Remote Sensing of Environment最新文献_第5页

Amplified deviation flood index (ADFI) for fast non-prior flood detection 用于快速非先验洪水检测的放大偏差洪水指数（ADFI）

IF 11.4 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2026-01-19 DOI: 10.1016/j.rse.2026.115258

Hui Zhang , Ming Luo , Zhixin Qi , Xing Li , Yongquan Zhao

Climate change causes widespread increases in the frequency, magnitude, and extent of flood events, which pose increasing threats to societal and natural systems and highlight the urgency for timely and accurate flood mapping. However, previous flood mapping methods often require prior knowledge (such as the timing and location) of flood events that is usually incomplete or even unavailable when studying historical floods. Here we propose a new amplified deviation flood index (ADFI) using the time-series anomaly statistics from the Synthetic Aperture Radar (SAR) data for mapping fully flooded areas without relying on prior knowledge of flood events. ADFI is constructed by considering two fundamentals of flood events: a decrease in backscatter intensity when ground objects are fully flooded and an increase in the variance of backscatter intensity owing to infrequently sudden occurrence of flood events, thus enabling a fast non-prior detection of flood events and extents. The performance of ADFI is assessed in four study areas across different climate zones of the globe, and the assessment shows that the overall accuracies of ADFI in all study areas exceed 93%, with precision >95% and recall >94%. Further comparison with two existing flood indices suggests that our proposed ADFI-based mapping method can improve the overall accuracy by 12.11%–3.97%, precision by 12.59%–10.17%, and recall by 54.32%–6.37%. A time-series flood mapping based on ADFI demonstrates that our proposed method enables a non-prior, precise, and fast detection of flood events and allows prompt monitoring of flood disasters. Our proposed approach enhances the efficiency and scalability of flood monitoring, providing a valuable tool for rapid disaster response and the reconstruction of long-term flood histories across diverse environments and climates.

气候变化导致洪水事件的频率、规模和范围普遍增加，对社会和自然系统构成越来越大的威胁，并突出了及时和准确绘制洪水地图的紧迫性。然而，以往的洪水制图方法往往需要洪水事件的先验知识（如时间和位置），而这些知识在研究历史洪水时通常是不完整的，甚至是不可用的。本文提出了一种新的放大偏差洪水指数（ADFI），该指数利用合成孔径雷达（SAR）数据的时间序列异常统计量来绘制全洪水区域，而不依赖于洪水事件的先验知识。ADFI的构建考虑了洪水事件的两个基本原理：地面物体被完全淹没时，后向散射强度会减小；洪水事件不经常突然发生，后向散射强度的方差会增大，从而可以快速地非先验地检测洪水事件和范围。在全球不同气候带的4个研究区对ADFI的性能进行了评估，评估结果表明，ADFI在所有研究区的总体准确度均超过93%，精密度为95%，召回率为94%。进一步与已有的2个洪水指数进行对比，表明基于adfi的制图方法整体精度提高12.11% ~ 3.97%，精密度提高12.59% ~ 10.17%，查全率提高54.32% ~ 6.37%。基于ADFI的时间序列洪水映射表明，我们提出的方法可以实现对洪水事件的非先验、精确和快速检测，并允许对洪水灾害进行及时监测。我们提出的方法提高了洪水监测的效率和可扩展性，为快速响应灾害和重建不同环境和气候下的长期洪水历史提供了有价值的工具。

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引用次数: 0

A snow properties-aware deep learning framework for penetration bias estimation of TanDEM-X DEMs over ice sheets 用于冰盖上TanDEM-X dem穿透偏差估计的雪属性感知深度学习框架

IF 11.4 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2026-01-17 DOI: 10.1016/j.rse.2026.115243

Alexandre Becker Campos , Antoine Diez-Latteur , José-Luis Bueso-Bello , Matthias H. Braun , Paola Rizzoli

