新疆内的高寒山区是高亚洲地区的重要组成部分，该区域内拥有大量规模较小的冰湖，部分冰湖在短时间内迅速扩大，并可能导致溃决洪水事件的发生。因此，提高对这些小型冰湖的制图精度对于深入理解冰川冰湖灾害机理至关重要。本研究利用Sentinel-2时序数据和DUNet语义分割模型，结合目视解译和质量控制，开展了2022年新疆高寒山区冰湖最大水域范围(≥新疆高寒山hm2)的提取；并根据冰湖与冰川的关系，将冰湖分为冰川补给湖（包括：冰面湖、冰川接触湖和冰川不接触湖3个亚类）和非冰川补给湖2类，最终得到2022年新疆高寒山区冰湖数据集。本数据集中冰湖总绝对面积误差为12.12 km，平均相对误差为6.14%。本数据集包括：（1）空间数据，即2022年研究区冰湖最大分布范围数据和新疆高寒山区分区；（2）表格数据，包括：2022年研究区不同区域、大小、类型、高程尺度下冰湖的数量与面积统计。数据集存储为shp和xlsx格式。可为新疆冰湖灾害预警、冰湖灾害评价提供数据支持和有效依据。

新疆内的高寒山区是高亚洲地区的重要组成部分，该区域内拥有大量规模较小的冰湖，部分冰湖在短时间内迅速扩大，并可能导致溃决洪水事件的发生。因此，提高对这些小型冰湖的制图精度对于深入理解冰川冰湖灾害机理至关重要。本研究利用Sentinel-2时序数据和DUNet语义分割模型，结合目视解译和质量控制，开展了2022年新疆高寒山区冰湖最大水域范围（≥新疆高寒山hm2）的提取；并根据冰湖与冰川的关系，将冰湖分为冰川补给湖（包括：冰面湖、冰川接触湖和冰川不接触湖3个亚类）和非冰川补给湖2类，最终得到2022年新疆高寒山区冰湖数据集。本数据集中冰湖总绝对面积误差为12.12 km2，平均相对误差为6.14%。本数据集包括：（1）空间数据，即2022年研究区冰湖最大分布范围数据和新疆高寒山区分区；（2）表格数据，包括：2022年研究区不同区域、大小、类型、高程尺度下冰湖的数量与面积统计。数据集存储为shp和xlsx格式。可为新疆冰湖灾害预警、冰湖灾害评价提供数据支持和有效依据。

新疆内的高寒山区是高亚洲地区的重要组成部分，该区域内拥有大量规模较小的冰湖，部分冰湖在短时间内迅速扩大，并可能导致溃决洪水事件的发生。因此，提高对这些小型冰湖的制图精度对于深入理解冰川冰湖灾害机理至关重要。本研究利用Sentinel-2时序数据和DUNet语义分割模型，结合目视解译和质量控制，开展了2022年新疆高寒山区冰湖最大水域范围(≥新疆高寒山hm2)的提取；并根据冰湖与冰川的关系，将冰湖分为冰川补给湖（包括：冰面湖、冰川接触湖和冰川不接触湖3个亚类）和非冰川补给湖2类，最终得到2022年新疆高寒山区冰湖数据集。本数据集中冰湖总绝对面积误差为12.12 km，平均相对误差为6.14%。本数据集包括：（1）空间数据，即2022年研究区冰湖最大分布范围数据和新疆高寒山区分区；（2）表格数据，包括：2022年研究区不同区域、大小、类型、高程尺度下冰湖的数量与面积统计。数据集存储为shp和xlsx格式。可为新疆冰湖灾害预警、冰湖灾害评价提供数据支持和有效依据。

