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Soil organic carbon in soil water content variability; detection and application in agricultural fields

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Title: Soil organic carbon in soil water content variability; detection and application in agricultural fields
Author: Manns, Hida
Department: Department of Geography
Program: Geography
Advisor: Berg, Aaron A.
Abstract: The promise of satellite remote sensing to deliver frequent and high resolution soil water content (SWC) on a global scale is dependent on the interpretation of the electrical energy reflectance from the earth surface. Soil texture and surface roughness have been taken into account in field surface variation, however, soil organic carbon (SOC) has not previously been considered as a factor in the passive remote sensing algorithm development. The SMAPVEX12 field sampling campaign, in preparation for the 2015 satellite launch provided an opportunity to test additional agricultural variables on multiple fields (55), and over a range of soil texture, soil organic matter, crops, crop maturity and rainfall. Soil organic carbon was more highly correlated to the average SWC at each sampling date than soil texture or bulk density although all soil variables were significantly correlated with each other. While soil texture was the dominant variable in multiple linear regression of SWC in wet conditions, in the driest sampling times, SOC alone explained the highest percentage of variability in SWC. In the analysis of the passive remotely sensed data from the aircraft measurements during SMAPVEX12, SOC also explained more variability in the absolute and relative SWC anomalies than soil texture. The ability of SOC to predict SWC and vice versa, developed from the SMAPVEX12 data was tested on fields sampled for SWC in 2008 in the same vicinity of Manitoba, in very dry weather. Both SOC and SWC were predicted to within field variability. A geostatistical analysis of data from SMAPVEX12 sites confirmed the existence of a significant cross-variogram with a range of 5 km, over which SOC and SWC expressed the identical variability in soil. Remote sensing of SWC may capitalize on this dual relationship to improve the interpretation of soil emissivity in passive remote sensing, to provide covariates in downscaling and modelling of SWC, and possibly to assist in measurement of SOC for ecosystem modelling.
Description: Evaluation of soil parameters in soil water content variability in ground sampling and in passive remote sensing
URI: http://hdl.handle.net/10214/8785
Date: 2015-04


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