DOI: 10.1002/2016MS000686
Scopus记录号: 2-s2.0-85010808903
论文题名: Mapping the global depth to bedrock for land surface modeling
作者: Shangguan W ; , Hengl T ; , Mendes de Jesus J ; , Yuan H ; , Dai Y
刊名: Journal of Advances in Modeling Earth Systems
ISSN: 19422466
出版年: 2017
卷: 9, 期: 1 起始页码: 65
结束页码: 88
语种: 英语
英文关键词: Decision trees
; Learning systems
; Radiometers
; Soil testing
; Soils
; Surface measurement
; Vegetation
; Well logging
; 10-fold cross-validation
; Biogeochemical process
; Depth to bedrock
; Earth system model
; Ensemble prediction
; Land surface modeling
; Pseudo-observations
; Spatial prediction modeling
; Forecasting
; algorithm
; bedrock
; borehole
; data set
; geological mapping
; land surface
; machine learning
; model validation
; MODIS
; prediction
; soil profile
; spatial analysis
; surface reflectance
英文摘要: Depth to bedrock serves as the lower boundary of land surface models, which controls hydrologic and biogeochemical processes. This paper presents a framework for global estimation of depth to bedrock (DTB). Observations were extracted from a global compilation of soil profile data (ca. 1,30,000 locations) and borehole data (ca. 1.6 million locations). Additional pseudo-observations generated by expert knowledge were added to fill in large sampling gaps. The model training points were then overlaid on a stack of 155 covariates including DEM-based hydrological and morphological derivatives, lithologic units, MODIS surface reflectance bands and vegetation indices derived from the MODIS land products. Global spatial prediction models were developed using random forest and Gradient Boosting Tree algorithms. The final predictions were generated at the spatial resolution of 250 m as an ensemble prediction of the two independently fitted models. The 10–fold cross-validation shows that the models explain 59% for absolute DTB and 34% for censored DTB (depths deep than 200 cm are predicted as 200 cm). The model for occurrence of R horizon (bedrock) within 200 cm does a good job. Visual comparisons of predictions in the study areas where more detailed maps of depth to bedrock exist show that there is a general match with spatial patterns from similar local studies. Limitation of the data set and extrapolation in data spare areas should not be ignored in applications. To improve accuracy of spatial prediction, more borehole drilling logs will need to be added to supplement the existing training points in under-represented areas. © 2016. The Authors. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/75808
Appears in Collections: 影响、适应和脆弱性 气候变化与战略
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作者单位: School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, China; ISRIC — World Soil Information, Wageningen, Netherlands
Recommended Citation:
Shangguan W,, Hengl T,, Mendes de Jesus J,et al. Mapping the global depth to bedrock for land surface modeling[J]. Journal of Advances in Modeling Earth Systems,2017-01-01,9(1)