Estimation of Leaf Area Index (LAI) in corn and soybeans using multi-polarization C- and L-band radar data

The feasibility of using optical remote sensing for crop monitoring has been widely studied over the past three decades. Considering the advantages offered with longer microwave wavelengths, most importantly an ability to collect data under cloudy conditions, the exploitation of Synthetic Aperture Radar (SAR) for monitoring crop condition is of great interest. In this study, multi-polarization RADARSAT-2 (C-band) and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) (L-band) were used to investigate the potential of SAR to estimate Leaf Area Index (LAI), a strong indicator of crop productivity. A new LAI estimation approach based on coupling of two existing models has been developed. The coupled model was calibrated and validated for two economically important crops, corn and soybeans, using the data collected during the Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12) over a site in the Canadian Prairies. The validation of the model generally showed high correlation coefficients (R) between ground measured and estimated LAI values, when dual like-cross polarizations were used (either HH–HV or VV–HV). At C-band, correlations were similar for both dual polarizations with corn R values of 0.83 and 0.81 for HH–HV and VV–HV, respectively; and with soybean R values of 0.80 for both HH–HV and VV–HV. L-band was less sensitive to corn LAI with R values ranging from 0.79 for HH–HV to 0.59 for VV–HV. Our method was not able to accurately estimate LAI for soybeans using UAVSAR. Errors of estimation (both root mean square error and mean average error) established that C-band for corn and soybeans canopies and L-band for corn canopies can be used to estimate LAI at accuracies similar to those reported in studies using satellite optical sensors. These optical satellite-based LAI measurements are currently used to correct estimates of yield and biomass from crop growth models. This study is important in that it demonstrates the capability of SAR to also estimate LAI but with an all-weather acquisition capability, offering an important advantage for operational crop growth monitoring.