Spectral and spatial kernel water quality mapping

Chu, Hsin; Jaelani, Lalu Muhamad; Nguyen, Mạnh Van; Lin, Ching Hsuan; Blanco, A. C.

doi:10.1007/s10661-020-08271-9

Cited by 5 publications

(1 citation statement)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Spatial interpolation for water quality mapping, such as inverse distance weighting, explicitly implements the assumption that the observations close together are more alike than those that are farther away [34,35]. Without considering the information of remote sensing, the spatial pattern of the Chla map (the result of spatial interpolation) is smoother [36]. Therefore, the water quality details can be observed using the spectral information of remote sensing, especially inland water.…”

Section: Global and Local Modelsmentioning

confidence: 99%

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

Chu

Chusnah

et al. 2021

Sustainability

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Global and Local Modelsmentioning

confidence: 99%