Assessing potential of vertical average soil moisture (0–40cm) estimation for drought monitoring using MODIS data: a case study

Abstract

Soil moisture is an important parameter in the research of hydrology, agriculture, and meteorology. The present study is designed to produce a near real time soil moisture estimation algorithm by linking optical/IR measurements to ground measured soil moisture, and then used to monitoring region drought. It has been found that the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) are related to surface soil moisture. Therefore, a relationship between ground measurement soil moisture and NDVI and LST can be developed. Six days’ NDVI and LST data calculated from Terra Moderate Resolution Imaging Spectroradiometer (MODIS) of Shandong province during October in 2009 to May in 2010 were combined with ground measured volumetric soil moisture in different depth (10cm, 20cm, 40cm, and mean in vertical (0-40cm)) and different soil type to determine regression relationships at a 1 km scale. Based on the regression relationships, mean volumetric soil moisture in vertical (0-40cm) at 1 km resolution can be calculated over the Shandong province, and then drought maps were obtained. The result shows that significantly relationship exists between the NDVI and LST and soil moisture at different soil depths, and regression relationships are soil type dependent. What is more, the drought monitoring results agree well with actual situation.