The accurate assessment of glacier volume and mass changes as well as snow depth is crucial for understanding glaciological processes and the impact of climate change. TanDEM-X, an X-band spaceborne interferometric synthetic aperture radar (InSAR) mission, offers global, high-resolution digital elevation models (DEMs) that are invaluable for these studies. However, the inherent variability in radar wave penetration into snow and ice creates challenges in accurately estimating surface elevation changes and snow depth. Variations in the acquisition geometry and snow properties can affect the estimation of the radar mean phase center, leading to penetration bias and an underestimation of the surface topographic height. In this work, we propose a novel deep learning framework for estimating the penetration bias of TanDEM-X DEMs over ice sheets, by combining the knowledge of the physical properties of snow and the InSAR system for the development of a robust regression framework. Due to the lack of extended reference data, which jeopardizes the use of fully-supervised data-driven approaches, we propose a deep learning approach based on two intrinsically connected tasks: a first unsupervised snow facies segmentation model designed to capture the overall properties of the snowpack independently of the single-pass InSAR acquisition geometries; and a subsequent downstream penetration bias regression model. To ensure that the robustness against the InSAR geometries of the first model is preserved, we propose two approaches: first, we employ a fine-tuning approach to transfer the weights of the segmentation model for a downstream regression task, leveraging the knowledge acquired by the pretext segmentation task for the regression of the penetration bias of TanDEM-X DEMs; and second, a multitask learning approach for the downstream task by jointly training both the segmentation and regression models, ensuring that the snow-related feature representations identified during the segmentation task are consistently leveraged to improve the final regression performance. We demonstrate that utilizing the first model as a pretext task improves convergence and overall performance, whereas the multitask approach enables better generalization. Experimental results over the Greenland Ice Sheet during boreal winter, using IceBridge laser altimeter measurements as reference data, demonstrate that our method estimates the penetration bias with a coefficient of determination

R^{2}

= 90% and RMSE of 0.65 m, independently of the InSAR acquisition geometry and snow properties. The work performed here is crucial for enhancing the accuracy of TanDEM-X DEMs over snow and ice-covered regions, thereby improving our understanding of glaciological processes and their climatic responses.

准确评估冰川体积和质量变化以及积雪深度对于理解冰川过程和气候变化的影响至关重要。TanDEM-X是一项x波段星载干涉合成孔径雷达（InSAR）任务，提供全球高分辨率数字高程模型（dem），这对这些研究来说是无价的。然而，雷达波穿透冰雪的固有变异性给准确估计地表高程变化和雪深带来了挑战。采集几何形状和积雪特性的变化会影响雷达平均相位中心的估计，导致侵彻偏差和地表地形高度的低估。在这项工作中，我们提出了一个新的深度学习框架来估计TanDEM-X dem在冰盖上的穿透偏差，通过结合雪的物理性质和InSAR系统的知识来开发一个鲁棒回归框架。由于缺乏扩展的参考数据，这危及了全监督数据驱动方法的使用，我们提出了一种基于两个内在联系的任务的深度学习方法：第一个无监督雪相分割模型，旨在独立于单次InSAR采集几何形状捕获积雪的整体属性；以及随后的下游渗透偏差回归模型。为了确保第一个模型对InSAR几何形状的鲁棒性，我们提出了两种方法：首先，我们采用微调方法将分割模型的权重转移到下游回归任务中，利用从假托分割任务中获得的知识来回归TanDEM-X dem的穿透偏差；其次，通过联合训练分割模型和回归模型，为下游任务提供多任务学习方法，确保在分割任务中识别的雪相关特征表示被一致地利用，以提高最终的回归性能。我们证明，利用第一个模型作为借口任务可以提高收敛性和整体性能，而多任务方法可以更好地泛化。以冰桥激光高度计测量数据为参考数据，在格陵兰冰盖北寒带冬季的实验结果表明，我们的方法估计的穿透偏差与InSAR采集几何形状和雪的性质无关，其决定系数R2R2 = 90%， RMSE为0.65 m。在这里进行的工作对于提高TanDEM-X dem在冰雪覆盖地区的准确性至关重要，从而提高我们对冰川过程及其气候响应的理解。

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引用次数: 0

RPI-GMM: A novel structure-based and phenology-independent algorithm for mapping latest 10-m resolution national-level rubber plantations RPI-GMM：一种基于结构和物候无关的新型算法，用于绘制最新10米分辨率的国家级橡胶种植园

IF 11.4 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2026-01-14 DOI: 10.1016/j.rse.2026.115241

Chiwei Xiao , Zilong Yue , Zhiming Feng , Jinwei Dong , Juliet Lu , Khin Htet Htet Pyone , Khampheng Boudmyxay

