Remote Sensing of Environment最新文献_第9页

A novel SIF framework for decoupling hierarchical water stress impacts on winter wheat photosynthesis 一种解耦等级水分胁迫对冬小麦光合作用影响的SIF框架

IF 11.4 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-12-24 DOI: 10.1016/j.rse.2025.115216

Kaiqi Du , Guilong Xiao , Xia Jing , Jie Zhang , Tingting Zhao , Xuecao Li , Yelu Zeng , Jianxi Huang

Drought has emerged as a critical constraint on sustainable agricultural development, particularly in water-scarce agroecosystems where multiscale hydrological stresses—such as rainfall deficits, soil moisture depletion, and groundwater exhaustion—interact to undermine crop productivity stability. However, current remote sensing frameworks lack the capacity to isolate and quantify the independent impacts of groundwater drought on crop photosynthesis and yield, leading to long-standing underestimation of deep-layer hydrological stress, especially in groundwater-dependent regions. To address this gap, we have proposed a dynamic monitoring framework based on solar-induced chlorophyll fluorescence (SIF) to assess the photosynthetic response characteristics of winter wheat across the Huang-Huai-Hai Plain under precipitation, surface moisture, and groundwater anomalies. This framework integrates dynamic time warping (DTW) for interannual phenological alignment and constructs a quantile-based dynamic baseline library that overcomes the limitations of traditional normality-based anomaly metrics. On this basis, we have developed a novel photosynthetic response anomaly index (PRAI) to characterize the spatiotemporal evolution of drought-induced photosynthetic stress. Results reveal that groundwater anomalies induce a significantly lagged crop response (mean lag ≈ +2.1 months, p < 0.01) and exert stronger influence on photosynthetic dynamics than soil surface moisture or rainfall. PRAI exhibits more concentrated and persistent negative anomalies during groundwater drought years, correlating more strongly with yield loss (R² = 0.53) than during meteorological drought years (R² = 0.30). Cross-validation using evapotranspiration (ET) and vegetation optical depth (VOD) further confirms PRAI reliability in capturing physiological stress. The proposed SIF-based dynamic monitoring framework not only deepens the understanding of crop eco-physiological response mechanisms to multiscale water stress, but also provides critical scientific support and methodological innovations for regional scale precision agriculture, crop model optimization, and sustainable water resource management.

干旱已成为可持续农业发展的关键制约因素，特别是在缺水的农业生态系统中，多尺度水文压力（如降雨不足、土壤水分枯竭和地下水枯竭）相互作用，破坏了作物生产力的稳定性。然而，目前的遥感框架缺乏分离和量化地下水干旱对作物光合作用和产量的独立影响的能力，导致长期低估深层水文应力，特别是在依赖地下水的地区。为了解决这一问题，我们提出了一个基于太阳诱导叶绿素荧光（SIF）的动态监测框架，以评估黄淮海平原冬小麦在降水、地表湿度和地下水异常下的光合响应特征。该框架集成了动态时间规整（DTW）用于年际物候比对，并构建了一个基于分位数的动态基线库，克服了传统基于正态的异常度量的局限性。在此基础上，我们建立了一个新的光合响应异常指数（PRAI）来表征干旱诱导的光合胁迫的时空演变。结果表明，地下水异常导致作物响应显著滞后（平均滞后≈+2.1个月，p < 0.01），对光合动态的影响强于土壤表面水分和降雨。PRAI在地下水干旱年表现出更集中和持续的负异常，与产量损失的相关性（R2 = 0.53）比气象干旱年更强（R2 = 0.30）。利用蒸散发（ET）和植被光学深度（VOD）进行交叉验证进一步证实了PRAI在捕捉生理胁迫方面的可靠性。基于sif的作物动态监测框架不仅深化了对作物多尺度水分胁迫生态生理响应机制的认识，而且为区域尺度精准农业、作物模式优化和水资源可持续管理提供了重要的科学支持和方法创新。

