Spatial composition of AMSR2 soil moisture products by conditional merging technique with ground soil moisture data
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- 作者：Dongkyun Kim ; Jaehyeon Lee ; Hyunglok Kim…
- 关键词：Soil moisture ; AMSR2 ; Remotely sensed soil moisture ; Conditional merging technique ; Kriging
- 刊名：Stochastic Environmental Research and Risk Assessment
- 出版年：2016
- 出版时间：December 2016
- 年：2016
- 卷：30
- 期：8
- 页码：2109-2126
- 全文大小：
- 刊物类别：Earth and Environmental Science
- 刊物主题：Math. Appl. in Environmental Science; Earth Sciences, general; Probability Theory and Stochastic Processes; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Computa
- 出版者：Springer Berlin Heidelberg
- ISSN：1436-3259
- 卷排序：30
文摘
The conditional merging (CM) spatial interpolation technique was applied to obtain the composite soil moisture products using the AMSR2 and in situ soil moisture for the 51 days of the summer through the late fall season of the year 2012 in Korean Peninsula. The ‘leave one out cross-validation’ analysis was conducted to assess the performance of the composite soil moisture products in estimating the soil moisture in ungagged locations. The control variable for comparison was the soil moisture products obtained by spatially interpolating the in situ soil moisture data measured at eight gage locations using the Ordinary Kriging (KR) technique. The results show that the composite soil moisture products are more accurate than the in situ only soil moisture products in estimating the soil moisture for the following cases: (1) when the spatial correlation of in situ soil moisture data is low. Such case includes when there is little rainfall and where the altitude is high (mountainous area) and (2) where the gage density is low or the area located further away from the in situ gages. For both cases, the KR method cannot use enough information due to the low spatial correlation of the in situ measurement for interpolation, while the CM method can take advantage of the satellite soil moisture measurement not affected by the spatial correlation of the in situ data.