Global Normalized Difference Vegetation Index forecasting from air temperature, soil moisture and precipitation using a deep neural network

Abstract

The complexity of the relationship between climate variables including temperature, precipitation, soil moisture, and the Normalized Difference Vegetation Index (NDVI) arises from the complex interaction between these factors. NDVI is a widely used index to analyze the characteristics of vegetation cover, including its dynamic patterns. It is a crucial parameter for examining vegetation stability, which is vital for ensuring sustainable food production. This study aims to develop a global-scale NDVI forecasting model based on deep learning algorithms that consider climate variables. The model was trained using three years of global data, including NDVI, temperature, precipitation, and soil moisture. The results of this study demonstrate the effectiveness of the deep learning model for forecasting NDVI. The model accurately predicted NDVI values, as evidenced by the high coefficient of determination (R2) values and the negligible average disparity between predicted and observed NDVI values. The study conducted an analysis of the model’s performance both temporally and spatially. The performance of the model was examined for each month and the overall performance of the model for months presented as the model’s temporal performance overall. Additionally, the model’s performance was analyzed at different latitudes, categorized as mid-latitude and low-latitude performance. The temporal analysis of the model demonstrated an overall R2 value of 0.85 and an RMSE of 0.096. Meanwhile, the spatial analysis of the model showed that it performed well at low-latitude, with an R2 value of 0.84 and an RMSE of 0.098, and at mid-latitude, with an R2 value of 0.82 and an RMSE of 0.095. This suggests that the model’s forecasted NDVI values showed a small average difference compared to actual values in both temporal and spatial analyses. Overall, the study supports the idea that deep learning models can effectively forecast NDVI using climate variables across various geographical zones and throughout different months of the year.