Surface soil moisture retrieval through combining LST-VI feature space with soil porosity

ABSTRACT

Surface soil moisture (SSM) is an important variable for controlling the energy exchange between earth’s surface and atmosphere. Accurate estimation of the distribution of SSM and its dynamic changes is increasingly essential in the study of hydrology, ecology, agriculture, geological hazard monitoring, and global climate change. This paper presents a new model for SSM retrieval (named, KSSM) through assimilating soil porosity with a two-dimensional feature space composed of land surface temperature and vegetation index (LST-VI). The KSSM model takes into account the physical properties of soil porosity, so it is suitable for SSM retrieval of various soil types in different regions. The KSSM model was validated using the measured data, and a comparison analysis with other data was conducted. Through the sensitivity analysis, it is concluded that the error of SSM retrieved by the KSSM model is less than 0.04 m³ m⁻³ and the KSSM model is robust. High accuracy of volumetric SSM estimation, error within 10% compared with the measured data, can be achieved by the KSSM model without the need for an in-situ SSM.

KEYWORDS:

• feature space
• karst
• land surface temperature
• optical remote sensing
• soil porosity

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the Natural Science Foundation of Guangxi Province (2022GXNSFBA035639); National Natural Science Foundation of China (41431179, 41961065, 42064003, 42267040); Scientific Research and Technology Development Program of Guangxi (GuikeAA18118038,GuikeAD19254002, GuikeAA18242048); (); Innovative Team and Outstanding Talent Program of Colleges and Universities in Guangxi (2019GXNSFGA245001); Guangxi Provincial Key Laboratory of Spatial Information and Geomatics (19-050-11-23).