Remote Sensing of Environment最新文献

{"title":"On the logic of remote detection of plastic litter in the aquatic environments: A revisit","authors":"Chuanmin Hu","doi":"10.1016/j.rse.2025.114911","DOIUrl":"10.1016/j.rse.2025.114911","url":null,"abstract":"<div><div>Remote detection of plastic litter in both marine and freshwater environments using satellite measurements has become a hot research topic in the past decade, where numerous papers have shown “successful” algorithm development and applications. However, many of these results appear to need some revisits because, in logic, the causality of A to B (i.e., A  =&gt; B) does not lead to the inference of B =&gt; A unless A is the <em>only</em> reason to cause B. In practice, even though plastics can lead to a certain type of signal anomaly (e.g., spectral, spatial, backscattering) from controlled experiments, the same anomaly detected from the natural environments cannot be used to infer plastics unless other possible reasons can all be ruled out. This is especially true when considering that non-plastic floating matters are much more ubiquitous in the aquatic environments. Unfortunately, this logic has been missing in many, if not most, publications. Here, using spectral reflectances of various types of floating matters and through demonstrations of several examples, I show why such logic is critical in remote detection of plastic litter and why pixel averaging and subtraction are necessary steps to spectrally discriminate the signal anomaly in multi-band optical remote sensing imagery. It is argued that unless other possibilities are ruled out using imaging spectroscopy or other means, it is premature to attribute the detected signal anomaly to plastic litter. After all, not every anomaly pixel is necessarily due to litter, and not every litter pixel is necessarily due to plastics unless proven otherwise.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"329 ","pages":"Article 114911"},"PeriodicalIF":11.1,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144710944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fusing SAR image and CYGNSS data for monitoring river water level changes by machine learning","authors":"Yan Jia ,&nbsp;Quan Liu ,&nbsp;Chunqiao Song ,&nbsp;Zhiyu Xiao ,&nbsp;Qiang Dai ,&nbsp;Shuanggen Jin ,&nbsp;Patrizia Savi","doi":"10.1016/j.rse.2025.114927","DOIUrl":"10.1016/j.rse.2025.114927","url":null,"abstract":"<div><div>Accurate river water level estimation is essential for effective flood monitoring and water resources management. However, traditional techniques and single satellite observations have low accuracy and resolution. In this paper, we propose a novel method to enhance river water level estimation by fusing Cyclone Global Navigation Satellite System (CYGNSS) data and Sentinel-1 Synthetic Aperture Radar (SAR) imagery based on advanced machine learning (ML) techniques. SAR provides high-resolution, all-weather surface imagery, while the GNSS-Reflectometry from the eight-satellite CYGNSS mission offers frequent and wide-coverage observations. Dynamic river water levels are obtained at a daily temporal resolution by extracting changes in Sentinel-1 backscattering coefficients and integrating them with the CYGNSS data's high temporal resolution feature. To guarantee the model's robustness, a ten-fold cross-validation (CV) procedure is used with incorporating 15 uniformly distributed gauge sites. Experimental results show that the data fusion method significantly improved the temporal resolution, and more importantly the precision of water level estimation. As opposed to the model without data fusion, the optimized fusion algorithm achieved a 50.74 % reduction in RMSE from 0.341 to 0.168 m, while the <em>R</em> was improved from 0.876 to 0.936. An improvement of over 35 % in RMSE was observed at 8 out of 15 stations. To further validate the model's generalizability, we tested it using data from 8 spatially and temporally independent hydrological stations. The fusion method reduced the RMSE from 0.479 to 0.202 m and increased the <em>R</em> from 0.848 to 0.927, further confirming its effectiveness in enhancing water level estimation. The findings indicate that integrating SAR imagery and CYGNSS time series data has complementary effects and enables better water level estimation.