Evaluation of models and drought-wetness factors contributing to predicting the vegetation health index in Dak Nong Province, Vietnam

Abstract

Monitoring and predicting vegetation health are essential for agricultural activities and food security. This study aimed to select a model and evaluate the factors contributing to predicting the vegetation health index (VHI) in the Dak Nong Province, Vietnam. Machine learning algorithms were evaluated, including multiple linear regression, xGBoost, and artificial neural networks (ANN). The input variables of the models included the standardized precipitation evapotranspiration index (SPEI), soil moisture (SM), and VHI in the previous periods. Research results showed that the ANN model gave the best prediction results. The accuracy of prediction results depended on the season of the year, in which the dry season gave a result with high accuracy. The results also indicated that SM from one to two previous months, SPEI1 from one to three previous months, SPEI3 and SPEI5 from three to six previous months, and VHI from one previous month contributed mainly to the prediction model. The relative contribution of SM and SPEI ranged from 42% to 52% in the last 4 months of the dry season. In addition, land use type also affected prediction quality.

Keywords

• vegetation health index
• SPEI
• soil moisture
• machine learning