Science of Remote Sensing最新文献

Satellite remote sensing of hydro-biogeochemical responses to near-coastal water dynamics in global river mouth areas 全球河口地区近海岸水动力的水文生物地球化学响应卫星遥感研究

IF 5.2 Q1 ENVIRONMENTAL SCIENCES

Science of Remote Sensing

Pub Date : 2026-06-01 Epub Date: 2026-01-23 DOI: 10.1016/j.srs.2026.100379

Youngwook Kim , Ji-Hyung Park , Jinyang Du

Land-margin ecosystems surrounding river mouths are hydro-biogeochemical hotspots where water, carbon, and nutrients are exchanged between land and ocean. The land-margin ecosystems have recently experienced significant variations in surface water extent (Fw) due to increasing intensity of climate and environmental changes. The variations of the Fw at the river mouth areas are closely linked with the changes in biogeochemical cycles, including greenhouse gas emissions, ocean chlorophyll production and nutrient exports from land-margin ecosystems. Multi-source environmental remote sensing data records were used to investigate how changes in Fw affect hydroclimates, including precipitation, surface soil moisture, and root-zone soil moisture, and biogeochemical fluxes associated with heterotrophic respiration, atmospheric CH₄, and terrigenous dissolved organic matter (tDOM). The study focused on 253 major river mouth sites, identified within the boundary of each river mouth using the MERIT-Hydro map derived from a digital elevation model. The long-term (2003–2022) satellite-derived Fw data showed a strong increasing trend in the mean annual Fw over global land areas and major river mouths. However, the Fw trends varied across aridity zones in response to climate and environmental changes —likely due to the changes in surface dryness and permafrost melting dynamics —with 46 % of river mouths showing a decreasing Fw trend, indicating lower surface wetness conditions. Fw generally showed positive correlations with heterotrophic respiration in the area surrounding river mouths. Its relationship with atmospheric CH₄ concentration was also positive in river mouth areas located in semi-arid and sub-humid zones. Particularly, in arid regions, the increasing Fw led to enhance heterotrophic respiration, but significantly reduced atmosphere CH₄ concentrations. The deceased flux of tDOM exported from land to water may be linked to the reduced runoffs from river mouth areas as indicated by the Fw decreases. The decreased Fw lowered tDOM exports to coastal waters in 61 % of the studied river mouth areas. The results highlight that long-term satellite-derived Fw observations, alongside multi-source remote sensing data, are critical for monitoring surface wetness in land-margin ecosystems and assessing its impact on hydro-biogeochemical fluxes in near-coastal environments.

河口周围的陆地边缘生态系统是水、碳和营养物质在陆地和海洋之间交换的水文生物地球化学热点。近年来，由于气候和环境变化的加剧，陆地边缘生态系统的地表水范围（Fw）发生了显著变化。河口区水势的变化与生物地球化学循环的变化密切相关，包括温室气体排放、海洋叶绿素产量和陆地边缘生态系统的养分输出。利用多源环境遥感数据记录，研究了Fw变化如何影响水文气候，包括降水、表层土壤水分和根区土壤水分，以及与异养呼吸、大气CH4和陆源溶解有机质（tDOM）相关的生物地球化学通量。这项研究的重点是253个主要的河口地点，利用数字高程模型衍生的MERIT-Hydro地图在每个河口的边界内确定。长期（2003-2022年）卫星数据显示，全球陆地区域和主要河口的年平均Fw呈强烈增加趋势。然而，随着气候和环境的变化（可能是由于地表干旱和永久冻土融化动力学的变化），不同干旱区的Fw趋势有所不同，46%的河口呈现出Fw减少的趋势，表明地表湿润条件较低。在河口周边地区，Fw与异养呼吸总体呈正相关。在半干旱和半湿润的河口地区，其与大气CH4浓度也呈正相关。特别是在干旱区，Fw的增加增加了异养呼吸，但显著降低了大气CH4浓度。从陆地向水输出的tDOM通量的减少可能与河口地区径流的减少有关，如Fw的减少所示。在研究的61%的河口地区，Fw的降低降低了tDOM向沿海水域的出口。结果强调，长期卫星衍生的Fw观测与多源遥感数据一起，对于监测陆地边缘生态系统的地表湿度并评估其对近岸环境中水文-生物地球化学通量的影响至关重要。

