Remote Sensing of Environment最新文献_第10页

Pan-Arctic winter sea ice classification using Sentinel-1 dual-polarized SAR images 基于Sentinel-1双极化SAR图像的泛北极冬季海冰分类

IF 11.4 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-11-19 DOI: 10.1016/j.rse.2025.115140

Yan Dai , Xiao-Ming Li , Haotian Yuan

Sea ice type information is important for climate change research and human activities in polar regions. Synthetic Aperture Radar (SAR) data, particularly from the Sentinel-1 (S1) mission, has become a key source for high-resolution sea ice mapping. With the growing volume of S1 SAR data, deep learning (DL) methods have been explored for sea ice classification. However, the limited availability of fine labels and overlapping backscatter intensity among ice types remain major obstacles to achieving automated fine-scale sea ice classification across the pan-Arctic region. This paper proposes a model, named IceDeepLab, for the automatic mapping of sea ice type across the pan-Arctic region in winter using S1 dual-polarized SAR images, based on the Deep Learning framework DeepLabv3+. The IceDeepLab can identify open water, young ice, first-year ice and old ice, generating pixel-wise classification maps at 400 m resolution. A sea ice type dataset, comprising 174 finely labelled S1 images from November 2019 to March 2020 and covering the pan-Arctic region with diverse sea ice conditions, is established for model training and validation. In particular, radar incidence angle is included as an input to mitigate its effects on HH-polarized SAR data. The model integrates the spatially optimised ConvNeXt backbone, the newly proposed Enhanced Atrous Spatial Pyramid Pooling module, and tailored training and inference schemes. These designs make it more suitable for processing whole SAR images, effectively reducing ambiguity between different sea ice types and eliminating stitching lines, thereby improving overall performance. Experiments on the test dataset show that IceDeepLab achieves an overall accuracy of 91.9%, a mean intersection over union of 82.3%, and a mean F1-score of 90.2%, significantly outperforming the traditional DeepLabv3+ by 16.0%, 10.0%, and 11.1%, respectively. When applied to 4153 S1 images collected from November 2020 to March 2021, IceDeepLab maintains over 90% agreement with operational ice charts in the pan-Arctic region, demonstrating the model's robustness across different years. Ablation experiments, feature visualizations, and statistical analyses further validate the model's effectiveness and offer insights for future improvements in sea ice classification algorithms. Furthermore, evaluations using both the radiometer-derived sea ice concentration product and a year-round ice type dataset are conducted to explore its potential for broader applications in operational sea ice monitoring.

海冰类型信息对气候变化研究和极地人类活动具有重要意义。合成孔径雷达（SAR）数据，特别是来自Sentinel-1 （S1）任务的数据，已成为高分辨率海冰制图的关键来源。随着S1 SAR数据量的不断增加，人们开始探索基于深度学习的海冰分类方法。然而，在泛北极地区实现高精度海冰自动分类的主要障碍仍然是精细标签的有限可用性和冰类型之间的反向散射强度重叠。本文基于深度学习框架DeepLabv3+，提出了一种基于S1双偏振SAR图像的泛北极地区冬季海冰类型自动制图模型IceDeepLab。IceDeepLab可以识别开放水域、新冰、新生冰和老冰，生成分辨率为400米的逐像素分类地图。建立了一个海冰类型数据集，该数据集包括2019年11月至2020年3月期间174张精细标记的S1图像，覆盖了具有不同海冰条件的泛北极地区，用于模型训练和验证。特别是，将雷达入射角作为输入，以减轻其对hh极化SAR数据的影响。该模型集成了空间优化的ConvNeXt骨干网、新提出的增强型空间金字塔池模块以及定制的训练和推理方案。这些设计使其更适合处理整个SAR图像，有效地减少了不同海冰类型之间的歧义，消除了拼接线，从而提高了整体性能。在测试数据集上进行的实验表明，IceDeepLab的总体准确率为91.9%，平均交集超过并集的准确率为82.3%，平均f1分数为90.2%，分别显著优于传统DeepLabv3+的16.0%、10.0%和11.1%。当应用于2020年11月至2021年3月收集的4153张S1图像时，IceDeepLab与泛北极地区的运行冰图保持了90%以上的一致性，证明了该模型在不同年份的稳健性。消融实验、特征可视化和统计分析进一步验证了模型的有效性，并为未来海冰分类算法的改进提供了见解。此外，利用辐射计衍生的海冰浓度产品和全年冰型数据集进行评估，以探索其在业务海冰监测中更广泛应用的潜力。

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引用次数: 0

Prediction and interpretation of high-temperature heat damage risk zones in the Eastern Tibetan Transport Corridor based on a location-information-fusion convolution neural network

IF 11.4 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-11-19 DOI: 10.1016/j.rse.2025.115155

Zhe Chen , Si Guo , Huadong Guo , Zhengbo Yu , Xiangqi Lei , Qingyun Ji , Ziqiong He , Ruichun Chang , Zhongchang Sun , Xiangjun Pei , Zhongli Zhou , Lorenzo Picco

