Predicting Gross Primary Productivity of the Forest Ecosystems using Machine Learning Techniques: A Review of Existing Approaches

Abstract

Photosynthesis is a biotic process in which the plants assimilate the atmospheric CO2 into the sugar molecules in the presence of solar energy. The carbon uptake by plants in this process is defined as gross primary productivity (GPP). A part of this assimilated carbon is used by the plants to support their physiological activities which are defined as the respiration. The sequestration of carbon by the terrestrial ecosystems holds significance as a vital element of Earth's carbon cycle and constitutes a major sink for the climate change mitigation. The crop yield of any agricultural field is directly linked with its GPP which is important in the aspect of food security and economy. Hence, quantifying the GPP of terrestrial ecosystems is an active branch of study and several methods have been used to address this. In recent times, the machine learning (ML) methods connecting the benefits of artificial intelligence (AI) have gained increased interest and different such methods are being used to address different scientific and technological problems. In addition to the traditional methods, several ML techniques have also been explored by several researchers for the GPP estimation. Studies have shown that ML models can produce GPP predictions with more accuracy. A comprehensive review of these methods will be helpful for the researchers due to a rapid development in this field. This paper offers a comprehensive analysis of various existing ML techniques to estimate the GPP, providing a comparative review of their effectiveness.