Contributing to Fishery Productivity in Colombia: A Machine Learning Approach to Predict Missing Chlorophyll-A Values Using MODIS Satellite Imagery

Abstract

The fisheries sector in Colombia plays a significant role, contributing 0.3% to the country's Gross Domestic Product (GDP) and generating exports worth US∃45.1 million, equivalent to 3.3% of the agricultural GDP. However, its management faces challenges such as non-target species fishing, inadequate control of overfishing, and resource management issues, among others, affecting fish production. This article highlights the necessity of enhancing chlorophyll-a measurement to op-timize fishery production. Chlorophyll-a measurements are vital indicators of marine ecosystem health. Utilizing satellite imagery like MODIS is crucial for accurate data collection. However, Colombia's geographic location, characterized by high cloud cover, compromises image quality for much of the year, posing significant limitations on reporting chlorophyll-a values. We propose a machine learning algorithm to predict chlorophyll-A values on MODIS images to address this issue. The approach demonstrates an accuracy exceeding 0.8 regarding R-squared for predicting missing chlorophyll-a values. By overcoming spatial limitations caused by cloud cover, this method enables a more precise assessment of fishing grounds. Various machine learning models were also applied and evaluated within the research's context. Results yielded a 5% recovery yield of chlorophyll-a values for 2023, enriching knowledge and management practices within Colombia's fishing sector.