High-temperature heat damage (HTHD) is a common geological hazard in underground engineering, and it poses a huge threat to the lives of the construction personnels and the safety of the project. The Eastern Tibetan Transport Corridor is an HTHD prone area. To address this hazard, an HTHD spatial database was built based on multi-source data such as Sustainable Development Goals Satellite-1 (SDGSAT-1) image. The spatial location relationship between geothermal samples, which is often overlooked in traditional HTHD risk zone prediction, was explored. An interpretable location-information-fusion convolutional neural network (LI-CNN) model was proposed to accurately predict and classify HTHD risk zones. The proposed LI-CNN model achieved a consistently superior performance across all evaluation metrics, with an AUC value of 0.81. The model significantly outperformed five traditional models, including logistic regression (AUC: 0.72), decision tree (AUC: 0.72), k-nearest neighbour (AUC: 0.72), extreme gradient boosting (AUC: 0.77), and convolutional neural network (AUC: 0.77). Finally, two deep learning interpretation models were combined to conduct both global and local interpretation. The results revealed that fault density, land surface temperature, and river density exerted the greatest influence on HTHD occurrence. Overall, our work contributes to the development of sustainable and disaster-resistant infrastructure in accordance with Sustainable Development Goal 9.1 to support economic development and improve human well-being.

高温热损伤是地下工程中一种常见的地质灾害，对施工人员的生命安全和工程安全构成巨大威胁。为了解决这一问题，基于可持续发展目标卫星1号（SDGSAT-1）图像等多源数据，建立了HTHD空间数据库。探讨了传统高温高温灾害危险区预测中常常被忽略的地热样品间的空间位置关系。提出了一种可解释的位置信息融合卷积神经网络（LI-CNN）模型，用于HTHD风险区的准确预测和分类。所提出的LI-CNN模型在所有评估指标上都取得了一致的优异性能，AUC值为0.81。该模型显著优于逻辑回归（AUC: 0.72）、决策树（AUC: 0.72）、k近邻（AUC: 0.72）、极端梯度增强（AUC: 0.77）和卷积神经网络（AUC: 0.77）等5种传统模型。最后，结合两种深度学习解释模型进行全局和局部解释。结果表明，断层密度、地表温度和河流密度对HTHD的发生影响最大。总体而言，我们的工作有助于根据可持续发展目标9.1发展可持续和抗灾的基础设施，以支持经济发展和改善人类福祉。

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引用次数: 0

Characterization of irrigation timing using thermal satellite observations, a data-driven approach 利用热卫星观测数据表征灌溉时间，这是一种数据驱动的方法

IF 11.4 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-11-18 DOI: 10.1016/j.rse.2025.115153

Ehsan Jalilvand , Sujay V. Kumar , Charles Truong , Erin Haacker , Sarith Mahanama

Irrigation is the major consumer of freshwater resources on Earth and represents the largest human intervention in the water cycle. Most irrigation modeling frameworks rely on simplified assumptions regarding the timing of irrigation that result in significant error in the irrigation water use estimation. In this study, we developed a generalized data-driven approach for estimating these irrigation timing attributes using a change point detection algorithm applied to thermal remote sensing data. Land surface temperature at cropland pixels was compared with hydrologically similar natural land cover pixels nearby to extract irrigation attributes. The approach was evaluated over two areas: Nebraska (NEB) and Mahabad, Iran (MAH), for which we had the in situ irrigation data. The method detected the start and end of the irrigation season with reasonable accuracy, exhibiting errors of 18 % and 15 % in estimating the duration of season, in NEB and MAH respectively. The cloud cover either at the start or the end of season was the primary source of error in both cases. Irrigation event detection accuracy across 10 NEB sites yielded F1-scores (Precision & recall combined score) of 0.59–0.74, varying with change point detection algorithm parameters. To optimize these parameters, extensive hyperparameter tuning was performed, leading to specific suggestions tailored for different irrigation practices. The results presented here demonstrate that the LST-based approach can be effective in characterizing interannual variations in irrigation timing attributes.

灌溉是地球上淡水资源的主要消耗者，是人类对水循环的最大干预。大多数灌溉建模框架依赖于关于灌溉时间的简化假设，这导致灌溉用水量估算出现重大误差。在这项研究中，我们开发了一种广义的数据驱动方法，通过应用于热遥感数据的变化点检测算法来估计这些灌溉时间属性。将农田像元的地表温度与附近水文相似的自然土地覆盖像元进行比较，提取灌溉属性。我们在两个地区对该方法进行了评估：内布拉斯加州（NEB）和伊朗马哈巴德（MAH），我们拥有这两个地区的原位灌溉数据。该方法以合理的精度检测灌溉季节的开始和结束，在估计季节持续时间时，NEB和MAH的误差分别为18%和15%。在这两种情况下，季节开始或结束时的云量是误差的主要来源。10个NEB站点的灌溉事件检测精度为0.59-0.74分（精度和召回率综合得分），随变化点检测算法参数的变化而变化。为了优化这些参数，进行了大量的超参数调整，从而为不同的灌溉实践提供了具体的建议。结果表明，基于lst的方法可以有效表征灌溉时间属性的年际变化。

