Comparative analysis of orbital sensors in soybean yield estimation by the random forest algorithm

Abstract

Remote sensing has proven to be a promising tool allowing crop monitoring over large geographic areas. In addition, when combined with machine learning methods, the algorithms can be used for estimating crop yield. This study sought to estimate soybean yield through the enhanced vegetation index and normalized difference vegetation index. These vegetation indices were obtained using moderate-resolution imaging spectro-radiometer (MODIS) sensors on AQUA and TERRA satellites and multispectral instrument (MSI) sensor on Sentinel-2 satellite. Random forest (RF) algorithm was used to predict soybean yield and the estimation models were compared with the actual plot’s yield. The RF algorithm showed good performance to estimate soybean yield with our models (R2 = 0.60 and RMSE = 0.50 for MSI; R² = 0.63 and RMSE = 0.59 for MODIS). Vegetation indices with imaging dates corresponding to the crop’s maturation had a higher degree of importance in its predictive ability. However, when comparing the actual and predicted soybean production values, differences of 145 kg ha-1 in contrast to 4 kg ha-1 were found for the MODIS and MSI models, respectively. Therefore, the MSI sensor integrated with machine learning algorithms accurately estimated crop yields.