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。

Key messageIntegrating airborne laser scanning and satellite time series data across the forest rotation enhances decision-making in precision forestry. This review supports forest managers by illustrating practical applications of these remote sensing technologies at different stages of intensive forest plantation management-such as site assessment, monitoring, and silviculture-helping improve productivity, sustainability, and operational efficiency.ContextIntensively managed forest plantations depend on high-resolution, timely data to guide silviculture and promote sustainability.AimsThis review explores how airborne laser scanning (ALS) and satellite time series data support precision forestry across key stages, including site assessment, establishment, monitoring, inventory updates, growth tracking, silvicultural interventions, and harvest planning.ResultsThe review highlights several key applications. ALS-derived digital elevation models and canopy metrics improve site productivity estimation by capturing micro-topographic variables and soil formation factors. Combining ALS with multispectral data enhances monitoring of seedling survival and health, although distinguishing seedlings from non-living components remains a challenge. ALS-based Enhanced Forest Inventories provide spatially detailed forest metrics, while satellite time series and vegetation indices support continuous monitoring of growth and early detection of drought, fire, and pest stress. ALS individual tree detection models offer insights into competition, stand structure, and spatial variability, informing thinning and fertilization decisions by identifying trees under stress or with high growth potential. These models also help mitigate drought and wind damage by guiding density and canopy structure management. ALS terrain data further support harvest planning by optimizing machinery routes and reducing environmental impacts.ConclusionDespite progresses, challenges remain in refining predictive models, expanding remote sensing applications, and developing tools that translate complex data into field operations. A major barrier is the technical expertise needed to interpret spatial data and integrate remote sensing into workflows. Continued research is needed to improve accessibility and operational relevance. High-resolution data still offer strong potential for adaptive management and sustainability.

Srinagar city is located in the heart of the Kashmir valley of the northwest Himalaya and is the largest urban center in the seismically active region. As yet, no direct deformation measurement or observation of any kind has been made in Srinagar and the surrounding areas using InSAR. We detect and quantify the ground deformation in the city's western flank using the InSAR time series. Stanford Method for Persistent Scatterer (StaMPS) is employed to process Sentinel-1A radar images acquired between 2015 and 2022 for ascending (161 scenes) and 2020 to 2022 for descending track (31 scenes). Generated velocity fields were decomposed into vertical rate maps, revealing a deformation of 17 mm year(-1) for ascending and 19 mm year(-1) for descending track. Time series analysis exhibits an identical deformation rate for both tracks on concurrent dates. Time-series GPS data was employed to validate the outcomes of our InSAR analysis. A field survey conducted in the main zone of deformation revealed extensive damage to structures in the form of wide cracks. Such cracks develop in older infrastructure (similar to 8 years) due to cumulative ground deformation over several years. Geotechnical investigation and strength calculation on a 30-m borehole of the subsiding region shows a vertical domination of high void, floodplain soils, with appreciable amounts of decomposed organic matter and lower shear strength parameters that are prone to volume reduction and particle rearrangement upon wetting and loading. The overall relevance of this study is in detecting and quantifying such subsidence in the Kashmir basin using SAR remote sensing. We also seek to establish a linkage of this deformation with the local stratum to allow for more consideration and efficient planning of civil infrastructure in the subsidence-prone regions of the citified zone and appropriate management of the subsidence-induced risk.

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

Remote sensing plays an increasingly important role in agriculture, especially in monitoring the quality of agricultural crops. Optical sensing is often limited in Central Europe due to cloud cover; therefore, synthetic aperture radar data is increasingly being used. However, synthetic aperture radar data is limited by more difficult interpretation mainly due to the influence of speckles. For this reason, its use is often limited to larger territorial units and field blocks. The main aim of this study therefore was to verify the possibility of using satellite synthetic aperture radar images to assess the within-field variability of winter wheat. The lowest radar vegetation index values corresponded to the area of the lowest production potential and the greatest damage to the stand. Also for VH and VV polarizations, the highest values corresponded to the area of the lowest stand quality. Qualitative changes in the stand across the zones defined by frost damage and production potential were assessed with the help of the logistic regression model with resampled data for 10, 50, and 100 m pixel size. The best correlation coefficients were achieved at a spatial resolution of 50 m for both options. The F-score still yielded a promising result ranging from 0.588 to 0.634 for frost damage categories. The regression model of the production potential performed slightly better in terms of the F-score, recall, and precision at higher resolutions. It was proved that modern statistical methods could be used to reduce problems associated with speckles of radar images for practical purposes.

