggesting a reduced vegetation interception together with a likely reduction of the soil infiltration capacity. The lowest VH backscattering values in the period immediately after the fire highlight the vegetation consumption. Nearly one year after the analyzed wildfire, both vegetation and soil conditions appear to have recovered to pre-fire levels. This study demonstrates the potentiality of integrating SAR and optical data to effectively monitor landscapes affected by wildfires. Future research should aim to combine this kind of data with hydrological models to improve post-fire risk mitigation strategies.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"12 ","pages":"Article 100339"},"PeriodicalIF":5.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145624121","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":"Corrigendum to “Investigating the contribution of understory to radiative transfer simulations through reconstructing 3-D realistic temperate broadleaf forest scenes based on multi-platform laser scanning” [Sci. Rem. Sens. 11 (2025) 100196]","authors":"Xiaohan Lin ,&nbsp;Ainong Li ,&nbsp;Jinhu Bian ,&nbsp;Zhengjian Zhang ,&nbsp;Xi Nan ,&nbsp;Limin Chen ,&nbsp;Yi Bai ,&nbsp;Yi Deng ,&nbsp;Siyuan Li","doi":"10.1016/j.srs.2025.100211","DOIUrl":"10.1016/j.srs.2025.100211","url":null,"abstract":"","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"12 ","pages":"Article 100211"},"PeriodicalIF":5.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145750165","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}