Multi-Reservoir Water Quality Mapping from Remote Sensing Using Spatial Regression

Hone-Jay Chu, Yu-Chen He, Wachidatin Nisa’ul Chusnah, Lalu Muhamad Jaelani and Chih-Hua Chang
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Hone-Jay Chu: Department of Geomatics, National Cheng Kung University, No.1, University Road, Tainan City 701, Taiwan
Yu-Chen He: Department of Geomatics, National Cheng Kung University, No.1, University Road, Tainan City 701, Taiwan
Wachidatin Nisa’ul Chusnah: Department of Geomatics, National Cheng Kung University, No.1, University Road, Tainan City 701, Taiwan
Lalu Muhamad Jaelani: Department of Geomatics Engineering, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia
Chih-Hua Chang: Department of Environmental Engineering, National Cheng Kung University, No.1, University Road, Tainan City 701, Taiwan

Sustainability, 2021, vol. 13, issue 11, 1-13

Abstract: Regional water quality mapping is the key practical issue in environmental monitoring. Global regression models transform measured spectral image data to water quality information without the consideration of spatially varying functions. However, it is extremely difficult to find a unified mapping algorithm in multiple reservoirs and lakes. The local model of water quality mapping can estimate water quality parameters effectively in multiple reservoirs using spatial regression. Experiments indicate that both models provide fine water quality mapping in low chlorophyll-a (Chla) concentration water (study area 1; root mean square error, RMSE: 0.435 and 0.413 mg m −3 in the best global and local models), whereas the local model provides better goodness-of-fit between the observed and derived Chla concentrations, especially in high-variance Chla concentration water (study area 2; RMSE: 20.75 and 6.49 mg m −3 in the best global and local models). In-situ water quality samples are collected and correlated with water surface reflectance derived from Sentinel-2 images. The blue-green band ratio and Maximum Chlorophyll Index (MCI)/Fluorescence Line Height (FLH) are feasible for estimating the Chla concentration in these waterbodies. Considering spatially-varying functions, the local model offers a robust approach for estimating the spatial patterns of Chla concentration in multiple reservoirs. The local model of water quality mapping can greatly improve the estimation accuracy in high-variance Chla concentration waters in multiple reservoirs.

Keywords: spatial regression; water quality mapping; Chla; Sentinel-2; band ratio (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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