Accurate and updated maps of rubber plantations are beneficial to eco-environmental and socio-economic impact assessment and sustainable agroforestry management. However, existing remotely-sensed approaches to identifying rubber plantations primarily rely on phenological signals from time-series optical data, which are limited by persistent cloud cover, regional phenological variability or inconsistency, and high data demands. To address these challenges, here, we propose an innovative phenology-independent framework that integrates a rubber plantation index (RPI) with an unsupervised Gaussian Mixture Model (GMM) classifier. The RPI is a structure sensitive index derived from dual-polarized Sentinel-1 SAR backscatter (VV/VH) and Sentinel-2 SWIR reflectance (Band 11), capturing plantation regularity and canopy moisture characteristics. We evaluated the RPI-GMM framework across six diverse sample areas of rubber plots in tropics representing variations in phenology, topography, and plantation structure. Results demonstrated high classification accuracy, with F1 scores over 0.87 under both phenologically strong and weak conditions, as well as across mountainous and fragmented landscapes. Our RPI-GMM method achieved an overall accuracy of 87.0% in Laos, and estimated 234,206 ha of rubber plots in 2024. Spatial analysis revealed that approximately 70% of rubber plantations are located in Laotian border areas near China and Vietnam, 90% are situated at elevations below 1000 m, and 80% are found on slopes with gradients ranging from 3^° to 16^°. Notably, our simple and integrated method of RPI-GMM requires no temporal or labeled data, ensuring robustness, cost-efficiency, and transferability. The results highlight valuable insights of structure-based SAR-optical fusion for future global or tropical monitoring of tree-plantation dynamics and support broader applications in agroforestry management.

准确和更新的橡胶园地图有利于生态环境和社会经济影响评价以及可持续农林业管理。然而，现有的识别橡胶种植园的遥感方法主要依赖于时间序列光学数据的物候信号，这些数据受到持续云层覆盖、区域物候变化或不一致以及高数据需求的限制。为了解决这些挑战，本文提出了一个创新的物候独立框架，该框架将橡胶种植指数（RPI）与无监督高斯混合模型（GMM）分类器集成在一起。RPI是基于Sentinel-1双偏振SAR后向散射（VV/VH）和Sentinel-2 SWIR反射率（波段11）的结构敏感指数，用于捕获人工林的规律性和冠层水分特征。我们评估了热带地区六个不同橡胶样地的RPI-GMM框架，这些橡胶样地代表了物候、地形和种植园结构的变化。结果表明，无论是物候强还是物候弱，无论是山地还是破碎化景观，分类精度均在0.87以上。我们的RPI-GMM方法在老挝实现了87.0%的总体精度，并在2024年估计了234,206公顷的橡胶地块。空间分析显示，约70%的橡胶种植园位于靠近中国和越南的老挝边境地区，90%的橡胶种植园位于海拔低于1000米的地区，80%的橡胶种植园位于坡度为3°至16°的斜坡上。值得注意的是，我们的简单和集成的RPI-GMM方法不需要时间或标记数据，确保鲁棒性，成本效益和可移植性。这些结果突出了基于结构的sar -光学融合对未来全球或热带人工林动态监测的有价值的见解，并支持在农林业管理中的更广泛应用。

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引用次数: 0

Why do classification models go wrong? The importance of adaptations and acclimations in driving landscape-level spectral variation in Fremont cottonwood 为什么分类模型会出错？适应与驯化在驱动白杨景观级光谱变化中的重要性

IF 11.4 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2026-01-14 DOI: 10.1016/j.rse.2026.115240

M.M. Seeley , B.C. Wiebe , G.P. Asner , A.J. Abraham , H.F. Cooper , C.A. Gehring , K.R. Hultine , G.J. Allan , T.G. Whitham , T. Goulden , C.E. Doughty

Spatially explicit predictions of species distributions can inform ecosystem processes and conservation, particularly under global change. While imaging spectroscopy could enable accurate species classifications, accuracy generally declines outside training regions, limiting its utility for regional-scale mapping. To investigate mechanisms constraining classification generalizability (e.g., spatial autocorrelation, local adaptation), we used National Ecological Observatory Network Airborne Observation Platform imaging spectroscopy data collected across riparian systems in Arizona, Colorado, and Utah. We extracted canopy spectral data of Populus fremontii (Fremont cottonwood), a foundation riparian tree known to form locally adapted ecotypes across its range, and spatially co-occurring species from seventeen 6 × 6 km sites. Combining this library with site-level environmental data, and support vector machine (SVM) models, we observed that environmental, not geographic, distance between training and test sites limited classification generalizability. Specifically, differences in mean annual temperature, winter precipitation, and spring precipitation, key drivers of local adaptation of P. fremontii, were associated with lower classification accuracy (∼50% lower). We then evaluated specific wavelength regions for improved generalizability. Classification models using only near-infrared (750–1400 nm) and shortwave infrared (1400–2500 nm) outperformed those using full-spectrum models in regions not represented in the training data, consistent with lower heritability in visible and red-edge wavelengths. In conclusion, spatially structured spectral phenotypes of P. fremontii, shaped by local adaptation and acclimation to environmental conditions, reduced species classification generalizability. By integrating ecology into remote sensing workflows, such as spectral band selection, we can improve species classification accuracy, thereby advancing scalable biodiversity monitoring and conservation efforts.