In this study, a methodology is proposed to use dual-polarimetric synthetic aperture radar (SAR) to identify the spatial distribution of soil liquefaction. The latter is a phenomenon that occurs in conjunction with seismic events of a magnitude generally higher than 5.5-6.0 and which affects loose sandy soils located below the water table level. The methodology consists of two steps: first the spatial distributions of soil liquefaction is estimated using a constant false alarm rate method applied to the SPAN metric, namely the total power associated with the measured polarimetric channels, which is ingested into a bitemporal approach to sort out dark areas not genuine. Second, the obtained masks are read in terms of the physical scattering mechanisms using a child parameter stemming from the eigendecomposition of the covariance matrix-namely the degree of polarization. The latter is evaluated using the coseismic scenes and contrasted with the preseismic one to have rough information on the time-variability of the scattering mechanisms occurred in the area affected by soil liquefaction. Finally, the obtained maps are qualitatively contrasted against state-of-the-art optical and interferometric SAR methodologies. Experimental results, obtained processing a time-series of ascending and descending Sentinel-1 SAR scenes acquired during the 2023 Turkiye-Syria earthquake, confirm the soundness of the proposed approach.

This paper describes the selected algorithm for the ESA climate change initiative vegetation parameters project. Multi- and hyper-spectral, multi-angular, or multi-sensor top-of-canopy reflectance data call for an efficient generic retrieval system which can improve the consistent retrieval of standard canopy parameters as albedo, Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (fAPAR) and their uncertainties, and exploit the information to retrieve additional parameters (e.g. leaf pigments). We present a retrieval system for canopy and sub-canopy parameters (OptiSAIL), which is based on a model comprising SAIL (canopy reflectance), PROSPECT-D (leaf properties), TARTES (snow properties), a soil model (soil reflectance anisotropy, moisture effect), and a cloud contamination model. The inversion is gradient based and uses codes created by Automatic Differentiation. The full per pixel covariance-matrix of the retrieved parameters is computed. For this demonstration, single observation data from the Sentinel-3 SY_2_SYN (synergy) product is used. The results are compared with the MODIS 4-day LAI/fAPAR product and PhenoCam site photography. OptiSAIL produces generally consistent and credible results, at least matching the quality of the technically quite different MODIS product. The system is computationally efficient with a rate of 150 pixel s(-1) (7 ms per pixel) for a single thread on a current desktop CPU using observations on 26 bands. Not all of the model parameters are well determined in all situations. Significant correlations between the parameters are found, which can change sign and magnitude over time. OptiSAIL appears to meet the design goals and puts real-time processing with this kind of system into reach.

Forests provide multiple ecosystem services including water and soil protection, biodiversity conservation, carbon sequestration, and recreation, which are crucial in sustaining human health and wellbeing. Global changes represent a serious threat to Mediterranean forests, and among known impacts, there is the spread of invasive pests and pathogens, often boosted by climate change and human pressure. Remote sensing can provide support to forest health monitoring, which is crucial to contrast degradation and adopt mitigation strategies. Here, different multispectral and SAR data are used to detect the incidence of ink disease driven by Phytophthora cinnamomi in forest sites in central Italy, dominated by chestnut and cork oak respectively. Sentinel 1, Sentinel 2, and PlanetScope data, together with ground information, served as input in Random Forests to model healthy and disease classes in the two sites. The results indicate that healthy and symptomatic trees are clearly distinguished, whereas the discrimination among disease classes of different severity (moderate and severe damage) is less accurate. Crown dimension and sampled spectral regions are a critical factors in the selection of the sensor; better results are obtained for the larger chestnut crowns with Sentinel 2 data. In both sites, the red and near infra-red bands from multispectral data resulted well suited to monitor the spread of the ink disease.

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).