Remote Sensing of Environment最新文献

{"title":"Hyperspectral surface reflectance improves GPP estimation in terrestrial biosphere modeling using model-data fusion","authors":"Haoran Liu ,&nbsp;Fa Li ,&nbsp;Hamid Dashti ,&nbsp;Min Chen","doi":"10.1016/j.rse.2025.114989","DOIUrl":"10.1016/j.rse.2025.114989","url":null,"abstract":"<div><div>Gross Primary Productivity (GPP) estimates from terrestrial biosphere models (TBMs) are often uncertain due to limited constraints on vegetation biochemical and biophysical properties. Remote sensing offers promising opportunities to reduce these uncertainties, yet its full potential remains understudied. Here, we conducted model-data fusion experiments, including Observing System Simulation Experiments (OSSEs), and Observing System Experiments (OSEs) at the Harvard Forest site, using the Terrestrial Ecosystem Carbon cycle simulator (TECs) with an embedded spectral invariant theory-based radiative transfer model. In OSSEs, we assimilated synthetic hyperspectral reflectance, multispectral reflectance, and Leaf Area Index (LAI) into TECs to evaluate their effect under the ideal conditions. In OSEs, we assimilated PRecursore IperSpettrale della Missione Applicativa (PRISMA) hyperspectral reflectance (620–1000 nm), MODerate resolution Imaging Spectroradiometer (MODIS) multispectral reflectance (broadband red and near-infrared), and MODIS-derived LAI to optimize model parameters, including several key vegetation traits such as leaf chlorophyll content (Cab), maximum carboxylation rate at 25 °C (V_cmax25), and LAI. Results show that hyperspectral reflectance consistently outperforms multispectral reflectance and LAI in improving GPP estimates and reducing uncertainties, with RMSE decreasing from 2.68 to 1.18 μmol CO₂ m⁻² s⁻¹ in OSSEs, and from 6.74 to 5.42 μmol CO₂ m⁻² s⁻¹ in OSEs. This is because hyperspectral information better constrains seasonal variations in canopy structure and Cab. Meanwhile, both hyperspectral and multispectral reflectance outperform LAI, with information from both canopy structural parameters and leaf biochemical properties, thus offering a joint constraint on GPP simulations. Our findings highlight that remotely sensed reflectance data, particularly hyperspectral reflectance, have great potential to improve photosynthesis modeling and reduce uncertainties in GPP estimates within TBMs.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"330 ","pages":"Article 114989"},"PeriodicalIF":11.4,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908241","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":"Landsat and dual random forest modelling reveal sediment fining in the Yellow River shaped by ecological restoration on China's loess plateau","authors":"Zhiqiang Qiu ,&nbsp;Dong Liu ,&nbsp;Nuoxiao Yan ,&nbsp;Yao Yan ,&nbsp;Chen Yang ,&nbsp;Chenxue Zhang ,&nbsp;Hongtao Duan","doi":"10.1016/j.rse.2025.114994","DOIUrl":"10.1016/j.rse.2025.114994","url":null,"abstract":"<div><div>Monitoring coarse-grained sediment is essential for managing riverbed stability, flood capacity, and ecological resilience in the Yellow River, where high sediment loads originate from the erosion-prone Loess Plateau. Although large-scale ecological restoration has been implemented since the 1980s, its long-term impact on sediment grain-size dynamics remains unclear due to limited field observations. This study developed a dual-layer random forest model that synergizes Landsat satellites reflectance (1986–2022) with multi-scale watershed attributes (hydrological information, vegetation coverage, erosion susceptibility) to remotely quantify particle size distribution (PSD) of suspended sediment. The model achieved high precision (root mean square error: 2.94–4.82 %; mean absolute percentage difference: 13.44–19.87 %), enabling the first basin-wide PSD reconstruction. Key findings reveal: (1) Medium-sized particles (0.01–0.05 mm) dominated the mainstream (63.96 %), while coarse (&gt;0.05 mm, 67.80 %) and fine particles (&lt;0.01 mm, 25.70 %) were concentrated in the Fen and Wei Rivers, respectively; (2) Median grain size decreased by 7.25 % during the 1980s–2020s, reflecting the cumulative effects of ecological restoration, though localized coarsening (1.27, 2.39 and 2.61 %) occurred in the Huangshui, Wei, and Jing Rivers; and (3) Vegetation expansion (8.50–51.23 %) and urbanization (impervious surfaces (12.90–17.04 %)) drove particle fining, while increased wind/water erosion increased the proportion of coarse particle. This study fills a critical gap in monitoring suspended particle size dynamics and provides a scalable framework for evaluating ecological restoration outcomes and informing suspended sediment management in large, sediment-rich watersheds.