Using a Modified Water Cloud Model to Retrive Leaf Area Index (LAI) from Radarsat-2 SAR Data Over an Agriculture Area

This reported study was intended to advance the retrieval of leaf area index (LAI) using synthetic aperture radar (SAR) data. A novel method was proposed by introducing the vegetation coverage into the Water Cloud Model (WCM) to improve the retrieval accuracy of the LAI. LAI is a strong indicator of crop productivity, and vegetation coverage has a strong relationship with the LAI (R²=0.9733), a function can be created to express their relation. Finally, the accuracy in this innovative LAI retrieval method were evaluated. The results showed that the accuracy of estimation was improved greatly (R² was increased to 0.6055 and 0.6422 from 0.3491 and 0.3561 in VH and HH polarization). Thus, the method has operational potential for the LAI retrieval of crop in agriculture regions.

1. Introduction

Timely information on crop growth condition is of great importance to crop production, yield forecast, and food security. Leaf Area Index (LAI) is a critical indicator of crop development and has strong correlation with crop productivity[1], [2]. Although LAI can be directly measured in situ, the amount of time and effort required prohibits frequent data collection over a large area.