- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Multi-Spectral Water Index (MuWI): A Native 10-m Multi-Spectral Water Index for Accurate Water Mapping on Sentinel-2
摘要： Accurate water mapping depends largely on the water index. However, most previously widely-adopted water index methods are developed from 30-m resolution Landsat imagery, with low-albedo commission error (e.g., shadow misclassified as water) and threshold instability being identified as the primary issues. Besides, since the shortwave-infrared (SWIR) spectral band (band 11) on Sentinel-2 is 20 m spatial resolution, current SWIR-included water index methods usually produce water maps at 20 m resolution instead of the highest 10 m resolution of Sentinel-2 bands, which limits the ability of Sentinel-2 to detect surface water at finer scales. This study aims to develop a water index from Sentinel-2 that improves native resolution and accuracy of water mapping at the same time. Support Vector Machine (SVM) is used to exploit the 10-m spectral bands among Sentinel-2 bands of three resolutions (10-m; 20-m; 60-m). The new Multi-Spectral Water Index (MuWI), consisting of the complete version and the revised version (MuWI-C and MuWI-R), is designed as the combination of normalized differences for threshold stability. The proposed method is assessed on coincident Sentinel-2 and sub-meter images covering a variety of water types. When compared to previous water indexes, results show that both versions of MuWI enable to produce native 10-m resolution water maps with higher classification accuracies (p-value < 0.01). Commission and omission errors are also significantly reduced particularly in terms of shadow and sunglint. Consistent accuracy over complex water mapping scenarios is obtained by MuWI due to high threshold stability. Overall, the proposed MuWI method is applicable to accurate water mapping with improved spatial resolution and accuracy, which possibly facilitates water mapping and its related studies and applications on growing Sentinel-2 images.
关键词： MNDWI,OSH,SVM,AWEI,water mapping,water classification,shadow,NDWI,Sentinel-2,MuWI,Landsat,water index,multi-spectral water index,sunglint,machine learning
更新于2025-09-23 15:21:01
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Selection and Fusion of spectral indices to improve water body discrimination
摘要： Spectral indices are widely used to emphasize water body information in satellite images. The selection of the appropriate index is one of the tasks that the remote sensing community faces when water bodies are studied. In this work we propose an approach for the selecting and fusing of spectral indices, in order to improve water discrimination. Firstly, we compute several spectral indices and analyze their discrimination power taking into account the accuracy value. Through a hierarchical clustering applied only on indices with accuracy value greater than a certain threshold, we cluster the water indices into different groups. The result of the clustering depends on 2 factors: the discrimination capacity of the computed indices and the features of the studied water body. Indices in each group are fused by means of a linear combination. Therefore, we obtain an adaptive fusion of different spectral indices. The previous information is used to compute the likelihoods belonging to water and non-water. These values are the inputs for a probabilistic classification framework named Gaussian Markov Measure Field. According to our experimental work the proposed selection and fusion approach improves the discrimination power of the studied indices.
关键词： spectral analysis,remote sensing,optimization,spectral water index,water resources
更新于2025-09-09 09:28:46
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - A CNN-Based Fusion Method for Super-Resolution of Sentinel-2 Data
摘要： Sentinel-2 data represent a rich source of information for the community due to the free access and to the temporal-spatial coverage assured. However, some of the spectral bands are sensed at reduced resolution due to a compromise between technological limitations and Copernicus program's objectives. For this reason in this work we present a new super-resolution method based on Convolutional Neural Networks (CNNs) to rise the resolution of the shortwave infra-red (SWIR) band from 20 to 10 meters, that is the highest resolution provided. This is accomplished by fusing the target band with the finer-resolution ones. The proposed solution compares favourably against several alternative methods according to different quality indexes. In addition we have also tested the use of the super-resolved band from an applicative perspective by detecting water basins through the Modified Normalized Difference Water Index (MNDWI).
关键词： convolutional neural network,Deep learning,Sentinel-2,pansharpening,normalized difference water index
更新于2025-09-09 09:28:46
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Airborne Hyperspectral Evaluation of Maximum Gross Photosynthesis, Gravimetric Water Content, and CO2 Uptake Efficiency of the Mer Bleue Ombrotrophic Peatland
摘要： Peatlands cover a large area in Canada and globally (12% and 3% of the landmass, respectively). These ecosystems play an important role in climate regulation through the sequestration of carbon dioxide from, and the release of methane to, the atmosphere. Monitoring approaches, required to understand the response of peatlands to climate change at large spatial scales, are challenged by their unique vegetation characteristics, intrinsic hydrological complexity, and rapid changes over short periods of time (e.g., seasonality). In this study, we demonstrate the use of multitemporal, high spatial resolution (1 m2) hyperspectral airborne imagery (Compact Airborne Spectrographic Imager (CASI) and Shortwave Airborne Spectrographic Imager (SASI) sensors) for assessing maximum instantaneous gross photosynthesis (PGmax) in hummocks, and gravimetric water content (GWC) and carbon uptake ef?ciency in hollows, at the Mer Bleue ombrotrophic bog. We applied empirical models (i.e., in situ data and spectral indices) and we derived spatial and temporal trends for the aforementioned variables. Our ?ndings revealed the distribution of hummocks (51.2%), hollows (12.7%), and tree cover (33.6%), which is the ?rst high spatial resolution map of this nature at Mer Bleue. For hummocks, we found growing season PGmax values between 8 μmol m?2 s?1 and 12 μmol m?2 s?1 were predominant (86.3% of the total area). For hollows, our results revealed, for the ?rst time, the spatial heterogeneity and seasonal trends for gravimetric water content and carbon uptake ef?ciency for the whole bog.
关键词： Shortwave Airborne Spectrographic Imager (SASI),Compact Airborne Spectrographic Imager (CASI),carbon uptake,gravimetric water content,normalized difference water index (NDWI),photosynthesis,airborne hyperspectral,bog,Mer Bleue,peatlands
更新于2025-09-04 15:30:14