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Evaluating the NASA MERRA-2 climate reanalysis and ESA CCI satellite remote sensing soil moisture over the contiguous United States.

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Title: Evaluating the NASA MERRA-2 climate reanalysis and ESA CCI satellite remote sensing soil moisture over the contiguous United States.
Authors: Valipour, Mohammad1 (AUTHOR) mvalipou@msudenver.edu, Bateni, Sayed M.2 (AUTHOR), Dietrich, Jörg3 (AUTHOR), Heggy, Essam4,5 (AUTHOR), Almazroui, Mansour6,7 (AUTHOR)
Source: International Journal of Remote Sensing. Aug2023, Vol. 44 Issue 15, p4639-4665. 27p.
Subjects: Soil moisture, Remote sensing, Government policy on climate change, Thematic mapper satellite, Soil testing, Conus
Geographic Terms: United States
Abstract: Accurate large-scale soil moisture (SM) retrievals using the daily NASA MERRA-2' climate reanalysis and ESA' Climate Change Initiative (CCI) remote sensing datasets are compromised by temporal and spatial ambiguities. To address this deficiency, we assess the accuracy of the above-mentioned datasets against the Soil Climate Analysis Network (SCAN) in-situ measurements at nine sites across the contiguous United States (CONUS) during 2014–2016. The sites are selected to represent different climate regions over the CONUS. SM dynamics from NASA MERRA-2 and ESA CCI are compared with those of SCAN, and an SM dataset is developed based on a spatiotemporal analysis. Our results show that the NASA MERRA-2 and ESA CCI SM datasets have different accuracies at the nine SCAN sites in different seasons. The MERRA-2 and CCI SM datasets have the highest accuracy at sites with the lowest number of extreme events, indicating that both datasets cannot robustly capture extremum soil moisture values. The highest (lowest) agreement between the MERRA-2/CCI and SCAN SM data is observed in April (February) with the nine-site average unbiased root-mean-square-difference (ubRMSD) of 0.0638 cm3/cm3 (0.0914 cm3/cm3). In all the nine sites, the CCI SM data are more accurate than those of MERRA-2 in April–October. The CCI SM data shows greater accuracy in the sites with lower SM values and/or higher SM variability. MERRA-2 and CCI SM datasets show higher and lower accuracy at the sites with pasture and agricultural vegetation, respectively. Finally, a new SM dataset is created by using the more accurate SM data from MERRA-2 and CCI in each site and season. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Remote Sensing is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 169922912
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  Label: Title
  Group: Ti
  Data: Evaluating the NASA MERRA-2 climate reanalysis and ESA CCI satellite remote sensing soil moisture over the contiguous United States.
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  Data: <searchLink fieldCode="AR" term="%22Valipour%2C+Mohammad%22">Valipour, Mohammad</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mvalipou@msudenver.edu</i><br /><searchLink fieldCode="AR" term="%22Bateni%2C+Sayed+M%2E%22">Bateni, Sayed M.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dietrich%2C+Jörg%22">Dietrich, Jörg</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Heggy%2C+Essam%22">Heggy, Essam</searchLink><relatesTo>4,5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Almazroui%2C+Mansour%22">Almazroui, Mansour</searchLink><relatesTo>6,7</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Remote+Sensing%22">International Journal of Remote Sensing</searchLink>. Aug2023, Vol. 44 Issue 15, p4639-4665. 27p.
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Soil+moisture%22">Soil moisture</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Government+policy+on+climate+change%22">Government policy on climate change</searchLink><br /><searchLink fieldCode="DE" term="%22Thematic+mapper+satellite%22">Thematic mapper satellite</searchLink><br /><searchLink fieldCode="DE" term="%22Soil+testing%22">Soil testing</searchLink><br /><searchLink fieldCode="DE" term="%22Conus%22">Conus</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22United+States%22">United States</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Accurate large-scale soil moisture (SM) retrievals using the daily NASA MERRA-2' climate reanalysis and ESA' Climate Change Initiative (CCI) remote sensing datasets are compromised by temporal and spatial ambiguities. To address this deficiency, we assess the accuracy of the above-mentioned datasets against the Soil Climate Analysis Network (SCAN) in-situ measurements at nine sites across the contiguous United States (CONUS) during 2014–2016. The sites are selected to represent different climate regions over the CONUS. SM dynamics from NASA MERRA-2 and ESA CCI are compared with those of SCAN, and an SM dataset is developed based on a spatiotemporal analysis. Our results show that the NASA MERRA-2 and ESA CCI SM datasets have different accuracies at the nine SCAN sites in different seasons. The MERRA-2 and CCI SM datasets have the highest accuracy at sites with the lowest number of extreme events, indicating that both datasets cannot robustly capture extremum soil moisture values. The highest (lowest) agreement between the MERRA-2/CCI and SCAN SM data is observed in April (February) with the nine-site average unbiased root-mean-square-difference (ubRMSD) of 0.0638 cm3/cm3 (0.0914 cm3/cm3). In all the nine sites, the CCI SM data are more accurate than those of MERRA-2 in April–October. The CCI SM data shows greater accuracy in the sites with lower SM values and/or higher SM variability. MERRA-2 and CCI SM datasets show higher and lower accuracy at the sites with pasture and agricultural vegetation, respectively. Finally, a new SM dataset is created by using the more accurate SM data from MERRA-2 and CCI in each site and season. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Remote Sensing is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1080/01431161.2023.2237665
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      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 27
        StartPage: 4639
    Subjects:
      – SubjectFull: Soil moisture
        Type: general
      – SubjectFull: Remote sensing
        Type: general
      – SubjectFull: Government policy on climate change
        Type: general
      – SubjectFull: Thematic mapper satellite
        Type: general
      – SubjectFull: Soil testing
        Type: general
      – SubjectFull: Conus
        Type: general
      – SubjectFull: United States
        Type: general
    Titles:
      – TitleFull: Evaluating the NASA MERRA-2 climate reanalysis and ESA CCI satellite remote sensing soil moisture over the contiguous United States.
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            NameFull: Valipour, Mohammad
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            NameFull: Bateni, Sayed M.
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            NameFull: Dietrich, Jörg
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            NameFull: Heggy, Essam
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            NameFull: Almazroui, Mansour
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            – D: 01
              M: 08
              Text: Aug2023
              Type: published
              Y: 2023
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              Value: 44
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              Value: 15
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            – TitleFull: International Journal of Remote Sensing
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