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Water Quality Analysis Tool (WQAT)
Citation:
Lehrter, J., B. Spiering, G. Gasser, AND J. Harcum. Water Quality Analysis Tool (WQAT). U.S. Environmental Protection Agency, Washington, DC, 2016.
Impact/Purpose:
Satellites provide spatially synoptic data at high frequency (daily to weekly). These characteristics are desirable for supplementing existing water quality observations and for providing information for large aquatic ecosystems that are historically under-sampled by field programs. Thus, the Water Quality Assessment Tool (WQAT) software tool was developed to support water quality managers that desire to use ocean color satellite remote sensing for water quality assessment. Included in this package are a power point presentation and a script that provide background for WQAT, software documentation and executable, a list of known software bugs, and optical observation datasets from several Gulf of Mexico estuaries. Funding is not available at this time to fix the bugs and update the software. This work was conducted through an Inter-Agency agreement between EPA and NASA with support from Lockheed Martin and Tetra Tech.
Description:
The purpose of the Water Quality Analysis Tool (WQAT) software is to provide a means for analyzing and producing useful remotely sensed data products for an entire estuary, a particular point or area of interest (AOI or POI) in estuaries, or water bodies of interest where pre-processed and geographically gridded remotely sensed images are available. A graphical user interface (GUI), was created to enable the user to select and display imagery from a variety of remote sensing data sources. The user can select a date (or date range) and location to extract pixels from the remotely sensed imagery. The GUI is used to obtain all available pixel values (i.e. pixel from all available bands of all available satellites) for a given location on a given date and time. The resultant data set can be analyzed or saved to a file for future use. The WQAT software provides users with a way to establish algorithms between remote sensing reflectance (Rrs) and any available in situ parameters, as well as statistical and regression analysis. The combined data sets can be used to improve water quality research and studies.