Remote Sensing of Environment最新文献

{"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":"2025-12-23","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":"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":"2025-12-18","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":"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":"2025-12-18","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":"Sentinel-1 imagery for wide-scale quantitative landslide vulnerability assessment of buildings","authors":"Francesco Poggi ,&nbsp;Francesco Caleca ,&nbsp;Olga Nardini ,&nbsp;Francesco Barbadori ,&nbsp;Matteo Del Soldato ,&nbsp;Claudio De Luca ,&nbsp;Francesco Casu ,&nbsp;Manuela Bonano ,&nbsp;Riccardo Lanari ,&nbsp;Veronica Tofani ,&nbsp;Federico Raspini","doi":"10.1016/j.rse.2025.115199","DOIUrl":"10.1016/j.rse.2025.115199","url":null,"abstract":"<div><div>The occurrence of landslides can potentially cause considerable economic impact on a global scale, resulting in damage to exposed structures and infrastructures, including buildings. In order to determine the most efficacious risk mitigation strategies, the scientific community is engaged in the analyses aimed at assessing the expected consequences of landslide activation/reactivation. This approach involves the implementation of quantitative risk assessment procedures, based on the definition of the landslide susceptibility and intensity, the identification of exposed elements, and the vulnerability assessment, which represents the most challenging parameter. The present work proposes an approach to the quantitative vulnerability assessment for buildings exposed to slow-kinematic landslides via empirical fragility and vulnerability curves, which express the probabilistic relationship between the damage severity to exposed buildings and the intensity of the landslide. A comprehensive database was compiled, collating information on landslide-induced damage to over four thousand buildings in the Northern Apennines of Central Italy. The landslide intensity is evaluated by exploiting the worldwide freely accessible Sentinel-1 SAR images, which have been processed by using the Parallel-Small BAseline (P-SBAS) – Differential SAR Interferometry (DInSAR) processing chain. The Institute for Electromagnetic Sensing of the Environment (IREA) of the National Research Council (CNR) of Italy is responsible for the processing of the Sentinel-1 SAR images, within the framework of an agreement with the Italian Ministry of Environment and Energy Security (MASE) for the detection and analysis of ground displacement over the entire Italian territory. The ultimate objective of the present study is the exploitation of the derived vulnerability curve into the quantitative risk assessment procedure for a comprehensive evaluation of buildings in the Northern Apennines.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115199"},"PeriodicalIF":11.4,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785692","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":"Development of an interference mitigation chlorophyll index for mitigating soil and canopy dependence to improve vegetation chlorophyll content monitoring","authors":"Tao Sun ,&nbsp;Zijun Tang ,&nbsp;Youzhen Xiang ,&nbsp;Junsheng Lu ,&nbsp;Yaohui Cai ,&nbsp;Wangyang Li ,&nbsp;Ruiqi Du ,&nbsp;Xianghui Lu ,&nbsp;Shouyang Liu ,&nbsp;Tianjie Zhao ,&nbsp;Zhijun Li ,&nbsp;Fucang Zhang","doi":"10.1016/j.rse.2025.115202","DOIUrl":"10.1016/j.rse.2025.115202","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Chlorophyll is a fundamental component of photosynthesis and a key indicator in quantitative vegetation remote sensing. However, accurate retrieval of leaf chlorophyll content from top-of-canopy (TOC) reflectance is often hindered by soil background and canopy structural effects. To address these challenges, we developed the Interference-Mitigation Chlorophyll Index (IMCI), derived from the spectral properties of leaves and soils together with canopy radiative transfer processes, to enhance robustness under complex soil and canopy conditions.