Farmland soil moisture retrieval using PROSAIL and water cloud model

Soil moisture, as an important component of soil, plays an important role in the process of energy exchange between soil surface and atmosphere. It is an important input parameter of hydrological, ecological and other physical models. Real-time and dynamic monitoring of soil moisture has a very important significance to agricultural production and crop yield estimation. Nowadays, optical and radar remote sensing are the 2 potential methods for quantifying soil moisture monitoring. However, optical remote sensing, vulnerable to the weather, cannot penetrate clouds and vegetation, which has great limitations in practical applications. Radar remote sensing is sensitive to the dielectric constant of soil and becomes one of the popular soil moisture acquisition methods. Obtaining surface soil moisture by using radar remote sensing is often affected by surface roughness. In areas covered with vegetation, it is also affected by vegetation layers. Many effective inversion models were proposed and the most commonly used model is water-cloud model. It, as a semi-empirical method for herbaceous vegetation, is often used for the retrieval of water content and biomass of vegetation and soil moisture. In the model, vegetation canopy is often regarded as a homogenous scatterer and volumetric scattering is the main form of herbaceous vegetation. However, in the natural condition, the distribution of herbaceous vegetation is not uniform, and especially in the case of complex land cover, the water-cloud model will be greatly limited in sparse vegetation covered area. Therefore, this paper presents a semi-empirical coupling algorithm combining water-cloud model and PROSAIL optical model. This algorithm introduces vegetation coverage to separate the crop scattering contribution from the surface direct scattering contribution of bare soil. Meanwhile, the actual distribution of vegetation is fully considered, especially for the sparse vegetation covered area. The coupling algorithm can eliminate the influence of crop canopy on radar signals to the maximum extent and establish a more accurate relationship between the surface direct backscatter contribution and soil moisture to obtain the soil moisture inversion value with a higher accuracy. The experimental results show that the inversion accuracy of the semi-empirical coupling algorithm can meet the demand of simulating the backscattering coefficients compared with the observations. In HH and VV polarizations, R2 values are 0.792 and 0.723, and RMSE (root mean square error) values are 0.600 and 0.837 dB, respectively. The coupling model introduces vegetation coverage to reduce the effect of vegetation gap on radar signals and characterize accurately the direct scattering contribution of bare soil. Meanwhile, the estimation accuracy of the semi-empirical coupling algorithm proposed in this paper is higher than that obtained by using the original water-cloud model, with R2 of 0.809 and RMSE of 0.043 cm3/cm3. Therefore, this algorithm has a high sensitivity to the vegetation ranging from relatively sparse to full cover and can eliminate the influence of vegetation canopy on radar backscatter coefficient by applying the optical data information to the inversion of soil moisture in the coupling model. It will provide ideas and theoretical support for soil moisture inversion in large area and complex land surface coverage. Because of limited experiment condition, errors of field measurement remain in backscattering simulation and soil moisture retrieval. Field experiments need to be conducted in complex and multi-vegetation cover areas to obtain enough measurements and further improve the coupling model.