Remote Sensing of Environment最新文献

{"title":"A novel land surface temperature retrieval method using channel correlation for atmospheric parameter modeling from SDGSAT-1 data","authors":"Li-Qin Cao ,&nbsp;Hang Zhao ,&nbsp;Du Wang ,&nbsp;Yan-Fei Zhong ,&nbsp;Fa-Wang Ye","doi":"10.1016/j.rse.2025.115190","DOIUrl":"10.1016/j.rse.2025.115190","url":null,"abstract":"<div><div>The Thermal Infrared Spectrometer (TIS) onboard Sustainable Development Science Satellite-1 (SDGSAT-1) features three unique channels with a broader spectral range compared to previous thermal infrared multi-channel sensors. The split-window (SW) and temperature-and-emissivity separation (TES) algorithms are suitable for land surface temperature (LST) retrieval from TIS data. However, both SW and TES require auxiliary information, and the temporal and spatial inconsistency of auxiliary information can lead to errors in LST retrieval. We propose a wide-band atmospheric correction TES algorithm, which can retrieve LST without any auxiliary atmospheric and land surface parameter input. By leveraging the stability of wide-band imaging, atmospheric transmittance and upward radiation are modeled, thereby reducing the number of unknowns in the radiative transfer equation. Additionally, a transmittance ratio refinement module is incorporated, which iteratively refines the transmittance. Experiments conducted on simulated datasets demonstrate that this method achieves an RMSE of 1.32 K, remaining stable at 1.39 K with estimated transmittance, indicating strong robustness to variations in water vapor content. Cross-validation results for the Wuhan region show a bias of −1.79 K and an RMSE of 2.28 K when compared to MODIS temperature products, suggesting that the retrieved LST captures more detailed information. Furthermore, a comparison with the general split-window (GSW) algorithm and MODTRAN-TES was conducted, selecting 108 validation points at Heihe, SURFRAD, ICOS, TERN, and BSRN stations for ground validation, yielding root mean square errors (RMSE) of 2.07 K, 1.55 K, 1.84 K, 1.72 K, and 2.14 K respectively, with an RMSE of 1.95 K across all validation sites. These results represent improvements of 0.25 K and 0.55 K over GSW and MODTRAN-TES, respectively, confirming the high accuracy of the proposed method.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115190"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785690","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":"Global retrieval of harmonized microwave land surface emissivity leveraging multi-sensor measurements from GMI, AMSR2 and MWRIs","authors":"Jiheng Hu ,&nbsp;Rui Li ,&nbsp;Peng Zhang ,&nbsp;Yu Wang ,&nbsp;Shengli Wu ,&nbsp;Husi Letu ,&nbsp;Fuzhong Weng","doi":"10.1016/j.rse.2025.115169","DOIUrl":"10.1016/j.rse.2025.115169","url":null,"abstract":"<div><div>Accurate accounting of microwave land surface emissivity (MLSE) facilitates applications associated with monitoring the ecohydrological dynamics in global terrestrial ecosystems, quantifying cross-sphere carbon and water exchanges, and meeting the critical accuracy required for assimilation in the global precipitation retrieval algorithm. However, collaborative applications of emissivity retrieved from individual sensors are severely hampered by discrepancies in retrieval techniques, instrumental configurations, calibration errors, and broken temporal periods. To mitigate this gap, we present an innovative framework to retrieve harmonized emissivity from observations of five passive microwave sensors, namely, GPM-CO/GMI, Fengyun-3B, −3C and −3D/MWRI, and GCOM-W1/AMSR2. Six geostationary visible and infrared imagers onboard three geostationary platforms, i.e. GOES-16/ABI, Himawari-8, −9/AHI, and MSG-1, −2, −3/SEVIRI, were collocated to jointly provide clear-sky masks covering the globe. The simultaneous conical overpass (SCO) recalibration technique was applied to scale all emissivity subsets retrieved from different sensors to be aligned with the GMI retrievals across various land types. Quantitative analyses reveal exceptionally strong consistency among emissivities across different subsets (Pearson R ≈ 0.95, RMSD &lt;0.011, and mean bias within ±0.005). Our estimates at 10.65 GHz show strong agreement with in-situ radiometer measurements over two grass and crop fields, with errors generally within ±0.01 at vertical polarization and a systematic underestimation of approximately −0.02 at horizontal polarization. Globally, we evaluate the recalibrated emissivities against four reference datasets derived using various techniques, which includes three single-sensor