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引用次数: 0

IHMSC: A novel iterative hybrid multiple scattering-corrected retrieval method for enhancing accuracy in ocean lidar profiling inversions IHMSC：一种提高海洋激光雷达剖面反演精度的迭代混合多重散射校正反演方法

IF 11.4 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-12-23 DOI: 10.1016/j.rse.2025.115209

Xinye Li , Siqi Zhang , Peng Chen , Zhanhua Zhang , Delu Pan

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 (K_d, bbp) to biogeochemical parameters (Chl, POC). Compared with the satellite ocean color products, the retrieval results derived from airborne and shipborne lidar observations show strong agreement: K_d (R = 0.76, RMSD = 0.01 m⁻¹, MAPD = 6.58 %), bbp (R = 0.80, RMSD = 0.00 m⁻¹, MAPD = 28.93 %), Chl (R = 0.61, RMSD = 0.29 μg/L, MAPD = 32.82 %), and POC (R = 0.88, RMSD = 20.55 μg/L, MAPD = 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.

海洋激光雷达技术是一种新兴的主动遥感技术，在揭示海洋次表层垂直结构，解决碳通量、浮游植物分析和生物地球化学监测等方面的挑战。然而，目前的激光雷达反演方法依赖于均匀水域的经验公式，忽略了光子多次散射，这带来了很大的不确定性，特别是在复杂的沿海生态系统中。针对这一问题，提出了一种基于南海117,456条垂直剖面（2017-2024）的迭代混合多重散射校正反演方法。该模型结合了后向散射衰减比、激光雷达比的优化和多重散射效应的半解析模拟，并结合XGBoost机器学习，将激光雷达衍生的光学特性（Kd, bbp）与生物地球化学参数（Chl， POC）联系起来。与卫星海洋颜色反演结果相比，机载和舰载激光雷达反演结果一致：Kd （R = 0.76, RMSD = 0.01 m−1,MAPD = 6.58%）、bbp （R = 0.80, RMSD = 0.00 m−1,MAPD = 28.93%）、Chl （R = 0.61, RMSD = 0.29 μg/L, MAPD = 32.82%）和POC （R = 0.88, RMSD = 20.55 μg/L, MAPD = 18.14%）。这些结果架起了主动和被动遥感的桥梁。研究还揭示了南海浮游植物地下层的三维动态特征，揭示了受环境因子影响的时空异质性。近岸次表层浮游植物层在潮汐作用下呈现出厚度和强度的日变化：白天增厚上升，夜晚变薄下降。较大的潮汐振幅与较浅的地层和较高的叶绿素-a浓度有关。这些发现证明了激光雷达技术在大规模、长期监测地下海洋剖面方面的潜力，为原位和被动卫星遥感数据提供了重要补充。

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引用次数: 0

Retrieving forest LAI from Landsat via 3D look-up table generated by realistic LiDAR scenes 利用真实LiDAR场景生成的三维查表从Landsat检索森林LAI

IF 11.4 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-12-18 DOI: 10.1016/j.rse.2025.115204

Jianbo Qi , Siying He , Xun Zhao , Su Ye , Tianjia Chu , Zhexiu Yu , Simei Lin , Huaguo Huang

<div><div>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<sup>2</sup>/m<sup>2</sup> and MAE from 0.73 to 1.00 m<sup>2</sup>/m<sup>2</sup>. 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

