积雪调节了北半球大部分地区的水循环和能量交换，研究北半球积雪持续时间、积雪面积及积雪物候的时空动态具有重要意义。本研究基于Google Earth Engine云平台，利用MODIS卫星数据对2000―2019年北半球积雪覆盖频率和积雪面积的时空变化特征，以及积雪物候的大尺度变化和贡献进行研究分析。结果表明：北半球积雪变化趋势存在着明显的年际和区域性差异，积雪覆盖频率在欧亚大陆（55°～65°N,30°～120°E）附近地区、蒙古高原、斯塔诺夫高原和格陵兰岛以0.4～0.6（d/a）的速率下降；北半球春冬季节积雪面积下降明显，平均每年冬季下降趋势达到了1.9×105 km2;哈萨克斯坦附近地区和北美洲中部平原地区积雪持续日数显著增加，平均每年增加1.1 d;北半球积雪物候变化主要归因于积雪结束日期的变化，二者相关性达到0.89;从区域对北半球积雪物候变化的贡献分析表明，北半球积雪物候变化主要是由欧亚大陆积雪物候变化主导的，贡献率达到60%左右。

积雪调节了北半球大部分地区的水循环和能量交换，研究北半球积雪持续时间、积雪面积及积雪物候的时空动态具有重要意义。本研究基于Google Earth Engine云平台，利用MODIS卫星数据对2000―2019年北半球积雪覆盖频率和积雪面积的时空变化特征，以及积雪物候的大尺度变化和贡献进行研究分析。结果表明：北半球积雪变化趋势存在着明显的年际和区域性差异，积雪覆盖频率在欧亚大陆（55°～65°N,30°～120°E）附近地区、蒙古高原、斯塔诺夫高原和格陵兰岛以0.4～0.6（d/a）的速率下降；北半球春冬季节积雪面积下降明显，平均每年冬季下降趋势达到了1.9×105 km2;哈萨克斯坦附近地区和北美洲中部平原地区积雪持续日数显著增加，平均每年增加1.1 d;北半球积雪物候变化主要归因于积雪结束日期的变化，二者相关性达到0.89;从区域对北半球积雪物候变化的贡献分析表明，北半球积雪物候变化主要是由欧亚大陆积雪物候变化主导的，贡献率达到60%左右。

积雪调节了北半球大部分地区的水循环和能量交换，研究北半球积雪持续时间、积雪面积及积雪物候的时空动态具有重要意义。本研究基于Google Earth Engine云平台，利用MODIS卫星数据对2000―2019年北半球积雪覆盖频率和积雪面积的时空变化特征，以及积雪物候的大尺度变化和贡献进行研究分析。结果表明：北半球积雪变化趋势存在着明显的年际和区域性差异，积雪覆盖频率在欧亚大陆（55°～65°N,30°～120°E）附近地区、蒙古高原、斯塔诺夫高原和格陵兰岛以0.4～0.6（d/a）的速率下降；北半球春冬季节积雪面积下降明显，平均每年冬季下降趋势达到了1.9×105 km2;哈萨克斯坦附近地区和北美洲中部平原地区积雪持续日数显著增加，平均每年增加1.1 d;北半球积雪物候变化主要归因于积雪结束日期的变化，二者相关性达到0.89;从区域对北半球积雪物候变化的贡献分析表明，北半球积雪物候变化主要是由欧亚大陆积雪物候变化主导的，贡献率达到60%左右。