retrievals from GMI and AMSR-E, as well as a climatology emissivity atlas generated using the Tool to Estimate Land Surface Emissivity at Microwaves and Millimeter waves (TELSEM). The results demonstrate strong consistencies at both vertical (<em>R</em> = 0.8–0.9, RMSD &lt;0.015 or ∼ 1.5 %) and horizontal (<em>R</em> = 0.9–0.95, RMSD &lt;0.02 or ∼ 2 %) polarizations on a monthly scale. The observed discrepancies are primarily attributed to differences in instrumental configurations, calibration accuracy, and retrieval methodologies. The harmonized retrieval algorithm and the sophisticated cross sensor calibrations facilitate its implementation as a self-consistent emissivity data for various applications associated with terrestrial ecohydrological dynamics, surface hydrological properties estimation, as well as the physical-based precipitation retrieval algorithms over land.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115169"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145703961","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":"Characterizing mangrove forest succession in Suriname using GEDI waveform metrics","authors":"Jasper Feyen ,&nbsp;Verginia Wortel ,&nbsp;Kim Calders ,&nbsp;John Armston ,&nbsp;Frieke Vancoillie","doi":"10.1016/j.rse.2026.115244","DOIUrl":"10.1016/j.rse.2026.115244","url":null,"abstract":"<div><div>Mangroves are critical coastal ecosystems known for their carbon storage capacity, biodiversity, and role in shoreline stabilization. In Suriname, mangroves develop within a dynamic coastal setting shaped by migrating mudbanks and high sedimentation rates. This study examines how 30 structural metrics derived from the Global Ecosystem Dynamics Investigation (GEDI) vary across gradients of mangrove stand age and seaward distance. Forest stand age and yearly coastline positions were derived from Landsat time series data, enabling the integration of temporal and spatial drivers to uncover patterns of mangrove succession and structural development. Nonlinear growth models, more specifically the Chapman–Richards function, captured early growth and stabilization phases, while Generalized Additive Models (GAMs) provided flexibility to represent more complex structural changes observed in mature and decaying stands. Results show that structural metrics related to forest growth, such as canopy height and aboveground biomass density, increase rapidly during early successional stages but plateau beyond approximately 12 years or 2 km from the coastline. Complexity-oriented metrics, such as Foliage Height Diversity (FHD) and the Waveform Structural Complexity Index (WSCI), continue to evolve, reflecting increased vertical stratification in mature stands. By combining GEDI spaceborne LiDAR with Landsat-derived chronosequences, this study demonstrates how remote sensing can be used to monitor mangrove successional trajectories and structural complexity, including in inaccessible coastal regions. Our findings extend traditional mangrove successional models by quantifying how both temporal (age) and spatial (seaward distance) gradients jointly determine mangrove structure across the Surinamese coastline.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115244"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146001413","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":"Retrieving forest LAI from Landsat via 3D look-up table generated by realistic LiDAR scenes","authors":"Jianbo Qi ,&nbsp;Siying He ,&nbsp;Xun Zhao ,&nbsp;Su Ye ,&nbsp;Tianjia Chu ,&nbsp;Zhexiu Yu ,&nbsp;Simei Lin ,&nbsp;Huaguo Huang","doi":"10.1016/j.rse.2025.115204","DOIUrl":"10.1016/j.rse.2025.115204","url":null,"abstract":"&lt;div&gt;&lt;div&gt;As a key biophysical parameter describing forest vegetation structure, Leaf Area Index (LAI) is an essential and widely used indicator for evaluating forest ecosystem function and health. LAI retrieval from remote sensing observations primarily relies on canopy radiative transfer models (RTMs) that quantitatively characterize the complex relationship between canopy parameters and reflectance. However, most physical models currently used for LAI retrieval are one-dimensional (1D) RTMs, which typically assume the canopy to be horizontally homogeneous and thus fail to capture the inherent heterogeneity within the canopy. Although three-dimensional (3D) RTMs can better characterize the structural complexity of forest canopies, their high computational demand and the difficulty of parameterization often limit their application to large-scale remote sensing retrievals. In this study, a novel 3D Look-Up