5.4. Water budget and management

competitive with thermal inertia procedures to derive surface soil moisture. However, one weak point with regard to microwave-based soil moisture (soil moisture active passive: SMAP) was noted, and it was a dry down process occurred after an antecedent rainfall that was too rapid for in-situ soil moisture measurement [50]. By contrast, the thermal inertiaderived soil moisture agreed fairly well with the in-situ soil moisture found in several dry down processes (M2018). This agreement may be because the sensing depth of the surface microwave-based soil moisture was shallower than the in-situ measurement and sensitive to the soil moisture itself [51], whereas the representative depth scale of the FRM is not as sensitive to soil moisture and almost agrees with the in-situ measuring depth (M2018). Regarding the spatial resolution of the satellite sensors, an Earth observing satellite with a more precise spatial resolution in the visible, near-infrared, and thermal-infrared bands, the Global Change Observation Mission-Climate (GCOM-C), was recently launched in 2017 by the Japan Aerospace Exploration Agency (JAXA), and the data will be available for general use within 1 year. Its LST spatial resolution is 500 m, twice than that of the MODIS resolution, which will benefit both data assimilation and thermal inertia procedures. Another GCOM-C type satellite will hopefully be able to be operated like MODIS. On the other hand, microwave-based soil moisture can be obtained almost every day regardless of the sky conditions (leading to partial lack of data in some regions due to the satellite orbit). There are trade-offs that have between

the above described advantages and disadvantages of the respective procedures.

and resources.

20 Soil Moisture

5.3. Dust emission

The Global Satellite Mapping of Precipitation (GSMaP) [52] operated by JAXA is a system that measures the spatio-temporal distribution of precipitation at the Earth's surface on a 0.1 spatial scale and a 1-hour temporal scale, and the latest data are added every hour. In arid and semi-arid regions far from rivers, short-term discharge and infiltration should be negligible, accordingly the water budget is calculated using the thermal inertia-derived soil moisture and GSMaP precipitation. Currently, there is not adequate accuracy for both variables to calculate a water budget, but it is worth tackling this issue to estimate regional water cycles

Dust emissions in arid and semi-arid regions have posed serious problems such as soil nutrition loss, crop and vegetation damage, and air quality deterioration. Dust emissions, for example, from Northeast Asia, influence not only individual arid or semi-arid regions but also regions across national boundaries and seas because some dust is raised by strong atmospheric convection and carried by strong westerly winds [53]. Dust emission from the Saharan Desert often harms the surrounding regions including regions far from Africa [53]. Wind erosion from agricultural land often causes local and regional problems according to tillage practices [54]. To predict these dust emissions in advance, monitoring and prediction of the surface soil moisture distribution over an area where dust emission occurrences are concentrated are important. Scheidt et al. [24] examined the spatial distribution of thermal inertia for five soil types in a desert of approximately 20 km using eight couples of the daytime and nighttime thermal-infrared surface temperatures observed by the Advanced Spaceborne Thermal Emission and Reflection (ASTER) and MODIS and then estimated the threshold wind speed based Monitoring the spatial distribution of surface soil moisture over a wide agricultural area is required for optimal water management. An example presented by Minecapllia et al. [30] showed the spatial distribution of thermal inertia over a small-scale cultivated field using airborne thermal images taken in the daytime and nighttime.

Root zone soil moisture has been examined in several studies [50, 59], using a thermal inertia procedure with the FRM applied to the soil water transport and data assimilation procedures, respectively. All of the studies noted that the initial values of the root zone soil moisture were significant for reducing the simulation error. It was noted that the FRM applied to soil moisture was not straight-forward like the soil temperature because of the nonlinearity in soil water transport that representatively appeared in the Richards equation [34, 59]. Various processes of water transport in soil such as infiltration, redistribution, and vapor transport should be improved [60].

The precise spatial resolution of satellite LST is better used for coinciding topography or land use on approximately a 1-km scale. Overlaying or assimilating thermal inertia-derived soil moisture over a common scale of topography or land use in the range of a watershed should contribute to the water budget estimation (discharge, infiltration, and evapotranspiration) when precipitation is known. If agricultural land use is resolved at a 1-km resolution, it is practically suitable for estimating thermal inertia and its applications using M2018 with GCOM-C LST.

Ki <sup>¼</sup> <sup>1</sup> 3 X 3

shape and orientation of the granules. Since P<sup>3</sup>

Ki <sup>¼</sup> <sup>2</sup> 3 1 <sup>þ</sup> <sup>λ</sup><sup>i</sup>

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

Chiba Institute of Technology, Narashino, Chiba, Japan

� �, the above equation reduces to:

gi<sup>1</sup> ¼ gi<sup>2</sup>

Author details

Dai Matsushima

References

1963. pp. 103-143

WR016i004p00787

(85)90038-0

DOI: 10.1029/JC083iC04p01889

DOI: 10.1016/0034-4257(83)90036-6

j¼1

<sup>λ</sup><sup>0</sup> � 1 � �gi h i þ

1 þ

where λ<sup>0</sup> is the conductivity of the medium and gij is a shape factor that takes into account the

λi λ0 � 1 � �gij � ��<sup>1</sup>

3 1 <sup>þ</sup> <sup>λ</sup><sup>i</sup>

[1] van Wijik WR, de Vries DA. Periodic temperature variations in a homogeneous soil. In: van Wijik WR, editor. Physics of Plant Environment. Amsterdam: North-Holland Publ. Co.;

[2] Deardorff JW. Efficient prediction of ground surface temperature and moisture, with inclusion of a layer of vegetation. Journal of Geophysical Research. 1978;83:1889-1903.

[3] Price JC. Thermal inertia mapping: A new view of the earth. Journal of Geophysical

[4] Price JC. The potential of remotely sensed thermal infrared data to infer surface soil moisture and evaporation. Water Resources Research. 1980;16:787-795. DOI: 10.1029/

[5] Price JC. Estimating surface temperatures from satellite thermal infrared data—A simple formulation for the atmospheric effect. Remote Sensing of Environment. 1983;13:353-361.

[6] Price JC. On the analysis of thermal infrared imagery: The limited utility of apparent thermal inertia. Remote Sensing of Environment. 1985;18:59-73. DOI: 10.1016/0034-4257

Research. 1977;82:2582-2590. DOI: 10.1029/JC082i018p02582

, (20)

23

http://dx.doi.org/10.5772/intechopen.80252

<sup>j</sup>¼<sup>1</sup> gij <sup>¼</sup> 1 and a spheroidal shape is assumed

Thermal Inertia-Based Method for Estimating Soil Moisture

� � h i : (21)

1

<sup>λ</sup><sup>0</sup> � 1 � � <sup>1</sup> � <sup>2</sup>gi
