气候变化正在加剧极端天气事件，导致水资源短缺和强降雨的不可预测性，对可持续发展、生物多样性以及饮水和卫生设施的获取构成威胁。本文旨在为不同气象条件下的综合水资源监测提供有价值的见解。研究提出对SEN2DWATER数据集进行扩展，以增强其流域分割能力。通过将来自Sentinel-1数据的时空对齐雷达信息与现有多光谱Sentinel-2数据整合，生成了一个新颖的多源多时相数据集。研究使用土壤水指数（Soil Water Index, SWI）、归一化差异水指数（Normalized Difference Water Index, NDWI）等指数以及无监督机器学习（Machine Learning, ML）分类器（k-means聚类）对增强后的数据集进行基准测试，取得了颇具前景的结果，同时还探讨了本研究衍生的潜在未来发展方向与应用场景。
英文摘要：
Climate change is intensifying extreme weather events, causing both water scarcity and severe rainfall unpredictability, and posing threats to sustainable development, biodiversity, and access to water and sanitation. This paper aims to provide valuable insights for comprehensive water resource monitoring under diverse meteorological conditions. An extension of the SEN2DWATER dataset is proposed to enhance its capabilities for water basin segmentation. Through the integration of temporally and spatially aligned radar information from Sentinel-1 data with the existing multispectral Sentinel-2 data, a novel multisource and multitemporal dataset is generated. Benchmarking the enhanced dataset involves the application of indices such as the Soil Water Index (SWI) and Normalized Difference Water Index (NDWI), along with an unsupervised Machine Learning (ML) classifier (k-means clustering). Promising results are obtained and potential future developments and applications arising from this research are also explored.
Avian botulism is a paralytic bacterial disease in birds often leading to high fatality. In-vitro diagnostic techniques such as Mouse Bioassay, ELISA, PCR are usually non-preventive, post-mortem in nature, and require invasive sample collection from affected sites or dead birds. In this study, we build a first-ever multi-spectral, remote-sensing imagery based global Bird-Area Water-bodies Dataset (BAWD) (i.e. fused satellite images of warm-water lakes/marshy-lands or similar water-body sites that are important for avian fauna) backed by on-ground reporting evidence of outbreaks. BAWD consists …
AI-driven multi-source data fusion for algal bloom severity classification in small inland water bodies: Leveraging Sentinel-2, DEM, and NOAA climate data
Extending Dynamic World Surface Water Mapping to Sentinel-1 with AlphaEarth Embeddings
Pacific Northwest National Laboratory（太平洋西北国家实验室）；NASA Goddard Space Flight Center（美国国家航空航天局戈达德太空飞行中心）；Fujitsu Research of America（富士通美国研究院）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）
An accurate and precise understanding of global irrigation usage is crucial for a variety of climate science efforts. Irrigation is highly energy-intensive, and as population growth continues at its current pace, increases in crop need and water usage will have an impact on climate change. Precise irrigation data can help with monitoring water usage and optimizing agricultural yield, particularly in developing countries. Irrigation data, in tandem with precipitation data, can be used to predict water budgets as well as climate and weather modeling. With our research, we produce an irrigation p…
Water quality is of great importance for humans and for the environment and has to be monitored continuously. It is determinable through proxies such as the chlorophyll a concentration, which can be monitored by remote sensing techniques. This study focuses on the trade-off between the spatial and the spectral resolution of six simulated satellite-based data sets when estimating the chlorophyll a concentration with supervised machine learning models. The initial dataset for the spectral simulation of the satellite missions contains spectrometer data and measured chlorophyll a concentration of …
Global surface water detection in very-high-resolution (VHR) satellite imagery can directly serve major applications such as refined flood mapping and water resource assessment. Although achievements have been made in detecting surface water in small-size satellite images corresponding to local geographic scales, datasets and methods suitable for mapping and analyzing global surface water have yet to be explored. To encourage the development of this task and facilitate the implementation of relevant applications, we propose the GLH-water dataset that consists of 250 satellite images and manual…
CloudBreaker: Breaking the Cloud Covers of Sentinel-2 Images using Multi-Stage Trained Conditional Flow Matching on Sentinel-1
Computer Science and Engineering（计算机科学与工程）；Bangladesh University of Engineering and Technology（孟加拉工程与技术大学）；Institute of Water and Flood Management（水与洪水管理研究所）
Global environment monitoring is a task that requires additional attention in the contemporary rapid climate change environment. This includes monitoring the rate of deforestation and areas affected by flooding. Satellite imaging has greatly helped monitor the earth, and deep learning techniques have helped to automate this monitoring process. This paper proposes a solution for observing the area covered by the forest and water. To achieve this task UNet model has been proposed, which is an image segmentation model. The model achieved a validation accuracy of 82.55% and 82.92% for the segmenta…
