Understanding the synergy between LST, NDVI, and solar radiation to evaluate root zone soil moisture dynamics in the forested area
Abstract
Knowing vegetation water stress status could be an efficient way to retrieve soil moisture conditions under forest canopies. Numerous studies have used a trapezoidal relationship between Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) to analyze soil moisture under variable vegetation cover. The synergy between LST and NDVI has nominal links with the evapotranspiration activities, which adequately highlight the severance of incoming solar radiation into sensible and latent heat. LST is a real-time indicator of vegetation water stress. At the same time, NDVI could lag with soil moisture dynamics. Moreover, both could strongly be influenced by the total incoming solar energy. This study has devised a new remote sensing index TVWSI (Temperature Vegetation Water Stress Index) using MODIS optical reflection and LST. Concerning the root zone soil moisture, a theoretical and empirical analysis has been carried out between LST, NDVI, and solar radiation. Besides, this study also aims to evaluate machine learning algorithms to evaluate this interaction in more detail.
Sixty grids (2 km X 2 km) each containing 16 pixels of daily MODIS-reflectance (band 1 - band 7, 500 m spatial resolution) and 4 pixels of daily MODIS-LST (1 km spatial resolution) were chosen over forested areas in Victoria representing most of the bioregions as classified by the Interim Biogeographic Regionalisation for Australia (IBRA7). From 2002 to 2018 daily (LST, NDVI, TVWSI, and Solar radiation) values of each grid were evaluated against the modeled daily available soil moisture content in the top 1 m of the soil profile, from the Australian Bureau of Meteorology (BOM). A high correlation was obtained between TVWSI vs. soil moisture with a coefficient of determination value of 0.6 (p<0.001). An improved correlation of 0.65 (p<0.001) was obtained using a multi regression model using TVWSI and solar radiation as a predictor. While correlation ranging (0.15-0.48, p<0.001) was obtained using dryness indices like Perpendicular Drought Index (PDI), Modified PDI (MPDI), Temperature Vegetation Dryness Index (TVDI) and Vegetation Supply Water Index (VSWI). The results show that TVWSI has coordination with the soil moisture's real-time dynamics and performs better than NDVI, LST, and other dryness indices mentioned in the literature and understanding the interaction between incoming solar energy with LST and NDVI is essential to describe the soil moisture dynamics accurately.- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2020
- Bibcode:
- 2020AGUFMB082.0005J
- Keywords:
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- 0439 Ecosystems;
- structure and dynamics;
- BIOGEOSCIENCES;
- 0476 Plant ecology;
- BIOGEOSCIENCES;
- 0480 Remote sensing;
- BIOGEOSCIENCES;
- 1812 Drought;
- HYDROLOGY