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引用次数: 0

A review of crop yield estimation on pixel and field scales from remotely sensed data 基于遥感数据的像元和田的作物产量估算综述

IF 5.2 Q1 ENVIRONMENTAL SCIENCES

Science of Remote Sensing

Pub Date : 2026-06-01 Epub Date: 2025-11-27 DOI: 10.1016/j.srs.2025.100342

Fengjiao Zhang , Shunlin Liang , Han Ma , Wenyuan Li , Yongzhe Chen , Tao He , Feng Tian , Jianglei Xu , Husheng Fang , Hui Liang , Yichuan Ma , Aolin Jia , Yuxiang Zhang

Crop yield estimation over large regions can provide critical data support for regional agricultural management and global food security assessments. The previous reviews mainly focused on the technological advancements of methods in specific areas such as crop growth, data assimilation, and machine learning. No reviews have summarized the progress in all these areas, particularly at the pixel and field scales. This review comprehensively evaluates various methods for estimating global and regional crop yield from different remotely sensed data, particularly on the pixel and field scales, in the past two decades. All estimation methods are grouped into four categories: empirical statistical, light use efficiency (LUE), data assimilation, and machine learning. We also identify remaining challenges in data consistency, update frequency, and crop type coverage, particularly in data-scarce developing regions. This review provides valuable insights for researchers in the field of remotely sensed data-based crop yield estimation, enabling a deeper understanding of the current status of global and regional datasets, the characteristics and challenges of existing estimation methods, and future research directions.

大区域作物产量估算可为区域农业管理和全球粮食安全评估提供关键数据支持。之前的综述主要集中在作物生长、数据同化和机器学习等特定领域的技术进展。没有一篇综述总结了所有这些领域的进展，特别是在像素和场尺度上的进展。本文综合评价了过去二十年来利用不同遥感数据估算全球和区域作物产量的各种方法，特别是在像元和田的尺度上。所有的估计方法分为四类：经验统计、光利用效率（LUE）、数据同化和机器学习。我们还确定了在数据一致性、更新频率和作物类型覆盖方面仍然存在的挑战，特别是在数据匮乏的发展中地区。本文综述为基于遥感数据的作物产量估算提供了有价值的见解，使研究人员能够更深入地了解全球和区域数据集的现状，现有估算方法的特点和挑战，以及未来的研究方向。

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引用次数: 0

Development of the satellite bio-optical algorithm for the shelf waters along the southern Kamchatka Peninsula: effect of optically active components variability on the spectral remote sensing reflectance 堪察加半岛南部陆架水域卫星生物光学算法的发展：光学有效成分变率对光谱遥感反射率的影响

IF 5.2 Q1 ENVIRONMENTAL SCIENCES

Science of Remote Sensing

Pub Date : 2026-06-01 Epub Date: 2026-01-06 DOI: 10.1016/j.srs.2026.100365

E. Korchemkina , T. Churilova , E. Skorokhod , N. Moiseeva , T. Efimova

A data set including in situ absorption of particulate and dissolved colored organic matter, absorption of phytoplankton and chlorophyll-a concentration was measured during the research cruise on board R/V “Professor Multanovsky” on August–September 2023 in shelf waters of the south Kamchatka Peninsula. In order to adjust the existing semi-analytical algorithm to the optically complex waters, the regional parameterization of in-water optically active components absorption was used. Based on in situ bio-optical properties the reflectance spectra were modeled and used for the algorithm testing. The algorithm allows to separate the absorption of phytoplankton and colored detrital matter using the separate spectral sites for their calculation. The total non-water absorption was retrieved with high accuracy, while phytoplankton absorption (chlorophyll-a concentration) was overestimated on average by 35 %, and CDM absorption was underestimated on average by 32 %. Accuracy of their retrievals allows the application of the algorithm for ecological monitoring. An analysis showed that the reflectance spectral shape is strictly determined by the total non-water absorption. The high variability of ratios between optically active components leads to weak connection between reflectance spectral shape and specific optical components. Further refining of the semi-analytical algorithm consists in a selection of more suitable spectral sites and switching between them based on the contributions of components to the total absorption.