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引用次数: 0

Soil moisture retrieval under different land cover conditions based on Sentinel-1 SAR 基于Sentinel-1 SAR的不同土地覆盖条件下土壤水分反演

IF 11.4 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-11-18 DOI: 10.1016/j.rse.2025.115147

Zhihao Shen , Qisheng He , Chenghui Yang , Zihao Cheng

Soil moisture plays a crucial role in regulating land-atmosphere interactions, directly influencing hydrological and ecological processes. Synthetic Aperture Radar (SAR) has significant advantages in soil moisture retrieval; however, the accuracy of retrieval under different land cover conditions requires further investigation. This study systematically evaluates the performance of optical and radar-derived Vegetation Indices (VIs) in the WCM-Oh coupling model using Sentinel-1C-band dual-polarization SAR data, covering five land cover types (grassland, crops, shrubland, forest and desert). Additionally, a new radar-based vegetation index, SVIdp, is proposed and tested for the first time. This index is based on the interaction of dual-polarization scattering components. The results show: (1) The WCM-Oh model significantly improved retrieval accuracy in typical vegetation areas (grasslands and crops), with the correlation coefficient (R) increasing by 0.10 and the root mean square error (RMSE) decreasing by 2 %–14 %, compared to the independent WCM model; (2) Under different land cover types, the WCM-Oh2004 coupling model based on GNDVI demonstrated high prediction accuracy (R = 0.66–0.85, ubRMSE = 0.0392–0.0698 m³/m³) in areas with moderate vegetation cover (e.g., grassland A, herbaceous vegetation A/B, wheat/corn rotation areas). However, areas such as alpine grasslands, artificial lawns, sparse vegetation, and tea plantations exhibited asymmetric errors, with underestimations in low-value regions and overestimations in high-value regions; (3) Performance of different vegetation indices: Biochemical optical indices (e.g., GNDVI, CVI) performed best in herbaceous vegetation areas, while structural radar indices (e.g., DpSVI, RVI) demonstrated superior stability in dense or structurally complex vegetation areas; (4) SVIdp outperformed DpRVI and NDVI in shrublands and grasslands. This study provides new insights into SAR-based soil moisture modeling and supports large-scale SAR soil moisture spatial mapping.

土壤水分在调节陆地-大气相互作用中起着至关重要的作用，直接影响水文和生态过程。合成孔径雷达（SAR）在土壤水分检索方面具有显著优势；但不同土地覆盖条件下的反演精度有待进一步研究。本研究利用sentinel - 1c波段双极化SAR数据，系统评估了WCM-Oh耦合模型中光学和雷达衍生植被指数（VIs）的性能，覆盖了5种土地覆盖类型（草地、农作物、灌丛、森林和沙漠）。此外，本文还首次提出了一种新的基于雷达的植被指数SVIdp，并对其进行了测试。该指标基于双偏振散射分量的相互作用。结果表明：(1)与独立WCM模型相比，WCM- oh模型显著提高了典型植被区（草地和农作物）的检索精度，相关系数(R)提高了0.10，均方根误差（RMSE）降低了2% ~ 14%；(2)在不同土地覆盖类型下，基于GNDVI的WCM-Oh2004耦合模型在中度植被覆盖区域（如草地A、草本植被A/B、小麦/玉米轮作区）具有较高的预测精度（R = 0.66 ~ 0.85, ubRMSE = 0.0392 ~ 0.0698 m3/m3）。而高寒草原、人工草坪、稀疏植被和茶园等区域则表现出不对称误差，低值区被低估，高值区被高估；(3)不同植被指数的表现：生物化学光学指数（如GNDVI、CVI）在草本植被区表现最好，而结构雷达指数（如DpSVI、RVI）在密集或结构复杂的植被区表现出较好的稳定性；(4)灌木林和草地的svidi表现优于DpRVI和NDVI。该研究为基于SAR的土壤水分建模提供了新的见解，并为大规模SAR土壤水分空间制图提供了支持。

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引用次数: 0

Tracking seasonal variability in plant traits from spaceborne PRISMA and NEON AOP across forest types and ecoregions 利用星载PRISMA和NEON AOP追踪不同森林类型和生态区植物性状的季节变化

IF 11.4 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-11-17 DOI: 10.1016/j.rse.2025.115149

Fujiang Ji , Ting Zheng , Alexey N. Shiklomanov , Ruqi Yang , Philip A. Townsend , Fa Li , Dalei Hao , Hamid Dashti , Kyle R. Kovach , Hangkai You , Junxiong Zhou , Min Chen