叶面积指数（Leaf Area Index， LAI）作为描述森林植被结构的重要生物物理参数，是评价森林生态系统功能和健康状况的重要指标。从遥感观测中获取LAI主要依赖于冠层辐射传输模型（RTMs），该模型定量表征了冠层参数与反射率之间的复杂关系。然而，目前用于LAI检索的大多数物理模型都是一维（1D） RTMs，这些模型通常假设冠层在水平方向上是均匀的，因此无法捕获冠层内部固有的异质性。虽然三维RTMs能更好地表征森林冠层结构的复杂性，但其计算量大、参数化困难，往往限制了其在大尺度遥感反演中的应用。本研究通过整合基于lidar的场景重建来参数化RTM，考虑森林内部的异质性，开发了一种新的基于Landsat的森林LAI三维查找表（3D- lut）方法。我们的方法不是使用理想的均匀层或简单的几何对象，而是使用机载激光雷达数据重建典型森林类型的真实和结构代表性的三维森林场景，包括落叶阔叶林（DBF）、落叶针叶林（DNF）、常绿阔叶林（EBF）和常绿针叶林（ENF）。基于这些重建的森林场景，利用基于三维路径长度分布和光谱不变性理论的精确高效的RTM分析模型PATH_RT，耦合三维RTM大尺度遥感数据和图像模拟（LESS），构建了特定类型的LAI查找表（LUTs），实现了对Landsat影像的精确LAI检索。将该方法与16个国家生态观测站网络（NEON）站点和8个综合碳观测系统（ICOS）站点收集的野外观测数据进行了比较，这些站点包含不同森林类型的代表性样本。此外，利用高分辨率全球陆地表面卫星（Hi-GLASS） LAI产品、简化二级原型处理器（SL2P）算法和MODIS LAI产品进行了对比。实测数据验证表明，该算法能够实现4种森林类型LAI的高精度检索，RMSE范围为0.93 ~ 1.20 m2/m2， MAE范围为0.73 ~ 1.00 m2/m2。对比结果表明，基于PROSAIL模型的检索算法（如SL2P）倾向于低估森林LAI。与基于深度学习框架的Hi-GLASS LAI产品和基于3D RTM的MODIS LAI产品相比，该算法总体上具有较强的一致性，支持了其在区域尺度森林LAI检索中的可靠性。利用激光雷达数据生成真实重建的三维森林结构模拟数据集，进一步推进了激光雷达在定量遥感检索中的应用。

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引用次数: 0

A novel land surface temperature retrieval method using channel correlation for atmospheric parameter modeling from SDGSAT-1 data 基于通道相关的SDGSAT-1大气参数模拟地表温度反演方法

IF 11.4 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-12-18 DOI: 10.1016/j.rse.2025.115190

Li-Qin Cao , Hang Zhao , Du Wang , Yan-Fei Zhong , Fa-Wang Ye

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.

可持续发展科学卫星-1 （SDGSAT-1）上的热红外光谱仪（TIS）具有三个独特的通道，与以前的热红外多通道传感器相比具有更宽的光谱范围。分窗（SW）和温度发射率分离（TES）算法适用于从TIS数据中检索地表温度（LST）。但是，遥感和TES都需要辅助信息，辅助信息的时空不一致会导致LST检索出现错误。本文提出了一种宽带大气校正TES算法，该算法可以在没有任何辅助大气和地表参数输入的情况下检索地表温度。利用宽带成像的稳定性，对大气透过率和向上辐射进行建模，从而减少辐射传递方程中的未知量。此外，还包含透光率细化模块，迭代地细化透光率。在模拟数据集上进行的实验表明，该方法的RMSE为1.32 K，在估计透光率下保持在1.39 K稳定，对水蒸气含量的变化具有较强的鲁棒性。与MODIS温度产品相比，武汉地区的交叉验证结果显示偏差为−1.79 K， RMSE为2.28 K，表明反演的地表温度捕获了更详细的信息。选择黑河、SURFRAD、ICOS、TERN和BSRN站的108个验证点进行地面验证，与通用分割窗（GSW）算法和modtrans - tes算法进行比较，得到的均方根误差（RMSE）分别为2.07 K、1.55 K、1.84 K、1.72 K和2.14 K，所有验证点的RMSE均为1.95 K。这些结果比GSW和modtrans - tes分别提高了0.25 K和0.55 K，证实了所提出方法的高精度。

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引用次数: 0

Sentinel-1 imagery for wide-scale quantitative landslide vulnerability assessment of buildings 大尺度定量滑坡易损性评价的Sentinel-1图像

IF 11.4 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-12-18 DOI: 10.1016/j.rse.2025.115199

Francesco Poggi , Francesco Caleca , Olga Nardini , Francesco Barbadori , Matteo Del Soldato , Claudio De Luca , Francesco Casu , Manuela Bonano , Riccardo Lanari , Veronica Tofani , Federico Raspini

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.