2023年8 - 9月，“马尔塔诺夫斯基教授”号科研船在堪察加半岛南部陆架水域测量了颗粒和溶解色有机物的原位吸收、浮游植物的吸收和叶绿素- A浓度。为了使现有的半解析算法适应光学复杂的水体，采用了水中光活性成分吸收的区域参数化方法。基于原位生物光学特性，对反射光谱进行建模，并用于算法测试。该算法允许分离浮游植物和彩色碎屑物质的吸收，使用单独的光谱位点进行计算。总非水分吸收量反演精度较高，而浮游植物吸收量（叶绿素-a浓度）平均高估35%，CDM吸收量平均低估32%。其检索的准确性允许将算法应用于生态监测。分析表明，反射光谱形状严格取决于总非吸水率。光学有效组分间比值的高变异性导致反射光谱形状与特定光学组分之间的联系较弱。半解析算法的进一步改进包括选择更合适的光谱位点，并根据各组分对总吸收的贡献在它们之间进行切换。

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引用次数: 0

High-resolution maize yield mapping across Africa using earth observation and machine learning, deep learning, and foundation model 利用地球观测、机器学习、深度学习和基础模型绘制全非洲高分辨率玉米产量地图

IF 5.2 Q1 ENVIRONMENTAL SCIENCES

Science of Remote Sensing

Pub Date : 2026-06-01 Epub Date: 2025-12-03 DOI: 10.1016/j.srs.2025.100344

Krishnagopal Halder , Frank Ewert , Anitabha Ghosh , Kaushik Muduchuru , Lily-belle Sweet , Radwa Elshawi , Jan Timko , Wenhi Zheng , Karam Alsafadi , Gang Zhao , Michael Maerker , Manmeet Singh , Lei Guoging , Thomas Gaiser , Dominik Behrend , Yue Shi , Liangxiu Han , Masahiro Ryo , Amit Kumar Srivastava

Africa's food security is increasingly threatened by climate change and population growth, creating an urgent need for high-resolution crop yield maps to support precision agriculture and climate adaptation; however, much of the continent remains poorly monitored due to limited ground observations. This study introduces a novel 250-m spatial resolution maize yield prediction framework for 42 African countries. Our methodology utilizes Net Primary Production (NPP) data to spatially disaggregate national FAO yield statistics, generating fine-scale training data for supervised learning. A comprehensive feature set of 296 variables was constructed by integrating multi-source Earth observation, climate, and soil data. We evaluated multiple benchmark models for tabular data, including XGBoost, LightGBM, a Hybrid Deep Neural Network (HDNN), and, for the first time in this context, a tabular foundation model, the Tabular Prior data Fitted Network (TabPFN). Using an expanding-window temporal cross-validation strategy, XGBoost achieved the highest temporal R² (0.78), while TabPFN demonstrated superior spatial generalization and the lowest mean absolute percentage error (MAPE ≈ 25 %). Causal inference and ablation analyses highlighted the predictive importance of vegetation indices (e.g., NDVI, NDWI), drought metrics, and soil properties. Model outputs closely matched national yields (R² > 0.75; MAPE ≈ 26–28 %). This study provides a scalable framework for yield monitoring in data-scarce regions and represents the first successful application of tabular foundation models in continental-scale agricultural prediction.

非洲的粮食安全日益受到气候变化和人口增长的威胁，迫切需要高分辨率作物产量地图，以支持精准农业和气候适应；然而，由于地面观测有限，非洲大陆的大部分地区仍然监测不足。本研究为42个非洲国家引入了一个新的250米空间分辨率玉米产量预测框架。我们的方法利用净初级生产（NPP）数据对粮农组织国家产量统计数据进行空间分解，生成用于监督学习的精细训练数据。综合多源对地观测、气候和土壤数据，构建了296个变量的综合特征集。我们评估了表格数据的多个基准模型，包括XGBoost， LightGBM，混合深度神经网络（HDNN），以及在此背景下首次使用的表格基础模型，表格先验数据拟合网络（TabPFN）。使用扩展窗口时间交叉验证策略，XGBoost获得了最高的时间R2(0.78)，而TabPFN表现出更好的空间泛化和最低的平均绝对百分比误差（MAPE≈25%）。因果推理和消融分析强调了植被指数（如NDVI、NDWI）、干旱指标和土壤特性的预测重要性。模型产出与国家产量密切匹配（R2 > 0.75; MAPE≈26 - 28%）。该研究为数据稀缺地区的产量监测提供了一个可扩展的框架，并代表了表格基础模型在大陆尺度农业预测中的首次成功应用。