物种分布的空间明确预测可以为生态系统过程和保护提供信息，特别是在全球变化的情况下。虽然成像光谱可以实现准确的物种分类，但在训练区域之外，精度通常会下降，限制了其在区域尺度制图中的应用。为了研究限制分类可泛化性的机制（如空间自相关、局部适应），我们使用了美国国家生态观测站网络机载观测平台在亚利桑那州、科罗拉多州和犹他州的河岸系统中收集的成像光谱数据。本文从17个6 × 6 km的地点提取了fremontii （Fremont cottonwood）和fremontii （Fremont cottonwood）的冠层光谱数据。fremontii是一种已知在其分布范围内形成局部适应生态型的基础河岸树种，以及空间上共存的物种。将该库与站点级环境数据和支持向量机（SVM）模型相结合，我们观察到训练和测试站点之间的环境距离（而不是地理距离）限制了分类的泛化性。具体而言，年平均气温、冬季降水和春季降水的差异（fremontii局部适应的关键驱动因素）与较低的分类精度相关（低约50%）。然后，我们评估了特定波长区域，以提高泛化性。仅使用近红外（750-1400 nm）和短波红外（1400-2500 nm）的分类模型在训练数据中未表示的区域优于使用全光谱模型的分类模型，这与可见光和红边波长的遗传率较低一致。综上所述，fremontii的空间结构光谱表型受局部适应和环境条件驯化的影响，降低了物种分类的普遍性。通过将生态学整合到遥感工作流程中，如光谱波段选择，我们可以提高物种分类的准确性，从而推进可扩展的生物多样性监测和保护工作。

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引用次数: 0

AnyTime-CD: Self-supervised change detection in arbitrary-length dense time series of high-resolution remote sensing images 任意长度高分辨率遥感影像密集时间序列的自监督变化检测

IF 11.4 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2026-01-14 DOI: 10.1016/j.rse.2026.115239

Yang Qu , JiaYi Li , Xin Huang

With the continuous enhancement of the observation capabilities of remote sensing satellites, dense multi-temporal high-resolution remote sensing images have become an important data source for monitoring surface changes. However, multi-temporal change detection (MTCD) remains challenging due to inconsistent time intervals, spatial misalignments, and a scarcity of labeled data, all of which hinder the generalization and practical deployment of deep learning models in large-scale applications. To address the above challenges, this paper proposed a multi-task self-supervised change detection framework (i.e., AnyTime-CD), which supports the input of time series of any length and can jointly model temporal-spectral-spatial features without manual annotation. This framework consists of three sub-tasks: temporal dynamic modeling, spectral semantic consistency learning, and spatial feature alignment, which respectively address the issues of irregular time interval, spectral perturbation, and spatial offset, thereby collaboratively optimize network performance. We evaluated AnyTime-CD on two globally distributed multi-temporal high-resolution datasets. The experimental results showed that, under an unsupervised setting, this method achieved performance close to or even surpassing that of the supervised methods. Compared with the best-performing self-supervised method, it achieved relative gains of over 13% (spatial) and 8% (temporal) in F1 scores (using a one-frame tolerance strategy), respectively. After sample fine-tuning, it further achieved F1 scores of 86.40% (spatial) and 79.50% (temporal), exceeding the state-of-the-art supervised method by 4.44% and 9.09%, respectively. Furthermore, AnyTime-CD showed good adaptability to different pre-training configurations. It is worth noting that it exhibited strong robustness under medium and low cloud cover conditions and could even utilize cloud perturbations as data augmentation to further enhance performance. In conclusion, AnyTime-CD offers a flexible and label-free solution for MTCD tasks, suitable for complex, dynamic, and interference-prone remote sensing scenarios, and is expected to promote the application of self-supervised methods in surface monitoring.

