Review of Iran's Climatic Zoning Based On Some Climate Variables Investigating the Capability of Satellite-Based Biophysical Indexes of Energy Balance Components and Evapotranspiration in Evaluating Soil Moisture Changes

Abstract

Surface soil moisture is one of the important variables in hydrological processes that affects the exchange of water and energy flow in relationship between land surface and atmosphere. Precise assessment of spatial and temporal variations in soil moisture is crucial for numerous environmental studies. Recent technological advances in satellite remote sensing indicates that the soil moisture can be measured using remote sensing methods. The purpose of this study is to estimate biophysical indices and evapotranspiration using SEBAL algorithm and to present soil moisture index using principal component regression method in the east of Bakhtegan Lake, Fars province. For this purpose, five Landsat 8 satellite images for March, April, May and June 2017 were selected and initially corrected. Meteorological data of Marvdasht synoptic station was used to execute SEBAL algorithm. Soil moisture index was modeled using biophysical indices such as albedo, net radiation flux, soil heat flux, evapotranspiration, and etc. by using principal component regression. TVDI index was used to validate the model. The coefficient of determination (R2) and F index are equal to 0.966 and 1651581.9, respectively, which indicates the high efficiency of the model to obtain soil moisture index for each pixel in different areas with different conditions and diverse vegetation. In addition to temperature and vegetation, other biophysical indicates of the region that affect the soil moisture should be taken into account.

Keywords

• principal component regression
• sebal
• normalized difference vegetation index
• temperature vegetation dryness index