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"329 ","pages":"Article 114927"},"PeriodicalIF":11.1,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144701437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping permafrost thaw stages in interior Alaska","authors":"Caiyun Zhang ,&nbsp;Thomas A. Douglas ,&nbsp;David Brodylo ,&nbsp;M. Torre Jorgenson ,&nbsp;Lauren V. Bosche","doi":"10.1016/j.rse.2025.114941","DOIUrl":"10.1016/j.rse.2025.114941","url":null,"abstract":"<div><div>Permafrost degradation has been recognized for decades due to climate warming, wildfire, and infrastructure development. However, a large-scale characterization of permafrost thaw status has not been attempted before due to difficulties in ground data collection, inherent complications and heterogeneity of thaw in ecosystem-protected permafrost, and constraints of remote sensor observations and process-based modeling techniques. Here we made a first effort to map the status of permafrost thaw across a large ice-rich lowland fire-influenced landscape (2500 km²) in interior Alaska by developing a new protocol and combining decades of field measurements, repeat airborne lidar, spaceborne WorldView-2, Sentinel-2, Landsat time series products, and a terrain elevation dataset. The repeat lidar and fine-resolution imagery offered a key to solving the bottleneck issue of thaw reference data collection, which further provided an opportunity to track post-fire thaw caused by six large fires in the past 25 years in four stages over time: old thaw, lateral thaw, vertical shallow thaw and vertical deep thaw. The developed protocol achieved an overall accuracy of 79 % in classifying these thaw stages and generated a reasonable thaw pattern mainly controlled by fires and locally modified by other drivers. Identifying degradation patterns can help understand the permafrost-fire-climate system. The protocol is a valuable alternative to current thermokarst mapping techniques.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"329 ","pages":"Article 114941"},"PeriodicalIF":11.1,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144701435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"First demonstration of spaceborne L-band bistatic single-polarization single-baseline SAR interferometry on the retrieval of forest vertical structural information","authors":"Yang Lei ,&nbsp;Weiliang Li ,&nbsp;Yanghai Yu ,&nbsp;Xiaotong Liu ,&nbsp;Jie Xu ,&nbsp;Anmin Fu ,&nbsp;Jie Wan ,&nbsp;Changcheng Wang ,&nbsp;Wenli Huang ,&nbsp;Zixuan Qiu ,&nbsp;Tao Li ,&nbsp;Haiqiang Fu ,&nbsp;Yu Liu ,&nbsp;Jiancheng Shi","doi":"10.1016/j.rse.2025.114916","DOIUrl":"10.1016/j.rse.2025.114916","url":null,"abstract":"<div><div>This paper shows the first demonstration of spaceborne L-band bistatic InSAR from the Chinese Lutan-1 mission for forest vertical structural information retrieval (in this work, namely, vertical profile, forest height, and underlying topography). With the single-polarization/baseline bistatic InSAR mode of Lutan-1, the measured few-look InSAR phase height histograms compare very well with the GEDI lidar waveforms, both capturing similar characteristics of the forest vertical structural profile. The ground finding approach based on the few-look InSAR phase height histogram is further adapted to incorporate spaceborne lidar measurements from GEDI and ICESat-2/ATLAS for more robust calibration. As for the DTM estimation, two ground finding strategies are developed: one using ample spaceborne lidar samples (with the lidar height as the feature), and the other using limited spaceborne lidar samples (with the few-look InSAR phase height standard deviation as the feature), both of which rely on the statistical model relating the underlying terrain elevation to the statistics of the few-look InSAR histogram. Then, forest height is inverted using the few-look histogram that mimics using lidar waveform to derive height metrics. The large-scale DTM and forest height mosaics of 2.74 million hectares are produced over tropical rainforest of the entire Hainan island of China. Through validation with airborne lidar data, the forest height is estimated to an accuracy of ∼5 m for tropical forest up to 45 m tall (relative error 10–15 %). The InSAR-derived DTM has a negligible bias (mean value of the radar-lidar DTM deviation) as referenced to airborne lidar DTM, with the uncertainties (median absolute deviation or MAD) being dependent on topographic surface slopes: 3 m (&lt;2°), 4 m (2°-6°), 7 m (6°-25°), and 9 m (&gt;25°). This approach sheds light on combining ascending/descending viewing geometries of spaceborne L-band bistatic InSAR data with single polarization/baseline (e.g. Lutan-1 and its follow-on) for large-scale wall-to-wall mapping of forest vertical structural profile, height metrics/biomass, underlying topography, as well as the changes of these forest parameters.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"329 ","pages":"Article 114916"},"PeriodicalIF":11.1,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spectral indices in remote sensing of soil: definition, popularity, and issues. A critical overview","authors":"Qianqian Chen ,&nbsp;Emmanuelle Vaudour ,&nbsp;Anne C. Richer-de-Forges ,&nbsp;Dominique Arrouays","doi":"10.1016/j.rse.2025.114918","DOIUrl":"10.1016/j.rse.2025.114918","url":null,"abstract":"<div><div>Serving as a powerful proxy in remote sensing studies, spectral indices can generate meaningful environmental interpretation from either raw or atmospherically corrected spectral data, and characterise and quantify some important properties of various objects on Earth’s surface. However, while numerous spectral indices have been developed over time, since the very launch of civilian satellites until now, some critical issues in their usage, such as comparability, remain scarcely studied, which may lead to incorrect, inconsistent, and unreliable results.