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"330 ","pages":"Article 114994"},"PeriodicalIF":11.4,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908240","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":"Microwave water vapor atmospheric motion vectors retrieval from polar-orbiting satellites","authors":"Zongru Yang ,&nbsp;Xuezhi Bai ,&nbsp;Gang Ma ,&nbsp;Peng Zhang ,&nbsp;Yangtian Yan ,&nbsp;Chunhong Zhou","doi":"10.1016/j.rse.2025.114983","DOIUrl":"10.1016/j.rse.2025.114983","url":null,"abstract":"<div><div>Atmospheric motion vectors (AMVs) constitute one of the most critical data sources assimilated in numerical weather prediction (NWP), yet current operational wind products fall short to meet forecast requirements. This study addresses a fundamental observational gap in satellite wind retrievals. Traditional polar-orbiting satellite retrievals are limited to high latitudes, and geostationary AMV products are restricted to mid-low latitudes. In the resulting gap regions, only morning-orbit Metop infrared AMVs currently provide limited coverage. This study introduces an optical flow-based atmospheric motion vector retrieval method employing spatiotemporal matching of 183.31 GHz microwave water vapor channel brightness temperatures from NOAA-20/21 Advanced Technology Microwave Sounders (ATMS), enabling highly vertically resolved wind retrievals with clear-sky pixels. Using a fixed 5° × 5° feature tracking regions (5° FTR), the wind speed bias ranges from 0.16 to 0.64 m·s⁻¹, the root mean square error (RMSE) ranges from 3.45 to 3.81 m·s⁻¹, and the wind direction bias was consistently constrained below 27.4°. The overall accuracy achieves the error levels of existing products.</div><div>For extremely wind speed conditions, a hybrid-scale FTR optimization model, 3° FTR for slow wind speed region and 10° FTR for those high wind speed region, is also proposed. It can expand the detectable wind speed range from 45 m·s⁻¹ to 70 m·s⁻¹ with a sample size increase of over 10 % per channel. The RMSE for 3° FTR reduces by 0.5 m·s⁻¹, while the 10° FTR achieves a 1.5° reduction in both angular deviations and their standard deviation (STD) at 500 and 450 hPa. For all the hybrid regions in all channels, the RMSE remains within 3.47–3.79 m·s⁻¹, the correlation coefficient is enhanced by about 10 % and the wind direction bias is almost the same as that of the fixed FTR. This hybrid-scale tracking strategy can effectively balance spatial resolution and statistical reliability, and thus provides a new technical paradigm for polar-orbiting microwave AMV retrieval. The resulting afternoon-orbit microwave AMVs deliver a novel wind data source for NWP assimilation systems.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"330 ","pages":"Article 114983"},"PeriodicalIF":11.4,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908239","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":"Predicting below-average NDVI anomalies for agricultural drought impact forecasting","authors":"Koen De Vos ,&nbsp;Sarah Gebruers ,&nbsp;Jeroen Degerickx ,&nbsp;Marian-Daniel Iordache ,&nbsp;Jessica Keune ,&nbsp;Francesca Di Giuseppe ,&nbsp;Francisco Vilela Pereira ,&nbsp;Hendrik Wouters ,&nbsp;Else Swinnen ,&nbsp;Koen Van Rossum ,&nbsp;Laurent Tits","doi":"10.1016/j.rse.2025.114980","DOIUrl":"10.1016/j.rse.2025.114980","url":null,"abstract":"<div><div>Agricultural droughts, driven by deficits in root-zone soil moisture, pose challenges to food security and economic stability in Africa, which is simultaneously vulnerable to frequent droughts and strongly relies on rainfed