Plant traits serve as critical indicators of how plants adapt to environmental changes and influence ecosystem functions. While airborne hyperspectral remote sensing effectively maps plant traits through detailed reflectance properties, it is limited by cost and scale, making large-scale and temporal studies challenging. The recently launched spaceborne hyperspectral imager, PRecursore IperSpettrale della Missione Applicativa (PRISMA), offers frequent, large scale and high-fidelity observations on a spatial resolution of 30 m and a revisit time of around 29 days, making it suitable for large-scale seasonal trait mapping. However, their potential remains largely unexplored. This study developed a multi-stage framework by leveraging the PRISMA spaceborne hyperspectral data and National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) hyperspectral data to investigate the seasonal dynamics of four key plant traits — chlorophyll content, carotenoid content, equivalent water thickness, and nitrogen content — across eleven NEON sites representing diverse forest types and ecoregions in the contiguous U.S. Our results demonstrated that PRISMA hyperspectral data can reliably track seasonal variability in plant traits, achieving overall R² values ranging from 0.78 to 0.88 and normalized root mean square error (NRMSE) values ranging from 5.4 % to 8.4 % for the four traits. Seasonal patterns revealed bell-shaped trajectories for chlorophyll and carotenoids, while equivalent water thickness decreased steadily across most sites, driven by structural changes during leaf maturation and senescence. Nitrogen content exhibited less pronounced seasonal variation but followed expected nutrient resorption patterns. Analysis of environmental drivers showed that seasonal variability is primarily controlled by solar radiation and day length in northern sites, vapor pressure in semi-arid regions, and temperature in mid-southeastern sites. Spatial variability, meanwhile, was primarily driven by soil properties, particularly during the peak growing season. However, the influence of soil variables slightly declines toward the end of the season at several sites, as climatic factors become more prominent. This study highlights the capability of PRISMA, and potentially other similar spaceborne hyperspectral data for large-scale, time-series plant trait mapping and provides valuable insights into the interactions between plant traits and environmental factors. These findings contribute to advancing our understanding of plant functional ecology and improving predictions of ecosystem responses to environmental changes.

植物性状是植物适应环境变化和影响生态系统功能的重要指标。虽然航空高光谱遥感可以通过详细的反射率特性有效地绘制植物性状，但受成本和规模的限制，使得大规模和时间研究具有挑战性。最近发射的星载高光谱成像仪precursoiperspettrale della Missione Applicativa （PRISMA）提供了30米空间分辨率的频繁、大规模和高保真观测，重访时间约为29天，适合大规模季节性特征制图。然而，它们的潜力在很大程度上仍未得到开发。利用PRISMA星载高光谱数据和美国国家生态观测站网络（NEON）机载观测平台（AOP）高光谱数据，构建了一个多阶段框架，研究了叶绿素含量、类胡萝卜素含量、等效水分厚度和氮含量等4个关键植物性状的季节动态结果表明，PRISMA高光谱数据能够可靠地跟踪植物性状的季节变化，4个性状的总体R2值在0.78 ~ 0.88之间，标准化均方根误差（NRMSE）在5.4% ~ 8.4%之间。叶绿素和类胡萝卜素的季节性变化呈钟形轨迹，而叶片成熟和衰老过程中结构变化导致的等效水厚度在大多数地点稳步下降。氮含量表现出不太明显的季节变化，但遵循预期的养分吸收模式。环境驱动因素分析表明，季节变化主要受北部站点的太阳辐射和日长、半干旱区的水汽压和中东南站点的温度控制。与此同时，空间变异主要受土壤性质驱动，特别是在生长旺季。然而，在一些地点，随着气候因素变得更加突出，土壤变量的影响在季节结束时略有下降。该研究突出了PRISMA和其他类似的星载高光谱数据在大规模、时间序列植物性状制图方面的能力，并为植物性状与环境因子之间的相互作用提供了有价值的见解。这些发现有助于提高我们对植物功能生态学的认识，并改善生态系统对环境变化响应的预测。

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引用次数: 0

Soil moisture retrieval from Sentinel-1: Lessons learned after more than a decade in orbit 从哨兵1号获取土壤水分：在轨道上运行十多年后的经验教训

IF 11.4 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-11-17 DOI: 10.1016/j.rse.2025.115146

Mehdi Rahmati , Anna Balenzano , Michel Bechtold , Luca Brocca , Anke Fluhrer , Thomas Jagdhuber , Kleanthis Karamvasis , David Mengen , Rolf H. Reichle , Seung-bum Kim , Ruhollah Taghizadeh-Mehrjardi , Jeffrey Walker , Liujun Zhu , Carsten Montzka

