Science of Remote Sensing最新文献_第2页

A novel approach to assessing the tracking accuracy of crop phenology for multi-orbit and multi-feature Sentinel-1 time series 基于多轨道多特征Sentinel-1时间序列作物物候跟踪精度评估新方法

IF 5.2 Q1 ENVIRONMENTAL SCIENCES

Science of Remote Sensing

Pub Date : 2026-01-13 DOI: 10.1016/j.srs.2026.100370

Johannes Löw , Christopher Conrad , Steven Hill , Michael Thiel , Tobias Ullmann , Insa Otte

This study presents a novel framework for quantifying uncertainties and variabilities related to the monitoring of crop phenology via Synthetic Aperture Radar (SAR) time series at the field scale. Therefore, the study investigated multi-orbit, multi-feature time series derived from Sentinel-1 (S1) VV/VH polarizations. This multi-feature approach encompasses backscatter intensity, interferometric coherence and alpha/entropy decomposition features. Crop phenology tracking is crucial for assessing agricultural resilience under climate change, yet existing approaches face challenges due to uncertainties and variability in SAR signal interpretation as well as in situ data. Building on previous landscape-level analyses, this work introduces the concept of trackability, defined as the temporal range during which SAR-derived time-series metrics (TSM), such as breakpoints in backscatter intensity or interferometric coherence, align with key phenological stages (e.g., stem elongation in winter wheat). A growing degree day (GDD)-based normalization contextualizes field-specific deviations relative to landscape averages, enabling quantification of uncertainties inherent in both SAR signals and ground observations. The framework captures the spatio-temporally variable nature of crop development by estimating the first and last phenologically relevant TSM occurrence within a defined uncertainty window, thus providing relational and relative indicators of phenological tracking. This approach reduces dependencies of extensive in situ data and enhances comparability across studies with differing SAR processing methods and their acquisition geometries. Results reproduce known feature-stage relationships (e.g., tracking for stem elongation by interferometric coherence) and reveal inter-seasonal variability influenced by weather conditions and acquisition parameters. On average relevant TSM occurrences were found at approximately 90 % of GDD progression of in situ reported phenological stages, while systematic differences of around 5 % by relative orbit were discovered. The study highlights the potential of integrating multiple S1 features and orbits without optimization-induced information loss, producing quality masks that identify optimal tracking performance at the field level. This framework advances SAR-based phenology monitoring by offering scalable, transferable insights for precision agriculture, while practical implementation still requires detailed field boundaries and early-season crop management information.

本研究提出了一种新的框架，用于量化田间尺度上合成孔径雷达（SAR）时间序列作物物候监测的不确定性和可变性。因此，该研究研究了Sentinel-1 (S1) VV/VH极化衍生的多轨道、多特征时间序列。这种多特征方法包括后向散射强度、干涉相干性和α /熵分解特征。作物物候跟踪对于评估气候变化下的农业恢复力至关重要，但由于SAR信号解释和原位数据的不确定性和可变性，现有方法面临挑战。在之前的景观级分析的基础上，本研究引入了可追踪性的概念，其定义为sar衍生的时间序列指标（TSM）的时间范围，如后向散射强度或干涉相干性的断点，与关键物候阶段（如冬小麦的茎伸长）一致。基于日数增长（GDD）的归一化处理了相对于景观平均值的特定区域偏差，从而可以量化SAR信号和地面观测中固有的不确定性。该框架通过在确定的不确定性窗口内估算第一次和最后一次物候相关的TSM发生来捕捉作物发育的时空变化性质，从而提供物候跟踪的相关和相对指标。这种方法减少了大量原位数据的依赖性，并增强了不同SAR处理方法及其获取几何形状的研究之间的可比性。结果再现了已知的特征阶段关系（例如，通过干涉相干跟踪茎伸长），并揭示了受天气条件和采集参数影响的季节间变化。平均而言，相关的TSM发生在约90%的原位报告物候阶段的GDD进展中，而相对轨道的系统差异约为5%。该研究强调了整合多个S1特征和轨道的潜力，而不会导致优化引起的信息损失，从而产生在现场水平上识别最佳跟踪性能的质量掩模。该框架通过为精准农业提供可扩展、可转移的见解，推进了基于sar的物候监测，而实际实施仍然需要详细的田地边界和早期作物管理信息。

