Science of Remote Sensing最新文献

{"title":"Divergent GPP dynamics in alpine and temperate grasslands: Hierarchical climatic controls across the Qinghai-Tibetan and Mongolian Plateaus","authors":"Yusi Zhang ,&nbsp;Gang Bao ,&nbsp;Zhonghua He ,&nbsp;Yuhai Bao ,&nbsp;Zhihui Yuan ,&nbsp;Siqin Tong","doi":"10.1016/j.srs.2025.100360","DOIUrl":"10.1016/j.srs.2025.100360","url":null,"abstract":"<div><div>Alpine grasslands on the Qinghai-Tibet Plateau and temperate grasslands on the Mongolian Plateau are key components of the global carbon cycle but differ markedly in their responses to climate change. To investigate the spatiotemporal variations in Gross Primary Productivity (GPP) and their response to climate change in these two types of grasslands, we developed a novel Random Forest Regression-Light Use Efficiency-Solar Induced Fluorescence (RFR-LUE-SIF) model that integrates machine-learning regression with physiological efficiency principles and satellite-derived SIF observations. This framework bridges tower-based GPP observations with large-scale remote-sensing estimates, improving model interpretability and accuracy. The model reproduced observed GPP with high fidelity (R² = 0.91), identifying EVI, NIRv, and GOSIF as the most influential predictors. Spatially, alpine grassland GPP decreases from southeast to northwest, while temperate grassland GPP declines from northeast to southwest. From 2001 to 2023, both grassland types exhibited increasing GPP trends, with temperate grasslands showing a faster rise, indicating stronger climatic sensitivity. Further, partial correlation analysis and Structural Equation Modeling (SEM) reveal that alpine grassland productivity is generally more sensitive to temperature, particularly under adequate moisture conditions, whereas temperate grasslands exhibited stronger dependence on precipitation and vapor pressure deficit (VPD). The proposed RFR-LUE-SIF model provides a scalable, data-driven, and physiologically consistent approach for assessing grassland carbon dynamics and their hierarchical climatic responses across contrasting ecosystems.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"13 ","pages":"Article 100360"},"PeriodicalIF":5.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939362","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-derived seasonal fluctuations in surface displacement and soil moisture: Implications for landslide activity","authors":"Chiao-Yin Lu ,&nbsp;Yu-Chang Chan ,&nbsp;Chung-Ray Chu ,&nbsp;Che-Hsin Liu ,&nbsp;Shih-Chiang Lee ,&nbsp;Yu-Chung Hsieh ,&nbsp;Jyr-Ching Hu ,&nbsp;Chih-Hsin Chang","doi":"10.1016/j.srs.2025.100354","DOIUrl":"10.1016/j.srs.2025.100354","url":null,"abstract":"<div><div>Seasonal surface fluctuations are commonly influenced by environmental variations, with both water presence and geological conditions playing key roles in surface deformation. These short-term signals often complicate the interpretation of time series data, particularly in complex and mountainous regions where in situ hydrological data such as groundwater levels, pore water pressure, and soil moisture are scarce or difficult to obtain. This study investigates two representative slow-moving landslides exhibiting seasonal variations by applying the small baseline subset (SBAS) method, one of the core approaches within the multitemporal InSAR (MTInSAR) framework. The SBAS-derived time series are further analyzed in combination with satellite-derived soil moisture data. Given the limited availability of high-resolution hydrological observations, satellite-derived soil moisture was adopted as a validated proxy to represent the average hydrological conditions over each landslide area. The results reveal notable patterns of seasonal surface fluctuations driven by hydrological variations, and demonstrate that their expression is further modulated by lithological conditions. Based on available data, we infer that in sedimentary rock areas, a high water storage coefficient causes hydrological loading to dominate seasonal surface displacement, resulting in a negative correlation. In contrast, pore water pressure plays a dominant role in metamorphic rock areas, leading to a positive correlation. This study demonstrates the potential of satellite-derived hydrological data to complement InSAR time series in regions with scarce in situ monitoring. These findings offer valuable and useful insights for a further understanding slow-moving landslide behaviors, particularly in distinguishing and identifying accelerated movement signals from seasonal fluctuations, and for improving slope failure early warning systems.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"13 ","pages":"Article 100354"},"PeriodicalIF":5.