Evaluation of Agricultural Soil Moisture Extremes in Canada Using Passive Microwave Remote Sensing

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Evaluation of Agricultural Soil Moisture Extremes in Canada Using Passive Microwave Remote Sensing

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Title: Evaluation of Agricultural Soil Moisture Extremes in Canada Using Passive Microwave Remote Sensing
Author: Champagne, Catherine
Department: Department of Geography
Program: Geography
Advisor: Berg, Aaron
Abstract: This research examines the potential to use passive microwave remote sensing for measuring soil moisture extremes that impact agricultural areas in Canada. A validation was made of three passive microwave remote sensing soil moisture data sets, with weekly averaged values from the Land Parameter Retrieval Model (LPRM) applied to AMSR-E C/X-Band data providing the most accurate results (root mean squared error of 5 to 10%). A further evaluation of this data set against a spatially distributed in situ soil moisture network in Alberta suggests that this data set may be less accurate in regions where dense vegetation or open water is present, particularly on the northern edges of the Canadian agricultural extent. A method to derive soil moisture anomalies was developed that uses homogenous regions to spatially aggregate soil moisture statistics to compensate for a short satellite data record. It was found that these anomalies can be estimated with errors of less than 5% when these regions are 15 pixels or more over a seven year time period. Surface soil moisture anomalies from LPRM showed weak but significant relationships to precipitation based drought indices, suggesting promise for using these anomalies for wider soil moisture extremes monitoring. Soil moisture anomalies from CLASS and in situ networks showed inconsistencies with LPRM anomalies in how they capture soil moisture conditions that are relevant to agricultural yield.. These data sets overall show that this approach to quantifying extremes has potential, but improvement to soil moisture retrieval from LPRM and CLASS, and an integration of the information they provide are needed to optimize these data sets for agricultural monitoring.
URI: http://hdl.handle.net/10214/2918
Date: 2011-08-25


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