TY - JOUR
T1 - Temperature-Soil Moisture Dryness Index for Remote Sensing of Surface Soil Moisture Assessment
AU - Son Le, Mai
AU - Liou, Yuei An
N1 - Publisher Copyright:
© 2004-2012 IEEE.
PY - 2022
Y1 - 2022
N2 - Surface water availability and its temperature are fundamental factors in describing the characteristics of land surface properties. In this study, a new temperature-soil moisture dryness index (TMDI) to quantify surface soil moisture (SSM) is proposed. It is defined as a function of land surface temperature variation and its relationship to surface water availability. The spatial pattern of TMDI has been analyzed over two time points of dry and rainy seasons for the plain area of Tainan, Taiwan, using Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) image acquired on January 20 and October 19, 2014. The effectiveness of TMDI in reflecting the SSM status was then evaluated by the results of the simulated evapotranspiration (ET) and verified by the near-surface air temperature ( T_{\text {air}} ) and humidity (RHair) measured by the ground-based weather stations. The results indicated that TMDI exhibited a significantly negative correlation with the simulated ET and positive correlation with in situ measured T_{\text {air}} from seven stations ( r = - 0.95 and -0.9 for simulated ET and r = 0.94 and 0.78 for T_{\text {air}} corresponding to January 20 and October 19, respectively). We further compared the performance of the TMDI with the existing remotely sensed dryness assessment methods, including temperature vegetation dryness index (TVDI) and Surface Energy Balance Algorithm for Land (SEBAL) model. The advantages of the TMDI in reflecting SSM, especially in the nonvegetation area, are clearly demonstrated. It is concluded that the TMDI is a reliable indicator for determining the SSM status with a large degree of freedom for further applications since it does not require any other ground-based measurements.
AB - Surface water availability and its temperature are fundamental factors in describing the characteristics of land surface properties. In this study, a new temperature-soil moisture dryness index (TMDI) to quantify surface soil moisture (SSM) is proposed. It is defined as a function of land surface temperature variation and its relationship to surface water availability. The spatial pattern of TMDI has been analyzed over two time points of dry and rainy seasons for the plain area of Tainan, Taiwan, using Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) image acquired on January 20 and October 19, 2014. The effectiveness of TMDI in reflecting the SSM status was then evaluated by the results of the simulated evapotranspiration (ET) and verified by the near-surface air temperature ( T_{\text {air}} ) and humidity (RHair) measured by the ground-based weather stations. The results indicated that TMDI exhibited a significantly negative correlation with the simulated ET and positive correlation with in situ measured T_{\text {air}} from seven stations ( r = - 0.95 and -0.9 for simulated ET and r = 0.94 and 0.78 for T_{\text {air}} corresponding to January 20 and October 19, respectively). We further compared the performance of the TMDI with the existing remotely sensed dryness assessment methods, including temperature vegetation dryness index (TVDI) and Surface Energy Balance Algorithm for Land (SEBAL) model. The advantages of the TMDI in reflecting SSM, especially in the nonvegetation area, are clearly demonstrated. It is concluded that the TMDI is a reliable indicator for determining the SSM status with a large degree of freedom for further applications since it does not require any other ground-based measurements.
KW - Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS)
KW - surface soil moisture (SSM)
KW - temperature-soil moisture dryness index (TMDI)
UR - http://www.scopus.com/inward/record.url?scp=85111581273&partnerID=8YFLogxK
U2 - 10.1109/LGRS.2021.3095170
DO - 10.1109/LGRS.2021.3095170
M3 - 期刊論文
AN - SCOPUS:85111581273
SN - 1545-598X
VL - 19
JO - IEEE Geoscience and Remote Sensing Letters
JF - IEEE Geoscience and Remote Sensing Letters
ER -