随着遥感卫星观测能力的不断增强，密集的多时相高分辨率遥感影像已成为监测地表变化的重要数据源。然而，由于时间间隔不一致、空间错位和标记数据的稀缺性，多时间变化检测（MTCD）仍然具有挑战性，所有这些都阻碍了深度学习模型在大规模应用中的推广和实际部署。针对上述问题，本文提出了一种多任务自监督变化检测框架（AnyTime-CD），该框架支持任意长度的时间序列输入，无需人工标注即可对时间-光谱-空间特征进行联合建模。该框架包括时间动态建模、频谱语义一致性学习和空间特征对齐三个子任务，分别解决了不规则时间间隔、频谱扰动和空间偏移问题，从而协同优化网络性能。我们在两个全球分布的多时相高分辨率数据集上评估了AnyTime-CD。实验结果表明，在无监督的情况下，该方法的性能接近甚至超过了有监督的方法。与表现最好的自监督方法相比，它在F1分数（使用一帧容忍策略）上分别获得了超过13%（空间）和8%（时间）的相对增益。经过样本微调后，该方法的F1得分分别为86.40%（空间）和79.50%（时间），分别比最先进的监督方法高4.44%和9.09%。此外，AnyTime-CD对不同的预训练配置具有良好的适应性。值得注意的是，它在中低云覆盖条件下表现出较强的鲁棒性，甚至可以利用云扰动作为数据增强来进一步提高性能。总之，AnyTime-CD为MTCD任务提供了一种灵活、无标签的解决方案，适用于复杂、动态、易受干扰的遥感场景，有望促进自监督方法在地面监测中的应用。

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引用次数: 0

Introduction to a 45-year (1979–2023) global daily snow cover fraction product from multiple AVHRR satellites with accuracy assessment 介绍了从多个AVHRR卫星获取的45年（1979-2023）全球日积雪分数产品及其精度评估

IF 11.4 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2026-01-14 DOI: 10.1016/j.rse.2026.115235

Xiongxin Xiao , Kathrin Naegeli , Valentina Premier , Shaopeng Li , Christoph Neuhaus , Andreas Wiesmann , Stefan Wunderle

Accurate monitoring of seasonal to decadal snow cover dynamics is essential for climate change attribution and sustainable water resource management. As part of the European Space Agency (ESA) Snow Climate Change Initiative (CCI+) Phase-2 project, this study introduces an Advanced Very High Resolution Radiometer (AVHRR) snow cover fraction (SCF) product portfolio, with a particular emphasis on the development of AVHRR10C1.V4—the first 45-year (1979–2023), global, daily SCF product. This dataset achieves unprecedented temporal consistency by addressing long-standing challenges such as orbital drift, inter-sensor inconsistencies, reduced cloud contamination, and enhanced SCF retrieval accuracy. The AVHRR10C1.V4 product was generated by integrating newly calibrated EUMETSAT AVHRR Fundamental Data Record (FDR) data from 16 AVHRR sensors on a 0.05° grid through a calibration method, modified cloud masking, an updated SCF retrieval method, refined post-processing, and a novel consolidation framework. Comprehensive validation against 66 high-resolution Landsat/Sentinel-2 SCF maps and extensive ground-based snow measurements confirms the robust accuracy of the AVHRR10C1.V4 product: root mean square errors of 16–19% for viewable snow cover fraction (SCFV) and 18–28% for ground snow cover fraction (SCFG), with overall accuracy (OA) ranging from 0.80 to 0.92. The consolidation approach remarkably reduces RMSEs by 7%–48% and lowers missing data rates by ∼30% compared to single-sensor products. The product maintains strong temporal consistency with ERA5-Land SCF product (R > 0.8) and in situ snow measurements. Statistical analysis over the 45-year record and multiple separate periods confirms minimal sensor-drift biases, revealing no statistically significant breakpoints or drift trends (Mann-Kendall p > 0.05). While the SCFV product proves robust to variations in viewing geometry and vegetation conditions, SCFG accuracy is more sensitive to forest cover density—exhibiting substantially increased estimation uncertainties in dense forest canopies (forest cover density > 50%) due to canopy radiative effects and the use of static land cover assumptions. This open-access, climate data record–quality AVHRR10C1.V4 product establishes a critical benchmark for studying snow-climate interactions and long-term cryospheric monitoring. It supports the development of next-generation global SCF products, enabling reliable detection of snow trends, improved hydrological modeling, and informed climate adaptation in a warming climate.

