Surface Soil Water Content Estimation from Thermal Remote Sensing based on the Temporal Variation of Land Surface Temperature

Remote Sensing

Dianjun Zhang³  Ronglin Tang³  Wei Zhao²  Bohui Tang³  Hua Wu³  Kun Shao¹ 

[1] School of Computer and Information, Hefei University of Technology, Hefei 230009, China; E-Mail:;Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China; E-Mail:;Key Laboratory of Resources and Environmental information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; E-Mails:

关键词: thermal infrared;    soil water content;    triangle method;    TRRVDI;    temperature rising rate;   

DOI  :  10.3390/rs6043170

来源: mdpi

【 摘 要 】

Soil water content (SWC) is a crucial variable in the thermal infrared research and is the major control for land surface hydrological processes at the watershed scale. Estimating the surface SWC from remotely sensed data using the triangle method proposed by Price has been demonstrated in previous studies. In this study, a new soil moisture index (Temperature Rising Rate Vegetation Dryness Index—TRRVDI) is proposed based on a triangle constructed using the mid-morning land surface temperature (LST) rising rate and the vegetation index to estimate the regional SWC. The temperature at the dry edge of the triangle is determined by the surface energy balance principle. The temperature at the wet edge is assumed to be equal to the air temperature. The mid-morning land surface temperature rising rate is calculated using Meteosat Second Generation—Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI) LST products over 4 cloud-free days (day of year: 206, 211, 212, 242) in 2007. The developed TRRVDI is validated by in situ measurements from 19 meteorological stations in Spain. The results indicate that the coefficient of determination (R²) between the TRRVDI derived using the theoretical limiting edges and the in situ SWC measurements is greater than that derived using the observed limiting edges. The R² values are 0.46 and 0.32; respectively (p < 0.05). Additionally, the TRRVDI is much better than the soil moisture index that was developed using one-time LST and fractional vegetation cover (FVC) with the theoretically determined limiting edges.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland