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Exploring spatiotemporal patterns of phosphorus concentrations in a coastal bay with MODIS images and machine learning models
Citation:
Chang, N., Z. Xuan, AND J. Yang. Exploring spatiotemporal patterns of phosphorus concentrations in a coastal bay with MODIS images and machine learning models. REMOTE SENSING OF ENVIRONMENT. Elsevier Science Ltd, New York, NY, 134:100-110, (2013).
Impact/Purpose:
This paper reports scientific information for sharing with public and academic communities. The subject concerns methodology and case study results on retrieval of nutrient concentration distribution in Tampa Bay, Florida based on MODIS data.
Description:
Excessive nutrients, which may be represented as Total Nitrogen (TN) and Total Phosphorus (TP) levels, in natural water systems have proven to cause high levels of algae production. The process of phytoplankton growth which consumes the excess nutrients in a water body can also be related to the changing water quality levels, such as Dissolved Oxygen (DO), Colored Dissolved Organic Matter (CDOM), chlorophyll-a, and turbidity. The Tampa Bay estuary has four major river basins that flow into it transporting TN and TP from the outfalls of terrestrial wastewater treatment plants, urban stormwater and agricultural runoffs. This impact associated with sources from atmospheric deposition collectively produces changes in the ecosystem states in the bay resulting in differing absorbance and reflection of actinic radiation from the sun on the surface of the water. This paper explores the spatiotemporal nutrient patterns in Tampa Bay, Florida with the aid of Moderate Resolution Imaging Spectroradiometer (MODIS) images and Genetic Programming (GP) models that are designed to link those relevant water quality parameters and remote sensing reflectance bands in aquatic environments with nutrients concentrations. In-situ data were drawn from a local database to support the calibration and validation of the GP model. The GP models show the effective capacity to demonstrating the snapshots of spatiotemporal distributions of TP across the Bay, which helps to delineate the short-term seasonality effect and the global trend of TP in the coastal bay.