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939361","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":"Discriminating winter wheat yellow rust and Fusarium head blight using Sentinel-2 imagery at a regional scale","authors":"Zhiqin Gui ,&nbsp;Huiqin Ma ,&nbsp;Jingcheng Zhang ,&nbsp;Wenjiang Huang ,&nbsp;Lin Yuan ,&nbsp;Kehui Ren","doi":"10.1016/j.srs.2026.100371","DOIUrl":"10.1016/j.srs.2026.100371","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Yellow rust (&lt;em&gt;Puccinia striiformis&lt;/em&gt; f. sp. &lt;em&gt;Tritici&lt;/em&gt;, YR) and Fusarium head blight (&lt;em&gt;Fusarium graminearum&lt;/em&gt;, FHB) are two major wheat diseases. These two diseases frequently pose concurrent risks to grain security, particularly in high-yielding wheat regions of eastern China. Accurate regional-scale discrimination of wheat YR and FHB is essential for developing effective green and intelligent disease management strategies. While satellite remote sensing shows potential for regional crop disease monitoring, conventional machine learning modeling approaches widely employed often fail to exploit the spectral-spatial information inherent in imagery. Meanwhile, the scarcity of ground-based disease survey samples limits the application of emerging sample-driven deep learning methods. This study evaluated the effectiveness of 27 sample-feature-algorithm combinatorial modeling strategies for discriminating regional-scale wheat YR and FHB using Sentinel-2 imagery. We augmented disease samples using a stepwise approach that combines marking diseased field vector boundaries with sliding window segmentation (SWS), horizontal-vertical flipping (HVF), and multi-angle rotation (MAR). Recursive feature elimination with cross-validation (RFECV) was employed to optimize spectral and textural features, yielding in two distinct feature sets: disease-sensitive spectral features (SFs) and spectral-textural combined features (STCFs). The original spectral bands (OSBs) served as a third feature set. These sample sets and feature sets were input into several fundamentally distinct algorithms to construct wheat YR and FHB discrimination models. These include three commonly used machine learning (ML) methods, namely, support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost). Additionally, include two deep learning methods, namely, the two-dimensional convolutional neural network (2D-CNN) and the spectral-spatial attention network (SSAN). The results indicated that three ML algorithms exhibited stable performance across all three feature sets under SWS-based sample augmentation. SVM yielded the best overall accuracy, but texture features provided only limited improvement over the SVM model compared with RF and XGBoost. The OSBs outperformed SFs and STCFs in 2D-CNN and SSAN modeling, achieving an overall accuracy (OA) comparable to that of SVM under SWS + HVF + MAR-based sample augmentation. Specifically, the SWS + HVF + MAR-OSBs-SSAN model demonstrated superior performance metrics. This model achieved an average accuracy of 81.8 %, a Kappa coefficient of 0.704, a G-means of 0.892, and an F1-score of 81.1 %. These accuracy results surpassed those of the SWS-STCFS-SVM model, even though the latter achieved the highest OA of 82.8 %. Sample augmentation yielded limited gains in modeling for the 2D-CNN but demonstrated more significant gains for the SSAN. Overall, the STCFs-based SVM modeling strategy remains preferab","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"13 ","pages":"Article 100371"},"PeriodicalIF":5.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037869","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":"Calculation of surface roughness using machine learning algorithms combined with knowledge distillation","authors":"Jianyong Cui ,&nbsp;Wenwen Gao ,&nbsp;Chunlei Meng","doi":"10.1016/j.srs.2026.100368","DOIUrl":"10.1016/j.srs.2026.100368","url":null,"abstract":"<div><div>Surface roughness is a key parameter in meteorological simulations and wind energy assessments, and its spatial distribution is influenced by various factors. However, the complexity of these factors makes it difficult to retrieve roughness. Although machine learning methods have partially addressed this issue, they still face the challenge of insufficient measurement data. To tackle this, the present study proposes a knowledge distillation framework that integrates physical models and machine learning. It establishes a “teacher-student” model to enable knowledge transfer from regions with sufficient data to target regions with zero samples. In the source domain, where abundant ground truth data is available, four models-Random Forest, Support Vector Regression, Multi-layer Perceptron, and Transformer—were trained. The Multi-layer Perceptron, which achieved the best performance (correlation coefficient: 0.81, RMSE: 0.74, MAE: 0.51), was selected as the teacher model. Then, using the knowledge distillation method, soft labels were generated from remote sensing data in the target region to guide the training of the student model. This facilitated cross-domain knowledge transfer. The results show that the student model's training accuracy improved to 0.89, with the RMSE and MAE reduced to 0.62 and 0.33, respectively, significantly outperforming the teacher model. Compared to ERA5 reanalysis data and land surface model results, the student model's inversion of surface roughness in the target region reduced the mean absolute error by approximately 18 %, effectively solving the parameter estimation problem under the condition of no measurement samples. This study significantly enhances the accuracy of surface roughness estimation and provides more reliable parameter input for meteorological simulations and numerical weather forecasting.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"13 ","pages":"Article 100368"},"PeriodicalIF":5.