新疆内的高寒山区是高亚洲地区的重要组成部分，该区域内拥有大量规模较小的冰湖，部分冰湖在短时间内迅速扩大，并可能导致溃决洪水事件的发生。因此，提高对这些小型冰湖的制图精度对于深入理解冰川冰湖灾害机理至关重要。本研究利用Sentinel-2时序数据和DUNet语义分割模型，结合目视解译和质量控制，开展了2022年新疆高寒山区冰湖最大水域范围(≥新疆高寒山hm2)的提取；并根据冰湖与冰川的关系，将冰湖分为冰川补给湖（包括：冰面湖、冰川接触湖和冰川不接触湖3个亚类）和非冰川补给湖2类，最终得到2022年新疆高寒山区冰湖数据集。本数据集中冰湖总绝对面积误差为12.12 km，平均相对误差为6.14%。本数据集包括：（1）空间数据，即2022年研究区冰湖最大分布范围数据和新疆高寒山区分区；（2）表格数据，包括：2022年研究区不同区域、大小、类型、高程尺度下冰湖的数量与面积统计。数据集存储为shp和xlsx格式。可为新疆冰湖灾害预警、冰湖灾害评价提供数据支持和有效依据。

Landslides can cause severe damage to property and human life. Identifying their locations and characteristics is crucial for emergency rescue and disaster risk assessment. However, existing methods need help in accurately detecting landslides because of their diverse characteristics and scales, as well as the differences in spectral features and spatial heterogeneity of remote sensing images. To overcome these challenges, a multiscale feature fusion landslide-detection network (MFLD-Net) is proposed. This network utilizes reflectance difference images from pre- and post-landslide Sentinel-2A images, along with digital elevation model (DEM) data. Moreover, a multichannel differential landslide dataset was constructed through spectral analysis of Sentinel-2A images, which facilitates network training and enables differentiation between landslides and other objects with similar spectral features, such as bare soil and buildings. The proposed MFLD-Net was tested in Shuzheng Valley and Detuo town in Sichuan, China, where earthquakes have occurred. The experimental results revealed that compared with advanced deep learning models, MFLD-Net has promising landslide detection performance. This study provides suggestions for selecting optimal deep learning methods and spectral band combinations for landslide detection and offers a publicly available landslide dataset for further research.

The European spruce bark beetle (Ips typographus) is an insect species that causes significant damage to Norway spruce (Picea abies) forests across Europe. Infestation by bark beetles can profoundly impact forest ecosystems, affecting their structure and composition and affecting the carbon cycle and biodiversity, including a decrease in net primary productivity (NPP), a key indicator of forest health. The primary objective of this study is to enhance our understanding of the interplay among NPP, bark beetle infestation, land surface temperature (LST), and soil moisture content as key components influencing the effects of climate change-related events (e.g., drought) during and after a drought event in the Bavarian Forest National Park in southeastern Germany. Earth observation data, specifically Landsat-8 TIR and Sentinel-2, were used to retrieve LST and leaf area index (LAI), respectively. Furthermore, for the first time, we incorporated a time series of high-resolution (20 m) LAI as a remote sensing biodiversity product into a process-based ecological model (LPJ-GUESS) to accurately generate high-resolution (20 m) NPP products. The study found a gradual decline in NPP values over time due to drought, increased LST, low precipitation, and a high rate of bark beetle infestation. We observed significantly lower LST in healthy Norway spruce stands compared to those infested by bark beetles. Likewise, low soil moisture content was associated with minimal NPP value. Our results suggest synergistic effects between bark beetle infestations and elevated LST, leading to amplified reductions in NPP value. This study highlights the critical role of integrating high-resolution remote sensing data with

石冰川空间分布制图对于研究寒旱区水文地质和气候变化具有重要意义。除野外实地调查和目视解译等传统手段外，深度学习与高分辨率自然彩色影像（RGB）相结合被应用于青藏高原的石冰川编目（TPRoGI）。然而，由于经典深度学习网络只接受三波段影像作为输入，拥有锐利的边界信息和丰富的光谱信息的近红外波段（NIR）尚未被应用于石冰川识别。因此，本次研究通过改进DeeplabV3+（IDNet），使其具备同时从RGB和NIR中提取和融合特征的能力。基于祁连山地区的Sentinel-2影像和TPRoGI训练IDNet，模型的准确率、精准度、召回率、特异度和mIoU（mean Inter over Union）分别为0.7830、0.7830、0.7840、0.7835和0.6916，各项指标均衡且稳定性高。IDNet模型在祁连山地区识别出459个被TPRoGI遗漏的石冰川，证明了基于IDNet和Sentinel-2 RGB-NIR影像识别石冰川可行，并能有效提升石冰川编目工作的效率和精度。