Soil moisture is a critical variable for hydrology, agriculture and climate. However, large-scale soil moisture observation remains difficult due to sparse in situ networks and the inability of optical sensors to capture it under cloud cover. Synthetic aperture radar (SAR) missions, e.g., Sentinel-1, yield unique all-weather, day and night observations with a fine spatial and temporal resolution that makes them of interest for development of global soil moisture monitoring. Consequently, this review discusses the application of C-band SAR observations from the Sentinel-1 satellite mission to estimate high-resolution near-surface soil moisture. First, the importance of SAR backscatter monitoring from Sentinel-1 is emphasized. Next, the current state-of-the-art in soil moisture retrieval from Sentinel-1 is presented. Although considerable progress has been made in near-surface soil moisture retrieval, several limitations remain. Factors such as the effects of vegetation and surface roughness on the signal, sensor and scattering model limitations, spatial and temporal constraints, and uncertainties, e.g. in data assimilation, pose challenges to its usage. While Artificial Intelligence (AI)-based retrieval methods have shown promise, their interpretability, dependence on large datasets, vulnerability to data quality, and computational burden have been major challenges. Beyond methods that rely on backscatter, there have been recent works indicating that SAR interferometric observables have the potential to estimate soil moisture, especially in arid and semi-arid regions where these are particularly sensitive to moisture changes. To address these challenges, this paper recommends integrating Sentinel-1 with other satellite mission data for a multi-sensor data integration approach (e.g., Sentinel-2 and Soil Moisture Active Passive - SMAP data), refining physical and semi-empirical models, developing advanced AI techniques able to consider physical principles, and combining with emerging data from other high temporal resolution SAR missions (e.g., NASA-ISRO SAR). The review concludes with identification of key research priorities, including standardization of retrieval frameworks, improved validation efforts on standardized reference sets, and cloud processing for real-time user cases. Overall, the review provides a thorough foundation for understanding, refining, and advancing Sentinel-1 based soil moisture retrieval methods.

土壤湿度是水文、农业和气候的关键变量。然而，由于原位网络稀疏，且光学传感器无法在云层下捕获土壤水分，因此大规模的土壤水分观测仍然是困难的。合成孔径雷达（SAR）任务，例如Sentinel-1，产生独特的全天候、白天和夜间观测，具有良好的空间和时间分辨率，使其成为全球土壤湿度监测发展的兴趣。因此，本文讨论了Sentinel-1卫星c波段SAR观测数据在估算高分辨率近地表土壤湿度方面的应用。首先，强调了Sentinel-1 SAR后向散射监测的重要性。其次，介绍了Sentinel-1土壤水分反演的最新进展。虽然在近地表土壤水分检索方面取得了相当大的进展，但仍然存在一些限制。植被和地表粗糙度对信号的影响、传感器和散射模式的限制、空间和时间限制以及数据同化等不确定性等因素对其使用构成了挑战。虽然基于人工智能（AI）的检索方法已经显示出前景，但它们的可解释性、对大型数据集的依赖性、数据质量的脆弱性以及计算负担一直是主要挑战。除了依赖于后向散射的方法之外，最近的研究表明，SAR干涉观测值具有估计土壤湿度的潜力，特别是在对湿度变化特别敏感的干旱和半干旱地区。为了应对这些挑战，本文建议将Sentinel-1与其他卫星任务数据集成为一种多传感器数据集成方法（例如，Sentinel-2和土壤湿度主动被动- SMAP数据），完善物理和半经验模型，开发能够考虑物理原理的先进人工智能技术，并结合其他高时间分辨率SAR任务（例如，NASA-ISRO SAR）的新兴数据。评审的结论是确定了关键的研究重点，包括检索框架的标准化、标准化参考集上改进的验证工作，以及实时用户用例的云处理。总之，该综述为理解、完善和推进基于Sentinel-1的土壤水分检索方法提供了全面的基础。

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引用次数: 0

Atmospheric dryness effects on canopy chlorophyll fluorescence and Gross Primary Production (GPP) in a deciduous forest during heat waves 热浪期间大气干燥对落叶林冠层叶绿素荧光和总初级生产量的影响

IF 11.4 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-11-17 DOI: 10.1016/j.rse.2025.115148

Zhaohui Li , Gabriel Hmimina , Gwendal Latouche , Daniel Berveiller , Abderrahmane Ounis , Yves Goulas , Kamel Soudani