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145939366","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":"Advancing vegetation segmentation from ALS point clouds: From benchmarking to GreenSegNet-A","authors":"Aditya Aditya ,&nbsp;Bharat Lohani ,&nbsp;Jagannath Aryal ,&nbsp;Stephan Winter","doi":"10.1016/j.srs.2026.100382","DOIUrl":"10.1016/j.srs.2026.100382","url":null,"abstract":"<div><div>Accurate large-scale vegetation segmentation is essential to maintain vegetation inventories, which are vital for informed ecological planning, landscape management, and long-term sustainability and liveability of the environment. Advancements in deep learning (DL) coupled with the increasing availability of airborne laser scanning (ALS) point clouds hold significant potential for detailed and large-scale vegetation segmentation. Yet, ALS-based vegetation segmentation has received limited attention, leading to ambiguity in model selection. To address this research gap, we present a comprehensive benchmarking of point-based DL models for vegetation segmentation. Seven representative DL models, KPConv, RandLANet, SCFNet, PointNeXt, SPoTr, PointMetaBase, and GreenSegNet, have been implemented on three different datasets, Eclair, Dales, and WHU-Urban3D. Evaluated through a ten-fold cross-validation strategy, the results reveal strong but inconsistent performances. KPConv records the highest mean intersection over union (mIoU) on the Eclair dataset with 96.24% while GreenSegNet dominates on Dales dataset, reaching 93.91%. GreenSegNet also outperforms other models on the WHU-Urban3D dataset, achieving a mIoU of 79.27%. These findings highlight both the promise and the limitations of existing models, including the vegetation-specific GreenSegNet, which also exhibited inconsistent behavior on ALS data due to sparsity, nadir-view perspective, and canopy occlusions. Building on these insights, we propose GreenSegNet-A, a DL architecture explicitly tailored for ALS vegetation segmentation. Incorporated with a novel ALS-adaptive module, GreenSegNet-A achieves mIoU scores of 96.56% (Eclair), 94.29% (Dales), and 80.87% (WHU-Urban3D). Statistical tests confirm its efficacy, while ablation studies validate the design choices. Although the model has a slightly higher parameter count than GreenSegNet, it remains lighter compared to other models. Overall, GreenSegNet-A establishes a strong performance baseline for ALS vegetation segmentation within the scope of our evaluation. The source code is available at <span><span>this URL</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"13 ","pages":"Article 100382"},"PeriodicalIF":5.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel approach to assessing the tracking accuracy of crop phenology for multi-orbit and multi-feature Sentinel-1 time series","authors":"Johannes Löw ,&nbsp;Christopher Conrad ,&nbsp;Steven Hill ,&nbsp;Michael Thiel ,&nbsp;Tobias Ullmann ,&nbsp;Insa Otte","doi":"10.1016/j.srs.2026.100370","DOIUrl":"10.1016/j.srs.2026.100370","url":null,"abstract":"<div><div>This study presents a novel framework for quantifying uncertainties and variabilities related to the monitoring of crop phenology via Synthetic Aperture Radar (SAR) time series at the field scale. Therefore, the study investigated multi-orbit, multi-feature time series derived from Sentinel-1 (S1) VV/VH polarizations. This multi-feature approach encompasses backscatter intensity, interferometric coherence and alpha/entropy decomposition features. Crop phenology tracking is crucial for assessing agricultural resilience under climate change, yet existing approaches face challenges due to uncertainties and variability in SAR signal interpretation as well as in situ data. Building on previous landscape-level analyses, this work introduces the concept of trackability, defined as the temporal range during which SAR-derived time-series metrics (TSM), such as breakpoints in backscatter intensity or interferometric coherence, align with key phenological stages (e.g., stem elongation in winter wheat). A growing degree day (GDD)-based normalization contextualizes field-specific deviations relative to landscape averages, enabling quantification of uncertainties inherent in both SAR signals and ground observations. The framework captures the spatio-temporally variable nature of crop development by estimating the first and last phenologically relevant TSM occurrence within a defined uncertainty window, thus providing relational and relative indicators of phenological tracking. This approach reduces dependencies of extensive in situ data and enhances comparability across studies with differing SAR processing methods and their acquisition geometries. Results reproduce known feature-stage relationships (e.g., tracking for stem elongation by interferometric coherence) and reveal inter-seasonal variability influenced by weather conditions and acquisition parameters. On average relevant TSM occurrences were found at approximately 90 % of GDD progression of in situ reported phenological stages, while systematic differences of around 5 % by relative orbit were discovered. The study highlights the potential of integrating multiple S1 features and orbits without optimization-induced information loss, producing quality masks that identify optimal tracking performance at the field level. This framework advances SAR-based phenology monitoring by offering scalable, transferable insights for precision agriculture, while practical implementation still requires detailed field boundaries and early-season crop management information.