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引用次数: 0

Improving national forest attribute maps of Sweden with machine learning 用机器学习改进瑞典国家森林属性图

IF 5.2 Q1 ENVIRONMENTAL SCIENCES

Science of Remote Sensing

Pub Date : 2026-06-01 Epub Date: 2026-02-11 DOI: 10.1016/j.srs.2026.100395

Dag Björnberg , Morgan Ericsson , Johan Lindeberg , Welf Löwe , Jonas Nordqvist , Jörgen Wallerman , Johan E.S. Fransson

Remote sensing techniques are widely used for mapping and monitoring forest attributes, providing valuable information on forest cover, biomass, and overall forest health. In recent years, national airborne laser scanning (ALS) campaigns have been conducted in several countries to map forest resources. When combining ALS with field inventory data, these datasets enable the development of nationwide models for prediction of forest attributes. In this study, we explore the potential of machine learning (ML) to enhance existing modeling approaches for nationwide forest attribute mapping in Sweden. We achieve this by relating ALS data from the most recent ALS campaign of Sweden with field data from the Swedish National Forest Inventory (NFI). By aggregating laser metrics from surveyed areas (NFI plots), as well as over surrounding areas to the plots, we investigate (1) if ML approaches can outperform existing linear regression baseline models and (2) if further enhancements of the predictive capacity can be achieved by including surrounding, spatially correlated ALS data. To this end, we used extreme gradient boosting (XGBoost), as well as a convolutional neural network (CNN), specialized to handle tabular data and spatially correlated data, respectively. The models were evaluated on five forest variables: basal-area weighted mean tree height, basal-area weighted mean stem diameter, basal area, stem volume, and above-ground biomass. All models were evaluated on several nested datasets to assess the robustness, showcasing consistent results across datasets. We achieved significant improvements in prediction accuracy across all investigated forest variables. Furthermore, incorporating surrounding information to the modeling rendered further improvements for diameter, basal area, and biomass predictions. The approaches tested and developed here thus form a promising basis for flexible modeling approaches that can be transferred globally for large-scale forest monitoring and management.

遥感技术被广泛用于绘制和监测森林属性，提供关于森林覆盖、生物量和整体森林健康的宝贵信息。近年来，一些国家开展了全国机载激光扫描（ALS）运动来绘制森林资源图。将ALS与实地清查数据结合起来，这些数据集能够开发用于预测森林属性的全国性模型。在这项研究中，我们探索了机器学习（ML）的潜力，以增强瑞典全国森林属性映射的现有建模方法。我们通过将瑞典最近的ALS运动中的ALS数据与瑞典国家森林清查（NFI）的实地数据相关联来实现这一目标。通过汇总来自调查区域（NFI图）以及周围区域的激光指标到图中，我们研究了(1)ML方法是否可以优于现有的线性回归基线模型；(2)是否可以通过包括周围空间相关的ALS数据来进一步增强预测能力。为此，我们使用了极端梯度增强（XGBoost）和卷积神经网络（CNN），分别专门用于处理表格数据和空间相关数据。对模型进行了5个森林变量的评价：基底面积加权平均树高、基底面积加权平均茎粗、基底面积、茎体积和地上生物量。所有模型都在几个嵌套的数据集上进行评估，以评估稳健性，显示跨数据集的一致结果。我们在所有被调查的森林变量中都取得了显著的预测精度提高。此外，将周围信息纳入建模，进一步改进了直径、基底面积和生物量的预测。因此，这里测试和发展的方法为灵活的建模方法奠定了有希望的基础，这些方法可以在全球范围内转移，用于大规模森林监测和管理。

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引用次数: 0

An improved optimization deep nonnegative matrix factorization for hyperspectral unmixing 一种改进的深度非负矩阵分解算法用于高光谱解混

