作者:Fan, L (Fan, Lei)[ 1,2,3 ] ; Xiao, Q (Xiao, Qing)[ 1 ] ; Wen, JG (Wen, Jianguang)[ 1,3 ] ; Liu, Q (Liu, Qiang)[ 4 ] ; Jin, R (Jin, Rui)[ 5,6 ] ; You, DQ (You, Dongqing)[ 1,3 ] ; Li, XW (Li, Xiaowen)[ 7 ]

Remote Sensing

卷: 7  期: 10  页: 13273-13297

DOI: 10.3390/rs71013273

出版年: OCT 2015

摘要

High spatial resolution soil moisture (SM) data are crucial in agricultural applications, river-basin management, and understanding hydrological processes. Merging multi-resource observations is one of the ways to improve the accuracy of high spatial resolution SM data in the heterogeneous cropland. In this paper, the Bayesian Maximum Entropy (BME) methodology is implemented to merge the following four types of observed data to obtain the spatial distribution of SM at 100 m scale: soil moisture observed by wireless sensor network (WSN), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)-derived soil evaporative efficiency (SEE), irrigation statistics, and Polarimetric L-band Multi-beam Radiometer (PLMR)-derived SM products (similar to 700 m). From the poor BME predictions obtained by merging only WSN and SEE data, we observed that the SM heterogeneity caused by irrigation and the attenuating sensitivity of the SEE data to SM caused by the canopies result in BME prediction errors. By adding irrigation statistics to the merged datasets, the overall RMSD of the BME predictions during the low-vegetated periods can be successively reduced from 0.052 m(3).m(-3) to 0.033 m(3).m(-3). The coefficient of determination (R-2) and slope between the predicted and in situ measured SM data increased from 0.32 to 0.64 and from 0.38 to 0.82, respectively, but large estimation errors occurred during the moderately vegetated periods (RMSD = 0.041 m(3.)m(-3), R = 0.43 and the slope = 0.41). Further adding the downscaled SM information from PLMR SM products to the merged datasets, the predictions were satisfactorily accurate with an RMSD of 0.034 m(3).m(-3), R-2 of 0.4 and a slope of 0.69 during moderately vegetated periods. Overall, the results demonstrated that merging multi-resource observations into SM estimations can yield improved accuracy in heterogeneous cropland.

通讯作者地址: Xiao, Q (通讯作者)

Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China.

地址:

[ 1 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China

[ 2 ] Univ Chinese Acad Sci, Beijing 100049, Peoples R China

[ 3 ] Joint Ctr Global Change Studies, Beijing 100875, Peoples R China

[ 4 ] Beijing Normal Univ, Coll Global Change & Earth Syst Sci, Beijing 100875, Peoples R China

[ 5 ] Chinese Acad Sci, Cold & Arid Reg Environm & Engn Res Inst, Lanzhou 730000, Peoples R China

[ 6 ] Chinese Acad Sci, Ctr Excellence Tibetan Plateau Earth Sci, Beijing 100101, Peoples R China

[ 7 ] Beijing Normal Univ, Sch Geog, Beijing 100875, Peoples R China