Development of global estimation algorithms for soil moisture and vegetation amount using Tropical Rainfall Measuring Mission (TRMM) data

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Two microwave sensors on board Tropical Rainfall Measuring Mission (TRMM), Precipitation Radar (PR) and TRMM Microwave Imager (TMI), are used to make gtobalsurface soil moisture map in this research. Firstly, a soil moisture estimation algorithm from TRMM/PR is developed. In this algorithm, the backscattering coefficients at land surface observed by TRMM/PR are used. As the backscattering coefficients are attenuated by strong rainfall, the data observed during rainfall is not included in our calculation. TRMM/PR has poor spatial resolution compared with Synthetic Aperture Radar (SAR), but the observation frequency (temporal resolution) is as high as passive microwave sensors. Though TRMM/PR observes by multiple incident angles from 0 to 18 degree, our algorithm is basically designed for the observations by 12 degree. It is shown by a sensitivity analysis that the backscattering coefficients are less affected by the ratio of surface vegetation cover when they are observed by 12 degree. … More However, if the temporal change of vegetation cover ratio is not significant, the observations by among 3 to 18 degree are well correlated with those by 12 degree. In such case, our algorithm can be applied to large number of observations by 3 to 18 degrees. Secondly, another soil moisture estimation algorithm from TRMM/TMI is developed. A microwave transfer model in soil-vegetation-atmosphere layers is used to retrieve soil moisture from single index such as polarization difference (PD) / frequency difference (FD) of brightness temperatures. Soil moisture estimates from different algorithms are compared to each other and are compared with estimates from TRMM/PR. In order to simulate realistic seasonal variation of soil moisture, it is necessary to give monthly Leaf Area Index (LAI) data from other satellite information. Other vegetation parameters (eg. Stem Area Index (SAI)) and soil roughness are also important because they significantly affect the sensitivity of PD/FD against LAI. PD of 10GHz is the best index to retrieve soil moisture because this is less affected by atmosphere and this has high sensitivily against soil moisture. Finally, these two algorithms by two sensors are applied to make global dataset of surface soil moisture in three years (1998-2000) with daily time step. Diurnal variations of these estimates are examined and the advantages of both methods are discussed. Less