石冰川空间分布制图对于研究寒旱区水文地质和气候变化具有重要意义。除野外实地调查和目视解译等传统手段外，深度学习与高分辨率自然彩色影像（RGB）相结合被应用于青藏高原的石冰川编目（TPRoGI）。然而，由于经典深度学习网络只接受三波段影像作为输入，拥有锐利的边界信息和丰富的光谱信息的近红外波段（NIR）尚未被应用于石冰川识别。因此，本次研究通过改进DeeplabV3+（IDNet），使其具备同时从RGB和NIR中提取和融合特征的能力。基于祁连山地区的Sentinel-2影像和TPRoGI训练IDNet，模型的准确率、精准度、召回率、特异度和mIoU（mean Inter over Union）分别为0.7830、0.7830、0.7840、0.7835和0.6916，各项指标均衡且稳定性高。IDNet模型在祁连山地区识别出459个被TPRoGI遗漏的石冰川，证明了基于IDNet和Sentinel-2 RGB-NIR影像识别石冰川可行，并能有效提升石冰川编目工作的效率和精度。

Heavy metal stress can lead to morphological and physiological variations in crops. We aimed to distinguish heavy metal stress levels based on the variations of morphological and physiological parameters from radiative transfer and statistical models. Sentinel-2 satellite images and in situ measured data were collected from heavy metal-contaminated soils of rice growing areas in Zhuzhou City, Hunan Province, China. The chlorophyll content (chlorophyll a + chlorophyll b, Cab) and leaf area index (LAI) were calculated using a PROSAIL radiative transfer model and the multilayer perceptron algorithm. A two-dimensional feature space was established from Cab-LAI. Furthermore, a normalized heavy metal stress index (HMSI) from the established Cab-LAI theoretical triangular model was explored to distinguish heavy metal stress levels in rice. The results indicated that (i) the PROSAIL and artificial neural network algorithm were successful at deriving physiological parameters with high estimation accuracy. Pearson's correlation coefficient between the predicted and measured Cab was 0.85; (ii) the correlation between the measured concentration of cadmium in the soil and the HMSI was 0.84, indicating that it is a good indicator of rice damage caused by heavy metal stress, with the maximum HMSI occurring in rice subjected to high pollution; and (iii) high pollution occurred on both sides of the Xiangjiang River, whereas moderate pollution mainly existed around the heavily polluted areas. Areas with non-pollution and mild pollution were distributed over most of the study area. Combining rice Cab with LAI is a feasible method to determine the distribution of rice heavy metal stress levels over a large area.

In recent years, China has gradually begun restoring native salt marsh vegetation such as Suaeda salsa (S. salsa) in coastal wetlands that were damaged by the long-term invasion of Spartina alterniflora. Chlorophyll content (C-ab), an important indicator of vegetation health, necessitates extensive and long-term monitoring using Sentinel-2. However, due to the influence of betacyanin (Beta), S. salsa exhibits different phenotypes (red and green) under various stress conditions, making remote sensing mechanism studies of this unique vegetation more challenging. In particular, satellite multispectral images are significantly affected by soil background in mixed pixels, making it imperative to mitigate this influence. This study explores the applicability of a recently proposed spectral separation of soil and vegetation (3SV) in Sentinel-2 multispectral and S. salsa vegetation from a remote sensing mechanism perspective, and further improves it. Additionally, a comparative analysis was conducted on the effectiveness of combining 3SV with several mainstream chlorophyll-sensitive indices. The advantages of machine learning algorithms were leveraged to develop a high-precision hybrid semi-empirical model for estimating C-ab in different S. salsa phenotypes. The research findings indicate that: (1) The 3SV algorithm, adjusted with slope compensation and B2 and B4 bands, is applicable to green S. salsa scenarios. For red S. salsa scenarios, further adjustment using B2 and B3 bands and coverage fraction is required. (2) The MTCI, MRENDVI, MND, and MNDRE indices combined best with the modified 3SV, significantly reducing the RMSE of the semi-empirical models, especially under wet soil conditions with soil fraction f(soil) < 0.5. (3) The highest accuracy (RMSE = 3.83 mu g/cm(2)) for C-ab estimation models for different S. salsa phenotypes was achieved by combining the modified 3SV soil-removed algorithm and the four indices with particle swarm optimization random forest regression (PSO-RFR).