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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 : 2026-01-09 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

Crop classification mapping in multi-cloud Regions: An integrated approach using GF-SG time series reconstruction and 3D deep convolutional networks 多云区域作物分类映射：使用GF-SG时间序列重建和3D深度卷积网络的集成方法

IF 5.2 Q1 ENVIRONMENTAL SCIENCES

Science of Remote Sensing

Pub Date : 2026-01-09 DOI: 10.1016/j.srs.2025.100361

Sihan Xue , Haibin Shi , Xianyue Li , Jianwen Yan , Weigang Wang , Qingfeng Miao , Yan Yan , Cong Hou , Yi Zhao , Xinlu Li

Persistent cloud contamination severely affects optical satellite-based crop classification, creating critical gaps in phenological monitoring essential for food security. This study presents GS-3D FCN, a novel framework integrating Gap Filling-Savitzky-Golay (GF-SG) time series reconstruction with 3D Fully Convolutional Networks (3D FCN) for robust crop mapping under extreme cloud conditions. The method reconstructs cloud-contaminated pixels using GF-SG algorithm and leverages MODIS-Landsat fusion to generate continuous NDVI time series. These reconstructed layers are integrated as spectral-temporal dimensions into the 3D FCN architecture, enabling hierarchical extraction of crop phenological patterns. An enhanced Automatic Cloud Cover Assessment algorithm adaptively adjusts decision criteria to maximize valid pixel retention. To address severe class imbalance from cloud occlusion, we apply Synthetic Minority Over-sampling Technique with Gaussian Noise (SMOGN) combined with Cross-entropy loss Middle Supervision (CE-MidS) and Supervised Contrastive loss Middle Supervision (SupCon-MidS) intermediate supervision strategies, facilitating discriminative feature learning. Applied to the cloud-prone South Bank Yellow River irrigation area in Inner Mongolia, GS-3D FCN achieves 93.80 % overall accuracy across four sites. The framework demonstrates exceptional stability, maintaining 85.2 % accuracy at 80 % cloud coverage compared to 33.6–37.2 % accuracy degradation in traditional methods. The GF-SG component achieves RMSE of 0.0853 with edge preservation index of 0.0085. Analysis reveals the critical role of dataset balance in crop mapping under cloud interference. The proposed GS-3D FCN framework provides a simple and effective solution for crop mapping in cloud-prone areas. By integrating near-real-time NDVI data sources, the framework provides a feasible path for accurate crop monitoring in global continuous cloudy areas, and provides a new perspective for future research on the interaction between extreme climate and crop mapping.

持续的云污染严重影响基于光学卫星的作物分类，造成对粮食安全至关重要的物候监测方面的严重空白。该研究提出了GS-3D FCN，这是一种将Gap填充- savitzky - golay （GF-SG）时间序列重建与3D全卷积网络（3D FCN）相结合的新框架，用于极端云条件下的鲁棒作物映射。该方法利用GF-SG算法重建被云污染的像元，并利用MODIS-Landsat融合生成连续NDVI时间序列。这些重建的层作为光谱-时间维度集成到3D FCN架构中，实现了作物物候模式的分层提取。一个增强的自动云覆盖评估算法自适应调整决策标准，以最大限度地提高有效像素保留。为了解决云遮挡造成的严重类不平衡问题，我们采用高斯噪声合成少数派过采样技术（SMOGN）结合交叉熵损失中间监督（CE-MidS）和监督对比损失中间监督（supcons - mids）中间监督策略，促进判别特征学习。GS-3D FCN应用于内蒙古黄南岸灌区多云地区，4个站点的整体精度达到93.80%。该框架表现出优异的稳定性，在80%的云覆盖率下保持85.2%的精度，而传统方法的精度下降了33.6 - 37.2%。GF-SG分量的均方根误差为0.0853，边缘保持指数为0.0085。分析表明，数据集平衡在云干扰下的作物制图中起着至关重要的作用。提出的GS-3D FCN框架为多云地区的作物制图提供了一个简单有效的解决方案。该框架通过整合近实时NDVI数据源，为全球连续多云地区作物精确监测提供了可行路径，为未来极端气候与作物制图的相互作用研究提供了新的视角。

