Soil moisture content inversion research using multi-source remote sensing data

Chengcai Zhang; Zule Zhu

doi:10.1117/12.2070618

8 November 2014 Soil moisture content inversion research using multi-source remote sensing data

Chengcai Zhang, Zule Zhu

Proceedings Volume 9260, Land Surface Remote Sensing II; 92600U (2014) https://doi.org/10.1117/12.2070618
Event: SPIE Asia-Pacific Remote Sensing, 2014, Beijing, China

Abstract

The method of apparent thermal inertia and the method of vegetation water supply index were used respectively for inversion of soil moisture content on the whole study area using the MODIS remote sensing data. The threshold value of NDVI that divided coverage of vegetation into high and low classification was determined as 0.3 after the correlative analysis between the result of the apparent thermal inertia model, the vegetation water supply index and the vegetation index of NDVI. Land use classification was worked out using the TM remote sensing data by ENVI software before the coverage of vegetation was divided into high and low classification. According to the result of land use classification, the VSWI method was used to retrieval soil moisture in the high vegetation coverage area using daily MODIS L1B data and the apparent thermal inertia model was used in low vegetation coverage using MODIS composite products data. The inversion results of two vegetation coverage types are integrated into a raster image after data normalization processing. Combining with the measured data, the precision of the result shows that the RMSE of this method is 8.3% and the MRE is 9.19% .Results show that the inversion accuracy is improved highly on the whole by the method adopted in this research.

Citation Download Citation

Chengcai Zhang and Zule Zhu "Soil moisture content inversion research using multi-source remote sensing data", Proc. SPIE 9260, Land Surface Remote Sensing II, 92600U (8 November 2014); https://doi.org/10.1117/12.2070618

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KEYWORDS

Vegetation

Soil science

Remote sensing

Thermal modeling

Data modeling

MODIS

Temperature metrology

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