&lt;/div&gt;&lt;div&gt;The development of IMCI consists of three steps. First, the soil single-scattering component (&lt;span&gt;&lt;math&gt;&lt;msubsup&gt;&lt;mi&gt;ρ&lt;/mi&gt;&lt;mi&gt;s&lt;/mi&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;/msubsup&gt;&lt;/math&gt;&lt;/span&gt;) is removed from TOC reflectance using two spectral features: (a) vegetation reflectance remains equivalent at 598 and 694 nm regardless of leaf color, while soil reflectance differs; and (b) soil reflectance exhibits an approximately linear dependence on wavelength within 590–750 nm. These features enable accurate estimation and correction of &lt;span&gt;&lt;math&gt;&lt;msubsup&gt;&lt;mi&gt;ρ&lt;/mi&gt;&lt;mi&gt;s&lt;/mi&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;/msubsup&gt;&lt;/math&gt;&lt;/span&gt;, yielding soil-adjusted TOC spectra (&lt;span&gt;&lt;math&gt;&lt;mover&gt;&lt;msub&gt;&lt;mi&gt;ρ&lt;/mi&gt;&lt;mi&gt;c&lt;/mi&gt;&lt;/msub&gt;&lt;mo&gt;∼&lt;/mo&gt;&lt;/mover&gt;&lt;/math&gt;&lt;/span&gt;). Second, a proxy for the vegetation single-scattering component (&lt;span&gt;&lt;math&gt;&lt;msubsup&gt;&lt;mi&gt;ρ&lt;/mi&gt;&lt;mi&gt;l&lt;/mi&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;/msubsup&gt;&lt;/math&gt;&lt;/span&gt;) is constructed from soil-adjusted red-edge difference–ratio reflectance values (&lt;span&gt;&lt;math&gt;&lt;mo&gt;∆&lt;/mo&gt;&lt;mover&gt;&lt;msub&gt;&lt;mi&gt;ρ&lt;/mi&gt;&lt;mi&gt;c&lt;/mi&gt;&lt;/msub&gt;&lt;mo&gt;∼&lt;/mo&gt;&lt;/mover&gt;&lt;mfenced&gt;&lt;msub&gt;&lt;mi&gt;λ&lt;/mi&gt;&lt;mi&gt;j&lt;/mi&gt;&lt;/msub&gt;&lt;/mfenced&gt;&lt;mo&gt;/&lt;/mo&gt;&lt;mo&gt;∆&lt;/mo&gt;&lt;mover&gt;&lt;msub&gt;&lt;mi&gt;ρ&lt;/mi&gt;&lt;mi&gt;c&lt;/mi&gt;&lt;/msub&gt;&lt;mo&gt;∼&lt;/mo&gt;&lt;/mover&gt;&lt;mfenced&gt;&lt;msub&gt;&lt;mi&gt;λ&lt;/mi&gt;&lt;mi&gt;i&lt;/mi&gt;&lt;/msub&gt;&lt;/mfenced&gt;&lt;/math&gt;&lt;/span&gt;). Exploiting the linearity of the red-edge, we demonstrated that the extrapolated intersection of &lt;span&gt;&lt;math&gt;&lt;mo&gt;∆&lt;/mo&gt;&lt;mover&gt;&lt;msub&gt;&lt;mi&gt;ρ&lt;/mi&gt;&lt;mi&gt;c&lt;/mi&gt;&lt;/msub&gt;&lt;mo&gt;∼&lt;/mo&gt;&lt;/mover&gt;&lt;/math&gt;&lt;/span&gt; and &lt;span&gt;&lt;math&gt;&lt;msubsup&gt;&lt;mi&gt;ρ&lt;/mi&gt;&lt;mi&gt;l&lt;/mi&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;/msubsup&gt;&lt;/math&gt;&lt;/span&gt; in the red-edge region converges to a reflectance value approaching 0 and further derived that &lt;span&gt;&lt;math&gt;&lt;mo&gt;∆&lt;/mo&gt;&lt;mover&gt;&lt;msub&gt;&lt;mi&gt;ρ&lt;/mi&gt;&lt;mi&gt;c&lt;/mi&gt;&lt;/msub&gt;&lt;mo&gt;∼&lt;/mo&gt;&lt;/mover&gt;&lt;mfenced&gt;&lt;msub&gt;&lt;mi&gt;λ&lt;/mi&gt;&lt;mi&gt;j&lt;/mi&gt;&lt;/msub&gt;&lt;/mfenced&gt;&lt;mo&gt;/&lt;/mo&gt;&lt;mo&gt;∆&lt;/mo&gt;&lt;mover&gt;&lt;msub&gt;&lt;mi&gt;ρ&lt;/mi&gt;&lt;mi&gt;c&lt;/mi&gt;&lt;/msub&gt;&lt;mo&gt;∼&lt;/mo&gt;&lt;/mover&gt;&lt;mfenced&gt;&lt;msub&gt;&lt;mi&gt;λ&lt;/mi&gt;&lt;mi&gt;i&lt;/mi&gt;&lt;/msub&gt;&lt;/mfenced&gt;&lt;/math&gt;&lt;/span&gt; can serve as a direct approximation of &lt;span&gt;&lt;math&gt;&lt;mo&gt;∆&lt;/mo&gt;&lt;msubsup&gt;&lt;mi&gt;ρ&lt;/mi&gt;&lt;mi&gt;l&lt;/mi&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;/msubsup&gt;&lt;mfenced&gt;&lt;msub&gt;&lt;mi&gt;λ&lt;/mi&gt;&lt;mi&gt;j&lt;/mi&gt;&lt;/msub&gt;&lt;/mfenced&gt;&lt;mo&gt;/&lt;/mo&gt;&lt;mo&gt;∆&lt;/mo&gt;&lt;msubsup&gt;&lt;mi&gt;ρ&lt;/mi&gt;&lt;mi&gt;l&lt;/mi&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;/msubsup&gt;&lt;mfenced&gt;&lt;msub&gt;&lt;mi&gt;λ&lt;/mi&gt;&lt;mi&gt;i&lt;/mi&gt;&lt;/msub&gt;&lt;/mfenced&gt;&lt;/math&gt;&lt;/span&gt;, which was adopted as the basic form of IMCI. Furthermore, under the chlorophyll-induced red-edge shift, we established a quantitative relationship between IMCI and red-edge displacement, confirming its sensitivity to chlorophyll content. Theoretical derivations and assumpt","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115202"},"PeriodicalIF":11.4,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785689","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":"A novel hybrid approach for mapping global surface solar radiation with DSCOVR/EPIC: Combining deep learning with physical algorithm","authors":"Wenjun Tang ,&nbsp;Jinwen Qi ,&nbsp;Junmei He ,&nbsp;Fuxin Zhu ,&nbsp;Chuanming Yuan ,&nbsp;Jinyan Yang ,&nbsp;Bing Hu","doi":"10.1016/j.rse.2025.115200","DOIUrl":"10.1016/j.rse.2025.115200","url":null,"abstract":"<div><div>Surface solar