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引用次数: 0

Solar-induced chlorophyll fluorescence (SIF) tracks variations in the soil-plant available water (PAW): a multiyear analysis on three crops 太阳诱导的叶绿素荧光（SIF）追踪土壤-植物有效水分（PAW）的变化：对三种作物的多年分析

IF 5.2 Q1 ENVIRONMENTAL SCIENCES

Science of Remote Sensing

Pub Date : 2026-01-08 DOI: 10.1016/j.srs.2026.100367

Juan Quiros-Vargas , Cosimo Brogi , Alexander Damm , Bastian Siegmann , Patrick Rademske , Vicente Burchard-Levine , Vera Krieger , Marius Schmidt , Jan Hanuš , Mauricio Martello , Lutz Weihermüller , Onno Muller , Uwe Rascher

Restrictions in the soil water availability can strongly impact crop productivity. The increasing frequency and severity of drought events, as a result of global warming, has made the assessment of drought stress effects on vegetation of utmost importance for meeting humanity's agricultural production needs. Recent advances in remote sensing of solar-induced chlorophyll fluorescence (SIF) provide a basis for new approaches to directly assess crop water status, since SIF is closely related to photosynthesis and, thus, to early plant physiological processes triggered by limitations in the water supply. This study provides new insights into the effect of varying levels of plant available water (PAW) in the soil on SIF emissions. We used several SIF datasets acquired with the high-performance airborne imaging spectrometer HyPlant during five subsequent vegetation periods (2018, 2019, 2020, 2021 and 2022), each having a different precipitation regime. We normalized the SIF maps for the underlying effects of canopy structure, calculated SIF emission efficiency (eSIF) and selected various crop fields including sugar beet, wheat and potato. Maps of eSIF were compared with spatial PAW patterns, which were derived from a forward soil infiltration model. Our results show positive correlation between eSIF and PAW in rainfed sugar beet fields at early growing stage, which remained consistent when accounting for variations in the leaf area index (LAI). This suggests that eSIF variations in sugar beet reflect the spatial reduction of photosynthesis caused by reduced PAW. In irrigated potato fields, conversely, no eSIF-PAW correlations were found. This indicates the absence of leaf-level water stress in these well-irrigated fields. In rainfed winter wheat fields that were already in a late developmental stage, the variations in the SIF signal were dominated by locally different ripening, i.e., chlorophyll degradation, and therefore not representative of changing PAW. With this study, we could demonstrate that normalized airborne SIF measurements are related to the functional water stress response in different crops. This study supports future investigations on the development of SIF-based tools for the improvement of water management in agriculture.