agriculture. Current Earth observation (EO)-based monitoring systems rely on a near-real-time assessment of vegetation conditions — often through monitoring the Normalized Difference Vegetation Index (NDVI)- and are thereby allowing for reactive rather than proactive drought management. This study presents a machine learning-based forecasting system to predict below-average NDVI anomalies as a proxy for agricultural drought impact, focusing on recently drought-affected and crises-prone countries. By integrating EO data, meteorological forecasts, soil moisture, and static environmental descriptors, we developed a system that forecasts below-average NDVI anomalies up to three months in advance and explicitly considers ensemble uncertainty. The forecast shows an improved accuracy over using near-real-time NDVI anomalies and similar temporal patterns during the 2021–2022 growing seasons, which was used for independent validation. Our forecasted results are comparable to existing NDVI-based monitoring products such as the Agricultural Stress Index System developed by FAO. Despite these advancements, the modelling system struggles during transitions between rainy and dry seasons, often coinciding with the start and end of the growing season. Uncertainties in meteorological forecasts burden effective estimates of important phenological dates such as emergence or harvest up to three months in advance. This study complements existing soil moisture forecasting tools with impact on vegetation and presents a benchmark for the potential of integrating predictive models into anticipatory strategies in existing drought management frameworks.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"330 ","pages":"Article 114980"},"PeriodicalIF":11.4,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144902940","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":"Spatial patterns of leaf angle distribution covary with canopy fluorescence yield, reflectance indices, and leaf chlorophyll content, in a mixed temperate forest","authors":"Andrew D. Jablonski,&nbsp;Rong Li,&nbsp;Jongmin Kim,&nbsp;Manuel Lerdau,&nbsp;Carmen Petras,&nbsp;Xi Yang","doi":"10.1016/j.rse.2025.114996","DOIUrl":"10.1016/j.rse.2025.114996","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Plant canopies are integrated units that coordinate their functional (e.g., foliar biochemistry) and structural properties. This coordination affects remote sensing observations of canopy reflectance and solar-induced chlorophyll fluorescence (SIF). One key canopy structural property is leaf angle. Despite the fact that radiative transfer models have shown the crucial role of leaf angle in modulating remote sensing signals, methodological and technological barriers have prevented detailed investigations of how leaf angle covaries with canopy function and remote sensing observations. In this study, we employ a novel uncrewed aerial system (UAS) called FluoSpecAir to study the spatial patterns in far-red (FR) SIF (SIF&lt;sub&gt;obs,FR&lt;/sub&gt;), near-infrared reflectance and radiance of vegetation (NIR&lt;sub&gt;V&lt;/sub&gt; and NIR&lt;sub&gt;V&lt;/sub&gt;R), normalized difference vegetation index (NDVI), and chlorophyll:carotenoid index (CCI), across individual tree canopies during two separate time periods. Additionally, we collected 3D scans of individual tree canopies using terrestrial laser scanning (TLS) and estimated foliar pigment content from leaf reflectance spectra. We used the 3D scans to calculate the leaf angle distribution (LAD) and leaf area voxel density (LAVD) of each canopy. We modeled LAD using a beta distribution, which is parameterized by μ and &lt;em&gt;ν&lt;/em&gt;, and the leaf inclination distribution function (LIDF), which is parameterized by LIDFa and LIDFb. We found that &lt;em&gt;ν&lt;/em&gt; and μ, which are inversely related to the variance in leaf angle, covaried with spatial patterns in peak growing season canopy CCI, NDVI, SIF&lt;sub&gt;obs,FR&lt;/sub&gt;, and &lt;span&gt;&lt;math&gt;&lt;mfrac&gt;&lt;msub&gt;&lt;mi&gt;SIF&lt;/mi&gt;&lt;mrow&gt;&lt;mi&gt;obs&lt;/mi&gt;&lt;mo&gt;,&lt;/mo&gt;&lt;mi&gt;FR&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;mrow&gt;&lt;msub&gt;&lt;mi&gt;NIR&lt;/mi&gt;&lt;mi&gt;V&lt;/mi&gt;&lt;/msub&gt;&lt;mi&gt;R&lt;/mi&gt;&lt;/mrow&gt;&lt;/mfrac&gt;&lt;/math&gt;&lt;/span&gt;, and