Demo for Upscaling in-situ Soil Moisture Based on MODIS-derived Apparent Thermal Inertia (ATI)

The whole upscaling algorithm is composed of three steps. First, a functional relationship between the station-averaged soil moisture and the pixel-averaged ATI is constructed. Second, this relationship is used to calculate the representative soil moisture time series at a certain spatial scale. Last, the Bayesian linear regression is applied to obtain the upscaled area-averaged soil moisture by using in-situ measurements as independent variables. The algorithm is validated using a network of in-situ moisture sensors in the central Tibetan Plateau. This demo is used to illustrate the last step and validation.

The users need to know the following points when running the demo.

(1) bayes_upscale_sm.m is the main matlab procedure to do upscaling.

(2) bayeslinearregression.m is the matlab procedure for bayesian linear regression called by bayes_upscale_sm.m.

(3) dailymat.mat is the 2010-2011 MODIS-derived ATI.

(4) finesite-info.txt is the information about the stations used in the upscaling experiment.

(5) *.txt (* represents station name) files are in-situ daily surface soil moisture values.

 

Reference

Qin, J., K. Yang, L. Ning, et al., 2013: Spatial upscaling of in-situ soil moisture measurements based on MODIS-derived apparent thermal inertia, Remote Sensing of Environment, submitted.

Jun Qin, Dr., Associated Professor

Institute of Tibetan Plateau Research, Chinese Academy of Sciences

P.O. Box 2871, Beijing 100085, China

Phone: +86-10-62847059, Fax: +86-10-62849886

Email: shuairenqin@itpcas.ac.cn

Web: http://dam.itpcas.ac.cn/