The University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）
Global warming is an urgent issue that is generating catastrophic environmental changes, such as the melting of sea ice and glaciers, particularly in the polar regions. The melting pattern and retreat of polar sea ice cover is an essential indicator of global warming. The Sentinel-2 satellite (S2) captures high-resolution optical imagery over the polar regions. This research aims at developing a robust and effective system for classifying polar sea ice as thick or snow-covered, young or thin, or open water using S2 images. A key challenge is the lack of labeled S2 training data to serve as the…
Analysis of Rainfall Variability and Water Extent of Selected Hydropower Reservoir Using Google Earth Engine (GEE): A Case Study from Two Tropical Countries, Sri Lanka and Vietnam
International Water Management Institute（国际水资源管理研究所）
IIT Bombay（印度理工学院孟买分校）；Rochester Institute of Technology（罗切斯特理工学院）；SGGSIE&T（SGGS工程技术学院）
Satellite Remote Sensing Technology is becoming a major milestone in the prediction of weather anomalies, natural disasters as well as finding alternative resources in proximity using multiple multi-spectral sensors emitting electromagnetic waves at distinct wavelengths. Hence, it is imperative to extract water bodies and burnt areas from orthorectified tiles and correspondingly rank them using similarity measures. Different objects in all the spheres of the earth have the inherent capability of absorbing electromagnetic waves of distant wavelengths. This creates various unique masks in terms …
PES University（PES大学）；Regional Remote Sensing Centre - South, ISRO（印度空间研究组织南部区域遥感中心）
Monitoring water is a complex task due to its dynamic nature, added pollutants, and land build-up. The availability of high-resolu-tion data by Sentinel-2 multispectral products makes implementing remote sensing applications feasible. However, overutilizing or underutilizing multispectral bands of the product can lead to inferior performance. In this work, we compare the performances of ten out of the thirteen bands available in a Sentinel-2 product for water segmentation using eight machine learning algorithms. We find that the shortwave infrared bands (B11 and B12) are the most superior for …
Improved flood mapping for efficient policy design by fusion of Sentinel-1, Sentinel-2, and Landsat-9 imagery to identify population and infrastructure exposed to floods
Lahore University of Management Sciences (LUMS)（拉合尔管理科学大学（LUMS））
National Scientific and Technical Research Council (CONICET)（国家科学技术研究委员会（CONICET））；Institute of Human Development, National University of General Sarmiento（萨米恩托将军国立大学人类发展研究所）；Faculty of Natural Sciences, National University of La Pampa（拉潘帕国立大学自然科学学院）
A method for segmenting water bodies in optical and synthetic aperture radar (SAR) satellite images is proposed. It makes use of the textural features of the different regions in the image for segmentation. The method consists in a multiscale analysis of the images, which allows us to study the images regularity both, locally and globally. As results of the analysis, coarse multifractal spectra of studied images and a group of images that associates each position (pixel) with its corresponding value of local regularity (or singularity) spectrum are obtained. Thresholds are then applied to the …
Geographically-Weighted Weakly Supervised Bayesian High-Resolution Transformer for 200m Resolution Pan-Arctic Sea Ice Concentration Mapping and Uncertainty Estimation using Sentinel-1, RCM, and AMSR2 Data
Department of Geomatics Engineering, Schulich School of Engineering, University of Calgary（地质工程系，Schulich 工程学院，卡尔加里大学）
City University of Hong Kong（香港城市大学）；Wake Forest University（维克森林大学）；Dartmouth College（达特茅斯学院）
Artisanal and Small-scale Gold Mining (ASGM) is an important source of income for many households, but it can have large social and environmental effects, especially in rainforests of developing countries. The Sentinel-2 satellites collect multispectral images that can be used for the purpose of detecting changes in water extent and quality which indicates the locations of mining sites. This work focuses on the recognition of ASGM activities in Peruvian Amazon rainforests. We tested several semi-supervised classifiers based on Support Vector Machines (SVMs) to detect the changes of water bodie…
School of Electronics Engineering, Vellore Institute of Technology Chennai Campus（韦洛尔理工大学金奈校区电子工程学院）