</div><div>In this study, we collected 471 spectral indices of various environment components (vegetation, water, and soil) that might be leveraged for soil studies, and traced their popularity in scientific publications over the past decades. The bibliometric analysis revealed a growing interest and utilisation of spectral indices as Earth-observing satellite technology advanced. Based on both literature and, for sake of complementation and illustration, some targeted regional-scale case studies, we discuss the issues of naming confusion, comparability, applicability, accuracy trade-offs, and reproducibility of using spectral indices.</div><div>Overall, this overview provides an extensive list of spectral indices, both soil indices and soil-related indices, that can be useful for characterising these environment components by remote sensing. It draws attention to some misuses and confusions that must be avoided to prevent scientific pitfalls. The comparisons between different spectral indices, sensors, and correction methods, highlight the confusing effects that the misuse and non-standardised practices of the spectral indices useful for soil, may have on soil property mapping and monitoring. Insights to the judicious and appropriate usage of spectral indices in the remote sensing of soil are provided.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"329 ","pages":"Article 114918"},"PeriodicalIF":11.1,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144684893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fine-scale poverty estimation by integrating SDGSAT-1 glimmer images and urban functional zoning data","authors":"Zejia Chen ,&nbsp;Huishan Luo ,&nbsp;Minting Li ,&nbsp;Jinyao Lin ,&nbsp;Xinchang Zhang ,&nbsp;Shaoying Li","doi":"10.1016/j.rse.2025.114925","DOIUrl":"10.1016/j.rse.2025.114925","url":null,"abstract":"<div><div>Poverty is a pervasive global issue that adversely affects human well-being. Traditional socioeconomic censuses are time-consuming and resource-intensive, suffering from temporal delays, while reliance on nighttime light data with low spatial resolution is insufficient for fine-scale identification of impoverished regions. Furthermore, the spatial heterogeneity of nighttime light in different urban functional zones has been overlooked. To address these shortcomings, we proposed a novel approach by integrating high-resolution SDGSAT-1 nighttime light data (10 m) with urban functional zoning data using a spatial overlay tool. A random forest model was then applied to predict county-level poverty identification in Guangdong, China. For comparative validation, traditional NPP-VIIRS nighttime light data (500 m) were also incorporated. This method effectively explored the nonlinear relationship between nighttime light, urban functional zones, and the multidimensional poverty index (MPI, serving as the dependent variable). Our experiments demonstrate that the integration of urban functional zoning with nighttime light moderately improves the accuracy of poverty estimates. Among the models tested, the one considering functional zoning-based indicators of “number of light pixels” and “sum of pixel light values” increased the correlation coefficient by 0.0158 compared to the model without considering these indicators. Additionally, comparative analysis revealed that high-resolution data from SDGSAT-1 exhibited a better fit with the MPI when integrated with functional zoning-based indicators. Specifically, the correlation coefficient of this combination was 0.0086 higher than that of traditional NPP-VIIRS data. This highlights that SDGSAT-1 can delineate the boundaries between dark and light regions more precisely, leading to a more accurate reflection of regional poverty levels. Our findings facilitate fine-scale poverty estimation across large regions. This approach can inform policy design, such as dynamic optimization of resource allocation based on poverty estimates, thus enabling timely and accurate poverty alleviation efforts.