土壤水分供应的限制会严重影响作物产量。由于全球变暖，干旱事件日益频繁和严重，因此评估干旱对植被的影响对满足人类农业生产需要至关重要。太阳诱导叶绿素荧光（SIF）遥感研究的最新进展为直接评估作物水分状况的新方法提供了基础，因为SIF与光合作用密切相关，因此与供水限制引发的植物早期生理过程密切相关。这项研究为土壤中不同水平的植物有效水分（PAW）对SIF排放的影响提供了新的见解。我们使用了高性能航空成像光谱仪HyPlant在随后的五个植被期（2018年、2019年、2020年、2021年和2022年）获取的几个SIF数据集，每个植被期都有不同的降水情况。我们对SIF图进行归一化，以确定冠层结构的潜在影响，计算SIF发射效率（eSIF），并选择甜菜、小麦和马铃薯等不同作物田。将eSIF图与基于前向土壤入渗模型的空间PAW图进行了比较。结果表明，旱作甜菜生长早期eSIF与PAW呈显著正相关，在考虑叶面积指数（LAI）变化的情况下，这一关系保持一致。这表明甜菜eSIF的变化反映了由于PAW的减少导致光合作用的空间减少。相反，在灌溉马铃薯田，没有发现eSIF-PAW相关性。这表明在这些灌溉良好的田地中不存在叶片水平的水分胁迫。在已经处于发育后期的旱作冬小麦地，SIF信号的变化主要受局部不同的成熟程度，即叶绿素降解的影响，因此不能代表PAW的变化。通过这项研究，我们可以证明标准化的空气SIF测量与不同作物的功能性水分胁迫响应有关。这项研究支持了未来基于sif的工具开发的研究，以改善农业用水管理。

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引用次数: 0

Altimetry river water level retrieval over complex environments: assessment and diagnosis of different strategies 复杂环境下的高程河流水位反演：不同策略的评估与诊断

IF 5.2 Q1 ENVIRONMENTAL SCIENCES

Science of Remote Sensing

Pub Date : 2026-01-07 DOI: 10.1016/j.srs.2026.100363

Tian Xia, Yanan Zhao, Liguang Jiang

Satellite altimetry has been increasingly used in monitoring inland water bodies. Waveform retracking plays a major role in water level retrieval. However, there remain many challenges to retrieving accurate river water levels, especially for rivers surrounded by various water bodies. In this study, we investigated this problem by diagnosing six retrackers in the Yangtze River, where the environment is very complex. Results show that the official retracker (i.e., OCOG and threshold retrackers) used in Sentinel-3 product exhibits varying performance across 12 virtual stations, with RMSE in the range of 0.55–2.76 m. Surprisingly, no one retracker performs consistently well across all virtual stations. The enhanced multiple waveform persistent peak (MWaPP+) retracker was slightly better than the others. Taking multiple waveforms into consideration is a better strategy than single waveform-based ones. Poor performance is due to irregular waveforms, which are attributed to various water bodies surrounding the river. The number, elevation, and proportion of anomalous water bodies within the footprint are found decisive. In such complex environments, a combination of multiple strategies is needed to improve the accuracy of retrieved water levels. The proposed strategy, by combining FFSAR and MWaPP+, substantially enhanced accuracy and the number of observations. Nevertheless, we call for a round robin exercise to test more retracking strategies to deal with this problem.

卫星测高已越来越多地用于监测内陆水体。波形重迹在水位反演中起着重要的作用。然而，对于被各种水体包围的河流来说，准确地获取河流水位仍然存在许多挑战。本文通过对环境复杂的长江流域6条河道的诊断，探讨了这一问题。结果表明，Sentinel-3产品中使用的官方回调器（即OCOG和阈值回调器）在12个虚拟站点中表现出不同的性能，RMSE范围为0.55-2.76 m。令人惊讶的是，没有一个回溯器在所有虚拟站点中表现一致。增强型多波形持续峰（MWaPP+）回拉器的效果略好于其他回拉器。考虑多种波形比基于单一波形的策略更好。性能差是由于不规则的波形，这是归因于河流周围的各种水体。发现足迹内异常水体的数量、海拔和比例是决定性的。在如此复杂的环境中，需要多种策略的结合来提高检索水位的准确性。该策略通过将FFSAR和MWaPP+相结合，大大提高了观测的精度和数量。然而，我们呼吁进行一轮循环，以测试更多的回溯策略来处理这个问题。

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引用次数: 0

Remote sensing facilitates the exploration of algal bloom dynamics and its hydrometeorological drivers in tributary bays of the Three Gorges Reservoir 遥感技术有助于研究三峡水库支流湾区藻华动态及其水文气象驱动因素