Whenever a person hears about pollution, more often than not, the first thought that comes to their mind is air pollution. One of the most under-mentioned and under-discussed pollution globally is that caused by the non-biodegradable waste in our water bodies. In the case of India, there is a lot of plastic waste on the surface of rivers and lakes. The Ganga river is one of the 10 rivers which account for 90 percent of the plastic that ends up in the sea and there are major cases of local nalaas and lakes being contaminated due to this waste. This limits the source of clean water which leads t…
Better Planet Laboratory, University of Colorado Boulder（科罗拉多大学博尔德分校更好星球实验室）；University of Colorado Boulder（科罗拉多大学博尔德分校）；Mortenson Center for Global Engineering and Resilience, University of Colorado Boulder（科罗拉多大学博尔德分校莫滕森全球工程与复原力中心）
Mapping bathymetry of inland water bodies on the North Slope of Alaska with Landsat using Random Forest
NASA Data Science Group, Goddard Space Flight Center（美国国家航空航天局戈达德太空飞行中心数据科学组）；NASA Biospheric Sciences Laboratory, Goddard Space Flight Center（美国国家航空航天局戈达德太空飞行中心生物圈科学实验室）；Science Systems and Applications Incorporated（科学系统应用公司）；University of Colorado Boulder（科罗拉多大学博尔德分校）；ASRC Federal Goddard Space Flight Center（ASRC联邦戈达德太空飞行中心）
Comparative Assessment of Multimodal Earth Observation Data for Soil Moisture Estimation
BEYOND EO Centre, IAASARS, National Observatory of Athens（BEYOND EO中心、IAASARS、雅典国家天文台）；Artificial Intelligence, Wageningen University & Research（人工智能，瓦赫宁根大学与研究所）
IRISA - University of Rennes 1（IRISA - 雷恩第一大学）；Total France（道达尔法国公司）
Detection of surface water in natural environment via multi-spectral imagery has been widely utilized in many fields, such land cover identification. However, due to the similarity of the spectra of water bodies, built-up areas, approaches based on high-resolution satellites sometimes confuse these features. A popular direction to detect water is spectral index, often requiring the ground truth to find appropriate thresholds manually. As for traditional machine learning methods, they identify water merely via differences of spectra of various land covers, without taking specific properties of …
The effects of global climate change on Peruvian glaciers have brought about several processes of deglaciation during the last few years. The immediate effect is the change of size of lakes and rivers. Public institutions that monitor water resources currently have only recent studies which make up less than 10% of the total. The effects of climate change and the lack of updated information intensify social-economic problems related to water resources in Peru. The objective of this research is to develop a software application to automate the Cadastral Registry of Water Bodies in Peru, using t…
Offshore oil and gas platform dynamics in the North Sea, Gulf of Mexico, and Persian Gulf: Exploiting the Sentinel-1 archive
German Remote Sensing Data Center, Earth Observation Center, EOC of the German Aerospace Center, DLR（德国遥感数据中心，地球观测中心，德国航空航天中心（DLR）地球观测中心）；Institute for Geography and Geology, Department of Remote Sensing, University of Würzburg（地理与地质研究所，遥感系，乌尔姆大学）
Advancing Earth Observation Through Machine Learning: A TorchGeo Tutorial
Microsoft AI for Good Research Lab（微软AI for Good研究实验室）；Chair of Data Science in Earth Observation, Technical University of Munich（地球观测数据科学主任，技术大学慕尼黑）；Chair of Earth Observation, University of the Bundeswehr Munich（地球观测主任，联邦国防军大学慕尼黑）；Department of Robotics, Perception and Learning, KTH Royal Institute of Technology（机器人、感知与学习系，皇家理工学院）；Wherobots（Wherobots公司）；National Water and Sanitation Agency, Brazil, Federal District, Brasilia（巴西国家水务与卫生局，联邦区，巴西利亚）
King’s College London（伦敦国王学院）；UN Environment World Conservation Monitoring Centre（联合国环境规划署世界保护监测中心）
Dam reservoirs play an important role in meeting sustainable development goals and global climate targets. However, particularly for small dam reservoirs, there is a lack of consistent data on their geographical location. To address this data gap, a promising approach is to perform automated dam reservoir extraction based on globally available remote sensing imagery. It can be considered as a fine-grained task of water body extraction, which involves extracting water areas in images and then separating dam reservoirs from natural water bodies. We propose a novel deep neural network (DNN) based…
Tallinn University of Technology（塔林理工大学）；University of Oxford（牛津大学）
The focus of this paper is using a convolutional machine learning model with a modified U-Net structure for creating land cover classification mapping based on satellite imagery. The aim of the research is to train and test convolutional models for automatic land cover mapping and to assess their usability in increasing land cover mapping accuracy and change detection. To solve these tasks, authors prepared a dataset and trained machine learning models for land cover classification and semantic segmentation from satellite images. The results were analysed on three different land classification…