IF 5.2 Q1 ENVIRONMENTAL SCIENCES

Science of Remote Sensing

Pub Date : 2026-06-01 Epub Date: 2026-01-09 DOI: 10.1016/j.srs.2025.100356

Jianhong Wu , Xiaojuan Liu , Longshan Yang , Yongze Song , Hong Cai , Bowen Yu

Hyperspectral unmixing (HU) is an essential HSI processing technique for separating the spectral signatures of endmembers and estimating their corresponding abundances within mixed pixels. While nonnegative matrix factorization (NMF) has been widely used for HU with a single-layer structure, it cannot fully explore the hidden features in a HSI. In this paper, an improved optimization deep nonnegative matrix factorization (IODNMF) method is proposed, which improves the parameter optimization approach of the deep NMF model for exploring the deep feature representation of HSI to achieve unsupervised HU. With both unknown endmembers and abundances, a heuristic algorithm is utilized to initialize the parameters of each layer to speed up the convergence of the proposed method. To ensure the nonnegativity of parameters and avoid gradient vanishing and explosion during training, a multiplicative update rule based on positive–negative separation is devised to update the endmembers and abundances in the pre-training and fine-tuning stages. In addition, two layer-setting strategies and three per-layer parameter setting strategies are proposed to effectively solve the network structure setting problem in deep NMF methods. Experimental results on synthetic and real HSI datasets show that the proposed deep NMF algorithm performs more effectively than other classical unmixing methods and achieves a significant improvement in unmixing performance.

高光谱解混（HU）是分离端元光谱特征并估计其在混合像元内对应丰度的基本HSI处理技术。虽然非负矩阵分解（NMF）已被广泛用于具有单层结构的HU，但它不能充分挖掘HSI中的隐藏特征。本文提出了一种改进的优化深度非负矩阵分解（IODNMF）方法，该方法改进了深度NMF模型的参数优化方法，用于探索HSI的深度特征表示，从而实现无监督HU。在端元和丰度都未知的情况下，采用启发式算法对各层参数进行初始化，加快了算法的收敛速度。为了保证训练过程中参数的非负性，避免梯度消失和爆炸，设计了一种基于正负分离的乘法更新规则，在预训练和微调阶段对端元和丰度进行更新。此外，提出了两种层设置策略和三种逐层参数设置策略，有效地解决了深度NMF方法中的网络结构设置问题。在合成和真实HSI数据集上的实验结果表明，所提出的深度NMF算法比其他经典解混方法更有效，解混性能显著提高。

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引用次数: 0

A cost-effective method for mapping land cover at national scale 一种在全国范围内绘制土地覆盖地图的经济有效方法

IF 5.2 Q1 ENVIRONMENTAL SCIENCES

Science of Remote Sensing

Pub Date : 2026-06-01 Epub Date: 2026-01-16 DOI: 10.1016/j.srs.2026.100376

John R. Dymond , James D. Shepherd , Richard Law , Brent Martin , Jan Schindler , Stella Belliss

Timely and accurate national-scale land-cover mapping is essential for resource management. However, achieving this with limited resources is a challenge, particularly in mountainous and ecologically diverse regions with frequent cloud cover like New Zealand. We present a cost-effective, scalable methodology for land-cover classification that integrates Sentinel-2 imagery, spectral decision rules, temporal NDVI analysis, and deep learning (U-Net) within a unified, reproducible workflow. Our approach generates land-cover maps at a spatial resolution of 10 m. National classification was generated in less than 12 h of computing time. Validation against 4500 samples stratified by map class yielded an overall classification accuracy of 96 %, outperforming leading global products. This method balances automation with expert-informed logic, enabling accurate differentiation of challenging classes such as exotic forest, indigenous forest, and croplands. Although developed for New Zealand, the workflow should be adaptable to other countries seeking low-cost, high-frequency land-cover mapping. These land-cover maps can support a range of environmental applications, including carbon accounting, biodiversity assessment, erosion modelling, and detection of land-use change.

