Spatiotemporal pattern validation of chlorophyll-a concentrations in Lake Okeechobee, Florida, using a comparative MODIS image mining approach

Abstract

A comparative analysis was conducted using three types of data-mining models produced from Moderate Resolution Imaging Spectroradiometer (MODIS) Terra Surface Reflectance 1-day or 8-day composite images to estimate chlorophyll-a (chl-a) concentrations in Lake Okeechobee, Florida. To understand the pros and cons of these three models, a genetic programming (GP) model was compared to an artificial neural network (ANN) model and multiple linear regression (MLR) model with respect to two different data sets related to model formulation. The first data set included the MODIS Terra bands from 1 to 7; the second data set extended the first data set by adding environmental parameters such as Secchi disc depth (SDD), total suspended solids (TSS), wind speed, water level, rainfall and air temperature collected around the lake in 2003 and 2004. The GP algorithm, which has an advantage in machine learning allowing us to select the appropriate input parameters that significantly impact the prediction accuracy, outperformed the other two models based on four statistical indices. Specifically, the GP modelling outputs revealed interesting determinations of chl-a concentrations for MODIS bands 3, 5, 6 and 7, corresponding to wavelengths 459–479, 1230–1250, 1628–1652 and 2105–2155 nm, respectively. The number of training data points is limited; therefore, the inclusion of additional environmental variables cannot improve the prediction accuracy of the GP-derived chl-a concentrations.