山体滑坡的发生可能在全球范围内造成相当大的经济影响，导致暴露的结构和基础设施受损，包括建筑物。为了确定最有效的减轻风险战略，科学界正在进行旨在评估滑坡激活/再激活的预期后果的分析。该方法涉及实施定量风险评估程序，基于滑坡易感性和强度的定义，暴露元素的识别，以及脆弱性评估，这是最具挑战性的参数。本文提出了一种通过经验易损性和易损性曲线来定量评估受慢动滑坡影响的建筑物易损性的方法，该方法表达了暴露建筑物的破坏程度与滑坡强度之间的概率关系。建立了一个综合数据库，整理了意大利中部亚平宁山脉北部4000多座建筑因山体滑坡而受损的信息。滑坡强度是通过利用全球免费获取的Sentinel-1 SAR图像来评估的，这些图像是通过平行小基线(P-SBAS) -差分SAR干涉测量（DInSAR）处理链处理的。意大利国家研究委员会（CNR）的环境电磁传感研究所（IREA）负责处理Sentinel-1 SAR图像，在与意大利环境和能源安全部（MASE）达成的协议框架内，用于探测和分析整个意大利领土的地面位移。本研究的最终目的是将导出的脆弱性曲线应用于定量风险评估程序，对亚平宁山脉北部的建筑物进行综合评估。

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引用次数: 0

Development of an interference mitigation chlorophyll index for mitigating soil and canopy dependence to improve vegetation chlorophyll content monitoring 建立干扰缓解叶绿素指数以减轻对土壤和冠层的依赖，以改善植被叶绿素含量监测

IF 11.4 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-12-18 DOI: 10.1016/j.rse.2025.115202

Tao Sun , Zijun Tang , Youzhen Xiang , Junsheng Lu , Yaohui Cai , Wangyang Li , Ruiqi Du , Xianghui Lu , Shouyang Liu , Tianjie Zhao , Zhijun Li , Fucang Zhang

<div><div>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.</div><div>The development of IMCI consists of three steps. First, the soil single-scattering component (<span><math><msubsup><mi>ρ</mi><mi>s</mi><mn>1</mn></msubsup></math></span>) 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 <span><math><msubsup><mi>ρ</mi><mi>s</mi><mn>1</mn></msubsup></math></span>, yielding soil-adjusted TOC spectra (<span><math><mover><msub><mi>ρ</mi><mi>c</mi></msub><mo>∼</mo></mover></math></span>). Second, a proxy for the vegetation single-scattering component (<span><math><msubsup><mi>ρ</mi><mi>l</mi><mn>1</mn></msubsup></math></span>) is constructed from soil-adjusted red-edge difference–ratio reflectance values (<span><math><mo>∆</mo><mover><msub><mi>ρ</mi><mi>c</mi></msub><mo>∼</mo></mover><mfenced><msub><mi>λ</mi><mi>j</mi></msub></mfenced><mo>/</mo><mo>∆</mo><mover><msub><mi>ρ</mi><mi>c</mi></msub><mo>∼</mo></mover><mfenced><msub><mi>λ</mi><mi>i</mi></msub></mfenced></math></span>). Exploiting the linearity of the red-edge, we demonstrated that the extrapolated intersection of <span><math><mo>∆</mo><mover><msub><mi>ρ</mi><mi>c</mi></msub><mo>∼</mo></mover></math></span> and <span><math><msubsup><mi>ρ</mi><mi>l</mi><mn>1</mn></msubsup></math></span> in the red-edge region converges to a reflectance value approaching 0 and further derived that <span><math><mo>∆</mo><mover><msub><mi>ρ</mi><mi>c</mi></msub><mo>∼</mo></mover><mfenced><msub><mi>λ</mi><mi>j</mi></msub></mfenced><mo>/</mo><mo>∆</mo><mover><msub><mi>ρ</mi><mi>c</mi></msub><mo>∼</mo></mover><mfenced><msub><mi>λ</mi><mi>i</mi></msub></mfenced></math></span> can serve as a direct approximation of <span><math><mo>∆</mo><msubsup><mi>ρ</mi><mi>l</mi><mn>1</mn></msubsup><mfenced><msub><mi>λ</mi><mi>j</mi></msub></mfenced><mo>/</mo><mo>∆</mo><msubsup><mi>ρ</mi><mi>l</mi><mn>1</mn></msubsup><mfenced><msub><mi>λ</mi><mi>i</mi></msub></mfenced></math></span>, 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

