Using temporal variability of land surface temperature and normalized vegetation index to estimate soil moisture condition on forest areas by means of remote sensing

Abstract

Land Surface Temperature (LST) against Vegetation Index (VI) scatterplot is a base concept of Temperature Vegetation Dryness Index (TVDI) which is a widely used drought index taking into account evaporation over heterogenic area. The main advantage of using TVDI is that it reflects rather a soil moisture conditions than volumetric soil moisture itself. This is particularly useful when the aim of conducting research is to assess water availability for plants rather than volumetric soil moisture at some depth. Results that have been obtained so far using indices based on LST-VI scatterplot proved their effectiveness in meadows, pastures or crops but were not satisfactory enough over forest areas. An important limitation of using TVDI is a need to collect LST and VI data from heterogenic areas, to provide different soil moisture conditions which is often difficult to ensure.In this paper, we proposed a new approach to the TVDI calculation method in which we use temporal variability of soil moisture conditions (shown in different satellite images of the same area) instead of their spatial heterogeneity.Calculations were conducted over the forest area in Tuczno Poland. To calculate the temporal TVDI model 4 Landsat 8 OLI/TIRS scenes were used, and calculation was performed in Python 3 using open-source packages. The average moisture conditions of each chosen scene were validated using field data, namely evapotranspiration determined from an eddy covariance.

Keywords

• Moisture condition
• Temperature Vegetation Dryness Index
• Landsat
• Python 3
• Eddy covariance