We demonstrate how to combine remote sensing data from satellite imagery(Sentinel-2)with in situ water quality gauging(USGS Super Gages and the Gybe hyperspectral radiometer)to create spatially dense maps of water qua...We demonstrate how to combine remote sensing data from satellite imagery(Sentinel-2)with in situ water quality gauging(USGS Super Gages and the Gybe hyperspectral radiometer)to create spatially dense maps of water quality parameters(chlorophyll-a concentration,turbidity,and nitrate plus nitrite con-centration)along the lower Kansas River.The water quality maps are created using locally tuned models of the target water quality parameters,and this study describes the steps used to design,calibrate,and validate the empirical correla-tions.Water quality parameters such as chlorophyll-a concentration are corre-lated with well-studied absorption and scattering features in the visible spectrum(roughly 400–700 nm).Nutrients(such as nitrate plus nitrite concentration)lack strong absorption features in the visible spectrum,and in those cases we describe a novel surrogate data modeling approach that identifies overlapping water parcels between the in situ gauging and the remote sensing imagery.Measure-ments from the overlapping water parcels yield excellent correlations(>R 0.92)for the target water quality parameters for limited windows of time(or limited sections of river reaches).Examples are provided illustrating how the water quality maps can be used to track river inputs from ungauged sources(such as creeks),or reveal the mixing patterns at the confluences.展开更多
基金Basic Energy Sciences,Grant/Award Number:DE-SC0020843。
文摘We demonstrate how to combine remote sensing data from satellite imagery(Sentinel-2)with in situ water quality gauging(USGS Super Gages and the Gybe hyperspectral radiometer)to create spatially dense maps of water quality parameters(chlorophyll-a concentration,turbidity,and nitrate plus nitrite con-centration)along the lower Kansas River.The water quality maps are created using locally tuned models of the target water quality parameters,and this study describes the steps used to design,calibrate,and validate the empirical correla-tions.Water quality parameters such as chlorophyll-a concentration are corre-lated with well-studied absorption and scattering features in the visible spectrum(roughly 400–700 nm).Nutrients(such as nitrate plus nitrite concentration)lack strong absorption features in the visible spectrum,and in those cases we describe a novel surrogate data modeling approach that identifies overlapping water parcels between the in situ gauging and the remote sensing imagery.Measure-ments from the overlapping water parcels yield excellent correlations(>R 0.92)for the target water quality parameters for limited windows of time(or limited sections of river reaches).Examples are provided illustrating how the water quality maps can be used to track river inputs from ungauged sources(such as creeks),or reveal the mixing patterns at the confluences.