叶绿素是光合作用的基本组分，是植被定量遥感的关键指标。然而，利用冠层顶部（TOC）反射率准确反演叶片叶绿素含量常常受到土壤背景和冠层结构效应的阻碍。为了应对这些挑战，我们开发了干扰缓解叶绿素指数（IMCI），该指数来源于叶片和土壤的光谱特性以及冠层辐射传输过程，以增强在复杂土壤和冠层条件下的鲁棒性。儿童疾病综合管理的发展分为三个步骤。首先，利用两个光谱特征从TOC反射率中去除土壤单散射分量（ρs1）：(a)无论叶片颜色如何，植被反射率在598和694 nm处保持相等，而土壤反射率不同；(b)在590 ~ 750 nm范围内，土壤反射率与波长呈近似线性关系。这些特征能够精确估计和校正ρs1，从而产生土壤调整的TOC谱（ρc ~）。其次，利用土壤调整后的红边差比反射率值（∆ρc ~ λj/∆ρc ~ λi）构建植被单散射分量（ρl1）的代表。利用红边的线性，我们证明了红边区域的∆ρc ~与ρl1的外推相交收敛于接近0的反射率值，并进一步推导出∆ρc ~ λj/∆ρc ~ λi可以作为∆ρl1λj/∆ρl1λi的直接近似，并将其作为IMCI的基本形式。此外，在叶绿素诱导的红边位移下，我们建立了IMCI与红边位移之间的定量关系，证实了其对叶绿素含量的敏感性。这些步骤背后的理论推导和假设得到了广泛的经验验证。利用多物种、多尺度的野外数据集和综合数据集对所提出的综合综合指数进行了评价。结果表明，估算值与实测值具有较强的一致性，R2在0.87 ~ 0.97之间，RMSE在2.87 ~ 6.47 μg·cm-2之间。对不同空间分辨率高光谱图像的测试进一步证实了IMCI在异质冠层中的鲁棒性。总体而言，IMCI提高了TOC反射率估算叶绿素的准确性和可靠性，为植被生理学定量遥感和实际监测应用提供了新的见解。

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引用次数: 0

A novel hybrid approach for mapping global surface solar radiation with DSCOVR/EPIC: Combining deep learning with physical algorithm 基于DSCOVR/EPIC的全球表面太阳辐射制图新方法：深度学习与物理算法相结合

IF 11.4 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-12-17 DOI: 10.1016/j.rse.2025.115200

Wenjun Tang , Jinwen Qi , Junmei He , Fuxin Zhu , Chuanming Yuan , Jinyan Yang , Bing Hu

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 Hao et al. (2020) 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.

地表太阳辐射（SSR，也称为全球辐射，Rg）对地球的能量、水和碳循环至关重要，但由于多源数据融合，现有的卫星衍生全球太阳辐射产品存在空间不一致性。为了解决这个问题，我们提出了一种新的混合方法，将深度学习与物理算法结合起来，利用深空气候观测站（DSCOVR/EPIC）的观测数据，该观测站位于太阳-地球拉格朗日-1点，连续观测地球的整个部分。与传统的物理算法或机器学习算法不同，该方法通过基于densenet的卷积神经网络（CNN）估计云透射率，使用物理参数化方案计算晴空Rg，并将这些结合起来得出全天Rg。同时，使用光梯度增强机（Light Gradient Boosting Machine, LightGBM）模型将直接分量和漫射分量（Rdir和Rdif）与估计的Rg进一步分离。利用基线地面辐射网（BSRN）的现场观测数据对方法进行了训练，并进一步与太阳辐射网（SOLRAD）、中国气象局（CMA）辐射站和全球能量平衡档案（GEBA）三个网络的现场观测数据进行了独立评估。独立评价表明，我们的混合方法具有良好的空间可扩展性。与Hao et al.（2020）从DSCOVR/EPIC观测中得到的产品进行比较验证表明，我们的方法可以生成更准确的Rg， Rdir和Rdif的全球产品。该方法的创新之处在于将机器学习与物理算法相结合，以利用它们的互补优势，同时克服了从DSCOVR/EPIC观测中检索云光学特性的高不确定性的局限性。这种方法将有助于绘制全球空间一致辐射产品的地图，克服地球静止卫星和极轨卫星的局限性。

