Evaluation of Soil Moisture Derived from Passive Microwave Remote Sensing Over Agricultural Sites in Canada Using Ground-based Soil Moisture Monitoring Networks.

Champagne, C., Berg, A.A., Bélanger, J., McNairn, H., and de Jeu, R. (2010). "Evaluation of Soil Moisture Derived from Passive Microwave Remote Sensing Over Agricultural Sites in Canada Using Ground-based Soil Moisture Monitoring Networks.", International Journal of Remote Sensing, 31(14), pp. 3669-3690. doi : 10.1080/01431161.2010.483485  Access to full text

Abstract

Passive microwave soil moisture datasets can be used as an input to provide an integrated assessment of climate variability as it relates to agricultural production. The objective of this research was to examine three passive microwave derived soil moisture datasets over multiple growing seasons in contrasting Canadian agricultural environments. Absolute and relative soil moisture was evaluated from two globally available datasets from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) sensor using different retrieval algorithms, as well as relative soil wetness at a weekly scale from the Special Sensor Microwave/Imager (SSM/I) sensor. At a daily scale, the Land Parameter Retrieval Model (LPRM) provides a better estimate of surface soil moisture conditions than the National Snow and Ice Data Center (NSIDC) dataset, with root mean squared errors ranging from 5 to 10% for LPRM and 12 to 18% for NSIDC soil moisture when a temporal smoothing is applied to the dataset. Both datasets provided better estimates of soil moisture over the temperate site near Elora, Ontario than the prairie site near Davidson, Saskatchewan. The LPRM dataset tends to overestimate soil moisture conditions at both sites, where the NSIDC dataset tends to underestimate absolute soil moisture. These differences in retrieval methods were independent of radiometric frequency used. At weekly scales, the LPRM dataset provides a better relative estimate of wetness conditions when compared to the NSIDC and the Basist Wetness Index (BWI) from SSM/I data, but the SSM/I dataset did provide a reasonably good relative indicator of moisture conditions. The high variability in accuracy of soil moisture estimation related to retrieval algorithms indicates that consistency is needed in these datasets if they are to be integrated in long term studies for yield estimation or data assimilation.

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