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Remote Sensing for Land & Resources    2018, Vol. 30 Issue (3) : 68-75     DOI: 10.6046/gtzyyg.2018.03.10
Error analysis of soil moisture based on Triple Collocation method
Kai WU, Hong SHU, Lei NIE, Zhenhang JIAO
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
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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.

Keywords soil moisture      error estimation      Triple Collocation     
:  TP237  
Issue Date: 10 September 2018
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Kai WU
Hong SHU
Zhenhang JIAO
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Kai WU,Hong SHU,Lei NIE, et al. Error analysis of soil moisture based on Triple Collocation method[J]. Remote Sensing for Land & Resources, 2018, 30(3): 68-75.
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Fig.1  2010 MODIS land cover type classification in study area
Fig.2  Correlation coefficient between ASCAT ,AMSR-E and ERA soil moisture in study area
Fig.3  The σ spatial distributions of ERA, ASCAT, AMSR-E soil moisture error and the corresponding histograms in study area
参数 μ估计值 μ置信区间 σ估计值 σ置信区间
ERA 4.920 9 [4.888 0,
4.953 9]
1.727 6 [1.704 6,
1.751 2]
ASCAT 12.728 9 [12.6437,
12.81 4]
4.468 4 [4.409 0,
4.529 4]
AMSR-E 5.214 6 [5.141 5,
5.287 7]
3.834 5 [3.783 6,
3.886 9]
Tab.1  Normal distribution parameter and interval estimation of ERA, ASCAT, AMSR-E soil moisture error
Fig.4  fMSE spatial distributions of ERA, ASCAT, AMSR-E soil moisture and corresponding histograms in study area
min~Q1 Q1~Q3 Q3~max min~Q1 Q1~Q3 Q3~max min~Q1 Q1~Q3 Q3~max
常绿阔叶林 65 51 19 18 79 38 0 25 110
混交林 147 148 68 33 129 201 1 40 322
稀疏灌丛 17 50 36 31 49 23 32 70 1
多树草原 199 306 77 180 311 91 4 278 300
草原 1 207 2 639 789 792 2 483 1 360 1 862 2 055 718
农田 433 1 412 1 354 954 1 641 604 178 2 022 999
耕地/自然植被镶嵌 77 242 192 105 296 110 29 350 132
裸地/低植被覆盖 477 416 92 515 272 198 535 421 29
Tab.2  Land cover type classification and σ of ERA, ASCAT, AMSR-E soil moisture error
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