Spectral and spatial kernel water quality mapping

Effects of anthropogenic activities on hydrochemical characteristics of ground water of Da'an irrigation area in Western of Jilin Province

The groundwater environment changes under the influence of anthropogenic activities. Because of the construction of the Da'an irrigation area, the amount of irrigation and fertilizer there has changed. Achieving the coordinated development of groundwater resources and economic benefits requires a deeper understanding of the impact of the construction of irrigation areas on groundwater chemistry. In this study, the variations in groundwater chemistry characteristics were studied using statistics and hydrogeochemical methods. Further, the groundwater quality was assessed using the support vector machine method. The results show that the primary water chemistry type was the HCO3 - Ca - Mg type, with local Fe3+ and F- pollution. After the construction of irrigation area, the SO42-, HCO3-, K+  + Na+, and Ca2+ contents decreased, but the Cl- and Mg2+ contents increased. The main nitrogen source in phreatic water was anthropogenic activities, and the main pollution component was NH4+. After the construction of the irrigation area, the NH4+ concentration increased significantly, and the ratio of samples exceeding the standard increased by 37.5%. The over-standard regions spread to the northwest, east, and southeast of Da'an City and east and southeast of the irrigation area. The groundwater quality was predominantly grade IV and V, which accounted for an increase of 16.35%, widely distributed in the south, east, and southwest of the irrigation area and urban areas. The construction of the irrigation area reduced the suitability of phreatic water for agricultural irrigation in the southeast but increased in the west and north.

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

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.