及时、准确的国家尺度土地覆盖测绘对资源管理至关重要。然而，在资源有限的情况下实现这一目标是一项挑战，特别是在像新西兰这样云量频繁的山区和生态多样性地区。我们提出了一种成本效益高、可扩展的土地覆盖分类方法，该方法将Sentinel-2图像、光谱决策规则、时间NDVI分析和深度学习（U-Net）集成在一个统一的、可重复的工作流程中。我们的方法生成空间分辨率为10米的土地覆盖地图。在不到12小时的计算时间内生成国家分类。对4500个按地图类别分层的样本进行验证，总体分类精度为96%，优于全球领先的产品。这种方法平衡了自动化和专家知情逻辑，能够准确区分具有挑战性的类别，如外来森林、原生森林和农田。虽然该工作流程是为新西兰开发的，但也应适用于寻求低成本、高频率土地覆盖测绘的其他国家。这些土地覆盖图可以支持一系列环境应用，包括碳核算、生物多样性评估、侵蚀建模和土地利用变化检测。

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引用次数: 0

Monitoring growth of the wildland-urban interface in 2000 and 2020 in Mediterranean ecosystems with Landsat satellite imagery 利用陆地卫星图像监测2000年和2020年地中海生态系统中荒地-城市界面的增长

IF 5.2 Q1 ENVIRONMENTAL SCIENCES

Science of Remote Sensing

Pub Date : 2026-06-01 Epub Date: 2026-02-03 DOI: 10.1016/j.srs.2026.100387

Kira A. Pfoch , Franz Schug , Heather R. Cox , Vu-Dong Pham , Sebastian van der Linden , Volker C. Radeloff

The wildland-urban interface (WUI) is the area where buildings meet or intermingle with wildland vegetation causing high wildfire risk, especially in fire-prone Mediterranean ecosystems. Mapping WUI growth can help to manage that risk but requires consistent information on building density and vegetation cover over time. Unfortunately, most WUI mapping methods cannot be applied to map long-term WUI growth due to the lack of historical data on building locations or density. We developed a novel method to map the WUI and its growth by comparing WUI extents in 2000 and 2020 using Landsat satellite data. Our goal was to: (a) accurately map the 2020 WUI with Landsat data, (b) distinguish WUI types by vegetation composition, and (c) map 2000 to 2020 WUI growth in five Mediterranean ecosystems: Adelaide, Cape Town, Lisbon, San Diego, and Santiago. We used spectral-temporal metrics and neural network regression to calculate impervious, woody, and non-woody vegetation fractions, and mapped the WUI from those. Our maps were accurate for both 2020 WUI (area-adjusted overall accuracies 72.3 to 90.2%), and WUI changes (56.2 to 68.8%). WUI was most prevalent in San Diego (30.9%), and grew rapidly in four sites, especially near cities and coasts. Woody WUI was dominant in all sites except Adelaide, where herbaceous WUI dominated. More broadly, we showed that fractions derived from Landsat data can consistently map WUI growth. That is important given predicted increases in wildfires due to climate change and more human ignitions, and may help to mitigate severe wildfires in fire-prone Mediterranean ecosystems.

荒地-城市界面（WUI）是建筑物与荒地植被相遇或混合的区域，具有很高的野火风险，特别是在火灾易发的地中海生态系统中。绘制WUI的增长地图可以帮助管理这一风险，但需要关于建筑密度和植被覆盖的一致信息。不幸的是，由于缺乏建筑位置或密度的历史数据，大多数WUI制图方法不能用于绘制长期WUI增长。我们开发了一种新的方法，通过比较2000年和2020年的陆地卫星数据来绘制无水地区及其增长。我们的目标是：(a)利用Landsat数据准确绘制2020年的WUI地图，(b)根据植被组成区分WUI类型，以及(c)绘制2000年至2020年五个地中海生态系统（阿德莱德、开普敦、里斯本、圣地亚哥和圣地亚哥）的WUI增长地图。我们使用光谱-时间度量和神经网络回归来计算不透水、木质和非木质植被组分，并从中绘制WUI。我们的地图对2020年WUI（面积调整后的总体精度为72.3 - 90.2%）和WUI变化（56.2 - 68.8%）都是准确的。WUI在圣地亚哥最为普遍（30.9%），并在四个地点迅速增长，特别是在城市和海岸附近。除阿德莱德以草本WUI为主外，其余各样地均以Woody WUI为主。更广泛地说，我们发现从陆地卫星数据中提取的分数可以一致地映射WUI的增长。考虑到由于气候变化和人为点火的增加，预计野火会增加，这一点很重要，并可能有助于减轻易发生火灾的地中海生态系统中的严重野火。

