Leaf area index and wet biomass retrieval in agricultural fields from C-band SAR data using the Water Cloud Model

Maintaining and increasing agrarian production is a significant challenge for agricultural policy in the face of global population expansion. In this context, acquiring accurate and prompt regional estimates of crop yields and development is crucial in informing and formulating food policies and mitigating food insecurity to support farm managers and decision-makers. Also, it is challenging to monitor vegetation conditions, plant diseases, irrigation, and fertilizer management on a large scale, which are influential factors in crop biophysical parameters. Therefore, remote sensing is suitable for bridging these gaps because it provides sufficient spatial and temporal information. The Water Cloud Model (WCM) is a vegetation contribution model widely used in crop biophysical parameters retrieval over large-scale vegetated areas. However, due to the lack of a standard technique for determining these descriptors, WCM must be calibrated to produce accurate estimates of crop parameters. This study proposes an innovative approach using WCM based on SAR data combined with ensemble learning models to retrieve crop biophysical parameters under all weather conditions accurately.

Faculty Supervisor:

Saeid Homayouni

Student:

Partner:

Shahid Chamran University of Ahvaz

Discipline:

Earth science

Sector:

Education

University:

Université du Québec : Institut national de la recherche scientifique

Program: