Active and passive cooperative algorithm at L-Band for bare soil moisture inversion

Abstract: Soil moisture (SM) is an important parameter for drought monitoring as well as soil erosion, crop production and surface temperature. Microwave remote sensing especially at L-band is one of the most promising approaches to monitor the variable of SM at the global scale with frequent revisiting times. We carried out a large number of experimental and theoretical studies to assess the potential of monitoring soil moisture by means of both active and passive microwave satellite observations. Improving the inversion accuracy of soil moisture by combining active and passive microwave remote sensing data is an important task for the development of quantitative remote sensing. In this paper, the main objective was to estimate soil volumetric water content (SVWC) of bare soil collaborative using the active and passive observations. A novel approach, which was based on the simulation database of the advanced integral equation model (AIEM), was presented. In the method, we took the advantages of the active and passive microwave remote sensing data to obtain SVWC. Based on the analysis of the simulation database, some significant patterns had been found that the characteristics of the vertical polarization microwave emissivity (EV) had stronger sensitivity to the soil moisture than the roughness while the active microwave backscatter coefficient was more sensitive to the soil roughness than passive microwave. Among the vertical polarization microwave emissivity (EV), horizontal polarization microwave emissivity (EH) and VV backscatter coefficient (σvv), the effect of roughness on the σvv was greater than on passive microwave surface emissivity (EV and EH) and the effect of roughness on EV was minimized. The EV and the σvv were used to estimate the soil surface roughness, and on the premise of knowing soil surface roughness, soil moisture can be accurately calculated by passive microwave data. This algorithm had following two steps, the first step was to estimate the soil surface roughness using the vertical polarization emissivity and VV polarization backscatter coefficient; and the second step was to estimate SVWC using a combination of the EV and EH at L band under the condition of soil roughness was known. At last, based on the dataset of the SMAP experimental campaigns carried out in 2012 (SMAPVEX12), we evaluated the superiority of the algorithm. The validation data were obtained through the online Data Pool at the National Snow and Ice Data Center of the SMAPVEX12, the good inversion results were achieved with R2 equal to 0.6637 and the RMSE equal to 0.0607 cm3/cm3. One of the biggest advantages of the algorithm developed in this paper was that the coefficient of algorithm was calculated by the simulated data set, which different from the conventional experience algorithm that depended on the field data, which reduced greatly the limitations of the algorithm in practical application. The threshold value 0.25 of NDVI was used to select the appropriate sampling points for validation and the error of the result partly came from the effect of the little vegetation covering the surface, so the algorithm proposed in this paper should be revalidated using the observed data of the bare soil in the future.