Chlorophyll Estimation from Multivariate Regression Analysis and Deep Learning Using Remote Sensing Data

Abstract

: The Orinico river is in Venezuela and flows into the Carribbean sea. The chlorophyll concentration in the Ocean delta changes due to the dust deposition from the Orinoco river which affects the primary productivity. The wet and dry deposition measurements are obtained from MERRA a NASA climate reanalysis of meteorology, atmospheric chemistry, land, ocean, and aero-sols data on a broad range of weather and climate time scales and places. Researchers are not sure how wet and dry deposition from the Orinoco river affects the chlorophyll concentration in the ocean. Aerosol optical depth (AOD), dry and wet deposition data are obtained from MERRA. Altimetry data of the Orinoco river and Chlorophyll concentration data are also obtained from the Giovanni database from 2016 to March, 2022. Linear regression analysis of altimetry and chlorophyll concentration show that the later does not depend on the water levels. Univariate models for each of the parameters of AOD, wet, and dry deposition are done. Bivariate models are done adding one additional variable at a time, and finally a multivariate model is built for prediction of chlorophyll concentration. From the analysis, it is seen that the multivariate models have higher correlation between chlorophyll and the independent variables. Of all the variables wet deposition is a better predictor of chlorophyll concentration. A deep learning neural network architecture is developed for performing forecasting of chlorophyll concentration from past values.