Sun-Induced chlorophyll Fluorescence (SIF) is the most promising remote-sensing proxy of Gross Primary Production (GPP) in terrestrial ecosystems. However, the estimation of GPP using SIF is challenging when plants experience stress, particularly during extreme climatic events whose frequency is projected to increase in the future. Recently, the feasibility of canopy-level active chlorophyll fluorescence measurements (LED-induced chlorophyll fluorescence), which directly measure the apparent fluorescence yield (F_yieldLIF), has provided new perspectives on detecting the responses of plants to stress. This study was conducted during the summer 2022 European heat waves in a mixed temperate deciduous broadleaf forest, located in the French Fontainebleau-Barbeau station. Continuous measurements of carbon dioxide (CO₂) and energy exchanges, SIF, F_yieldLIF, and ancillary environmental variables were acquired. We investigated how heat-wave induced high atmospheric dryness, measured as Vapor Pressure Deficit, affected canopy chlorophyll fluorescence (both SIF and F_yieldLIF) and GPP, as well as their relationships. At the half-hourly scale, our results revealed a decrease of the correlation between SIF and GPP (R² decreased from 0.49 to 0.17) at high atmospheric dryness. In contrast, the correlation between F_yieldLIF and GPP increased significantly under high atmospheric dryness (R² increased from 0.07 to 0.43). However, at the daily scale, the correlations between SIF and GPP and between F_yieldLIF and GPP showed an overall increase compared to the half-hourly scale, suggesting a time-scale-dependent response of these relationships to atmospheric dryness. This study also highlighted F_yieldLIF's advantage in detecting plant responses to high atmospheric dryness, and emphasized the potential of canopy-level active chlorophyll fluorescence for assessing the chlorophyll fluorescence-photosynthesis relationship under extreme climatic conditions.

太阳诱导叶绿素荧光（SIF）是陆地生态系统中最有前途的遥感指标。然而，当植物遭受胁迫时，特别是在预计未来频率会增加的极端气候事件期间，使用SIF估计GPP是具有挑战性的。近年来，直接测量表观荧光量（FyieldLIF）的冠层水平活性叶绿素荧光测量（led诱导的叶绿素荧光）的可行性为检测植物对胁迫的响应提供了新的视角。这项研究是在2022年夏季欧洲热浪期间在位于法国枫丹白露-巴博站的混合温带落叶阔叶林中进行的。连续测量二氧化碳（CO2）和能量交换、SIF、FyieldLIF和辅助环境变量。我们研究了热浪引起的高大气干燥度（测量为蒸汽压差）如何影响冠层叶绿素荧光（SIF和FyieldLIF）和GPP，以及它们之间的关系。在半小时尺度上，我们的研究结果显示，在高大气干燥度下，SIF与GPP的相关性降低（R2从0.49降至0.17）。高干度条件下，FyieldLIF与GPP的相关系数显著增加（R2由0.07增加到0.43）。然而，在日尺度上，与半小时尺度相比，SIF和GPP之间以及FyieldLIF和GPP之间的相关性总体上有所增加，这表明这些关系对大气干燥的响应具有时间尺度依赖性。本研究还强调了FyieldLIF在检测植物对高大气干燥度的响应方面的优势，并强调了冠层活性叶绿素荧光在评估极端气候条件下叶绿素荧光-光合作用关系方面的潜力。

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引用次数: 0

Characterizing land use changes triggered by crop-aquaculture co-cultivation from 2013 to 2022 based on a robust classification framework: Illustration in Jianghan Plain, China 基于稳健分类框架的2013 - 2022年种植业与水产养殖共生引发的土地利用变化特征研究——以江汉平原为例

IF 11.4 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-11-17 DOI: 10.1016/j.rse.2025.115142

Yanbing Wei , Wenjuan Li , Peng Zhu , Qiangyi Yu , Wenbin Wu

The rapid expansion of rice-crayfish farming in China has triggered significant land-use transformations, yet long-term mapping of these patterns remains challenging due to sample limitations and spectral complexities. This study developed a robust classification framework integrating synergistic sample generation and hierarchical classification to address this gap. We first proposed a sample generation method integrating temporal migration with feature-based enlargement strategy, then designed a two-layer stratified classification approach combining machine learning (Random Forest) with phenology-based techniques. Applied to the Jianghan Plain (2013−2022), our framework achieved high accuracy, with overall accuracy higher than 87 % annually and correlation around 0.90 with statistical data. Critical land use dynamics were noticed as follows: (1) Land-use transitions accelerated during 2016–2022, with rice-crayfish expanding predominantly at the expense of traditional rice cultivation (77 % ± 4.76 %) of rice-crayfish fields originated from rice-based cropping). (2) Single-rice areas declined by 24 % ± 3.02 %, while rapeseed-rice and wheat-rice systems decreased by 21 % ± 5.41 % and 26 % ± 5.32 %, respectively. (3) Conversions from dryland and water bodies to rice-crayfish emerged during 2019–2022, a later phase of expansion when the conversion to rice-crayfish became widespread. Overall, this study proposed a robust land use type classification framework for complex regions with limited samples in long-term, providing a transferable solution for monitoring land-system changes under rapid transitions. By revealing the transformative impact of rice-crayfish system expansion on traditional land use patterns, this study highlights its substantial effects on conventional rice cultivation and offers valuable insights for formulating adaptive land management strategies that support ecological sustainability and regional food security.