IF 5.2 Q1 ENVIRONMENTAL SCIENCES

Science of Remote Sensing

Pub Date : 2026-01-07 DOI: 10.1016/j.srs.2026.100369

Aiping Jiang , Dongsheng Wang , Tiantian Jin , Peng Li , Tao Xu , Di Zhang , Boran Zhu , Junqiang Lin , Qidong Peng

Reservoir construction has led to frequent algal blooms in tributaries of backwater areas, threatening water quality, aquatic ecological safety, and public health. The mechanisms governing algal blooms in reservoir bays are complex, leading to spatiotemporal occurrence patterns that are difficult to accurately identify. In this study, Sentinel-2 imagery from 2019 to 2023 were used to characterize the spatiotemporal distribution of algal blooms in Xiangxi Bay (XXB) near the dam of the Three Gorges Reservoir (TGR). The Google Earth Engine (GEE) platform was used for remote sensing image selection, preprocessing, and spectral index calculation, while random forest (RF) model and polynomial regression were employed to develop chlorophyll-a (Chl-a) retrieval model. Box plots and the Mann–Whitney U test were used to compare bloom and non-bloom conditions across nine hydrometeorological variables from the perspective of external influences, while the internal bloom mechanisms were analyzed by integrating the critical depth theory of algal growth with the hydrodynamic mixing characteristics and bloom occurrence patterns in XXB. The results indicated that: (1) Sentinel-2 imagery effectively captures bloom dynamics in narrow tributary embayments, and the Normalized Difference Chlorophyll Index (NDCI) accurately retrieves Chl-a, with the retrieval model achieving an R² of 0.76; (2) In recent years, algal blooms in XXB have been more likely to occur in March, July, August, and September, particularly in August, with approximately 18 % of the XXB experiencing blooms, predominantly at level II intensity. High-frequency bloom areas are mainly distributed in the mid-to upper reaches of the backwater zone; (3) During the flood season, mid-layer intrusion from the Yangtze River and upstream tributary inflow promote stable stratification in XXB, especially under low-rainfall conditions, thereby favoring algal bloom formation in the mid-to upper reaches. Enhancing hydrodynamic disturbance through physical mixing or reservoir inflow and water-level regulation, particularly under low 10-day rainfall (<35 mm), can effectively suppress bloom development.

水库建设导致回水地区支流藻华频发，对水质、水生生态安全和公众健康构成威胁。水库海湾藻华的控制机制复杂，导致其时空发生模式难以准确识别。利用2019 - 2023年的Sentinel-2遥感影像，对三峡水库大坝附近湘西湾（XXB）赤潮的时空分布特征进行了研究。利用谷歌Earth Engine （GEE）平台进行遥感影像选择、预处理和光谱指数计算，采用随机森林（RF）模型和多项式回归建立叶绿素-a （Chl-a）检索模型。通过箱形图和Mann-Whitney U检验，从外部影响的角度比较了9个水文气象变量的开花和不开花情况，并将藻类生长的临界深度理论与XXB的水动力混合特性和开花发生模式相结合，分析了内部的开花机制。结果表明：(1)Sentinel-2遥感影像能有效捕获狭窄支流河口的水华动态，归一化叶绿素指数（NDCI）能准确地反演出叶绿素a，反演模型的R2为0.76；(2)近年来，XXB的赤潮多发生在3月、7月、8月和9月，特别是8月，赤潮发生率约为18%，以II级赤潮为主。高频水华区主要分布在回水区中上游；(3)汛期，长江中层的入侵和上游支流的流入促进了XXB的稳定分层，特别是在低降雨条件下，从而有利于中上游藻华的形成。通过物理混合或水库入流和水位调节等方式增强水动力扰动，特别是在低10天降雨（35 mm）条件下，可以有效抑制水华的发展。

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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 : 2026-01-06 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

Rule-based training sample generation using Sentinel-2 GCVI time series for winter wheat mapping 基于规则的Sentinel-2 GCVI时间序列冬小麦制图训练样本生成

