PROSAIL模型和水云模型耦合反演农田土壤水分

土壤水分的实时、动态监测对农业生产及作物估产有着非常重要的意义。该文提出一种光学和雷达遥感半经验耦合模型，该模型通过引入植被覆盖度将作物覆盖下的散射贡献与裸露地表的直接散射贡献区分开，结合水云模型和PROSAIL模型对农田区域土壤水分进行反演研究。结果表明：该耦合模型模拟得到的后向散射系数与实测值之间具有较好的线性关系，在HH和VV极化下决定系数R2分别为0.792和0.723，RMSE分别为0.600和0.837 dB。同时该模型对农田区域土壤水分的反演精度也较高，其R2为0.809，RMSE为0.043 cm3/cm3。因此该模型可以有效分离农田作物及裸露土壤对雷达信号的影响，准确建立地表直接后向散射贡献与土壤水分的关系，为大面积复杂地表类型覆盖区域的土壤水分反演提供研究思路和理论支持。

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.