Leaf Area Index Inversion of Winter Wheat Using Modified Water-Cloud Model

Abstract

The inversion of vegetation parameters using microwave remote sensing is usually affected by the heterogeneous distribution of vegetation, sparse vegetation cover, and bare soil, which leads to unsatisfactory results in parameter estimation of agricultural applications. In this letter, in order to solve the problem of surface vegetation parameter retrieval by using microwave remote sensing, a modified water-cloud model (WCM) was developed to retrieve leaf area index (LAI) by adding vegetation coverage and direct effect of bare soil on the total backscatter coefficients, which fully took into account the distribution of vegetation cover. The modified model was validated between the simulated backscatter coefficients and measurements based on ground observations and RADARSAT-2 data in China. Then, a look-up table algorithm was applied to calculate the value of vegetation water content and retrieve LAI according to a linear relationship between vegetation water content and LAI. Results indicated that the modified model was more sensitive to vegetation condition and the estimation accuracy was higher than that of the original WCM. $R^{2}$ and rmse were 85.0% and 0.918 dB in HH polarization, and 73.9% and 1.475 dB in VV polarization, respectively. Meanwhile, the modified model could separate the scattering influences produced by the vegetation cover and bare soil components on the backscatter coefficients effectively. The accuracy of LAI retrieval was significantly high with $R^{2}$ and rmse of 84.1% and 0.233 m2/m2, respectively. This method will provide support for estimating LAI of winter wheat by using radar data in a wide range.