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A novel water optical types framework for Chinese inland waters with the application of multitype satellite sensor
- Source :
- International Journal of Digital Earth, Vol 17, Iss 1 (2024)
- Publication Year :
- 2024
- Publisher :
- Taylor & Francis Group, 2024.
-
Abstract
- ABSTRACTOptical Water Type (OWT) analysis is crucial for comprehending water composition and quality, key factors in assessing water quality over extensive areas. However, China’s inland waters lack a standardized system for such analysis. To quantitatively analyze the classification results, our study compared three K-means clustering methods, for analyzing 1310 spectral data from various Chinese lakes and reservoirs, thereby addressing this gap. The innovative split-merge K-means method identified 13 distinct OWTs that more closely adhere to the principles of minimizing intra-class distance and maximizing inter-class distance. These were categorized into four groups: clear water, turbid water, eutrophic water, and special type water. Additionally, we developed a method based on Spectral Angle Distance (SAD) to evaluate the classification capabilities of 12 satellite sensors. The results show that Sentinel-3 OLCI (Ocean and Land Color Instrument), MERIS (Medium Resolution Imaging Spectrometer), and Sentinel-2 MSI (Multispectral Instrument) have the best water classification capabilities, making them well-suited for large-scale monitoring of OWT changes. Conversely, other sensors, such as the Sustainable Development Scientific Satellite-1 (SDGSAT-1), Landsat-8, GaoFen-6, GaoFen-1, GaoFen-2, Landsat-5, Landsat-7, Moderate Resolution Imaging Spectroradiometer (MODIS), and HuanJing-1, necessitate the consolidation of water types for effective categorization, indicative of their more limited classification capabilities.
Details
- Language :
- English
- ISSN :
- 17538947 and 17538955
- Volume :
- 17
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- International Journal of Digital Earth
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.980cd11a656b4e27b8dcb93687f28e7b
- Document Type :
- article
- Full Text :
- https://doi.org/10.1080/17538947.2024.2327834