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引用次数: 0

Improved hybrid algorithm for land surface temperature retrieval from Chinese GF-5B satellite 中国GF-5B卫星地表温度反演的改进混合算法

IF 5.2 Q1 ENVIRONMENTAL SCIENCES

Science of Remote Sensing

Pub Date : 2026-06-01 Epub Date: 2025-12-02 DOI: 10.1016/j.srs.2025.100343

Zhengyu Shen , Honglian Huang , Xiao Liu , Rufang Ti , Xiaobing Sun , Qidong Chen

Land surface temperature (LST) is a key parameter in understanding surface–atmosphere energy exchanges. Conventional temperature and emissivity separation (TES) and split-window (SW) algorithms often suffer from limited accuracy when applied to surfaces with strong spectral contrast or complex atmospheric conditions. To overcome these issues, this study proposes a Maximum Emissivity Approximation OSTES (MEAOSTES) algorithm that integrates the generalized radiance-based split-window (GRSW) model with the optimized smoothing for temperature and emissivity separation (OSTES) framework. The MEAOSTES introduces a maximum-emissivity adjustment and iterative rollback mechanism to enhance the coupling between LST and emissivity retrievals. Furthermore, in this paper, the SW-TES algorithm is extended to the SW-OSTES algorithm, and the retrieval accuracy of different combination schemes of the SW and OSTES algorithms is discussed. Using data from the GF-5B satellite, which provides four thermal infrared channels at 40 m spatial resolution, the algorithms’ performance were evaluated against simulated datasets, in-situ measurements, and the MODIS MOD21 LST product. The results show that the MEAOSTES achieves the best overall accuracy, root-mean-square errors (RMSEs) of 0.92 K and 1.77 K for simulation and in-situ validation, outperforming both the OSTES and SW-OSTES approaches. The proposed algorithm effectively reduces sensitivity to atmospheric parameters and maintains consistent performance across surfaces with varying spectral contrast. These improvements demonstrate the robustness of MEAOSTES for high-resolution LST retrieval and provide insights for future hybrid algorithm development.

地表温度（LST）是了解地表-大气能量交换的关键参数。传统的温度和发射率分离（TES）和分窗（SW）算法在应用于具有强光谱对比度或复杂大气条件的表面时，精度往往有限。为了克服这些问题，本研究提出了一种最大发射率近似OSTES （MEAOSTES）算法，该算法将基于广义辐射的分窗（GRSW）模型与温度和发射率分离（OSTES）框架的优化平滑相结合。MEAOSTES引入了最大发射率调整和迭代回滚机制，以增强地表温度和发射率检索之间的耦合。在此基础上，本文将SW- tes算法扩展到SW-OSTES算法，讨论了SW和OSTES算法不同组合方案的检索精度。利用GF-5B卫星提供的4个40米空间分辨率的热红外通道数据，通过模拟数据集、现场测量和MODIS MOD21 LST产品对算法的性能进行了评估。结果表明，MEAOSTES方法总体精度最高，模拟和原位验证的均方根误差（rmse）分别为0.92 K和1.77 K，优于OSTES和SW-OSTES方法。该算法有效地降低了对大气参数的敏感性，并在不同光谱对比度的表面上保持一致的性能。这些改进证明了MEAOSTES在高分辨率LST检索方面的鲁棒性，并为未来混合算法的开发提供了见解。

