A Framework for Crop Yield Estimation and Change Detection Using Image Fusion of Microwave and Optical Satellite Dataset

Crop yield prediction is one of the crucial components of agriculture that plays an important role in the decision-making process for sustainable agriculture. Remote sensing provides the most efficient and cost-effective solution for the measurement of important agricultural parameters such as soil moisture level, but retrieval of the soil moisture contents from coarse resolution datasets, especially microwave datasets, remains a challenging task. In the present work, a machine learning-based framework is proposed to generate the enhanced resolution soil moisture products, i.e., classified maps and change maps, using an optical-based moderate resolution imaging spectroradiometer (MODIS) and microwave-based scatterometer satellite (SCATSAT-1) datasets. In the proposed framework, nearest-neighbor-based image fusion (NNIF), artificial neural networks (ANN), and post-classification-based change detection (PCCD) have been integrated to generate thematic and change maps. To confirm the effectiveness of the proposed framework, random forest post-classification-based change detection (RFPCD) has also been implemented, and it is concluded that the proposed framework achieved better results (88.67–91.80%) as compared to the RFPCD (86.80–87.80%) in the computation of change maps with σ°-HH. This study is important in terms of crop yield prediction analysis via the delivery of enhanced-resolution soil moisture products under all weather conditions.