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Blending Noah, SMOS, and in Situ Soil Moisture Using Multiple Weighting and Sampling Schemes

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  • 1 a Department of Geography, The Ohio State University, Columbus, Ohio
  • | 2 b University of California Agriculture and Natural Resources, Davis, California
  • | 3 c Illinois State Water Survey, University of Illinois at Urbana–Champaign, Urbana, Illinois
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Abstract

Soil moisture can be obtained from in situ measurements, satellite observations, and model simulations. This study evaluates the importance of in situ observations in soil moisture blending, and compares different weighting and sampling methods for combining model, satellite, and in situ soil moisture data to generate an accurate and spatially continuous soil moisture product at 4-km resolution. Four different datasets are used: the antecedent precipitation index (API); KAPI, which incorporates in situ soil moisture observations with the API using regression kriging; SMOS L3 soil moisture; and model-simulated soil moisture from the Noah model as part of the North American Land Data Assimilation System (NLDAS). Triple collocation, least squares weighting, and equal weighting are used to generate blended soil moisture products. An enumerated weighting scheme is designed to investigate the impact of different weighting schemes. The sensitivity of the blended soil moisture products to sampling schemes, station density, and data formats (absolute, anomalies, and percentiles) are also investigated. The results reveal that KAPI outperforms API. This indicates that incorporating in situ soil moisture improves the accuracy of the blended soil moisture products. There are no statistically significant (p > 0.05) differences between blended soil moisture using triple collocation and equal weighting approaches, and both methods provide suboptimal weighting. Optimal weighting is achieved by assigning larger weights to KAPI and smaller weights to SMOS. Using multiple sources of soil moisture is helpful for reducing uncertainty and improving accuracy, especially when the sampling density is low, or the sampling stations are less representative. These results are consistent regardless of how soil moisture is represented (absolute, anomalies, or percentiles).

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Ning Zhang, zhang.7819@osu.edu

Abstract

Soil moisture can be obtained from in situ measurements, satellite observations, and model simulations. This study evaluates the importance of in situ observations in soil moisture blending, and compares different weighting and sampling methods for combining model, satellite, and in situ soil moisture data to generate an accurate and spatially continuous soil moisture product at 4-km resolution. Four different datasets are used: the antecedent precipitation index (API); KAPI, which incorporates in situ soil moisture observations with the API using regression kriging; SMOS L3 soil moisture; and model-simulated soil moisture from the Noah model as part of the North American Land Data Assimilation System (NLDAS). Triple collocation, least squares weighting, and equal weighting are used to generate blended soil moisture products. An enumerated weighting scheme is designed to investigate the impact of different weighting schemes. The sensitivity of the blended soil moisture products to sampling schemes, station density, and data formats (absolute, anomalies, and percentiles) are also investigated. The results reveal that KAPI outperforms API. This indicates that incorporating in situ soil moisture improves the accuracy of the blended soil moisture products. There are no statistically significant (p > 0.05) differences between blended soil moisture using triple collocation and equal weighting approaches, and both methods provide suboptimal weighting. Optimal weighting is achieved by assigning larger weights to KAPI and smaller weights to SMOS. Using multiple sources of soil moisture is helpful for reducing uncertainty and improving accuracy, especially when the sampling density is low, or the sampling stations are less representative. These results are consistent regardless of how soil moisture is represented (absolute, anomalies, or percentiles).

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Ning Zhang, zhang.7819@osu.edu

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