Abstract
The vegetation supply water index (VSWI = NDVI/LST) is an effective metric estimating soil moisture in areas with moderate to dense vegetation cover. However, the normalized difference vegetation index (NDVI) exhibits a long water stress lag and the land surface temperature (LST), sensitive to water stress, does not contribute considerably to surface soil moisture monitoring due to the constraints of the mathematical characteristics of VSWI: LST influences VSWI less when LST value is sufficiently high. This paper mathematically analyzes the characteristics of VSWI and proposes a new operational dryness index (surface water content temperature index, SWCTI) for the assessment of surface soil moisture status. SWCTI uses the surface water content index (SWCI), which provides a more accurate estimation of surface soil moisture than that of NDVI, as the numerator and the modified surface temperature, which has a greater influence on SWCTI than that of LST, as the denominator. The validation work includes comparison of SWCTI with in situ soil moisture and other remote sensing indices. The results show SWCTI demonstrates the highest correlation with in situ soil moisture; the highest correlation R = 0.801 is found between SWCTI and the 0–5 cm soil moisture in a sandy loam. SWCTI is a functional and effective method that has a great potential in surface soil moisture monitoring.
Highlights
Soil moisture plays an important role in the conversion of water and energy among the hydrosphere, biosphere, and atmosphere
In the study of surface soil moisture estimation, many remote sensing methods have been developed based on VIR, near infrared (NIR), shortwave infrared (SWIR), and thermal infrared (TIR) data
Ghulam [5] proposed the perpendicular drought index (PDI) on the basis of the spatial characteristics of the soil moisture distribution in NIR–red space, which is applicable to areas covered by only bare soil or low vegetation
Summary
Soil moisture plays an important role in the conversion of water and energy among the hydrosphere, biosphere, and atmosphere. In the study of surface soil moisture estimation, many remote sensing methods have been developed based on VIR, NIR, SWIR, and TIR data. Ghulam [5] proposed the perpendicular drought index (PDI) on the basis of the spatial characteristics of the soil moisture distribution in NIR–red space, which is applicable to areas covered by only bare soil or low vegetation. The vegetation supply water index (VSWI) is a relatively simple and effective metric to estimate surface soil moisture and has been shown to be significantly related to crop moisture content and soil moisture content under most climate types and land cover types [17]. This paper aims to provide an operational and effective method for estimating surface soil moisture in areas with high vegetation cover by proposing the surface water content temperature index (SWCTI), which is developed by combining the water-sensitive SWCI with the modified surface temperature. To validate SWCTI, we analyze the relationship between SWCTI and in situ soil moisture and compare SWCTI with other commonly used remote sensing soil moisture indices, including VSWI, TVDI, SIWSI, NMDI, and SWCI
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