Triple Collocation of Ground-, Satellite- and Land Surface Model-Based Surface Soil Moisture Products in Oklahoma Part II: New Multi-Sensor Soil Moisture (MSSM) Product
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Triple Collocation of Ground-, Satellite- and Land Surface Model-Based Surface Soil Moisture Products in Oklahoma Part II: New Multi-Sensor Soil Moisture (MSSM) Product

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Details:

  • Journal Title:
    Remote Sensing
  • Personal Author:
  • NOAA Program & Office:
    CESSRST (Cooperative Science Center for Earth System Sciences and Remote Sensing Technologies)
  • Description:
    This study develops a triple-collocation (TC) based, multi-source shallow-soil moisture product for Oklahoma. The method uses a least squared weights (LSW) optimization to find the set of parameters that result in the lowest root mean squared error (RMSE) with respect to the “unknown truth”. Soil moisture information from multiple sources and resolutions, including the Soil Moisture Active Passive SMAP L3_SM_P_E (9 km, daily), the physically-based, land surface model (LSM) estimates from NLDAS_NOAH0125_H (1/8°, hourly), and the Oklahoma Mesonet ground sensor network (9 km interpolated from point, 30 min) is merged into a 9 km spatial and daily temporal resolution product across the state of Oklahoma from April 2015 to July 2019. This multi-sensor surface soil moisture (MSSM) product is assessed in terms of a state-wide benchmark and previously tested, in situ-based soil moisture product and SMAP L4. Results show that: (1) independent source products have differential values according to the regional conditions they represent, including land cover type, soils, irrigation, or climate regime; (2) beyond serving as validation sets, in situ measurements are of significant value for improving the accuracy of multi-sensor soil moisture datasets through TC; and (3) state-wide RMSE values obtained with MSSM are similar to the typical measurement error found on in situ ground measurements which provides some degree of confidence on the new product. MSSM is an improvement over currently available products in Oklahoma due to its minimized uncertainty, easiness of production, and continuous temporal and geographic coverage. Nevertheless, to exploit its utility, further tests of this methodology are needed in different climates, land cover types, geographic regions, and for other independent products and spatiotemporal resolutions.
  • Keywords:
  • Source:
    Remote Sensing. 2023; 15(13):3450
  • DOI:
  • Format:
    PDF
  • Document Type:
    Journal Article
  • Funding:
    Grant no. NA22SEC4810016
  • Place as Subject:
    Oklahoma
  • License:
  • Rights Information:
    CC BY
  • Compliance:
    Submitted
  • Main Document Checksum:
  • Download URL:
  • File Type:

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