准确监测季节至年代际积雪动态对气候变化归因和可持续水资源管理至关重要。作为欧洲航天局（ESA）积雪气候变化计划（CCI+）第二阶段项目的一部分，本研究介绍了先进甚高分辨率辐射计（AVHRR）积雪覆盖率（SCF）产品组合，特别强调了AVHRR10C1的开发。v4 -第一个45年（1979-2023），全球，每日SCF产品。该数据集通过解决轨道漂移、传感器间不一致、减少云污染和提高SCF检索精度等长期存在的挑战，实现了前所未有的时间一致性。AVHRR10C1。V4产品通过校准方法、改进的云遮蔽、更新的SCF检索方法、改进的后处理和新的整合框架，在0.05°网格上整合来自16个AVHRR传感器的新校准EUMETSAT AVHRR基本数据记录（FDR）数据，生成。针对66张高分辨率Landsat/Sentinel-2 SCF地图和广泛的地面积雪测量进行的综合验证证实了AVHRR10C1的强大准确性。V4产品：可视积雪分数（SCFV）的均方根误差为16-19%，地面积雪分数（SCFG）的均方根误差为18-28%，总体精度（OA）在0.80 ~ 0.92之间。与单传感器产品相比，整合方法显着降低了7%-48%的均方根误差，并降低了约30%的丢失数据率。该产品与ERA5-Land SCF产品（R > 0.8）和现场积雪测量值保持较强的时间一致性。对45年记录和多个独立时期的统计分析证实了最小的传感器漂移偏差，没有显示统计上显著的断点或漂移趋势（Mann-Kendall p > 0.05）。虽然SCFV产品对观测几何形状和植被条件的变化具有鲁强性，但SCFG精度对森林覆盖密度更为敏感——由于冠层辐射效应和使用静态土地覆盖假设，在茂密的森林冠层（森林覆盖密度>； 50%）中，SCFG精度的估计不确定性大大增加。这个开放获取的气候数据记录质量AVHRR10C1。V4产品为研究雪-气候相互作用和长期冰冻圈监测建立了关键基准。它支持下一代全球SCF产品的开发，能够可靠地检测降雪趋势，改进水文建模，并在气候变暖的情况下适应气候。

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引用次数: 0

Multi-satellite derived data reveals spatiotemporal dynamics of carbon-water coupling and its drivers in tropical ecosystems 多卫星数据揭示了热带生态系统碳-水耦合的时空动态及其驱动因素

IF 11.4 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2026-01-13 DOI: 10.1016/j.rse.2026.115242

Xiang Wang , Zheng Fu , Philippe Ciais , Josep Peñuelas , Jingfeng Xiao , Xing Li , Xiangzhong Luo , Chi Chen , Haoyu Xia , Tao Zhou , Paul C. Stoy , Julia K. Green , Fangyue Zhang

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.

气候变化显著影响了热带水分利用效率（WUE），即总初级生产力（GPP）与蒸散（ET）的比值。然而，这些地区WUE的时空动态和控制因素，特别是极端El Niño事件的影响尚不清楚。利用多个卫星衍生的GPP和ET大尺度观测数据集，我们量化了2001 - 2020年的WUE趋势，并评估了2015/16年El Niño干旱对热带地区WUE的影响。我们的分析显示，热带地区的用水效率呈显著上升趋势，增长率为0.007±0.001 g C kg−1 H2O /年（平均±标准差），其中亚洲热带地区的增幅最大（0.01±0.001 g C kg−1 H2O /年）。在空间上，三个独立的遥感驱动数据集一致显示，32%-54%的热带地区WUE显著增加，而只有1%-3%的热带地区WUE显著下降。此外，热带生态系统GPP显著增加（5.47±0.60 g C m−2 yr−1），其中亚洲热带地区增幅最大（11.45±0.37 g C m−2 yr−1），而ET变化较小。这表明热带生态系统的WUE变化主要是由GPP的增加而不是ET的增加驱动的。进一步的分析发现，叶面积是影响热带地区WUE、GPP和ET趋势的主要因素。我们还发现，2015/16年El Niño事件期间的极端干旱导致WUE的净减少（- 0.03±0.01 g C kg - 1 H2O），到2016/17年转变为净增加（0.04±0.01 g C kg - 1 H2O）。与卫星驱动的结果相比，大多数陆地表面模式捕获了热带WUE趋势的方向，但模拟的变化率较慢，模式之间预测的趋势强度存在很大差异。该研究促进了我们对热带生态系统水分利用效率动态的理解，并为预测持续气候变化下未来水分利用效率的变化提供了重要见解，为热带脆弱地区的碳封存和水资源管理策略提供了信息。

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引用次数: 0

Quantification of phytoplankton primary production from space: A revisit based on high-frequency observations with the aid of Himawari-8/AHI 空间浮游植物初级产量的量化：借助Himawari-8/AHI基于高频观测的重访

IF 11.4 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2026-01-13 DOI: 10.1016/j.rse.2026.115238

