Different Rates of Soil Drying after Rainfall Are Observed by the SMOS Satellite and the South Fork in situ Soil Moisture Network

Author:

Rondinelli Wesley J.1,Hornbuckle Brian K.1,Patton Jason C.1,Cosh Michael H.2,Walker Victoria A.1,Carr Benjamin D.1,Logsdon Sally D.3

Affiliation:

1. Iowa State University of Science and Technology, Ames, Iowa

2. Hydrology and Remote Sensing Laboratory, Agricultural Research Service, USDA, Beltsville, Maryland

3. National Laboratory for Agriculture and the Environment, Agricultural Research Service, USDA, Ames, Iowa

Abstract

Abstract Soil moisture affects the spatial variation of land–atmosphere interactions through its influence on the balance of latent and sensible heat fluxes. Wetter soils are more prone to flooding because a smaller fraction of rainfall can infiltrate into the soil. The Soil Moisture Ocean Salinity (SMOS) satellite carries a remote sensing instrument able to make estimates of near-surface soil moisture on a global scale. One way to validate satellite observations is by comparing them with observations made with sparse networks of in situ soil moisture sensors that match the extent of satellite footprints. The rate of soil drying after significant rainfall observed by SMOS is found to be higher than the rate observed by a U.S. Department of Agriculture (USDA) soil moisture network in the watershed of the South Fork Iowa River. This leads to the conclusion that SMOS and the network observe different layers of the soil: SMOS observes a layer of soil at the soil surface that is a few centimeters thick, while the network observes a deeper soil layer centered at the depth at which the in situ soil moisture sensors are buried. It is also found that SMOS near-surface soil moisture is drier than the South Fork network soil moisture, on average. The conclusion that SMOS and the network observe different layers of the soil, and therefore different soil moisture dynamics, cannot explain the dry bias. However, it can account for some of the root-mean-square error in the relationship. In addition, SMOS observations are noisier than the network observations.

Publisher

American Meteorological Society

Subject

Atmospheric Science

Reference48 articles.

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2. Evaluation of SMOS soil moisture products over continental U.S. using the SCAN/SNOTEL network;Al Bitar;IEEE Trans. Geosci. Remote Sens.,2012

3. From near-surface to root-zone soil moisture using year-round data;Calvet;J. Hydrometeor.,2000

4. Carr, B. D. , 2014: Evaluation of an agroecosystem model using cosmic-ray neutron soil moisture. M.S. thesis, Paper 14006, Dept. of Agronomy, Iowa State University of Science and Technology, 122 pp.

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