The Massarosa wildfire, which occurred in July 2022 in Northwestern Tuscany (Italy), burned over 800 hectares, leading to significant environmental and geomorphological issues, including an increase in soil erosion rates. This study applied the Revised Universal Soil Loss Equation (RUSLE) model to estimate soil erosion rates with a multi-temporal approach, investigating three main scenarios: before, immediately after, and one-year post-fire. All the analyses were carried out using the Google Earth Engine (GEE) platform with free-access geospatial data and satellite images in order to exploit the cloud computing potentialities. The results indicate a differentiated impact of the fire across the study area, whereby the central parts suffered the highest damages, both in terms of fire-related RUSLE factors and soil loss rates. A sharp increase in erosion rates immediately after the fire was detected, with an increase in maximum soil loss rate from 0.11 ton x ha-1 x yr-1 to 1.29 ton x ha-1 x yr-1, exceeding the precautionary threshold for sustainable soil erosion. In contrast, in the mid-term analysis, the maximum soil loss rate decreased to 0.74 ton x ha-1 x yr-1, although the behavior of the fire-related factors caused an increase in soil erosion variability. The results suggest the need to plan mitigation strategies towards reducing soil erodibility, directly and indirectly, with a continuous monitoring of erosion rates and the application of machine learning algorithms to thoroughly understand the relationships between variables.

Despite its crucial role in flood defense for downstream regions, the catastrophic breach of the Kakhovka Dam on June 6, 2023, along the Dnipro River in Ukraine caused extensive flooding and damage both upstream and downstream. In addition, the subsequent significant drying up of the dam reservoir poses serious challenges, including hindered electricity generation, compromised flood control measures, and disrupted aquatic ecosystems. This study aims to address knowledge gaps related to the event by employing multi-temporal change detection of pre- and post-event Sentinel-1 synthetic aperture radar (SAR) imagery, analyzed using the Google Earth Engine (GEE) platform, to map flood extent and impacts. Furthermore, we assessed the impacts of dam breaches on soil organic carbon (SOC) sequestration potential in both the drying reservoir region upstream and the flooded areas downstream. The results estimated the total area of the flood extent to be approximately 379.41 km2, with an overall accuracy (OA) of 94% and a Kappa index (K) of 0.89. Quantitative analysis revealed that 81.15 km2 of urban areas, 82.59 km2 of agricultural lands, and 215.56 km2 of herbaceous wetlands were submerged by floodwaters. Both flooding and reservoir drawdown from dam collapses can significantly affect soil organic carbon (SOC) sequestration rates in affected soils. The quantification of post-disaster impacts underscores the pressing need for restoration practices and sustainable management efforts to lessen the environmental impacts and enhance the recovery of the affected regions.

天山位于亚欧大陆中部，是现代冰川的主要分布区之一，该地区冰川融水形成了数量多且分布广泛的冰湖。冰湖是气候变化的重要指示器，也是中国西北干旱与半干旱地区重要的地表水及地下水供给来源。由于地形因素和地物光谱特征的影响，使用单一的水体指数进行遥感影像的冰湖提取时，难以较好地区分出冰湖、山体阴影和积雪。本研究以天山地区为研究区，基于Google Earth Engine云平台，以Landsat 8遥感影像为数据源，根据冰湖的空间位置（缓冲区范围）、地形特征（坡度、高程）以及光谱特征，构建了冰湖决策树提取方法，并与NDWI（归一化水体指数）、MNDWI（改进的归一化水体指数）阈值法进行了精度比较。结果表明：决策树法能够有效减小山体阴影和积雪影响，更有效地提取冰湖信息，提取结果总体精度为89.14%,Kappa系数为0.783,F1分数为87.85%。结合了空间位置、地形特征和光谱特征的决策树方法为冰湖的动态监测与研究分析提供了一种较为高效的提取方法。

天山位于亚欧大陆中部，是现代冰川的主要分布区之一，该地区冰川融水形成了数量多且分布广泛的冰湖。冰湖是气候变化的重要指示器，也是中国西北干旱与半干旱地区重要的地表水及地下水供给来源。由于地形因素和地物光谱特征的影响，使用单一的水体指数进行遥感影像的冰湖提取时，难以较好地区分出冰湖、山体阴影和积雪。本研究以天山地区为研究区，基于Google Earth Engine云平台，以Landsat 8遥感影像为数据源，根据冰湖的空间位置（缓冲区范围）、地形特征（坡度、高程）以及光谱特征，构建了冰湖决策树提取方法，并与NDWI（归一化水体指数）、MNDWI（改进的归一化水体指数）阈值法进行了精度比较。结果表明：决策树法能够有效减小山体阴影和积雪影响，更有效地提取冰湖信息，提取结果总体精度为89.14%,Kappa系数为0.783,F1分数为87.85%。结合了空间位置、地形特征和光谱特征的决策树方法为冰湖的动态监测与研究分析提供了一种较为高效的提取方法。

