Retrieving Surface Soil Moisture in Cotton Fields Using ASAR and MODIS Data Without the Auxiliary Data in SIHU Region, Hubei Province, China

Surface soil moisture is variable, and it plays a crucial role in many processes in the soil-atmosphere interface. The knowledge of the surface soil moisture is very helpful for retrieving the spatial-temporal distribution of water-logging of agriculture field. The capability of microwave remote sensing has been proven that due to its all-weather, day-round measurement and sensitive backscattering coefficient to soil water content, it can derive quantitative soil moisture information from active and passive sensor systems at various spatial resolutions. Soil surface roughness and vegetation of agriculture field are also two main factors influenced on radar backscattering coefficient. So many models have been developed to calculate surface soil moisture, for example, Integral Equation Model (IEM) for bare field, the semi-empirical water cloud model and a novel method for agriculture field. However, these models need too many parameters. It is difficult to use them easily and widely. The paper retrieved the surface soil moisture in cotton fields using 79 ASAR GM images data from 2007-2011 during cotton growing periods. The auxiliary parameters were calculated using MODIS data. The main method was following: (1) got the spatial distribution of land-use classification using crop time series characteristics and MODIS data, (2) corrected the matrix pixels data of backscattering coefficient using the land-use classification data, (3) separated backscattering coefficient influenced by soil and vegetation using the semi-empirical water-cloud model and calculated the model´s parameters by ASAR GM data under water saturation state using non-linear statistic mode and the vegetation water content from NDVI data calculated by MODIS data, (4) calculated soil surface moisture by ASAR GM time-series data using the corrected soil backscattering coefficient. This method does not require any auxiliary data beforehand. Compared the method value with the measured data sitting on cotton- field in SIHU region, the results indicated that this method was corrected (R2=0.779 n=25). And the spatial temporal distribution of surface soil moisture during cotton growing periods was calculated.