Zhaoxin Li , Wei Yang , Huangrong Chen , Chong Shi , Husi Letu , Fang Shen

Synoptic quantification of phytoplankton depth-integrated primary production (IPP) has advanced significantly over recent decades by leveraging satellite observations and sophisticated IPP models. However, monthly mean upstream products from polar-orbiting satellites, e.g., the Moderate Resolution Imaging Spectroradiometer (MODIS), are commonly used to generate IPP products, raising a concern about whether neglecting diurnal or daily IPP variabilities may compromise the accuracy of monthly- and annual-scale quantifications. Here, we aim to investigate this concern by comparing IPP quantified using high-frequency data at multiple timescales. A theoretical time-resolved model (TPM) was utilized for IPP modeling, driven by either diurnal photosynthetically available radiation (PAR) from the Advanced Himawari Imager (AHI) onboard Himawari-8 (H8) or daily PAR from MODIS. Preliminary evaluation against in situ measurements corroborated the superiority of AHI-based daily IPP estimation over MODIS, attributed to the robustness of AHI PAR data across varying sky conditions. Satellite IPP products were generated in the full-disk area of H8 between 2016 and 2019 under “daily-to-monthly-to-annual” (DtA) and “monthly-to-annual” (MtA) scenarios for comparison, using other requisite daily and gap-free biogeochemical products. Our analysis unveiled moderate spatiotemporal discrepancies between DtA-based IPP products from AHI and MODIS, confirming an overestimation in MODIS-derived monthly (< 8%) and annual total IPP (∼5%). In contrast, under the MtA scenario, MODIS substantially overestimated monthly (∼14–30%) and annual total IPP (∼20%) and gave biased temporal trends (∼1.3–1.6 times higher) compared to DtA-based IPP estimates of AHI. The discrepancies between IPP products were largely subject to the cloud-induced variabilities in daily PAR products and ocean color data coverage. By upscaling our results to the global ocean, it is anticipated that the annual total IPP previously estimated from MODIS with TPM-like models is overestimated by at least 19%. This study emphasizes the necessity of modeling IPP at finer timescales using high-frequency observations and provides insights for improving IPP quantification with the aid of geostationary satellites.

近几十年来，利用卫星观测和复杂的IPP模型，浮游植物深度综合初级产量（IPP）的天气性量化取得了显著进展。然而，来自极轨卫星的月平均上游产品，例如中分辨率成像光谱仪（MODIS），通常用于生成IPP产品，这引起了人们的关注，即忽略日或日IPP变化是否会损害月和年尺度量化的准确性。在这里，我们的目的是通过比较在多个时间尺度上使用高频数据量化的IPP来研究这一问题。IPP模型采用理论时间分辨模型（TPM），由Himawari-8 （H8）上的高级Himawari成像仪（AHI）的日光合有效辐射（PAR）或MODIS的日PAR驱动。对原位测量的初步评估证实了基于AHI的每日IPP估计优于MODIS，这归因于AHI PAR数据在不同天空条件下的稳健性。利用其他必要的每日和无间隙的生物地球化学产品，在2016 - 2019年H8全盘区域在“日-月-年”（DtA）和“月-年”（MtA）情景下生成卫星IPP产品进行比较。我们的分析揭示了AHI和MODIS基于dta的IPP产品之间存在适度的时空差异，证实了MODIS得出的月度（< 8%）和年度总IPP（~ 5%）的高估。相比之下，在MtA情景下，MODIS大大高估了月度（~ 14-30%）和年度总IPP(~ 20%)，并且与基于dta的AHI IPP估计值相比，给出了有偏差的时间趋势（高~ 1.3-1.6倍）。IPP产品之间的差异主要受日PAR产品和海洋颜色数据覆盖范围的云诱导变化的影响。通过将我们的结果升级到全球海洋，预计以前使用类似tpm模式的MODIS估计的年总IPP至少被高估了19%。本研究强调了利用高频观测在更精细的时间尺度上对IPP进行建模的必要性，并为借助地球静止卫星改进IPP量化提供了见解。

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引用次数: 0

Mapping the structural diversity of Central African and Western US forests using GEDI 利用GEDI绘制中非和美国西部森林结构多样性图

IF 11.4 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2026-01-10 DOI: 10.1016/j.rse.2025.115215

Fabian D. Schneider , Morgan Dean , Elsa M. Ordway , Moses B. Libalah , Antonio Ferraz

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.