IF 5.2 Q1 ENVIRONMENTAL SCIENCES

Science of Remote Sensing

Pub Date : 2026-01-06 DOI: 10.1016/j.srs.2026.100364

Fangjie Li , Inbal Becker-Reshef , Josef Wagner , Françoise Nerry

Timely and accurate winter wheat mapping is essential for agricultural monitoring and food security. However, efficiently acquiring high-quality training data for supervised classification remains a challenge. In this study, we developed a rule-based method to automatically generate training samples using Sentinel-2 green chlorophyll vegetation index (GCVI) time series. Then, the key phenological periods were identified through feature importance analysis, and spectral features from these periods were used with a Random Forest (RF) classifier to produce 10 m resolution winter wheat distribution maps for Hengshui, Kaifeng, and Xiangyang in 2022 and 2023. To evaluate temporal transferability, the automatically generated training samples from 2022 to 2023 were transferred to subsequent years, enabling winter wheat mapping for 2023 and 2024 based on cross-year training data. Accuracy assessments showed that the proposed method achieved high performance, with average overall accuracy (OA) of 96.04 ± 1.97 % and 94.81 ± 2.14 % in 2022 and 2023, respectively, and average F1 scores of 91.21 % and 90.83 %. The winter wheat maps generated using transferred samples also demonstrated good temporal transferability, and maintained high accuracy, with average OA of 94.06 ± 2.19 % in 2023 and 94.58 ± 2.01 % in 2024. Area estimates from stratified random sampling showed that winter wheat planting areas in Hengshui, Kaifeng, and Xiangyang were 290.8 ± 16.82, 199.16 ± 10.65, and 318.05 ± 44.16 thousand hectares (kha) in 2022, increasing to 379.34 ± 19.75, 209.27 ± 12.68, and 342.02 ± 42.81 kha in 2023, respectively. Compared with existing winter wheat products, the map generated in this study achieved higher classification accuracy and finer spatial detail. Overall, this study provides a practical and effective approach for automatic training sample generation in winter wheat mapping, and offers valuable guidance for large-scale, long-term agricultural monitoring.

及时、准确的冬小麦制图对农业监测和粮食安全至关重要。然而，如何有效地获取高质量的监督分类训练数据仍然是一个挑战。在这项研究中，我们开发了一种基于规则的方法，以哨兵-2绿色叶绿素植被指数（GCVI）时间序列自动生成训练样本。然后，通过特征重要性分析确定关键物候期，利用这些物候期的光谱特征与随机森林（Random Forest， RF）分类器构建2022年和2023年衡水、开封和襄阳地区10 m分辨率冬小麦分布图。为了评估时间可转移性，将自动生成的2022 - 2023年训练样本转移到后续年份，实现了基于跨年训练数据的2023年和2024年冬小麦制图。准确率评估表明，该方法取得了良好的性能，2022年和2023年的平均总体准确率（OA）分别为96.04±1.97%和94.81±2.14%，平均F1分数为91.21%和90.83%。利用转移样本生成的冬小麦图谱具有良好的时间可转移性，保持了较高的精度，2023年和2024年的平均OA分别为94.06±2.19%和94.58±2.01%。分层随机抽样估算结果显示，2022年衡水、开封和襄阳冬小麦种植面积分别为290.8±16.82、199.16±10.65和318.05±44.16千公顷，2023年分别增加到379.34±19.75、209.27±12.68和342.02±42.81千公顷。与现有冬小麦产品相比，本研究生成的地图具有更高的分类精度和更精细的空间细节。综上所述，本研究为冬小麦制图中训练样本的自动生成提供了实用有效的方法，对大规模、长期的农业监测具有重要的指导意义。

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引用次数: 0

Major improvements in spaceborne early fire detection and small-fire FRP retrieval with the meteosat third generation flexible combined imager 气象卫星第三代柔性组合成像仪在星载早期火灾探测和小火灾玻璃钢回收方面的重大改进

