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Estimating the spatial distribution of soil moisture based on Bayesian maximum entropy method with auxiliary data from remote sensing

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Gao, S.G., Zhu, Z.L., Liu, S.M., Jin, R., Yang, G.C., Tan, L. (2014). Estimating spatial distribution of soil moisture based on Bayesian maximum entropy method with auxiliary data from remote sensing. International Journal of Applied Earth Observation and Geoinformation, 32, 54-66. doi:10.1016/j.jag.2014.03.003.

Literature information
Type Of Reference JOUR
Title Estimating the spatial distribution of soil moisture based on Bayesian maximum entropy method with auxiliary data from remote sensing
Authors Gao, Shengguo| Zhu, Zhongli| Liu, Shaomin| Jin, Rui| Yang, Guangchao| Tan, Lei|
Secondary Title International Journal of Applied Earth Observation and Geoinformation
Abstract Soil moisture (SM) plays a fundamental role in the land–atmosphere exchange process. Spatial estimation based on multi in situ (network) data is a critical way to understand the spatial structure and variation of land surface soil moisture. Theoretically, integrating densely sampled auxiliary data spatially correlated with soil moisture into the procedure of spatial estimation can improve its accuracy. In this study, we present a novel approach to estimate the spatial pattern of soil moisture by using the BME method based on wireless sensor network data and auxiliary information from ASTER (Terra) land surface temperature measurements. For comparison, three traditional geostatistic methods were also applied: ordinary kriging (OK), which used the wireless sensor network data only, regression kriging (RK) and ordinary co-kriging (Co-OK) which both integrated the ASTER land surface temperature as a covariate. In Co-OK, LST was linearly contained in the estimator, in RK, estimator is expressed as the sum of the regression estimate and the kriged estimate of the spatially correlated residual, but in BME, the ASTER land surface temperature was first retrieved as soil moisture based on the linear regression, then, the t-distributed prediction interval (PI) of soil moisture was estimated and used as soft data in probability form. The results indicate that all three methods provide reasonable estimations. Co-OK, RK and BME can provide a more accurate spatial estimation by integrating the auxiliary information Compared to OK. RK and BME shows more obvious improvement compared to Co-OK, and even BME can perform slightly better than RK. The inherent issue of spatial estimation (overestimation in the range of low values and underestimation in the range of high values) can also be further improved in both RK and BME. We can conclude that integrating auxiliary data into spatial estimation can indeed improve the accuracy, BME and RK take better advantage of the auxiliary information compared to Co-OK, and BME outperforms RK by integrating the auxiliary data in a probability form.
Date 2014///
Year 2014
DOI 10.1016/j.jag.2014.03.003
Database Provider ScienceDirect
volume 32
Start Page 54
Alternate Title1 International Journal of Applied Earth Observation and Geoinformation
Issn 0303-2434
Url http://www.sciencedirect.com/science/article/pii/S0303243414000531
Access Date 2015/01/23/04:23:54
Keywords Bayesian maximum entropy (BME)| Remote sensing| Soft data| soil moisture| wireless sensor network (WSN)|
file_attachments2 http://www.sciencedirect.com/science/article/pii/S0303243414000531
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