Year Established: 2014 Start Date: 2014-03-01 End Date: 2015-02-28
Total Federal Funds: $14,998 Total Non-Federal Funds: $29,996
Principal Investigators: Keith Cherkauer, Indrajeet Chaubey
Abstract: Abstract Water quality is a major issue for rivers in the agriculturally dominated Midwestern United States. Most monitoring of water quality relies on the installation of expensive real-time monitoring stations or the selection of permanent sampling sites where samples are collected by hand, which is often expensive, labor-consuming and spatially and/or temporally limited. While remote sensing has been widely used to quantify optically active constituents (OACs) in open oceans, coastal areas and lakes due to its great spatial and temporal coverage, it is still limited in riverine systems due to both the coarse resolution of satellite sensors and the challenge of estimating OACs using empirical modeling methods. Physical models based on the radiative transfer process within the water column and with consideration of all OACs at one time have been little used in river systems due the required investment in hardware and software. The lack of inherent optical properties (IOPs) measurements is another limitation of developing physical models for inland waters. This project will continue the development of a database with measurements of the geochemical state of the Wabash River and Tippecanoe River and coincident spectral reflectance measurements. As part of this project, we will collect highresolution spectral data over the two rivers using Unmanned Aerial Vehicles (UAVs), and will use all data in the development and evaluation of physical radiative transfer models relating spectral properties to the geochemical state of the river system. Such models should be better able to estimate the geochemical state of the river from remote sensing imagery, and more transferable to other locations. We expect this project to result in a database of IOP and water quality measurements of the Wabash River under various environmental conditions and algorithms that are effective for the remote sensing of the Wabash River and other major Midwestern rivers.