IF 5.2 Q1 ENVIRONMENTAL SCIENCES

Science of Remote Sensing

Pub Date : 2026-01-05 DOI: 10.1016/j.srs.2026.100366

Weidong Xu , Martin J. Wooster , Jiangping He , Andrea Meraner , Jose Gomez-Dans , Zixia Liu , Isabel F. Trigo , Emanuel Dutra

Geostationary Earth Observation satellites, originally developed for weather forecasting, offer unique high temporal resolution imaging capabilities increasingly suited for detecting the fast-changing dynamics of landscape fires. The newly operational Meteosat Third Generation (MTG) satellite carries a Flexible Combined Imager (FCI) that greatly improves on the spatial, temporal and radiometric characteristics of the predecessor Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) system. Here we describe for the first time the application of an active fire (AF) detection algorithm to FCI data, and the retrieval of fire radiative power (FRP) estimates from the detected AF pixels. The algorithm used is the Fire Thermal Anomaly (FTA) approach, currently used to generate the operational SEVIRI AF data products at the EUMETSAT Land Surface Analysis Satellite Application Facility (LSA SAF). A comparative analysis between the FCI-derived outputs and those obtained from the existing SEVIRI system is undertaken in order to evaluate the benefits provided by FCI. We also include in this comparison data products from the polar-orbiting Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) systems. Our intercomparisons made in detail over specific Portuguese and Greek wildfires, and systematically across Africa and Europe, reveals four key findings: (1) FCI detects fire onset up to 4 h earlier than SEVIRI for the specific fires examined, and 2 h before MODIS and 4 h before VIIRS; (2) FCI generated 5 × more AF pixel detections than SEVIRI, due to a much reduced minimum FRP detection threshold (∼10 MW versus ∼40 MW) enabling the detection of the many AF pixels missed by SEVIRI; (3) FCI AF detection errors of omission were 38 % compared to MODIS centre-of-scan data, and 68 % compared to VIIRS, substantially improving on SEVIRI's 83 % and 89 % respectively; while commission errors compared to these two remained low at 12 % and 10 % respectively; (4) FCI FRP retrievals showed very strong agreement with the matching ones provided by MODIS (r² = 0.97, slope = 0.93). FCI offers detections every 10 min over the full disk, and 2.5 min over Europe when rapid-scan commences after launch of the second MTG Imagery platform. The results shown here suggest that the operational active fire data products based on FCI and planned to be issued from the EUMETSAT LSA SAF using the FTA algorithm should deliver a substantial improvement in satellite-based fire monitoring across Europe and Africa compared to the already successful products currently generated from MSG.

地球同步观测卫星最初是为天气预报而开发的，它提供了独特的高时间分辨率成像能力，越来越适合于探测快速变化的景观火灾动态。新运行的气象卫星第三代（MTG）卫星携带一个柔性组合成像仪（FCI），大大改善了前身气象卫星第二代（MSG）旋转增强可见光和红外成像仪（SEVIRI）系统的空间、时间和辐射特性。在这里，我们首次描述了在FCI数据中应用主动火灾（AF）检测算法，并从检测到的AF像素中检索火灾辐射功率（FRP）估计。使用的算法是火热异常（FTA）方法，目前用于在EUMETSAT陆地表面分析卫星应用设施（LSA SAF）生成可操作的SEVIRI AF数据产品。为了评价FCI提供的效益，对FCI衍生的产出与现有SEVIRI系统获得的产出进行了比较分析。我们还包括了极轨中分辨率成像光谱仪（MODIS）和可见光红外成像辐射计套件（VIIRS）系统的数据产品。我们对葡萄牙和希腊的野火以及整个非洲和欧洲的野火进行了详细的相互比较，揭示了四个关键发现：(1)FCI比SEVIRI检测的特定火灾早4小时，比MODIS早2小时，比VIIRS早4小时；(2) FCI产生的AF像素检测比SEVIRI多5倍，因为最低FRP检测阈值大大降低（~ 10 MW对~ 40 MW），能够检测到SEVIRI错过的许多AF像素；(3)与MODIS扫描中心数据相比，FCI AF遗漏检测误差为38%，与VIIRS相比为68%，大大改善了SEVIRI的83%和89%；与这两家公司相比，佣金错误率仍然很低，分别为12%和10%；(4) FCI FRP反演结果与MODIS反演结果吻合较好（r2 = 0.97，斜率= 0.93）。FCI在整个磁盘上每10分钟提供一次检测，在第二个MTG图像平台启动后开始快速扫描时，在欧洲每2.5分钟提供一次检测。这里显示的结果表明，与目前由MSG生成的成功产品相比，基于FCI并计划由EUMETSAT LSA SAF使用FTA算法发布的可操作的主动火灾数据产品应该在欧洲和非洲的基于卫星的火灾监测方面提供实质性的改进。