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"13 ","pages":"Article 100370"},"PeriodicalIF":5.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977579","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 ground deformation in Mandalay, Myanmar, using InSAR with Sentinel-1 data after the March 2025 earthquake","authors":"Behzad Taghi-Lou,&nbsp;Michael Schultz,&nbsp;Andreas Braun,&nbsp;Volker Hochschild","doi":"10.1016/j.srs.2025.100358","DOIUrl":"10.1016/j.srs.2025.100358","url":null,"abstract":"<div><div>This study quantifies coseismic ground deformation associated with the March 28, 2025 earthquake sequence (Mw 6.7–7.7) in the Mandalay Region of Myanmar. Differential interferometric synthetic-aperture radar (DInSAR) was applied to Sentinel-1A single-look complex (SLC) data, using pre-event images acquired six and four days before the earthquakes and post-event images acquired five and nineteen days after. The interferograms were processed in ESA SNAP, phase-unwrapped with SNAPHU, and geocoded using the 30 m SRTM digital elevation model (DEM). A coherence threshold of 0.35 was applied to ensure reliable phase retrieval. Peak line-of-sight (LOS) displacements reach +0.24 m (uplift) and −0.62 m (subsidence) for the ascending pair, and +0.22 m (uplift) and −0.64 m (subsidence) for the descending pair. Decomposition of the paired LOS maps yields vertical motion between +0.74 m and −1.57 m and east–west offsets between −1.99 m (westward) and +1.29 m (eastward), corresponding to a horizontal-to-vertical displacement ratio of 1.27. The deformation field closely follows the surface trace of the dextral Sagaing Fault, confirming it as the primary seismogenic structure. Opposite-sense deformation is observed across the fault, with subsidence and eastward motion on the western block and uplift and westward motion on the eastern block. These results provide a high-resolution deformation field for this event and highlight the value of open-access Sentinel-1 data for rapid seismic hazard assessment in data-scarce regions.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"13 ","pages":"Article 100358"},"PeriodicalIF":5.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938943","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":"Establishing a hyperspectral library for Hong Kong mangroves: Species differentiation and leaf decay dynamics","authors":"Tahir Sattar ,&nbsp;Majid Nazeer ,&nbsp;Man Sing Wong ,&nbsp;Janet Elizabeth Nichol ,&nbsp;Xiaolin Zhu","doi":"10.1016/j.srs.2025.100362","DOIUrl":"10.1016/j.srs.2025.100362","url":null,"abstract":"<div><div>Mangroves are the resistant species found in the intertidal zones, providing ecosystem services such as protection of shorelines, provision of habitats to flora and fauna, and contributing to nutrient cycling. Study of their leaf properties has always been challenging, but this has been facilitated by the advent of Hyperspectral Imaging (HSI) systems. In such a context, this study undertook the development of a hyperspectral library offering the reflectance characteristics for adaxial and abaxial surfaces of mangrove species found in Hong Kong, on the temporal scale of seven days to facilitate the species identification and monitor the leaf decay. This library contained species level data, plot level data, and decay level data. Field surveys in fifteen plots (900 m² each) conducted in the Eastern and Western regions of Hong Kong collected hyperspectral data of five mangrove species, namely: <em>Ceriops tagal</em>, <em>Kandelia obovata, Avicennia marina, Avicennia germinans, and Aegiceras corniculatum,</em> using two different types of HSI systems i.e., Specim IQ (in-field data) and NEO Hyspex (in-lab data) hyperspectral cameras. A comparison of sensors unveiled a notably higher reflectance in field collected data than that of the lab-collected data, with a range of 11.8 % (Kandelia obovate) to 73.1 % (<em>Aegiceras corniculatum</em>). The Root Mean Square Error (RMSE) indicated deviation between the two sensors, i.e., 0.211 for <em>Ceriops tagal</em>, followed by <em>Kandelia obovata</em> (0.233), <em>Avicennia marina</em> (0.317), <em>Avicennia germinans</em>, and <em>Aegiceras corniculatum</em> (0.349). This freely available comprehensive hyperspectral library will serve as the foundation for training datasets to achieve automated classification with enhanced accuracy. This open access hyperspectral library will assist the researchers to relate the physiological and anatomical variations in leaves with the changes in hyperspectral reflectance on the temporal scale.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"13 ","pages":"Article 100362"},"PeriodicalIF":5.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938947","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":"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":"2026-06-01","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":"Impacts of using solar spectra adjusted for solar cycle variability in the radiometric correction of retrieved multi-band parameters from ETM+/Landsat-7 data","authors":"Jonathan Gil Müller,&nbsp;Alexander Christian Vibrans","doi":"10.1016/j.srs.2026.100384","DOIUrl":"10.1016/j.srs.2026.100384","url":null,"abstract":"","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"13 ","pages":"Article 100384"},"PeriodicalIF":5.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187970","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}