leaf chlorophyll content. Canopies with greater variation in LAD, thus lower &lt;em&gt;ν&lt;/em&gt; and μ, have larger values of NDVI, CCI, SIF&lt;sub&gt;obs,FR&lt;/sub&gt;, &lt;span&gt;&lt;math&gt;&lt;mfrac&gt;&lt;msub&gt;&lt;mi&gt;SIF&lt;/mi&gt;&lt;mrow&gt;&lt;mi&gt;obs&lt;/mi&gt;&lt;mo&gt;,&lt;/mo&gt;&lt;mi&gt;FR&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;mrow&gt;&lt;msub&gt;&lt;mi&gt;NIR&lt;/mi&gt;&lt;mi&gt;V&lt;/mi&gt;&lt;/msub&gt;&lt;mi&gt;R&lt;/mi&gt;&lt;/mrow&gt;&lt;/mfrac&gt;&lt;/math&gt;&lt;/span&gt;, and leaf chlorophyll content, while LAVD is not correlated with these remote sensing metrics. We found positive correlations between leaf chlorophyll content and canopy NDVI, SIF&lt;sub&gt;obs,FR&lt;/sub&gt;, and &lt;span&gt;&lt;math&gt;&lt;mfrac&gt;&lt;msub&gt;&lt;mi&gt;SIF&lt;/mi&gt;&lt;mrow&gt;&lt;mi&gt;obs&lt;/mi&gt;&lt;mo&gt;,&lt;/mo&gt;&lt;mi&gt;FR&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;mrow&gt;&lt;msub&gt;&lt;mi&gt;NIR&lt;/mi&gt;&lt;mi&gt;V&lt;/mi&gt;&lt;/msub&gt;&lt;mi&gt;R&lt;/mi&gt;&lt;/mrow&gt;&lt;/mfrac&gt;&lt;/math&gt;&lt;/span&gt;, as well. Together, our results show that across our study site during the peak growing season, spatial variability in remote sensing variables is driven by the coordination between LAD and leaf chlorophyll content. These findings provide important context for how we interpret landscape level variability in SIF and &lt;span&gt;&lt;math&gt;&lt;mfrac&gt;&lt;msub&gt;&lt;mi&gt;SIF&lt;/mi&gt;&lt;mrow&gt;&lt;mi&gt;obs&lt;/mi&gt;&lt;mo&gt;,&lt;/mo&gt;&lt;mi&gt;FR&lt;/mi&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;mrow&gt;&lt;msub&gt;&lt;mi&gt;NIR&lt;/mi&gt;&lt;mi&gt;V&lt;/mi&gt;&lt;/msub&gt;&lt;mi&gt;R&lt;/mi&gt;&lt;/mrow&gt;&lt;","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"330 ","pages":"Article 114996"},"PeriodicalIF":11.4,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144906082","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":"High-resolution global soil salinity and sodicity mapping (1980–2024): Box-Cox-based sample optimization, multi-source remote sensing features, and uncertainty quantification","authors":"Tiantian Wang ,&nbsp;Jinwei Dong ,&nbsp;Binyan Lyu ,&nbsp;Xuan Gao ,&nbsp;Nan Wang ,&nbsp;Zhou Shi","doi":"10.1016/j.rse.2025.114991","DOIUrl":"10.1016/j.rse.2025.114991","url":null,"abstract":"<div><div>Global soil salinization and sodification threaten food security by causing excessive salt accumulation in soils, degrading productivity. However, their spatiotemporal patterns have not been well documented due to the lack of high-accuracy estimates of soil salinity and sodicity with a long-term perspective. Here we propose a novel framework combining the Box-Cox transformation that addresses the skewed distribution of samples and more critical predictors as well as remote sensing indices, to predict global soil salinity (electrical conductivity of the saturated soil extract, ECe) and sodicity (exchangeable sodium percentage, ESP) from 1980 to 2024 with a 1 km × 1 km resolution using random forest. Model accuracy is higher than previous studies, with root mean square error (RMSE) of ECe and ESP as 2.24 and 6.04 respectively, and an <em>R</em>² of 0.65, and 0.60 respectively. The implementation of the Box-Cox transformation significantly improved the model performance (<em>R</em>²) by approximately 100 % (from 0.35 to 0.79 for ECe and 0.59 to 0.85 for ESP), while the additional predictors further enhanced the performance (<em>R</em>² increased by 15 %), ranking in the top 30 % of the feature importance list. Results revealed that global multiple-year average salinization and sodification are primarily concentrated in arid regions characterized by low precipitation and high temperatures. We also found a significant increasing trend of soil salinization in 20 % of global land and of sodification in 48 % of global land from 1980 to 2024, both most pronounced near the equator, as well as in central and eastern North America, Europe, southeastern China, and Mongolia. This study provides updated long-term soil salinity and sodicity maps with improved accuracies, offering critical insights for sustainable land management under climate change, serving as an essential resource for addressing food security and land degradation challenges worldwide.