PROGRESS IN GEOGRAPHY ›› 2006, Vol. 25 ›› Issue (2): 123-130.doi: 10.11820/dlkxjz.2006.02.014

• Original Articles • Previous Articles     Next Articles

Retrieved Deduction of Soil Moisture Spatial Distribution and Drought Discrimination Based on Remote Sensing

LI Yuhuan11,2, 21, WANG Jing11   

  1. 1. Key Laboratory of Land Use, Ministry of Land and Resources, China Land Surveying &|Planning Institute, Beijing,100035|
    2. Shandong Agricultural University College of Resources and Environment, Taian 271018
  • Received:2006-01-01 Revised:2006-02-01 Online:2006-03-25 Published:2006-03-25


Soil moisture monitoring acts as an important role in reasonable water resource utilization and scientific management and decision-making of drought-fight. Soil moisture has relation with vegetation growth index(NDVI) and land surface temperature(LST). The NDVI was derived from the red and the NIR bands and the LST from the one or two thermal bands. The paper adopted LST retrieved from TM/ETM+ by mono-window algorithm and the modified soil adjustment vegetation index(MSAVI) based on ground soil spectrum line parameters and the earth reflection by COST model from the same satellite data. By analyzing the linear relation between LST and MSAVI, soil moisture indicators were put forward in terms of three geometrical expressions based on the two extreme points of the LST-MSAVI scatterplots, and drought discrimination function(DF) which was be used to discriminate the drought years or areas from the wet ones based on the DFij received either positive or negative values or the regress between the DFij values of the respective geometrical index and soil moisture component by Laboratory. a clear trend was exhibited between the drought-year cluster (negative values) and the wet-year cluster (positive values). The crossing point between the regression line and the DF= 0 line could be used to quantify the threshold between wet and drought regions in terms of soil moisture component. The retrieved model was built to illuminate soil moisture spatial distribution depending on linear regression analysis between soil moisture and LST or MSAVI. Results of the discrimination function for each of the drought indicators are presented. The “length” indicator is able to successfully separate the two drought years(1990, 2001) from the wet years(1991, 2002). The drought-land was discriminated by the DFj values as a function of the soil moisture of each region with DFj=0 and soil moisture=10~11%. Therefore the result showed that more information was mined by combining LST and MSAVI; length indicator could present valuable information for drought based on TM/ETM+; It wasn’t remarkable for the retrieved soil moisture to be tested by T-test; and the retrieved model from LST was better and more valuable than that from MSAVI.

Key words: drought discrimination function, LST, MSAVI, soil moisture

CLC Number: 

  • P934