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引用次数: 0

Calculation of surface roughness using machine learning algorithms combined with knowledge distillation 结合知识蒸馏的机器学习算法计算表面粗糙度

IF 5.2 Q1 ENVIRONMENTAL SCIENCES

Science of Remote Sensing

Pub Date : 2026-01-05 DOI: 10.1016/j.srs.2026.100368

Jianyong Cui , Wenwen Gao , Chunlei Meng

Surface roughness is a key parameter in meteorological simulations and wind energy assessments, and its spatial distribution is influenced by various factors. However, the complexity of these factors makes it difficult to retrieve roughness. Although machine learning methods have partially addressed this issue, they still face the challenge of insufficient measurement data. To tackle this, the present study proposes a knowledge distillation framework that integrates physical models and machine learning. It establishes a “teacher-student” model to enable knowledge transfer from regions with sufficient data to target regions with zero samples. In the source domain, where abundant ground truth data is available, four models-Random Forest, Support Vector Regression, Multi-layer Perceptron, and Transformer—were trained. The Multi-layer Perceptron, which achieved the best performance (correlation coefficient: 0.81, RMSE: 0.74, MAE: 0.51), was selected as the teacher model. Then, using the knowledge distillation method, soft labels were generated from remote sensing data in the target region to guide the training of the student model. This facilitated cross-domain knowledge transfer. The results show that the student model's training accuracy improved to 0.89, with the RMSE and MAE reduced to 0.62 and 0.33, respectively, significantly outperforming the teacher model. Compared to ERA5 reanalysis data and land surface model results, the student model's inversion of surface roughness in the target region reduced the mean absolute error by approximately 18 %, effectively solving the parameter estimation problem under the condition of no measurement samples. This study significantly enhances the accuracy of surface roughness estimation and provides more reliable parameter input for meteorological simulations and numerical weather forecasting.

地表粗糙度是气象模拟和风能评价的关键参数，其空间分布受多种因素的影响。然而，这些因素的复杂性使得粗糙度很难恢复。虽然机器学习方法已经部分解决了这个问题，但它们仍然面临着测量数据不足的挑战。为了解决这个问题，本研究提出了一个集成物理模型和机器学习的知识蒸馏框架。建立“师生”模型，实现知识从数据充足的区域向零样本的目标区域转移。在源域，有丰富的地面真值数据可用，四个模型-随机森林，支持向量回归，多层感知器和变压器-被训练。选择表现最佳的多层感知器（相关系数：0.81,RMSE: 0.74, MAE: 0.51）作为教师模型。然后，利用知识蒸馏方法，从目标区域的遥感数据中生成软标签，指导学生模型的训练。这促进了跨领域的知识转移。结果表明，学生模型的训练准确率提高到0.89，RMSE和MAE分别降低到0.62和0.33，显著优于教师模型。与ERA5再分析数据和地表模型结果相比，学生模型反演目标区域表面粗糙度的平均绝对误差降低了约18%，有效解决了无测量样本条件下的参数估计问题。该研究显著提高了地表粗糙度估算的精度，为气象模拟和数值天气预报提供了更可靠的参数输入。

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引用次数: 0