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"330 ","pages":"Article 114991"},"PeriodicalIF":11.4,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144902909","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":"Satellite observations of water transparency from VIIRS in global aquatic ecosystems","authors":"Jianwei Wei ,&nbsp;Menghua Wang ,&nbsp;Lide Jiang ,&nbsp;Zhongping Lee ,&nbsp;Richard Kirby ,&nbsp;Karlis Mikelsons ,&nbsp;Gong Lin","doi":"10.1016/j.rse.2025.114981","DOIUrl":"10.1016/j.rse.2025.114981","url":null,"abstract":"<div><div>This study presents a new satellite ocean color data record of Secchi depth (<em>Z</em>_<em>SD</em>) observations from the Visible Infrared Imaging Radiometer Suite (VIIRS). As part of the NOAA enterprise satellite data processing, the decade-long <em>Z</em>_<em>SD</em> data are derived from the visible and near-infrared reflectance measurements over oceanic, coastal, and inland waters. Based on in situ data, the model is excellent in generating low-uncertainty <em>Z</em>_<em>SD</em> data with an absolute percentage difference (APD) of 15%–29%. The satellite and in situ matchups confirm reliable satellite retrievals with APD = 19%–26% over the <em>Z</em>_<em>SD</em> range of 0.1–60 m. Although the product uncertainties are dependent on optical water types, assessments show that the satellite <em>Z</em>_<em>SD</em> estimations are very reliable, especially where <em>Z</em>_<em>SD</em> ≥ 1 m. This new satellite product has enabled the ability to access Level-2 daily <em>Z</em>_<em>SD</em> imagery and information as well as Level-3 data aggregated on daily to monthly scales. Our examination indicates that the ocean transparent windows are situated at 443 and 486 nm in the vast open oceans and 551 nm for most coastal waters. They shift to the red band at 671 nm for extremely turbid environments, such as large river estuaries. From a global perspective, the <em>Z</em>_<em>SD</em> data extend from less than half a meter in nearshore environments to &gt;70 m in the South Pacific Gyre, while demonstrating a strong dependency on the optical water types. Short-term fluctuations over time are registered in the satellite <em>Z</em>_<em>SD</em> daily and monthly data from almost every aquatic environment. Trend analyses reveal significant increases in water transparency over many regions, especially the open ocean. We stress the necessity of normalizing satellite <em>Z</em>_<em>SD</em> estimations to eliminate the uncertainties induced by different solar-zenith angles. The present satellite products can be further improved by accounting for the limitations imposed by the multispectral reflectance data with hyperspectral ocean color spectra.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"330 ","pages":"Article 114981"},"PeriodicalIF":11.4,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144902943","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":"Corrigendum to “Investigating the anisotropy of nighttime light using an unmanned aerial vehicle combined with scaled experiments” [Remote Sensing of Environment 329 (2025) 114960]","authors":"Ji Wu,&nbsp;Xi Li,&nbsp;Deren Li","doi":"10.1016/j.rse.2025.114992","DOIUrl":"10.1016/j.rse.2025.114992","url":null,"abstract":"","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"330 ","pages":"Article 114992"},"PeriodicalIF":11.4,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144900843","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":"Exploring the ecological potential of SDGSAT-1 MII and TIS data: Methods, applications, and comparisons","authors":"Hanqiu Xu ,&nbsp;Guifen Su ,&nbsp;Guojin He ,&nbsp;Mengmeng Wang ,&nbsp;Yafen Bai ,&nbsp;Jiahui Chen ,&nbsp;Mengjie Ren ,&nbsp;Tengfei Long","doi":"10.1016/j.rse.2025.114976","DOIUrl":"10.1016/j.rse.2025.114976","url":null,"abstract":"<div><div>As global urbanization accelerates and