Initial Modeling of Yojoa Secchi

The processing pipeline in this repository utilizes Landsat Collection 2 Surface Reflectance data and European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) data to develop machine learning models and stepwise linear regression models to estimate Secchi depth at Lake Yojoa, Honduras. This codebase acquires and harmonizes the Landsat and ERA5 data using the Google Earth Engine (GEE) Python API (Gorelick, et al. 2017). Remote sensing data are matched with in situ Secchi depth measurements and the matchups are partitioned into train-test (‘stringent’ data handling) or train-test-validate (‘very stringent’ data handling) data sets. Models were created using both xgboost gbtree methods and stepwise linear regression methods.