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

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.

Time Varying Spatial Downscaling of Satellite-Based Drought Index

“…A time-dependent spatial downscaling model shows that the regional variation is the specific combining and modeling spatial relationships each period [26,27]. In the fitting step, the spatial regression is further extended to allow for a spatially varying function between the SPI 0 , NDV I 0 and LST 0 each time step.…”

“…The approach is a remote-sensing-based downscaling with spatial-weighted calibration. The model is an effective calibration process for the spatio-temporal mapping and estimation [27,32]. Spatial uncertainty of SPI characteristics can be conducted by spatially varying parameters using spatial regression.…”

Remote Sensing for Water Quality Monitoring – A Study Case for the Marateca Reservoir, Portugal

“…For more than four decades, remote sensing has illustrated strong capabilities to monitor and evaluate the quality of inland waters [1,[6][7][8][9]. Indeed, optically active Disclaimer/Publisher's Note: The statements, opinions, and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s).…”

“…The most measured qualitative parameters of water obtained using remote sensing include chlorophyll-a (Chl-a), coloured dissolved organic matters (CDOM), Secchi disk depth (SDD), turbidity, total suspended sediments (TSS), water temperature (WT), total phosphorus (TP), sea surface salinity (SSS), dissolved oxygen (DO), biochemical oxygen demand (BOD) and chemical oxygen demand (COD) [1]. However, most of these parameters are correlated between themselves [1,[6][7][8][9].…”

“…For instance, the Sentinel-2 Multispectral Imager (MSI) imagery and the corresponding Sentinel-3 Ocean and Land Colour Instrument (OLCI) products have been used for aquatic applications to assess the parameter chlorophyll-a (Chl-a) and turbidity [7]. Indeed, Chl-a was mapped by spatial regression using the Sentinel-2 MSI imagery [8]. Other authors modelled a water quality index, that aggregates nine non-correlated water quality parameters, through machine learning algorithms using as explanatory variables several water spectral indices [9].…”