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引用次数: 0

GPP-net: a robust high-resolution GPP estimation network for Sentinel-2 using only surface reflectance and photosynthetically active radiation GPP-net: Sentinel-2的高分辨率GPP估计网络，仅使用表面反射率和光合有效辐射

IF 11.4 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-12-16 DOI: 10.1016/j.rse.2025.115198

Shaoyu Wang , Youngryel Ryu , Benjamin Dechant , Helin Zhang , Huaize Feng , Jeongho Lee , Changhyun Choi

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.

高分辨率的总初级生产力（GPP）估算对于需要精细空间细节来捕捉GPP异质性的生态和农业应用至关重要。基于卫星的GPP估算通常依赖于土地覆盖和气象数据。然而，土地覆被数据的错误分类和气象数据的粗分辨率大大增加了不确定性。在这里，我们提出了一个鲁棒的高分辨率GPP估计深度学习（DL）网络，命名为GPP-net，仅使用来自Sentinel-2的卫星表面反射率（SR）和光合有效辐射（PAR）。具体来说，GPP-net是基于一个全一维卷积编码器-解码器网络，并结合了一个频谱频带重要性估计模块。为了提高GPP-net的泛化能力，我们运行了土壤-冠层能量平衡辐射传输（SCOPE）模型，然后将SCOPE模拟的反射率数据与FLUXNET2015提取的GPP和PAR数据相结合，对GPP-net进行了预训练。与包括植被近红外反射率乘以入射阳光（NIRvP）、偏最小二乘（PLS）和随机森林（RF）在内的基准模型相比，GPP-net提高了包括四种森林类型、农田、草地和湿地在内的七种植物功能类型（PFTs）的半小时和每日GPP检索。由于其鲁棒的非线性特征学习能力，GPP-net还可以实现C3和C4植被的鲁棒GPP估计。我们发现GPP-net可以可靠地估计干旱和热浪条件下的GPP，而将蒸汽压差（VPD）作为预测因子的改进很小。此外，GPP-net在GPP制图中对土壤效应具有较强的鲁棒性，具有较强的捕捉GPP年际变化的能力。预训练范式使我们能够充分利用历史数据，DL框架确保随着新数据的集成，模型泛化能力不断提高。我们的模型省去了土地覆盖数据，并最大限度地减少了对高分辨率GPP估算的粗分辨率气象数据的要求，这可以支持未来全球高分辨率GPP制图的努力。

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引用次数: 0

Hyperspectral OCI/PACE observations of the Atlantic Sargassum 大西洋马尾藻的高光谱OCI/PACE观测

IF 11.4 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-12-15 DOI: 10.1016/j.rse.2025.115185

Lin Qi , Menghua Wang , Chuanmin Hu , Yuyuan Xie , Brian B. Barnes

Since the first appearance of the annually recurrent Great Atlantic Sargassum Belt (GASB) in 2011, satellite remote sensing has been used as a primary technique to monitor and track the pelagic Sargassum fluitans/natans 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 Sargassum, 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 Sargassum 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 Sargassum-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 Sargassum pixels than MODIS, attributed to the larger swath and a finer spatial resolution, respectively. For these reasons, both OCI and VIIRS detected substantially more Sargassum than MODIS at daily, weekly, and monthly scales, although the spatial distributions and temporal changes of Sargassum revealed by the three sensors are similar. Finally, because of the hyperspectral capability, OCI is the only sensor that can spectrally discriminate Sargassum 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.