Table (3D-LUT) approach was developed for retrieving forest LAI from Landsat by accounting for the heterogeneity within forests through the integration of LiDAR-based scene reconstructions to parameterize the RTM. Instead of using idealized homogeneous layers or simple geometric objects, our approach used airborne LiDAR data to reconstruct realistic and structurally representative 3D forest scenes for typical forest types, including Deciduous Broadleaf Forest (DBF), Deciduous Needleleaf Forest (DNF), Evergreen Broadleaf Forest (EBF), and Evergreen Needleleaf Forest (ENF). Based on these reconstructed forest scenes, type-specific LAI look-up tables (LUTs) were built by coupling the 3D RTM Large-scalE remote Sensing data and image Simulation (LESS) with an analytical model PATH_RT, an accurate and efficient RTM based on 3D path-length distribution and spectral invariant theory, enabling accurate LAI retrieval from Landsat imagery. This method was compared against field observations collected from 16 National Ecological Observatory Network (NEON) sites and 8 Integrated Carbon Observation System (ICOS) sites, which comprise a representative sample of different forest types. Additionally, intercomparison was conducted using the High-resolution Global LAnd Surface Satellite (Hi-GLASS) LAI product, Simplified Level-2 Prototype Processor (SL2P) algorithm and the MODIS LAI product. Validation against in situ data demonstrated that the proposed algorithm can achieve high-accuracy retrieval of LAI across four forest types, with RMSE ranging from 0.93 to 1.20 m&lt;sup&gt;2&lt;/sup&gt;/m&lt;sup&gt;2&lt;/sup&gt; and MAE from 0.73 to 1.00 m&lt;sup&gt;2&lt;/sup&gt;/m&lt;sup&gt;2&lt;/sup&gt;. The intercomparison results revealed that retrieval algorithms based on the PROSAIL model, such as SL2P, tend to underestimate forest LAI. In contrast, the proposed algorithm shows strong overall agreement with the Hi-GLASS LAI product and MODIS LAI product, which are derived from a deep learning framework and a 3D RTM, respectively, supporting its reliability for regional-scale forest LAI retrieval. By generating the s","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115204"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145771666","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":"Multi-satellite derived data reveals spatiotemporal dynamics of carbon-water coupling and its drivers in tropical ecosystems","authors":"Xiang Wang ,&nbsp;Zheng Fu ,&nbsp;Philippe Ciais ,&nbsp;Josep Peñuelas ,&nbsp;Jingfeng Xiao ,&nbsp;Xing Li ,&nbsp;Xiangzhong Luo ,&nbsp;Chi Chen ,&nbsp;Haoyu Xia ,&nbsp;Tao Zhou ,&nbsp;Paul C. Stoy ,&nbsp;Julia K. Green ,&nbsp;Fangyue Zhang","doi":"10.1016/j.rse.2026.115242","DOIUrl":"10.1016/j.rse.2026.115242","url":null,"abstract":"<div><div>Climate change has significantly impacted tropical water use efficiency (WUE), defined as the ratio of gross primary productivity (GPP) to evapotranspiration (ET). However, the spatiotemporal dynamics and controlling factors of WUE in these regions—particularly the effects of extreme El Niño events—remain unclear. Using multiple satellite-derived GPP and ET datasets with large-scale observations, here we quantified WUE trends from 2001 to 2020 and assessed the impact of the 2015/16 El Niño drought on WUE in the tropics. Our analysis revealed a significant upward trend in tropical WUE, increasing at a rate of 0.007 ± 0.001 g C kg⁻¹ H₂O yr⁻¹ (mean ± standard deviation), with the largest increase observed in tropical Asia (0.01 ± 0.001 g C kg⁻¹ H₂O yr⁻¹). Spatially, three independent remote sensing-driven datasets consistently showed a significant WUE increase in 32%–54% of tropical regions, while only 1%–3% experienced a significant decline. Furthermore, tropical ecosystems exhibited a substantial increase in GPP (5.47 ± 0.60 g C m⁻² yr⁻¹), with the highest growth rate in tropical Asia (11.45 ± 0.37 g C m⁻² yr⁻¹), whereas ET showed minor changes. This suggests that WUE changes in tropical ecosystems are primarily driven by increases of GPP rather than ET. Further analysis identified leaf area as the dominant factor influencing WUE, GPP, and ET trends across the tropics. We also found that the extreme drought during the 2015/16 El Niño event resulted in a net decrease in WUE (−0.03 ± 0.01 g C kg⁻¹ H₂O), which transitioned to a net increase (0.04 ± 0.01 g C kg⁻¹ H₂O) by 2016/17. Compared to satellite-driven results, most land surface models captured the direction of tropical WUE trends but simulated a slower rate of change, with substantial variation in predicted trend intensities among models. This study