ecological challenges intensify, effective monitoring and assessing ecological conditions have become critical for sustainable development. Remote sensing technologies play an increasingly crucial role in this context. The Sustainable Development Goals Science Satellite 1 (SDGSAT-1), a next-generation remote sensing satellite, provides 10-m spatial resolution and multispectral imaging capabilities, offering new opportunities for ecological monitoring. This study explores the ecological potential of SDGSAT-1 data, focusing on the comprehensive assessment of urban heat islands (UHI), urban vegetation coverage, and regional ecological conditions. This is achieved through a detailed comparison with the widely-used Landsat-8/9 data. The study develops several methodologies for cloud detection, atmospheric correction, and land dryness retrieval. Validation shows that the cloud removal effect achieved by the proposed SDGSAT Cloud Mask (SCM) algorithm is comparable to, or slightly better than, those of the CFMask algorithm for Landsat-9 and the machine learning-based S2cloudless algorithm for Sentinel-2A, with F1 scores greater than 0.92. The results show that the monitoring of regional ecological conditions by SDGSAT-1 is very similar to that of Landsat-8/9, with differences generally under 5 %. Because SDGSAT-1's multispectral and thermal infrared imagery has higher spatial resolution than Landsat-8/9, it can detect 5.6 % more vegetation area and 2.6 times larger high-temperature areas within urban environments than Landsat data. SDGSAT-1's finer resolution enables more detailed ecological assessments, supporting urban sustainability applications. However, due to the lack of shortwave infrared bands in the SDGSAT-1 imagery, it is less effective than Landsat-8/9 in interpreting land surface dryness and moisture content.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"330 ","pages":"Article 114976"},"PeriodicalIF":11.4,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144896143","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":"100 m PM2.5 mapping from SDGSAT-1 TOA reflectance: Model development and -evaluation","authors":"Kaixu Bai ,&nbsp;Zhe Zheng ,&nbsp;Songyun Qiu ,&nbsp;Ke Li ,&nbsp;Liuqing Shao ,&nbsp;Chaoshun Liu ,&nbsp;Ni-Bin Chang","doi":"10.1016/j.rse.2025.114977","DOIUrl":"10.1016/j.rse.2025.114977","url":null,"abstract":"<div><div>Satellite-based fine-resolution (∼100 m) PM_2.5 mapping remains challenging because broadband multispectral imagery struggles to decouple land and atmospheric signals, limiting accurate local emission source detection in regions with sparse ground monitoring networks. Here, we introduce LAD-GAT, a novel deep learning framework for 100 m-resolution PM_2.5 estimation from SDGSAT-1––the first science satellite mission dedicated to the UN Sustainable Development Goals. Specifically, LAD-GAT builds a high-dimensional scene-attribute graph by combining PM_2.5-relevant geographical features, meteorological dynamics, top-of-the-atmosphere (TOA) reflectance, and estimated surface reflectance (SR). A specialized land-atmosphere decoupling (LAD) module is introduced to separate latent aerosol signals from ground surface contributions, and a graph attention network (GAT) models nonlinear associations between in-situ PM_2.5 observations and the input graph structure. In 10-fold cross-validation, LAD-GAT achieved RMSE = 5.042 μg m⁻³ (R² = 0.875) using SDGSAT-1 TOA reflectance, and RMSE = 9.428 μg m⁻³ (R² = 0.862) with Sentinel-2 TOA reflectance. Incorporating daily SR yielded an 8.68 % accuracy gain over TOA reflectance alone and outperformed multi-day composites by 7.27 %, highlighting the benefit of accounting for SR dynamics in fine-scale PM_2.5 mapping. Overall, leveraging the proposed novel LAD-GAT method, SDGSAT-derived PM_2.5 estimates rival those from Sentinel-2, providing fine-scale data to better support SDG 11.6.2 monitoring and targeted air-quality interventions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"330 ","pages":"Article 114977"},"PeriodicalIF":11.4,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144902941","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}