这项研究利用NASA的GEDI星载激光雷达绘制了森林结构多样性，这是生态系统多样性的关键组成部分，为支持保护和恢复工作提供了新的机会。以非洲中部和美国西部的生物多样性热点地区为研究对象，通过将GEDI衍生的指标与391 km2的机载激光扫描（ALS）数据进行比较，评估了GEDI捕获森林冠层结构空间变化的能力。森林结构特征以1 km2的分辨率进行评估，GEDI显示与ALS有很强的相关性，特别是在茂密和平坦的中非森林中（r2高达0.85），在加利福尼亚内华达山脉更为异质性的地形中（r2高达0.55），GEDI与ALS的相关性中等。我们使用基于概率密度的多元多样性框架将结构多样性量化为冠层结构的水平变化。GEDI冠层高度（rh98）、冠层盖度和叶高多样性共同捕获了冠层变化的独立轴，并解释了壁面-壁面ALS的结构多样性（r2 = 0.37, 1 km2）。结果显示，美国西部的中高海拔和沿海森林以及中非的森林-稀树草原过渡和火山山脉的结构多样性很高，与扰动、地形和干旱的梯度一致。尽管采样密度、波形噪声和地形复杂性存在局限性，但该研究表明，GEDI的足迹级指标可以直接用于量化森林结构多样性。这些发现强调了星载激光雷达在提供可扩展的、基于特征的结构多样性指标方面的潜力，同时强调了解释仍然依赖于环境，并受益于与生态数据、环境驱动因素和干扰制度的整合。

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引用次数: 0

Unveiling the long-term cascading effects of the 2018 Baige landslide and subsequent outburst flood with satellite radar observations 利用卫星雷达观测揭示2018年白葛山滑坡及其溃决洪水的长期级联效应

IF 11.4 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2026-01-10 DOI: 10.1016/j.rse.2026.115231

Bo Chen , Zhenhong Li , Chuang Song , Roberto Tomás , Chen Yu , Wu Zhu , Jianbing Peng

Landslide-dammed lakes (LDL) and landslide lake outburst flood (LLOF) can significantly alter the kinematic behavior of upstream and downstream landslides, posing severe threats to human life and infrastructure. However, the long-term impacts of LDL and LLOF on surrounding landslide stability remain poorly understood. In this study, we systematically examine the cascading effects triggered by the 2018 Baige LDL and LLOF on adjacent landslides, based on time series interferometric synthetic aperture radar (InSAR) analysis of 1437 satellite radar images. Unlike previous studies that focused on individual landslides or localized areas, we developed an automated method to detect the onset of landslide acceleration, leading to the establishment of an inventory of 65 accelerated landslides (ALs) and a quantitative evaluation of their controlling factors. Our results show that approximately 30 % of the flood-affected active landslides changed their deformation mechanisms, which can be categorized into five distinct types. Among the landslides accelerated by the Baige event, 43 % exhibited persistent acceleration, whereas 57 % showed signs of self-recovery. For the latter, deformation velocity typically decayed by 90 % within an average of 9.3 years after the outburst, returning to near pre-event levels. Furthermore, compared to 378 flood-involved but non-ALs, ALs preferentially occur on gentler slopes and in areas with lower vegetation cover. More notably, those ALs generally experienced greater flood depth, higher flow velocity, and stronger flood power. This study is the first to assess the long-lasting cascading effects of LDL and LLOF on creep landslides. These findings advance our understanding of LDL and LLOF-induced landslide mechanisms and offer valuable insights for the long-term risk assessment and geohazard mitigation of landslide-prone regions affected by similar cascading processes.

滑坡堰塞湖（LDL）和滑坡湖溃决洪水（LLOF）可以显著改变上下游滑坡的运动行为，对人类生命和基础设施构成严重威胁。然而，低密度脂蛋白和低密度脂蛋白对周围滑坡稳定性的长期影响仍然知之甚少。基于时间序列干涉合成孔径雷达（InSAR）对1437张卫星雷达图像的分析，系统研究了2018年白格低密度脂蛋白和低密度脂蛋白对相邻滑坡的级联效应。与以往专注于单个滑坡或局部区域的研究不同，我们开发了一种自动检测滑坡加速开始的方法，从而建立了65个加速滑坡（al）的清单，并对其控制因素进行了定量评估。结果表明，约30%的受洪水影响的活动性滑坡发生了变形机制的改变，可分为5种不同的类型。在因白葛山事件加速的滑坡中，43%表现出持续加速，57%表现出自我恢复的迹象。对于后者，变形速度通常在突出后平均9.3年内衰减90%，恢复到接近事件前的水平。此外，与378种与洪水有关但非ALs相比，ALs优先发生在较平缓的斜坡和植被覆盖较低的地区。更值得注意的是，这些al普遍具有更大的洪水深度，更高的流速和更强的洪水功率。这项研究首次评估了LDL和LLOF对蠕变滑坡的长期级联效应。这些发现促进了我们对低密度脂蛋白和低密度脂蛋白诱发的滑坡机制的理解，并为受类似级联过程影响的滑坡易发地区的长期风险评估和地质灾害缓解提供了有价值的见解。

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