advances our understanding of tropical ecosystem WUE dynamics and provides critical insights for predicting future WUE changes under ongoing climate change, informing strategies for carbon sequestration and water resource management in vulnerable tropical regions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115242"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145962391","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":"IHMSC: A novel iterative hybrid multiple scattering-corrected retrieval method for enhancing accuracy in ocean lidar profiling inversions","authors":"Xinye Li ,&nbsp;Siqi Zhang ,&nbsp;Peng Chen ,&nbsp;Zhanhua Zhang ,&nbsp;Delu Pan","doi":"10.1016/j.rse.2025.115209","DOIUrl":"10.1016/j.rse.2025.115209","url":null,"abstract":"<div><div>Ocean lidar technology, an emerging active remote sensing method, excels at revealing the vertical structure of subsurface ocean layers, addressing challenges in carbon flux, phytoplankton analysis, and biogeochemical monitoring. Current lidar inversion methods, however, rely on empirical formulations for homogeneous waters and overlook photon multiple scattering, which introduces significant uncertainties, especially in complex coastal ecosystems. To overcome this, we present an iterative hybrid multiple scattering-corrected retrieval method based on 117,456 vertical profiles (2017–2024) in the South China Sea. The model combines the optimization of backscatter–attenuation ratios, lidar ratios, and semianalytical simulations of multiple scattering effects integrated with XGBoost machine learning to relate lidar-derived optical properties (<em>K</em>_d<em>, bbp</em>) to biogeochemical parameters (<em>Chl</em>, <em>POC</em>). Compared with the satellite ocean color products, the retrieval results derived from airborne and shipborne lidar observations show strong agreement: <em>K</em>_d (<em>R =</em> 0.76<em>, RMSD =</em> 0.01 m⁻¹<em>, MAPD =</em> 6.58 %)<em>, bbp</em> (<em>R =</em> 0.80<em>, RMSD =</em> 0.00 m⁻¹<em>, MAPD =</em> 28.93 %)<em>, Chl</em> (<em>R =</em> 0.61<em>, RMSD =</em> 0.29 μg/L<em>, MAPD =</em> 32.82 %)<em>, and POC</em> (<em>R =</em> 0.88<em>, RMSD =</em> 20.55 μg/L<em>, MAPD =</em> 18.14 %). These results bridge active and passive remote sensing. This study also reveals the dynamic three-dimensional characteristics of the subsurface phytoplankton layer in the South China Sea, revealing spatial and temporal heterogeneity influenced by environment factors. The nearshore subsurface phytoplankton layer shows diurnal variations in thickness and intensity driven by tidal processes: it thickens and ascends during the day and thins and descends at night. Larger tidal amplitudes are linked to shallower layers and higher chlorophyll-a concentrations. These findings demonstrate the potential of lidar technology for large-scale, long-term monitoring of subsurface ocean profiles, offering an important complement to in situ and passive satellite remote sensing data.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115209"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823767","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":"AgriFM: A multi-source temporal remote sensing foundation model for Agriculture mapping","authors":"Wenyuan Li ,&nbsp;Shunlin Liang ,&nbsp;Keyan Chen ,&nbsp;Yongzhe Chen ,&nbsp;Han Ma ,&nbsp;Jianglei Xu ,&nbsp;Yichuan Ma ,&nbsp;Yuxiang Zhang ,&nbsp;Shikang Guan ,&nbsp;Husheng Fang ,&nbsp;Zhenwei Shi","doi":"10.1016/j.rse.2026.115234","DOIUrl":"10.1016/j.rse.2026.115234","url":null,"abstract":"<div><div>Climate change and population growth intensify the demand for precise agriculture mapping to enhance food security. Such mapping tasks require robust modeling of multi-scale spatiotemporal patterns from fine field textures to landscape context, and from short-term phenology to full growing-season dynamics. Existing methods often process spatial and temporal features separately, limiting their ability to capture essential agricultural dynamics. While transformer-based remote sensing foundation models (RSFMs) offer unified spatiotemporal modeling ability, most of them remain suboptimal: they either use fixed windows that ignore multi-scale crop characteristics or neglect temporal information entirely. To address these gaps, we propose AgriFM, a multi-source, multi-temporal foundation model for agriculture mapping. AgriFM introduces a synchronized spatiotemporal downsampling strategy within a Video Swin Transformer backbone, enabling efficient handling of long and variable-length satellite time series while preserving multi-scale spatial and