کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6409458 1332870 2016 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Research papersImproved water quality retrieval by identifying optically unique water classes
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات فرآیندهای سطح زمین
پیش نمایش صفحه اول مقاله
Research papersImproved water quality retrieval by identifying optically unique water classes
چکیده انگلیسی


- Uses multi-temporal and multi-sensor data for water quality analysis.
- Defines optically different water classes by clustering.
- Water quality estimates are improved using the defined water zones.
- The defined zones and models can be used in routine water quality monitoring.

Accurate remote sensing retrieval of water quality parameters in complex coastal environments is challenging due to variability of the coastal environment. For example, in the coastal waters of Hong Kong water quality varies from east to west. The currently existing water zones, defined by the Hong Kong Environmental Protection Department (EPD) are based on ease of access to sampling locations rather than on water quality alone. In this study an archive of fifty-seven Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) and HJ-1 A/B Charged Couple Device (CCD) images over a 13-year period (January 2000-December 2012) was used to define optically distinct water classes by Fuzzy c-Means (FCM) clustering. The clustering was applied by combining the Surface Reflectance (SR) derived from the first four bands of Landsat and HJ-1 scenes with 240 insitu samples of Chlorophyll-a (Chl-a) and Suspended Solid (SS) concentrations collected within 2 h of image acquisition. The FCM clustering suggested the existence of five optically different water classes in the region. The significance of the defined water classes was tested in terms of the water SR behaviour in each band. The SR for Classes 1 and 2 in bands 1-3 was lower than in other classes, and band 4 showed the lowest reflectance, indicating that these classes represent a clearer type of water. Class 3 showed intermediate reflectance in all bands, while Classes 4 and 5 showed overall higher reflectance indicating high sediment contribution from the Pearl River Delta. Application of water quality retrievals within individual classes showed much greater confidence with Root Mean Square Error (RMSE) of 1.32 μg/l (1.21 mg/l) for Chl-a (SS) concentrations, compared with 5.97 μg/l (2.98 mg/l) when applied to the whole spectrum of different water types across the region.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Hydrology - Volume 541, Part B, October 2016, Pages 1119-1132
نویسندگان
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