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practically suitable for estimating thermal inertia and its applications using M2018 with

A vast number of studies have been proposed for estimating soil moisture, and thermal inertiabased methods have been improving for the last five decades. These methods are based on the thermal diffusion equation combined with sinusoidal function boundary conditions at the Earth's surface and are constant at an infinite depth. There are two solutions for retrieving thermal inertia of which both use satellite thermal-infrared-based LST. One uses the Fourier series expansion, and the other employs the force-restore method. There are advantages and disadvantages in their formulation, calculation procedures, time, and adjustment schemes; however, both solutions are essentially the same in principle. The parameterization for converting thermal inertia to soil moisture is also important. Two ways to perform this parameterization have been proposed. One uses the relationships of soil moisture to the volumetric heat capacity and thermal conductivity, and the other is analogous to the Johansen type thermal conductivity model. The individual studies proposed so far combined a method for retrieving thermal inertia and parameterization for converting it to soil moisture. The accuracy of estimating surface soil moisture for individual studies was not significantly different. The current and future applications of thermal inertia-derived soil moisture are discussed. The thermal inertia approach will be competitive with the assimilation method combining microwavebased soil moisture and satellite data from the other wavelength bands (visible, near-infrared, and thermal-infrared). There are advantages and disadvantages to both approaches in regard to spatial resolution, sky conditions, and the dry down process. Issues to be tackled remain for dust emission processes, especially in relation to soil moisture. Regional water budgeting and management can be applied to arid land water resource and agricultural practice considering

This chapter is based on an aggregation of contributions from personal collaborations and research groups. This chapter is also partly supported by JSPS KAKENHI Grant Number JP

the fusion of other satellite data such as GSMaP precipitation.

Details of the weighting factor Ki formulation in Section 3.

Details of the formulation of Ki are given as

GCOM-C LST.

22 Soil Moisture

6. Conclusions

Acknowledgements

26289159.

Appendix

#### Dai Matsushima

Address all correspondence to: matsushima.dai@it-chiba.ac.jp

Chiba Institute of Technology, Narashino, Chiba, Japan