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"329 ","pages":"Article 114925"},"PeriodicalIF":11.1,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144684894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Space-time explainable modelling of regional hillslope deformation, an example from the Tibetan Plateau","authors":"Jun He ,&nbsp;Hakan Tanyas ,&nbsp;Da Huang ,&nbsp;Luigi Lombardo","doi":"10.1016/j.rse.2025.114924","DOIUrl":"10.1016/j.rse.2025.114924","url":null,"abstract":"<div><div>The future of InSAR applications will undoubtedly involve data-driven solutions to predict deformation across space and time. Recent advancements in subsidence research have already integrated such approaches, primarily in flat to near-flat landscapes. However, in mountainous terrains, space-time InSAR modelling has so far focused mainly on individual slopes or small catchments. Here, we propose a modelling protocol based on a deep learning architecture capable of predicting InSAR-derived hillslope deformation. This approach is developed primarily using morphometric and meteorological variables over extensive mountainous areas (∼15,000 km²) and extended time windows (∼7 years). By aggregating the deformation signal at the Slope Unit scale while maintaining 12-day temporal intervals consistent with Sentinel-1 acquisitions, we achieve high modelling performance (PCC = 0.7). If validated in other regions, this method could represent a crucial step towards a large-scale, consistent, and highly effective scenario-based warning system for hillslope deformation.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"329 ","pages":"Article 114924"},"PeriodicalIF":11.1,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144685098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analytical modeling and correction of the ocean colour bidirectional reflectance across water types","authors":"Jaime Pitarch ,&nbsp;Vittorio Ernesto Brando ,&nbsp;Marco Talone ,&nbsp;Constant Mazeran ,&nbsp;Davide D'Alimonte ,&nbsp;Tamito Kajiyama ,&nbsp;Ewa Kwiatkowska ,&nbsp;David Dessailly ,&nbsp;Juan Ignacio Gossn","doi":"10.1016/j.rse.2025.114920","DOIUrl":"10.1016/j.rse.2025.114920","url":null,"abstract":"<div><div>The remote-sensing reflectance (<span><math><msub><mi>R</mi><mi>rs</mi></msub></math></span>) varies with the illumination and viewing geometry, an effect referred to as anisotropy, bidirectionality, or bidirectional reflectance distribution function (BRDF). In the aquatic environment, bidirectionality arises from the anisotropic downwelling illumination, scattered by water and particles in varying proportions as a function of the scattering angle, and modulated by the two-way interaction with the sea surface. For remote sensing applications, it is desirable that the reflectance only depends on the inherent optical properties (IOPs). This process implies transforming <span><math><msub><mi>R</mi><mi>rs</mi></msub></math></span> into a “corrected” or “normalized” <span><math><msub><mi>R</mi><mrow><mi>rs</mi><mo>,</mo><mi>N</mi></mrow></msub></math></span>, referred to the sun at the zenith and the sensor zenith angle at the nadir. A recent review study (D'Alimonte et al., 2025) compared published BRDF methods, showing the better performance of that developed by Lee et al. (2011, henceforth L11) with respect to those proposed by Morel et al. (2002) and Park and Ruddick (2005). This article presents a new method starting from L11's analytical framework, named O25 after OLCI, the Ocean Colour sensor on Sentinel-3 satellite. O25 has been calibrated with a recently published synthetic dataset tailored to its needs (Pitarch and Brando, 2025). A comparative assessment using the same datasets as in D'Alimonte et al. (2025) concludes that O25 outperforms L11 and hence all pre-existing methods. O25 includes complementary operational features: (1) applicability range, (2) uncertainty estimates, and (3) a demonstrated reversibility of the bidirectional correction. O25's look-up tables are generic to any in situ and satellite sensors, including hyperspectral ones. For sensors such as Landsat/Sentinel 2, the IOPs retrieval component of O25 can easily be reformulated.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"329 ","pages":"Article 114920"},"PeriodicalIF":11.1,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144684681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using UAV images and deep learning to enhance the mapping of deadwood in boreal forests","authors":"Janne Mäyrä ,&nbsp;Topi Tanhuanpää ,&nbsp;Anton Kuzmin ,&nbsp;Einari Heinaro ,&nbsp;Timo Kumpula ,&nbsp;Petteri Vihervaara","doi":"10.1016/j.rse.2025.114906","DOIUrl":"10.1016/j.rse.2025.114906","url":null,"abstract":"<div><div>Deadwood and decaying wood are the most important components for the biodiversity of boreal forests, and around a quarter of all flora and fauna in Finnish forests depend on them, with third of these species being red-listed. However, there is a severe lack of stand-level deadwood data in Finland, as the operational inventories either focus on the large-scale estimates or omit deadwood altogether. Unoccupied Aerial Vehicles (UAVs) are a cost-effective method for remotely mapping small objects, such as fallen deadwood, over compartment-level areas, as even the most spatially accurate commercial satellites and aerial photography provide 30 cm ground sampling distance, compared to less than 5 cm that is easily achievable with UAVs.