radiation (SSR, also known as global radiation, R_g), is critical for Earth's energy, water, and carbon cycles, yet existing satellite-derived global R_g products suffer from spatial inconsistencies due to multi-source data fusion. To address this, we propose a novel hybrid approach integrating deep learning with physical algorithms using observations from the Deep Space Climate Observatory (DSCOVR/EPIC), positioned at the Sun-Earth Lagrange-1 point and continuously observed the entire portion of the Earth with sunshine. Unlike traditional physical algorithms or machine learning algorithms, this method estimates cloud transmittance via a DenseNet-based convolutional neural network (CNN), calculates clear-sky R_g using a physical parameterization scheme, and combines these to derive all-sky R_g. Meanwhile, the direct and diffuse components (R_dir and R_dif) are further separated from the estimated R_g using a Light Gradient Boosting Machine (LightGBM) model. The method was trained with in-situ observations from the Baseline Surface Radiation Network (BSRN), and further independently evaluated against in-situ observations from three networks of the Solar Radiation (SOLRAD), China Meteorological Administration (CMA) radiation stations and Global Energy Balance Archive (GEBA). Independent evaluation demonstrates that our hybrid method exhibits excellent spatial scalability. Comparative validation against the product of <span><span>Hao et al. (2020)</span></span> derived from DSCOVR/EPIC observations demonstrates our method can generate more accurate global products of R_g, R_dir and R_dif. The innovation of our method lies in integrating machine learning with physical algorithms to leverage their complementary strengths, while overcoming the limitations of high uncertainty associated with cloud optical property retrievals from DSCOVR/EPIC observations. This approach will contribute to the mapping of global spatially consistent radiation products, overcoming the limitations of geostationary and polar-orbiting satellites.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115200"},"PeriodicalIF":11.4,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785694","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":"GPP-net: a robust high-resolution GPP estimation network for Sentinel-2 using only surface reflectance and photosynthetically active radiation","authors":"Shaoyu Wang ,&nbsp;Youngryel Ryu ,&nbsp;Benjamin Dechant ,&nbsp;Helin Zhang ,&nbsp;Huaize Feng ,&nbsp;Jeongho Lee ,&nbsp;Changhyun Choi","doi":"10.1016/j.rse.2025.115198","DOIUrl":"10.1016/j.rse.2025.115198","url":null,"abstract":"<div><div>High-resolution gross primary productivity (GPP) estimation is crucial for ecological and agricultural applications that require fine spatial details to capture GPP heterogeneity. Satellite-based GPP estimation usually relies on land cover and meteorological data. However, the misclassification of land cover data and coarse resolution of meteorological data greatly increase the uncertainty. Here, we propose a robust high-resolution GPP estimation deep learning (DL) network, named GPP-net, using only satellite surface reflectance (SR) from Sentinel-2 and photosynthetically active radiation (PAR). Specifically, GPP-net is based on a fully 1-D convolutional encoder-decoder network combined with a spectral band importance estimation module. To enhance the generalization of GPP-net, we ran the soil-canopy energy balance radiative transfer (SCOPE) model, and then combined these SCOPE-simulated reflectance data with GPP and PAR data extracted from FLUXNET2015 to pre-train GPP-net. Compared to benchmark models including near-infrared reflectance of vegetation multiplied by incoming sunlight (NIRvP), partial least squares (PLS) and random forest (RF), GPP-net improved half-hourly and daily GPP retrieval across seven plant functional types (PFTs) including four forest types, cropland, grassland and wetland. Owing to its robust nonlinear feature learning capabilities, GPP-net also facilitated robust GPP estimation across both C3 and C4 vegetation. We found that GPP-net could reliably estimate GPP under drought and heatwave conditions, with minimal improvement from including vapor pressure deficit (VPD) as a predictor. Furthermore, GPP-net demonstrated great robustness to soil effects in GPP mapping, and had strong ability in capturing inter-annual variability of GPP. The pretraining paradigm enabled us to fully leverage historical data, and the DL framework ensured that the model generalization continually improves as new data is integrated. Our model dispenses with land cover data and minimizes the requirements of coarse-resolution meteorological data for high-resolution GPP estimation, which could support future efforts in global high-resolution GPP mapping.