Evaluation of Temperature-Soil Moisture Dryness Index for Surface Soil Moisture and Evapotranspiration Analysis

EasyChair Preprint 7130

Abstract

In the context of climate change, the surface soil moisture (SSM) assessment is crucial for the precise irrigation of crops and the economical use of water resources. The recently developed Temperature-Soil Moisture Dryness Index (TMDI) has shown better potentials in the assessment of SSM. The TMDI is empirically calculated by the Land Surface Temperature (LST) and Normalized Difference Latent Heat Index (NDLI) derived by Landsat-8 data. In the current study, SSM assessment was carried out using the TMDI to analyze the dryness conditions and water availability for irrigation over southwestern Taiwan on October 11, 2017. To evaluate the TMDI applicability in SSM monitoring, this study used two indices, Temperature Vegetation Dryness Index (TVDI) derived by Sandholt’s concept and Evapotranspiration (ET) product obtained by the Surface Energy Balance Algorithm for Land developed within Google Earth Engine platform (geeSEBAL). Meanwhile, the geeSEBAL-based ET was taken as the reference data to evaluate the sensitiveness of the TMDI and TVDI for observing the SSM and ET at the regional extent. The outcomes showed a good correlation (negative) between the TMDI and ET with R (-0.90), while a relatively lower R-value (-0.83) was found between TVDI and ET. Hence, the TMDI application for the extraction of SSM will be better due to its strong correlation with the ET.

Keyphrases: Evapotranspiration (ET), Landsat-8 data, Temperature-Soil Moisture Dryness Index (TMDI), geeSEBAL, surface soil moisture (SSM)

@booklet{EasyChair:7130,
  author    = {Thai Minh-Tin and Liou Yuei-An},
  title     = {Evaluation of Temperature-Soil Moisture Dryness Index for Surface Soil Moisture and Evapotranspiration Analysis},
  howpublished = {EasyChair Preprint 7130},
  year      = {EasyChair, 2021}}