</div><div>In this study, we utilized YOLOv8 by Ultralytics for detecting and segmenting standing and fallen deadwood instances from RGB UAV imagery. Our study consists of two geographically distinct study areas in Finland, Hiidenportti National Park and Evo. We manually annotated around 13 800 deadwood instances to be used as the training and validation data for the instance segmentation models. These annotations were also compared to field-measured deadwood data from Hiidenportti to assess the extent on how much of the deadwood can even be seen from UAV imagery. We also compared how the models perform on another area than from which its training dataset was from, and whether adding data from another areas to the training dataset improves the performance compared to training only with images from one location.</div><div>The best performing model achieved test set mask mAP50 score of 0.682 for Hiidenportti and 0.651 for Sudenpesänkangas datasets. For both areas, including imagery from the another area improved the instance segmentation metrics, whereas using data only from another site to train the models produced significantly worse results. While methods utilizing UAV imagery cannot completely replace traditional field work, they should still be considered as an additional tool for field campaigns.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"329 ","pages":"Article 114906"},"PeriodicalIF":11.1,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144678296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Two decades of river discharge from multi-mission multispectral data","authors":"Paolo Filippucci ,&nbsp;Debi Prasad Sahoo ,&nbsp;Angelica Tarpanelli","doi":"10.1016/j.rse.2025.114919","DOIUrl":"10.1016/j.rse.2025.114919","url":null,"abstract":"<div><div>Long-term river discharge time series are essential for assessing water availability, seasonal variability, and the impacts of climate change. However, in-situ data do not ensure continuity and large-scale availability, as they are constrained by the limitations of monitoring networks, which are affected by high maintenance costs, geopolitical factors, and the remoteness of many river basins. Satellite remote sensing offers a valuable alternative, with multispectral data providing information on river discharge dynamics. Nevertheless, also satellite data suffer discontinuity, being periodically decommissioned and substituted by sensors with different characteristics.</div><div>This study applies and refines the Calibration-Measurement (<em>CM</em>) approach across 54 river sites worldwide, using 10 different multispectral satellite products from 8 satellite sensors to examine long time series of data. The methodology optimizes the selection of reflectance indices based on local hydrological conditions, evaluating the best procedure to obtain river discharge proxies according to the specific flow regimes and climatic condition. Multi-mission data are then combined to improve temporal coverage and accuracy, obtaining long-term timeseries of river discharge proxies. A new uncalibrated procedure is also introduced to extract river discharge information in sites with decommissioned stations or to obtain the CM proxies in ungauged basins.</div><div>Results demonstrate that integrating multiple satellite sources substantially improves river discharge estimation, due to factor such as the superior performance of high-resolution sensors (e.g., Sentinel-2) over coarse-resolution datasets (e.g. MODIS) in narrow rivers, and the complementary value of MODIS finer temporal resolution. The proposed merging approach enhances data consistency and reduces gaps while maintaining good performance values, while the uncalibrated method proves effective in many cases but remains challenging for frozen rivers, cloud-prone regions and areas with a low ratio between river width and satellite sensor spatial resolution. Specifically, after the application of a Cumulative Distribution Function Merging (CDF) matching to obtain river discharge estimates from <em>CM</em> proxies, the average and maximum value of Spearman's correlation for the calibrated approaches are respectively 0.78 and 0.92 whereas for uncalibrated approach are 0.55 and 0.89; in terms of Kling-Gupta Efficiency (KGE) the average and maximum value are 0.41 and 0.85 for calibrated approach and 0.29 and 0.8 for the uncalibrated one.</div><div>These findings highlight the potential of multi-sensor approaches for global river discharge monitoring and lay the groundwork for future operational applications.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"329 ","pages":"Article 114919"},"PeriodicalIF":11.1,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144669932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}