phenological information. It is pre-trained on a globally representative dataset comprising over 25 million samples from MODIS, Landsat-8/9, and Sentinel-2 with land cover fractions as pre-training supervision. AgriFM further integrates a versatile decoder specifically designed to dynamically fuse multi-source features from different stages of backbone and accommodate varying temporal lengths, thereby supporting consistent and scalable agriculture mapping across diverse satellite sources and task requirements. It supports diverse tasks including agricultural land mapping, field boundary delineation, agricultural land use/land cover mapping, and specific crop mapping (e.g., winter wheat and paddy rice) with different data sources. Comprehensive evaluations show that AgriFM consistently outperforms existing deep learning models and general-purpose RSFMs across multiple agriculture mapping tasks. Codes and models are available at <span><span>https://github.com/flyakon/AgriFM</span><svg><path></path></svg></span> and <span><span>https://glass.hku.hk</span><svg><path></path></svg></span></div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115234"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938802","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 the structural diversity of Central African and Western US forests using GEDI","authors":"Fabian D. Schneider ,&nbsp;Morgan Dean ,&nbsp;Elsa M. Ordway ,&nbsp;Moses B. Libalah ,&nbsp;Antonio Ferraz","doi":"10.1016/j.rse.2025.115215","DOIUrl":"10.1016/j.rse.2025.115215","url":null,"abstract":"<div><div>This study maps forest structural diversity, a key component of ecosystem diversity, using NASA's GEDI spaceborne lidar, providing new opportunities to support conservation and restoration efforts. Focusing on biodiversity hotspots in Central Africa and the Western US, we evaluated GEDI's ability to capture spatial variation in forest canopy structure by comparing GEDI-derived metrics with 391 km² of airborne laser scanning (ALS) data. Forest structural traits were assessed at 1 km² resolution, with GEDI showing robust correlations with ALS, particularly in dense and flat Central African forests (r² up to 0.85), and moderate agreement in more heterogeneous terrain in the California Sierra Nevada (r² up to 0.55). We quantified structural diversity as horizontal variation in canopy structure using a probability density-based multivariate diversity framework. GEDI canopy height (rh98), canopy cover, and foliage height diversity jointly captured independent axes of canopy variation and explained structural diversity derived from wall-to-wall ALS (r² = 0.37 at 1 km²). Resulting maps reveal high structural diversity in mid-elevation and coastal forests of the Western US and in forest-savanna transitions and volcanic ranges of Central Africa, consistent with gradients in disturbance, topography and aridity. Despite limitations related to sampling density, waveform noise and terrain complexity, this study demonstrates that GEDI's footprint-level metrics can be used directly to quantify forest structural diversity. These findings highlight the potential of spaceborne lidar to provide scalable, trait-based structural diversity indicators, while emphasizing that interpretation remains context dependent and benefits from integration with ecological data, environmental drivers and disturbance regimes.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115215"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938804","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":"Highest quality remote sensing reflectance database compiled from 20+ years of MODIS-aqua measurements","authors":"Longteng Zhao,&nbsp;Zhongping Lee,&nbsp;Xiaolong Yu,&nbsp;Tianhao Wang,&nbsp;Daosheng Wang,&nbsp;Shaoling Shang","doi":"10.1016/j.rse.2025.115195","DOIUrl":"10.1016/j.rse.2025.115195","url":null,"abstract":"<div><div>Remote sensing reflectance (<em>R</em>_<em>rs</em>) is a fundamental property in satellite ocean color remote sensing, which is critical for retrieving optical-biogeochemical properties and data-driven atmospheric correction algorithms. In this study, with three criteria applicable to ∼91% of the global ocean, we compiled a database of the highest quality <em>R</em>_<em>rs</em> (HQ_MODISA-<em>R</em>_<em>rs</em>) of oceanic waters based on 20+ years of ocean color measurements by the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. While removing a large number of daily “standard” data products, our evaluation showed that the criteria for the highest-quality <em>R</em>_<em>rs</em> (CHQR) improved MODIS <em>R</em>_<em>rs</em> data consistency with benchmark <em>in situ R</em>_<em>rs</em> datasets, such as those from MOBY and AERONET-OC. After applying CHQR, analysis of imagery products in the South Pacific Ocean revealed that the coefficient of variation (CV) of <em>R</em>_<em>rs</em> among pixels reduced from 0.042 (standard quality control) to 0.030, along with enhanced temporal consistency, which indicates that this approach effectively filters abnormal data products. While such a dataset played a key role in the development of the cross-satellite atmospheric correction algorithm (Lee et al., 2024), we here further demonstrate that applications of HQ_MODISA-<em>R</em>_<em>rs</em> have ∼21.0% of oceanic areas between 50°S and 50°N showing reversed long-term trends of <em>R</em>_<em>rs</em> compared to the trend based on the standard <em>R</em>_<em>rs</em> product. We anticipate that this highest-quality <em>R</em>_<em>rs</em> database would not only improve our evaluation and understanding of long-term changes in various <em>R</em>_<em>rs</em>-derivative bio-optical properties of the global ocean, but also help to obtain consistent products among various satellite ocean color missions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115195"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731548","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":"An integrated atmospheric-topographic correction framework for land surface reflectance estimation using a spatial-spectral Attention U-Net model","authors":"Yichuan Ma ,&nbsp;Shunlin Liang ,&nbsp;Han Ma ,&nbsp;Tao He ,&nbsp;Xiran Shi ,&nbsp;Wenyuan Li ,&nbsp;Dejun Cai ,&nbsp;Xiongxin Xiao ,&nbsp;Shikang Guan ,&nbsp;Weiwei Liu ,&nbsp;Jianglei Xu ,&nbsp;Yongzhe Chen ,&nbsp;Yuxiang Zhang","doi":"10.1016/j.rse.2025.115188","DOIUrl":"10.1016/j.rse.2025.115188","url":null,"abstract":"<div><div>Surface reflectance, a fundamental parameter for deriving high-level satellite products, is typically obtained through atmospheric correction. However, in rugged terrains, topographic effects substantially distort surface reflectance estimates. Many post-topographic correction methods for surface reflectance were proposed and applied to mitigate topographic effects. However, the coupled atmospheric and topographic influences in radiative transfer processes cause significant errors when these corrections are performed separately. For example, we observed over 50 % of pixels in the official Landsat 8 surface reflectance data across a complex terrain exhibited physically implausible negative values, predominantly in shadowed areas. Moreover, traditional pixel-by-pixel methods failed to leverage valuable spatial information for estimation. To address these limitations, we developed an integrated atmospheric and topographic correction framework, Unet-TopoFlat, leveraging a spatial-spectral Attention U-Net algorithm and a novel pseudo-topographic synthetic strategy. The pseudo-topographic synthetic strategy generated sufficient and robust topographically incorporated TOA radiance samples across different terrains, surfaces, and atmospheric conditions, using surface reflectance over flat terrains based on radiative transfer models, atmospheric parameters, random forest regression, and a mountainous radiative transfer parameterization scheme. Using Landsat 8 data as a proxy for evaluation, the Unet-TopoFlat was trained on 47,398 samples (256 × 256 pixels), leveraging multiple datasets. The Unet-TopoFlat model effectively captured the spatial and spectral relationships between TOA radiance and surface reflectance, achieving a relative root mean square error (rRMSE) of 4.5 %–6.2 % across 20,314 samples spanning different terrain, temporal, and spectral bands. Compared to the baseline Unet-FLAT model, which lacked topographic consideration and exhibited substantial uncertainties, Unet-TopoFlat effectively reduced topographic effects, lowering negative reflectance ratios from 55.5 % to 2.8 % while accurately recovering surface information and preserving spectral information. Moreover, the leaf area index (LAI) and snow cover mapping using our estimated surface reflectance were superior to those using official products, and deviations reached up to 2.4 for LAI and 8 % for snow cover mapping at the regional scale. Our proposed framework is not sensor-specific and can be potentially applied to multiple optical remotely sensed data.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115188"},"PeriodicalIF":11.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145823488","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}