Recent research on the Himalayan cryosphere has increasingly been focused on climate uncertainty and regional variations, considering features such as glacier recession, lake expansion, outburst floods, and regional hazards. The Bhilangana river basin, located in the central Himalayas, is predominantly characterized by increased elevation-dependent warming and declining seasonal precipitation. Our study shows that high-elevation temperature increased from 2000 to 2022 (0.05(degrees)C/year, p = 20 m/sec). Quantification of the regional hazard reveals potentially severe downstream challenges for low-to-medium-scale hydropower stations, local settlements, and road and railway bridges near Devling and Ghuttu villages.

Artificial human-induced soil sealing has numerous negative consequences. The extent of impervious surfaces is a key indicator of the location and intensity of human activity; however, it is also proof of damage to the natural environment as a result of the sealing and modification of ecosystems. Remote sensing techniques can help detect and monitor changes in land use and cover over an extended period. However, the limited availability of consistent satellite images with high spatiotemporal resolutions covering several decades poses major challenges for achieving high overall classification accuracy. An accurate methodology for the multitemporal detection of artificial land cover classes was developed and applied to a case study of the metropolitan area of Murcia (Spain) with its challenging landscape conditions due to the frequent presence of bare soil. For this purpose, a variety of high-resolution satellite images from SPOT 5, Rapid Eye, and PlanetScope covering a period of 20 years were used. To improve the automated detection of built-up areas, the reflectance values of the images, normalised difference vegetation index (NDVI) and soil adjusted vegetation index (SAVI), and a building surface digital model were used as inputs for the supervised classification model. We applied a random forest algorithm to non-public, high-resolution images in the Google Earth Engine (GEE) as a processing environment to identify eight target land cover classes. The results show that the proposed methodology leads to a substantial improvement, after including the indices and the digital building model, in the overall accuracy (from 93.16 to 95.97%) and in all classes. This improvement was significant for the artificial classes and was particularly noticeable for the built-up areas (from 91.1 to 95.64%) because their confusion with bare soil was considerably reduced. This work demonstrates the effectiveness of the building-surface digital model as a tool for training the classification model, as it reduces uncertainty in confusion with other spectrally similar classes and its applicability to multisource imagery.

帕米尔高原是亚洲高山区最大的冰川作用中心，其冰川融水在区域水资源与水循环中发挥关键作用。然而，近年来对帕米尔高原冰川变化的认识仍存在争议，一方面认为喀喇昆仑-帕米尔高原冰川存在异常前进现象，另一方面认为帕米尔冰川正在加剧退缩。究其原因，一是研究时空范围不重合，二是研究中对冰川的定义有所不同。为明晰帕米尔高原无表碛覆盖冰川近30年来的时空变化情况，本研究基于Google Earth Engine（GEE）平台，利用Landsat 5 TM和Landsat 8 OLI遥感影像数据，消除掉云层遮蔽、季节性积雪和冰川表碛覆盖对无表碛覆盖冰川面积的影响，获取了帕米尔高原1990—2020年期间无表碛覆盖冰川时空变化特征。结果显示，在过去30年帕米尔高原无表碛覆盖冰川面积以116.42 km2·a-1的速率由（12 108.98±250.38） km2缩减到（8 616.44±7.72） km2。在空间上，帕米尔高原西部无表碛覆盖冰川面积总体上呈退缩趋势，而帕米尔东部无表碛覆盖冰川面积则相对稳定。特别是在200...