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115198"},"PeriodicalIF":11.4,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145771648","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":"Hyperspectral OCI/PACE observations of the Atlantic Sargassum","authors":"Lin Qi ,&nbsp;Menghua Wang ,&nbsp;Chuanmin Hu ,&nbsp;Yuyuan Xie ,&nbsp;Brian B. Barnes","doi":"10.1016/j.rse.2025.115185","DOIUrl":"10.1016/j.rse.2025.115185","url":null,"abstract":"<div><div>Since the first appearance of the annually recurrent Great Atlantic <em>Sargassum</em> Belt (GASB) in 2011, satellite remote sensing has been used as a primary technique to monitor and track the pelagic <em>Sargassum fluitans/natans</em> in the Atlantic Ocean. The Ocean Color Instrument (OCI) on the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE, 2024–present) mission is a first-ever operational hyperspectral sensor designed to measure the surface ocean's biological and biogeochemical properties at global scale on a near-daily basis, which is expected to provide improved performance over traditional multi-band polar-orbiting ocean color sensors. Here, we evaluate the capacity of OCI in detecting and quantifying the Atlantic <em>Sargassum</em>, referenced against heritage multi-band satellite sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS on Aqua) and Visible Infrared Imaging Radiometer Suite (VIIRS on NOAA-20). Our research involved development of a shared deep-learning <em>Sargassum</em> detection algorithm, fine-tuned for each sensor. As such, we found that OCI not only provided 47% more valid observations (# of valid pixels) than MODIS in the central west Atlantic for the study period of May–August 2024, but from the common valid pixels OCI also detected 51% more <em>Sargassum</em>-containing pixels. While the former is mostly due to the OCI's ability to avoid sun glint, the latter appears to be due to band binning and fewer artifacts around clouds. Using VIIRS as a bridge, it is further found that such a difference between OCI and MODIS is not due to the reported MODIS sensor degradation after 2023, but is inherent to sensor and measurement characteristics. On the other hand, VIIRS also showed more valid observations and detected more <em>Sargassum</em> pixels than MODIS, attributed to the larger swath and a finer spatial resolution, respectively. For these reasons, both OCI and VIIRS detected substantially more <em>Sargassum</em> than MODIS at daily, weekly, and monthly scales, although the spatial distributions and temporal changes of <em>Sargassum</em> revealed by the three sensors are similar. Finally, because of the hyperspectral capability, OCI is the only sensor that can spectrally discriminate <em>Sargassum</em> pixels without ambiguity. Such improved performance will make OCI a unique sensor to map both macroalgae mats and microalgae scums at global scale in both near real-time and retrospective analyses.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115185"},"PeriodicalIF":11.4,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145759817","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":"A synergistic super-ellipsoidal particle shape and ice cloud optical thickness retrieval method based on satellite polarimetric observations","authors":"Yizhen Meng ,&nbsp;Lei Bi ,&nbsp;Wei Han","doi":"10.1016/j.rse.2025.115172","DOIUrl":"10.1016/j.rse.2025.115172","url":null,"abstract":"<div><div>Ice clouds, composed of irregular ice crystals, play a critical role in the Earth's radiative balance and climate regulation. Satellite polarimetric observations, such as those from the POLarization and Directionality of Earth Reflectance-3 (POLDER-3), exhibit high sensitivity to particle