As one of the important driving parameters in hydrologic cycle, soil moisture has remarkable effect on weather variations. The development of remote sensing technology makes large-area and dynamic soil moisture observation possible, but the accurate estimation of error in remote sensing soil moisture data remains to be further studied. Based on TC method, the authors used ERA-Interim reanalysis soil moisture data and soil moisture derived from ASCAT and AMSR-E in the study area (15°N~55°N,73°E~135°E) to estimate error variance and SNR (Signal Noise Ratio) of these three soil moisture data, and also employed MODIS land cover data to analyze the error characteristics of these three soil moisture data. Study results are as follows: vegetation cover has an influence on TC method to estimate error variance and SNR of remote sensing soil moisture data; From the perspective of error variance estimation, ERA soil moisture has the highest precision, AMSR-E possesses the second place, and ASCAT is the lowest; From the perspective of SNR, ASCAT soil moisture has the highest SNR, ERA’s SNR is higher than AMSR-E and lower than ASCAT, and AMSR-E has the lowest SNR. Mostly, TC result is distributed in grasslands, croplands and barren or sparsely vegetated area through analyzing TC result related to MODIS land cover data, and TC result corresponds to objective reality.
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2018, Vol. 30 
