Machine Learning Model to Predict Manganese Micronutrient Content in Oil Palm Plantation Soil Using Sentinel 1A and Sentinel 2A Image Integration

This study aims to predict manganese micronutrients in oil palm plantation soil using machine learning. Materials and technological tools use remote sensing with the integration of Sentinel 1A and Sentinel 2A satellites for monitoring micronutrients in peat soil in oil palm plantations. Integrating Sentinel 1A with Sentinel 2A will complement the shortcomings of Sentinel 2A, which is not free from cloud cover. Sentinel 1A has the advantage of being free from cloud cover. Meanwhile, Sentinel 2A has a high spectral resolution with 12 to 13 bands, which Sentinel 1A does not have, and only has dual polarization (VV-VH) and local incident angle (LIA). This study uses a machine learning method to obtain a model with a random forest regression algorithm and 103 soil samples in Central Kalimantan and Riau locations. The results of the model performance evaluation using integration showed MAPE and correctness of 25% and 75%, respectively. Suppose using Sentinel 1A, MAPE, and accuracy are 59.63% and 40.23%. Using Sentinel 2A, the MAPE and accuracy obtained are 48.40% and 51.59%. These results suggest that the integration of Sentinel 1A and Sentinel 2A plays a significant role, given their good predictive power. The implications of this study are the status of nutrient distribution maps, which can help determine the status of manganese micronutrients in soil in oil palm plantations for fertilizer application plans according to the needs of each oil palm plant.