Dryness Indices Based on Remotely Sensed Vegetation and Land Surface Temperature for Evaluating the Soil Moisture Status in Cropland-Forest-Dominant Watersheds

Abstract

The Temperature Vegetation Dryness Index (TVDI) was derived from the relationship between remotely sensed vegetation indices and land surface temperature (TS) in this study for assessing the soil moisture status at regional scale in South Korea. The Leaf Area Index (LAI) is newly applied in this method to overcome the increasing uncertainty of using the Normalized Difference Vegetation Index (NDVI) at high vegetation conditions. Both dryness indices were found to be well correlated with in situ soil moisture and 8-day average precipitation at most of the in situ measurement sites. The dryness indices accuracy was found to be influenced by rainfall events. An average correlation coefficient was improved from -0.253 to -0.329 when LAI was used instead of NDVI in calculating the TVDI. In the spatial analysis between the dryness indices and Advanced SCATterometer (ASCAT) surface soil moisture (SSM) using geographically weighted regression (GWR), the results showed the average negative correlation (R) between the variables, while LAI-induced TVDI was more strongly correlated with SSM on average with the R value improved from -0.59 to -0.62. Both dryness indices and ASCAT SSM mappings generally showed coherent patterns under low vegetation and dry conditions. Based on these results, the LAI-induced TVDI accuracy as an index for soil moisture status was validated and found appropriate for use as an alternative and complementary method for NDVI-induced TVDI.

Keywords

• geophysics
• geology
• atmospheric science
• space science
• oceanic science
• hydrology