DOI: 10.1016/j.jag.2014.03.003
Scopus记录号: 2-s2.0-84904500663
论文题名: Estimating the spatial distribution of soil moisture based on Bayesian maximum entropy method with auxiliary data from remote sensing
作者: Gao S ; , Zhu Z ; , Liu S ; , Jin R ; , Yang G ; , Tan L
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2014
卷: 32, 期: 1 起始页码: 54
结束页码: 66
语种: 英语
英文关键词: Bayesian maximum entropy (BME)
; Remote sensing
; Soft data
; Soil moisture
; Wireless sensor network (WSN)
Scopus关键词: accuracy assessment
; ASTER
; atmosphere-biosphere interaction
; Bayesian analysis
; estimation method
; kriging
; remote sensing
; sensor
; soil moisture
; spatial data
; spatial distribution
; surface temperature
英文摘要: 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 regressionestimate 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 amore 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 underestima-tion in the range of high values) can also be further improved in both RK and BME. We can conclude thatintegrating auxiliary data into spatial estimation can indeed improve the accuracy, BME and RK take bet-ter advantage of the auxiliary information compared to Co-OK, and BME outperforms RK by integratingthe auxiliary data in a probability form. © 2014 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79702
Appears in Collections: 气候变化事实与影响
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作者单位: School of Geography, Beijing Normal University, Beijing 100875, China; State Key Laboratory of Remote Sensing Science, Beijing Normal University and Institute of Remote Sensing, Digital Earth ChineseAcademy of Sciences, Beijing 100875, China; Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China
Recommended Citation:
Gao S,, Zhu Z,, Liu S,et al. Estimating the spatial distribution of soil moisture based on Bayesian maximum entropy method with auxiliary data from remote sensing[J]. International Journal of Applied Earth Observation and Geoinformation,2014-01-01,32(1)