Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2018WR024535 |
Mesoscale Soil Moisture Patterns Revealed Using a Sparse In Situ Network and Regression Kriging | |
Ochsner, Tyson E.1; Linde, Evan2; Haffner, Matthew3; Dong, Jingnuo1 | |
2019-06-01 | |
发表期刊 | WATER RESOURCES RESEARCH |
ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2019 |
卷号 | 55期号:6页码:4785-4800 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | Soil moisture spatial patterns with length scales of 1-100 km influence hydrological, ecological, and agricultural processes, but the footprint or support volume of existing monitoring systems, for example, satellite-based radiometers and sparse in situ monitoring networks, is often either too large or too small to effectively observe these mesoscale patterns. This measurement scale gap hinders our understanding of soil water processes and complicates calibration and validation of hydrologic models and soil moisture satellites. One possible solution is to utilize geostatistical techniques that have proven effective for mapping static patterns in soil properties. The objective of this study was to determine how effectively dynamic, mesoscale soil moisture patterns can be mapped by applying regression kriging to the data from a sparse, large-scale in situ network. The fully automated system developed here uses several data sets: daily soil moisture measurements from the Oklahoma Mesonet, sand content estimates from the Natural Resource Conservation Service Soil Survey Geographic Database, and an antecedent precipitation index computed from National Weather Service multisensor precipitation estimates. A multiple linear regression model is fitted daily to the observed data, and the residuals of that model are used in a semivariogram estimation and kriging routine to produce daily statewide maps of soil moisture at 5-, 25-, and 60-cm depths at 800-m resolution. During over 3 years of operation, this mapping system has revealed complex, dynamic, and depth-specific mesoscale patterns, reflecting the shifting influences of both soil texture and precipitation, with a mean absolute error of <= 0.0576 cm(3)/cm(3) across all three depths. |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000477616900016 |
WOS关键词 | SPATIAL VARIABILITY ; HIGH-RESOLUTION ; SCALE ; TEMPERATURE ; WATER ; PRECIPITATION ; CLIMATE ; INTERPOLATION ; PRODUCTS ; MODEL |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/183970 |
专题 | 资源环境科学 |
作者单位 | 1.Oklahoma State Univ, Dept Plant & Soil Sci, Stillwater, OK 74078 USA; 2.Oklahoma State Univ, Ctr High Performance Comp, Stillwater, OK 74078 USA; 3.Univ Wisconsin, Geog & Anthropol, Eau Claire, WI 54701 USA |
推荐引用方式 GB/T 7714 | Ochsner, Tyson E.,Linde, Evan,Haffner, Matthew,et al. Mesoscale Soil Moisture Patterns Revealed Using a Sparse In Situ Network and Regression Kriging[J]. WATER RESOURCES RESEARCH,2019,55(6):4785-4800. |
APA | Ochsner, Tyson E.,Linde, Evan,Haffner, Matthew,&Dong, Jingnuo.(2019).Mesoscale Soil Moisture Patterns Revealed Using a Sparse In Situ Network and Regression Kriging.WATER RESOURCES RESEARCH,55(6),4785-4800. |
MLA | Ochsner, Tyson E.,et al."Mesoscale Soil Moisture Patterns Revealed Using a Sparse In Situ Network and Regression Kriging".WATER RESOURCES RESEARCH 55.6(2019):4785-4800. |
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