{"title":"Improved hybrid algorithm for land surface temperature retrieval from Chinese GF-5B satellite","authors":"Zhengyu Shen , Honglian Huang , Xiao Liu , Rufang Ti , Xiaobing Sun , Qidong Chen","doi":"10.1016/j.srs.2025.100343","DOIUrl":"10.1016/j.srs.2025.100343","url":null,"abstract":"<div><div>Land surface temperature (LST) is a key parameter in understanding surface–atmosphere energy exchanges. Conventional temperature and emissivity separation (TES) and split-window (SW) algorithms often suffer from limited accuracy when applied to surfaces with strong spectral contrast or complex atmospheric conditions. To overcome these issues, this study proposes a Maximum Emissivity Approximation OSTES (MEAOSTES) algorithm that integrates the generalized radiance-based split-window (GRSW) model with the optimized smoothing for temperature and emissivity separation (OSTES) framework. The MEAOSTES introduces a maximum-emissivity adjustment and iterative rollback mechanism to enhance the coupling between LST and emissivity retrievals. Furthermore, in this paper, the SW-TES algorithm is extended to the SW-OSTES algorithm, and the retrieval accuracy of different combination schemes of the SW and OSTES algorithms is discussed. Using data from the GF-5B satellite, which provides four thermal infrared channels at 40 m spatial resolution, the algorithms’ performance were evaluated against simulated datasets, in-situ measurements, and the MODIS MOD21 LST product. The results show that the MEAOSTES achieves the best overall accuracy, root-mean-square errors (RMSEs) of 0.92 K and 1.77 K for simulation and in-situ validation, outperforming both the OSTES and SW-OSTES approaches. The proposed algorithm effectively reduces sensitivity to atmospheric parameters and maintains consistent performance across surfaces with varying spectral contrast. These improvements demonstrate the robustness of MEAOSTES for high-resolution LST retrieval and provide insights for future hybrid algorithm development.</div></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"13 ","pages":"Article 100343"},"PeriodicalIF":5.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145658617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Satellite remote sensing and field analyses of megaripples and dunes in the Skeleton Coast National Park, Namibia 纳米比亚骷髅海岸国家公园的巨型水坑和沙丘的卫星遥感和野外分析

IF 5.2 Q1 ENVIRONMENTAL SCIENCES

Science of Remote Sensing

Pub Date : 2026-06-01 Epub Date: 2025-12-04 DOI: 10.1016/j.srs.2025.100347

A.L. Cohen-Zada , D.A. Vaz , I. Katra , L. Berger , L. Saban , S. Silvestro , H. Yizhaq

The morphology of aeolian bedforms is controlled by the boundary conditions during their formation and evolution, playing a crucial role in reconstructing current and past environmental settings. This study investigates the morphology and dynamics of multiscale aeolian bedforms, specifically megaripples and barchan dunes, in Namibia's Skeleton Coast National Park using satellite remote sensing, reanalysis wind data, and field observations. Newly mapped megaripples show average wavelengths between 3.3 and 4.4 m, with crest orientations aligned predominantly with regional southerly winds. Remote sensing data reveal a dune migration rate of ∼30 m yr⁻¹ and an average sand flux of ∼140 m³ m⁻¹ yr⁻¹, while field-based storm observations yield a peak flux of 256 m³ m⁻¹ yr⁻¹. A novel correlation between armoring layer thickness and grain-size median (D₅₀) suggests a feedback loop where larger grains promote ripple growth. Seasonal wind shifts inferred from dune patterns and wind streaks are corroborated by ERA5 reanalysis, indicating a dynamic yet low-energy aeolian system. The results provide insights into sediment transport mechanisms across scales, the evolution of ripple morphometry, and grain-size feedbacks influencing megaripple formation, offering valuable analogs for similar processes on Mars. This multiscale assessment supports improved modeling of terrestrial and planetary aeolian systems.

风成地貌的形态在其形成和演化过程中受边界条件的控制，在重建当前和过去的环境条件中起着至关重要的作用。本研究利用卫星遥感、再分析风数据和野外观测，研究了纳米比亚骷髅海岸国家公园多尺度风成地貌的形态和动力学，特别是巨波纹和barchan沙丘。新绘制的巨虹点显示平均波长在3.3到4.4米之间，其波峰方向主要与区域南风对齐。遥感资料显示，沙丘迁移速率为~ 30 m yr - 1，平均沙通量为~ 140 m3 m - 1 yr - 1，而野外风暴观测的峰值通量为256 m3 m - 1 yr - 1。装甲层厚度和晶粒尺寸中值（D50）之间的新相关性表明，一个反馈回路，其中较大的晶粒促进波纹生长。ERA5再分析证实了从沙丘型态和风条推断出的季节风移，表明这是一个动态的低能风成系统。研究结果提供了对沉积物跨尺度运输机制、波纹形态的演变以及影响巨波纹形成的粒度反馈的见解，为火星上的类似过程提供了有价值的类似物。这种多尺度评估支持改进陆地和行星风成系统的模拟。

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引用次数: 0