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Performance Metrics for Soil Moisture Retrievals and Application Requirements

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  • 1 Ralph M. Parsons Laboratory for Environmental Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts
  • | 2 Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland
  • | 3 USDA/ARS Hydrology and Remote Sensing Laboratory, Beltsville, Maryland
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Abstract

Quadratic performance metrics such as root-mean-square error (RMSE) and time series correlation are often used to assess the accuracy of geophysical retrievals (satellite measurements) with respect to true fields. These metrics are related; nevertheless, each has advantages and disadvantages. In this study the authors explore the relation between the RMSE and correlation metrics in the presence of biases in the mean as well as in the amplitude of fluctuations (standard deviation) between estimated and true fields. Such biases are common, for example, in satellite retrievals of soil moisture and impose constraints on achievable and meaningful RMSE targets. Last, an approach is introduced for converting a requirement in an application’s product into a corresponding requirement for soil moisture accuracy. The approach can help with the formulation of soil moisture measurement requirements. It can also help determine the utility of a given retrieval product for applications.

Corresponding author address: Dara Entekhabi, 48-216G, MIT, Cambridge, MA 02139. Email: darae@mit.edu

Abstract

Quadratic performance metrics such as root-mean-square error (RMSE) and time series correlation are often used to assess the accuracy of geophysical retrievals (satellite measurements) with respect to true fields. These metrics are related; nevertheless, each has advantages and disadvantages. In this study the authors explore the relation between the RMSE and correlation metrics in the presence of biases in the mean as well as in the amplitude of fluctuations (standard deviation) between estimated and true fields. Such biases are common, for example, in satellite retrievals of soil moisture and impose constraints on achievable and meaningful RMSE targets. Last, an approach is introduced for converting a requirement in an application’s product into a corresponding requirement for soil moisture accuracy. The approach can help with the formulation of soil moisture measurement requirements. It can also help determine the utility of a given retrieval product for applications.

Corresponding author address: Dara Entekhabi, 48-216G, MIT, Cambridge, MA 02139. Email: darae@mit.edu

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