A Multi-Sensor Approach for Satellite Soil Moisture Monitoring for Agricultural Climate Risk Assessment

Thursday, 18 December 2014: 4:00 PM
Catherine Champagne, Agriculture and Agri-Food Canada, National AgroClimate Information Service, Ottawa, ON, Canada, Patrick Cherneski, Agriculture and Agri-Food Canada, National AgroClimate Information Service, Regina, SK, Canada, Trevor A Hadwen, Agriculture and Agri-Food Canada, Regina, Canada and Andrew Davidson, Agriculture Canada, Ottawa, ON, Canada
Satellite missions specifically dedicated to soil moisture retrieval have become a reality in the past few years, with the launch of SMOS in 2009 and SMAP in 2014. While much of the work on applications around these missions has focussed on data assimilation systems for numerical weather prediction, there is also potential to use the data to support agricultural applications such as drought and flood assessment and yield forecasting. Previous work has examined the potential for using SMOS soil moisture for detecting spatial and temporal patterns of agroclimate risk, such as drought and excess wetness. This research builds upon that work through the examination of a data set with a longer reference period to determine if the dataset can be used as a baseline for detecting anomalies from normal conditions. Surface satellite soil moisture from a multi-sensor climate reference data set (1993 to 2010) and the SMOS surface soil moisture data (2010 – 2014) set were examined in hindsight to detect relevant trends for monitoring the climate conditions in agricultural regions of Canada. Soil moisture and soil moisture anomalies were examined against precipitation and temperature records over the relevant time periods, and compared against agroclimatic drought risk indicators, including the Palmer Drought Severity Index, the Standardized Precipitation Index and the MODIS Normalized Difference Vegetation Condition anomalies. High impact events, including the 2002 drought in the Canadian Prairies, excess wetness in the southern Manitoba in 2009 and 2011 were evaluated in detail. The potential for using these data sets in near real time to support agricultural decision making will be discussed.