Project Database

Climate change and global geopolitical conflicts have jeopardized food security worldwide. Given Taiwan’s unique geopolitical situation and heavy reliance on food imports, along with the continuous shrink of farm land area, safeguarding national food production has become imperative. Among all, rice crop is the most critical one for maintaining people’s daily livelihood. Accurate prediction of annual rice yields is crucial for policy making and resource distribution, yet current existing models have not fully sufficed the accuracy level required. This proposal addresses this challenge by employing both optical and synthetic aperture radar (SAR) satellite imagery in a machine learning (ML) framework. There are two innovative elements in this proposal: multi-frequency SAR polarimetry (PolSAR) and canopy structure dynamics model (CSDM). To obtain multi-frequency SAR images suitable for polarimetric analysis, the proposal will attempt to task repeat-pass, high-resolution, full-polarimetric sensing modes over the target area in central western Taiwan. The optical and PolSAR information will then be linked to rice growth stages for feature selection, and then further to rice yields by using multiple ML models. In addition, a SAR-optical integrated CSDM will be created to fill the gaps when no observation data is available. The CSDM model will be examined separately for the use in yield prediction and utilized jointly within the ML models. If this project proves successful, we expect that the government will achieve better budget/resource allocation, import/export decision, crop price stabilization during ordinary years, water resource and pesticide management, as well as hazard mitigation plans, all critical to the long-term goals of sustainable agriculture.