characteristics, making them valuable for deriving ice cloud microphysical properties. Conventional ice cloud remote sensing methods typically rely on single-particle models, which assume a prior particle shape across entire regions, thereby neglecting the inherent spatial heterogeneity. Under this context, the super-ellipsoidal particle model was developed, enabling continuous variation in surface morphology through three parameters (i.e., aspect ratio, roundness, and surface roughness), thus facilitating the retrieval of particle shape variations. To comprehensively consider the spatial heterogeneity of ice crystals and assess the effectiveness of the super-ellipsoidal multi-particle model, a synergistic retrieval of particle shape parameters and ice cloud optical thickness (IOT) was conducted across six tropical cyclone (TC) and cloud cases. The retrieval framework was built upon vector radiative transfer simulations derived from the adding-doubling model, linking POLDER-3 observations with the super-ellipsoidal particle models and IOT. The retrieved particle shapes and IOT were validated by comparing re-simulated radiance with satellite observations. The findings indicate an order-of-magnitude enhancement over single-particle models in retrieval performance, with root mean square errors (RMSEs) for normalized radiance decreasing from [0.0371, 0.1063] to [0.0023, 0.0042], and for polarized radiance reducing from [0.0036, 0.0061] to [0.0008, 0.0018]. The proposed novel method offers substantial improvements in retrieving IOT, contributing valuable insights for advancing ice cloud remote sensing techniques.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115172"},"PeriodicalIF":11.4,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145752888","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":"A review of forward modelling and retrieval approaches for forest soil moisture and vegetation optical depth using L-band radiometry","authors":"Andreas Colliander ,&nbsp;Mike Schwank ,&nbsp;Yiwen Zhou ,&nbsp;Mehmet Kurum ,&nbsp;Cristina Vittucci ,&nbsp;Leung Tsang ,&nbsp;Alex Roy ,&nbsp;Aaron Berg","doi":"10.1016/j.rse.2025.115158","DOIUrl":"10.1016/j.rse.2025.115158","url":null,"abstract":"<div><div>Forests are a critical component of the Earth system, accounting for approximately one-third of global photosynthetic activity and carbon storage. They also provide essential habitats for countless species and vital resources for human activities. Low-frequency (L-band; 1–2 GHz) microwave radiometry enables the measurement of forest soil moisture (SM) and L-band vegetation optical depth (L-VOD), offering valuable insights into processes such as tree growth, water infiltration, soil fertility, fuel moisture, carbon stocks, wildfire vulnerability, and biodiversity dynamics. These measurements also support the study of carbon and water fluxes, tree responses to hydrological stress (e.g., drought), and fuel moisture estimation. However, existing algorithms for retrieving SM and L-VOD were primarily developed for low-biomass vegetation types (e.g., grasslands and croplands), differing structurally from forests. This motivates the present review to evaluate the current retrieval approaches, their performance assessment methods, and available validation resources. The review found that systematic uncertainties persist in forest retrievals, despite the demonstrated sensitivity of L-band brightness temperature (TB) to forest SM and L-VOD. Moreover, the focus on non-forest ecosystems has led to a lack of suitable ground truth and reference data for validating forest SM and L-VOD products, and current validation techniques remain underdeveloped. To fully harness the potential of L-band radiometry in forest monitoring, new retrieval algorithms that account for the unique structural and compositional characteristics of forests are required. Additionally, validation efforts must be enhanced both quantitatively and qualitatively—particularly for L-VOD—to improve confidence in these remote sensing products.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"334 ","pages":"Article 115158"},"PeriodicalIF":11.4,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145759818","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}