Leaf Area Index prediction in the Tropics using Machine Learning and Remote Sensing

Evapotranspiration (ET) is of paramount importance due to its crucial role in the water cycle, moving water from land to the atmosphere. This process is critical for sustaining atmospheric rivers and guiding water management operations. Process-based hydrological modeling is commonly used to predict ET in various ecosystems. However, while plant growth dynamics are better understood in temperate regions, the accuracy of ET predictions in tropical areas remains limited. This reduced accuracy is primarily due to challenges in simulating the Leaf Area Index (LAI), the intensity of mass and energy exchanges, and the prevalence of energy-limited conditions.

In this study, we explore the potential of data-driven models to estimate LAI in the tropics and improve ET predictions. We implemented a Multilayer Perceptron (MLP) model trained using climatological variables from ERA-5 and CHIRPS as inputs. The model's performance was evaluated using LAI values from MODIS at the Cesar River Watershed in northern Colombia, South America.

Comparisons between the selected MLP model and SWAT reveal an improvement over the default LAI simulated by the latter. Particularly, SWAT underestimates foliage growth and fails to capture the bimodal behavior observed in the study area. The MLP model, tested at the watershed and Hydrologic Response Unit (HRU) scales, demonstrated promising results. The performance of the proposed MLP model was evaluated using shuffled and sequential schemes, achieving validation Nash-Sutcliffe efficiencies between 0.5 and 0.99 at the tested scales. In addition, the results show that the MLP model is especially sensitive to the seasonal component of relative humidity. By leveraging remote sensing data, data-driven models become a potential tool to simulate remote sensed LAI with greater accuracy. This potential of the MLP model to significantly improve LAI and ET predictions can enhance hydrologic models' reliability, especially under shifting environmental conditions, and offers an enhanced outlook for better simulating multiple water compartments in the tropics.