自2011年每年周期性的大大西洋马尾藻带首次出现以来，卫星遥感一直被用作监测和跟踪大西洋中上层马尾藻的主要技术。浮游生物、气溶胶、云、海洋生态系统（PACE， 2024年至今）任务上的海洋颜色仪器（OCI）是有史以来第一个可操作的高光谱传感器，旨在几乎每天测量全球范围内海洋表面的生物和生物地球化学特性，预计将提供比传统多波段极轨道海洋颜色传感器更好的性能。在这里，我们评估了OCI在探测和量化大西洋马尾藻方面的能力，参考了传统的多波段卫星传感器，如Aqua上的中分辨率成像光谱仪（MODIS）和NOAA-20上的可见红外成像辐射计套件（VIIRS）。我们的研究涉及开发共享的深度学习马尾藻检测算法，并对每个传感器进行微调。因此，我们发现，在2024年5月至8月的研究期间，OCI不仅比MODIS在大西洋中西部提供了47%的有效观测值（有效像元数），而且从普通有效像元中，OCI检测到的含sarg假设的像元也多51%。前者主要是由于OCI有能力避免太阳的闪光，而后者似乎是由于带化和云周围的人工制品较少。利用VIIRS作为桥梁，进一步发现OCI和MODIS之间的这种差异不是由于2023年之后报道的MODIS传感器退化，而是传感器和测量特性固有的。另一方面，VIIRS也比MODIS显示了更有效的观测结果，并检测到更多的马尾藻像素，这分别归功于更大的条带和更精细的空间分辨率。由于这些原因，OCI和VIIRS在日、周、月尺度上对马尾藻的探测明显多于MODIS，尽管这三种传感器所揭示的马尾藻的空间分布和时间变化相似。最后，由于具有高光谱特性，OCI是唯一能够准确分辨马尾藻像素的传感器。这种改进的性能将使OCI成为一种独特的传感器，可以在近实时和回顾性分析中绘制全球范围内的大藻垫和微藻浮渣。

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引用次数: 0

A synergistic super-ellipsoidal particle shape and ice cloud optical thickness retrieval method based on satellite polarimetric observations 基于卫星极化观测的超椭球粒子形状和冰云光学厚度协同反演方法

IF 11.4 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-12-15 DOI: 10.1016/j.rse.2025.115172

Yizhen Meng , Lei Bi , Wei Han

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.

由不规则冰晶组成的冰云在地球的辐射平衡和气候调节中起着至关重要的作用。卫星极化观测，例如来自地球反射率-3 （POLDER-3）偏振和方向性的观测，对粒子特征表现出很高的灵敏度，这使得它们对推导冰云微物理特性很有价值。传统的冰云遥感方法通常依赖于单粒子模型，该模型假设整个区域的先验粒子形状，从而忽略了固有的空间异质性。在此背景下，建立了超椭球粒子模型，通过三个参数（即纵横比、圆度和表面粗糙度）实现表面形貌的连续变化，从而便于颗粒形状变化的检索。为了综合考虑冰晶的空间异质性，评估超椭球多粒子模型的有效性，在6个热带气旋（TC）和云的情况下，对粒子形状参数和冰云光学厚度（IOT）进行了协同检索。检索框架建立在从添加加倍模型导出的矢量辐射传输模拟的基础上，将POLDER-3观测与超椭球粒子模型和物联网联系起来。通过将重新模拟的辐射与卫星观测结果进行比较，验证了检索到的粒子形状和物联网。结果表明，与单粒子模型相比，该模型的检索性能提高了一个数量级，归一化辐射的均方根误差（rmse）从[0.0371,0.1063]降至[0.0023,0.0042]，极化辐射的均方根误差（rmse）从[0.0036,0.0061]降至[0.0008,0.0018]。提出的新方法在检索物联网方面提供了实质性的改进，为推进冰云遥感技术提供了有价值的见解。

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