Evaluation of Soil Moisture Extremes for Agricultural Productivity in the Canadian Prairies.

Champagne, C., Berg, A.A., McNairn, H., Drewitt, G.B., and Huffman, E.C. (2012). "Evaluation of Soil Moisture Extremes for Agricultural Productivity in the Canadian Prairies.", Agricultural and Forest Meteorology, 165, pp. 1-11. doi : 10.1016/j.agrformet.2012.06.003  Access to full text

Abstract

Soil moisture is a critical variable in determining crop productivity in the Canadian prairies. Methods to estimate and measure soil moisture extremes all have limitations related to spatial coverage, data accuracy and temporal record length. Three soil moisture data sets (in situ, satellite and modelled) were evaluated to determine how well they capture extreme moisture conditions that impact crop productivity. The limited temporal baseline of satellite datasets appears to provide a relatively robust measure of normal conditions, with a bias toward capturing drier than normal conditions. In situ soil moisture measurements showed few weeks with a significant relationship to agricultural yield, but the relationship was strong when it was significant, suggesting that accurate soil moisture could be a good indicator of yield anomalies if more spatially distributed data were available. Surface satellite and surface layer modelled soil moisture from the Canadian Land Surface Scheme (CLASS) showed a stronger relationship to yield during critical growth stages than did modelled soil moisture at depth. This observation indicates that improvements in the CLASS soil hydrology estimation procedure are needed, particularly for deeper soil layers. Overall, while models and satellite-detected soil moisture show promise for capturing crop yield variation, improvements are needed to increase accuracy in both approaches if they are to be used in routine agricultural monitoring. The use of agricultural productivity as a means of evaluating errors and limitations of soil moisture data sets provides an alternative to traditional data validation as it focuses on the application of the data rather than the absolute accuracy.

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