Monitoring agricultural soil moisture extremes in Canada using passive microwave remote sensing.

Champagne, C., McNairn, H., and Berg, A.A. (2011). "Monitoring agricultural soil moisture extremes in Canada using passive microwave remote sensing.", Remote Sensing of Environment, 115(10), pp. 2434-2444. doi : 10.1016/j.rse.2011.04.030  Access to full text


Current methods to assess soil moisture extremes rely primarily on point-based in situ meteorological stations which typically provide precipitation and temperature rather than direct measurements of soil moisture. Microwave remote sensing offers the possibility of quantifying surface soil moisture conditions over large spatial extents. Capturing soil moisture anomalies normally requires a long temporal record of data, which most operating satellites do not have. This research examines the use of surface soil moisture from the AMSR-E passive microwave satellite to derive surface soil moisture anomalies by exploiting spatial resolution to compensate for the shorter temporal record of the satellite sensor. Four methods were used to spatially aggregate information to develop a surface soil moisture anomaly (SMA). Two of these methods used soil survey and climatological zones to define regions of homogeneity, based on the Soil Landscapes of Canada (SLC) and the EcoDistrict nested hierarchy. The second two methods (ObShp3 and ObShp5) used zones defined by a data driven segmentation of the satellite soil moisture data. The level of sensitivity of the calculated SMA decreased as the number of pixels used in the spatial aggregation increased, with the average error reducing to less than 5% when more than 15 pixels are used. All methods of spatial aggregation showed somewhat weak but consistent relationship to in situ soil moisture anomalies and meteorological drought indices. The size of the regions used for aggregation was more important than the method used to create the regions. Based on the error and the relationship to the in situ and ancillary data sets, the EcoDistrict or ObShp3 scale appears to provide the lowest error in calculating the SMA baseline. This research demonstrates that the use of spatial aggregation can provide useful information on soil moisture anomalies where satellite records of data are temporally short.

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