中国水稻-小龙虾养殖的快速扩张引发了重大的土地利用转变，但由于样本限制和光谱复杂性，这些模式的长期绘图仍然具有挑战性。本研究开发了一个强大的分类框架，整合了协同样本生成和分层分类来解决这一差距。我们首先提出了一种结合时间迁移和基于特征的扩展策略的样本生成方法，然后设计了一种结合机器学习（随机森林）和基于物候的两层分层分类方法。应用于江汉平原（2013 ~ 2022），总体精度在87%以上，与统计数据的相关系数在0.90左右。(1) 2016-2022年土地利用转型加速，稻田小龙虾的扩张以传统水稻种植为代价（稻田小龙虾田占77%±4.76%）。(2)单稻面积减少24%±3.02%，油菜-水稻和小麦-水稻系统面积分别减少21%±5.41%和26%±5.32%。(3)从旱地和水体向水稻-小龙虾的转变出现在2019-2022年，这是向水稻-小龙虾转变的后期扩张阶段。总体而言，本研究为样本有限的复杂区域提供了一个长期稳健的土地利用类型分类框架，为快速过渡下的土地系统变化监测提供了可转移的解决方案。通过揭示水稻-小龙虾系统扩展对传统土地利用模式的变革性影响，本研究强调了其对传统水稻种植的实质性影响，并为制定支持生态可持续性和区域粮食安全的适应性土地管理战略提供了有价值的见解。

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引用次数: 0

Multi-source assessment of permafrost deformation along the Bei'an–Hei'he highway in Northeast China 东北北安-黑河高速公路沿线冻土变形多源评价

IF 11.4 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-11-17 DOI: 10.1016/j.rse.2025.115143

Aoxiang Yan , Shanzhen Li , Xiaoying Jin , Shuai Huang , Wenhui Wang , Jianjun Tang , Anyuan Li , Ze Zhang , Shengrong Zhang , Jinbang Zhai , Lanzhi Lü , Ruixia He , Xiaoying Li , Wei Shan , Ying Guo , Huijun Jin

This study assesses the stability of the Bei'an–Hei'he Highway (BHH), located near the southern limit of latitudinal permafrost in the Xiao Xing'anling Mountains, Northeast China, where permafrost degradation is intensifying under combined climatic and anthropogenic influences. Freeze-thaw-induced ground deformation and related periglacial hazards remain poorly quantified, limiting regional infrastructure resilience. We developed an integrated framework that fuses multi-source InSAR (ALOS, Sentinel-1, ALOS-2), unmanned aerial vehicle (UAV) photogrammetry, electrical resistivity tomography (ERT), and theoretical modeling to characterize cumulative deformation, evaluate present stability, and project future dynamics. Results reveal long-term deformation rates from −35 to +40 mm/yr within a 1-km buffer on each side of the BHH, with seasonal amplitudes up to 11 mm. Sentinel-1, with its 12-day revisit cycle, demonstrated superior capability for monitoring the Xing'an permafrost. Deformation patterns were primarily controlled by air temperature, while precipitation and the topographic wetness index enhanced spatial heterogeneity through thermo-hydrological coupling. Wavelet analysis identified a 334-day deformation cycle, lagging climate forcing by ∼107 days due to the insulating effects of peat. Early-warning analysis classified 4.99 % of the highway length as high-risk (subsidence <−18.18 mm/yr or frost heave >10.91 mm/yr). The InSAR-based landslide prediction model achieved high accuracy (Area Under the Receiver Operating Characteristic (ROC) Curve, or AUC = 0.9486), validated through field surveys of subsidence, cracking, and slow-moving failures. The proposed ‘past-present-future’ framework demonstrates the potential of multi-sensor integration for permafrost monitoring and provides a transferable approach for assessing infrastructure stability in cold regions.

本研究评估的稳定性贝'an-Hei 'he公路(BHH)附近的南纬度的永冻层的限制小邢'anling山脉,中国东北,冻土退化加剧下结合气候和人为影响。冻融引起的地面变形和相关的冰周灾害的量化仍然很差，限制了区域基础设施的恢复能力。我们开发了一个集成框架，融合了多源InSAR (ALOS, Sentinel-1, ALOS-2)，无人机（UAV）摄影测量，电阻率层析成像（ERT）和理论建模，以表征累积变形，评估当前稳定性，并预测未来动态。结果显示，在BHH两侧各1公里的缓冲区内，长期变形率为- 35至+40 mm/年，季节性振幅高达11 mm。哨兵1号以其12天的重访周期，展示了对兴安永久冻土的卓越监测能力。变形模式主要受气温控制，而降水和地形湿度指数通过热-水文耦合增强了空间异质性。小波分析确定了334天的变形周期，由于泥炭的绝缘作用，滞后于气候强迫约107天。预警分析将4.99%的公路长度划分为高风险路段（沉降<；−18.18 mm/年或冻胀>；10.91 mm/年）。基于insar的滑坡预测模型获得了较高的精度（ROC曲线下面积，AUC = 0.9486），并通过沉降、裂缝和缓慢移动破坏的实地调查得到验证。提出的“过去-现在-未来”框架展示了多传感器集成用于永久冻土监测的潜力，并为评估寒冷地区基础设施稳定性提供了一种可转移的方法。

