Multi-reservoir water quality mapping from remote sensing using spatial regression

Hone Jay Chu, Yu Chen He, Wachidatin Nisa’Ul Chusnah, Lalu Muhamad Jaelani, Chih Hua Chang

研究成果: Article同行評審

2 引文 斯高帕斯(Scopus)

摘要

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 regres-sion. 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.

原文English
文章編號6416
期刊Sustainability (Switzerland)
13
發行號11
DOIs
出版狀態Published - 2021 6月 1

All Science Journal Classification (ASJC) codes

  • 地理、規劃與發展
  • 可再生能源、永續發展與環境
  • 環境科學(雜項)
  • 能源工程與電力技術
  • 管理、監督、政策法律

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