Determining the dry boundary of the LST/FVC space for soil moisture monitoring: a semi-empirical method

ABSTRACT

Land surface temperature and fractional vegetation coverage (LST/FVC) space is a classical model for monitoring soil moisture (SMC) from optical/thermal remote sensing. However, its applications are critically constrained by the determination of the dry and wet boundaries. In this study, a semi-empirical method was provided for determining the dry boundary by introducing a parallel resistance following the Biome-BGC (BioGeochemical Cycles) model to determine the temperature endmembers on the dry boundary. The semi-empirical method was evaluated by comparing with typical theoretical calculation methods in calculating soil moisture index (SMI) based on the LST/FVC space. Public datasets from SMAPVEX12 (Soil Moisture Active Passive mission Validation Experiment 2012) experiment, MODIS (Moderate Resolution Imaging Spectroradiometer), and NLDAS-2 (North American Land Data Assimilation System) Forcing Dataset were utilized in the evaluation. Results demonstrated that the semi-empirical method has comparable performances with the theoretical methods in monitoring the spatial variation of SMC. The SMIs based on the semi-empirical and theoretical methods are significantly correlated at a high level of Pearson’s correlation coefficients (r) around 0.8–0.9 with p-value = 0.05. More importantly, this semi-empirical method requires fewer parameters and does not require a complex iteration calculating process as compared with previous theoretical methods.

Acknowledgement

This research was funded by National Natural Science Foundation of China, grant number 41,871,338; Ningxia Key Research and Development Program, grant number 2018BEG03069; Yue Qi Young Scholar Project, CUMTB; and the Fundamental Research Funds for the Central Universities, grant number 2015QD02. The author would like to thank the National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory (ESRL) Physical Sciences Division (PSD) Boulder, Colorado, USA for providing NARR data. Thanks are also expressed to NASA Distributed Active Archive Center at NSIDC and NASA Land Processes Distributed Active Archive Center for providing the SMAPVEX12 and MODIS data.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [41871338]; Ningxia Key Research and Development Program [2018BEG03069]; Yue Qi Young Scholar Project, CUMTB; Fundamental Research Funds For the Central Universities [2015QD02].