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引用次数: 0

Intercomparison of Earth Observation products for hyper-resolution hydrological modelling over Europe 欧洲超分辨率水文模拟地球观测产品的相互比较

IF 11.4 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment

Pub Date : 2025-11-15 DOI: 10.1016/j.rse.2025.115131

Almudena García-García , Pietro Stradiotti , Federico Di Paolo , Paolo Filippucci , Milan Fischer , Matěj Orság , Luca Brocca , Jian Peng , Wouter Dorigo , Alexander Gruber , Bram Droppers , Niko Wanders , Arjen Haag , Albrecht Weerts , Ehsan Modiri , Oldrich Rakovec , Félix Francés , Matteo Dall’Amico , Martha Anderson , Christopher Hain , Luis Samaniego

The increasing frequency and severity of hydrological extremes demand the development of early warning systems and effective adaptation and mitigation strategies. Such systems and strategies require spatially detailed hydrological predictions, mostly provided by hydrological models. However, current state-of-the-art hydrological predictions remain limited in their spatial resolution. A proposed solution is the integration of high-resolution (

<

1 km) Earth observation (EO) products in hydrological modelling in order to reach hyper-resolution (approximately 1 km²). Nonetheless, proper use of these data in hydrological modelling requires a comprehensive characterization of their uncertainties. Here, we evaluate the performance of high-resolution EO products of four hydrological variables (7 precipitation products, 5 snow cover area products, 6 surface soil moisture products, and 6 actual evapotranspiration products) against observational references. Two merged EO precipitation products at 1 km resolution (merged IMERG-SM2A and merged ERA5-IMERG-SM2A) reached correlation coefficients

>

0.5 with the benchmark reference over most areas and are recommended for hyper-resolution hydrological modelling over Europe. The MODIS (resolution of 250 m) and Sentinel-2/Landsat-8 (resolution of 20/30 m) snow cover products showed the highest classification accuracy and were selected as the best choice for the use of snow cover area products in hyper-resolution hydrological modelling. For surface soil moisture, the NSIDC SMAP product at 1 km resolution yielded correlation coefficients

>

0.6 at most stations and is recommended for hyper-resolution hydrological modelling. Finally, evapotranspiration products showed similar performances at the selected flux sites (correlations coefficients

>

0.8). While the MODIS-Terra/Aqua evapotranspiration products (MOD16A2/MYD16A2) offer higher spatial resolution (500 m), making them potentially advantageous for hyper-resolution hydrological modelling, their temporal resolution is coarser (8-day intervals). In contrast, products like ETMonitor (1 km), ALEXI, and HOLAPS (5 km) provide daily estimates, albeit at lower spatial resolution. The assimilation of the proposed high-resolution products in models individually or in combination could lead us to hyper-resolution hydrological modelling. Still, developing integration workflows is required to overcome difficulties related to scale mismatches and data-gaps.

水文极端事件日益频繁和严重，要求发展早期预警系统和有效的适应和缓解战略。这样的系统和策略需要空间上详细的水文预测，主要由水文模型提供。然而，目前最先进的水文预测在空间分辨率上仍然有限。一个建议的解决方案是将高分辨率（<<；1公里）地球观测（EO）产品集成到水文建模中，以达到超分辨率（约1平方公里）。尽管如此，在水文建模中正确使用这些数据需要对其不确定性进行全面表征。本文针对观测资料，对4个水文变量（7个降水产品、5个积雪面积产品、6个地表土壤湿度产品和6个实际蒸散量产品）的高分辨率EO产品的性能进行了评价。两个合并的1公里分辨率EO降水产品（合并的imergs - sm2a和合并的era5 - imergs - sm2a）在大多数地区与基准参考的相关系数达到>；>0.5，被推荐用于欧洲的超分辨率水文建模。MODIS（分辨率为250 m）和Sentinel-2/Landsat-8（分辨率为20/30 m）积雪产品分类精度最高，被认为是使用积雪面积产品进行超分辨率水文模拟的最佳选择。对于地表土壤湿度，NSIDC在1公里分辨率下的SMAP产品在大多数站点的相关系数为>；>0.6，推荐用于超分辨率水文建模。最后，蒸散发产物在选定通量点表现出相似的性能（相关系数>；> 0.8）。虽然MODIS-Terra/Aqua蒸散发产品（MOD16A2/MYD16A2）提供更高的空间分辨率（500米），使其对超分辨率水文建模具有潜在的优势，但其时间分辨率较粗（8天间隔）。相比之下，ETMonitor（1公里）、ALEXI和HOLAPS（5公里）等产品提供每日估计，尽管空间分辨率较低。在模型中单独或组合同化所提出的高分辨率产品可能导致我们获得超分辨率水文模型。尽管如此，开发集成工作流还是需要克服与规模不匹配和数据差距相关的困难。

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引用次数: 0