IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i11p9112-d1164261.html
   My bibliography  Save this article

Evaluation of Remote Sensing and Reanalysis Products for Global Soil Moisture Characteristics

Author

Listed:
  • Peng Zhang

    (College of Geographical Science, Inner Mongolia Normal University, Hohhot 010022, China)

  • Hongbo Yu

    (College of Geographical Science, Inner Mongolia Normal University, Hohhot 010022, China
    Inner Mongolia Key Laboratory of Remote Sensing and Geographic Information Systems, Hohhot 010022, China
    Provincial Key Laboratory of Mongolian Plateau’s Climate System, Hohhot 010022, China)

  • Yibo Gao

    (College of Tourism and Urban-Rural Planning, Chengdu University of Technology, Chengdu 610059, China)

  • Qiaofeng Zhang

    (College of Geographical Science, Inner Mongolia Normal University, Hohhot 010022, China
    Inner Mongolia Key Laboratory of Remote Sensing and Geographic Information Systems, Hohhot 010022, China)

Abstract

Soil moisture (SM) exists at the land-atmosphere interface and serves as a key driving variable that affects global water balance and vegetation growth. Its importance in climate and earth system studies necessitates a comprehensive evaluation and comparison of mainstream global remote sensing/reanalysis SM products. In this study, we conducted a thorough verification of ten global remote sensing/reanalysis SM products: SMAP DCA, SMAP SCA-H, SMAP SCA-V, SMAP-IB, SMOS IC, SMOS L3, LPRM_C1, LPRM_C2, LPRM_X, and ERA5-Land. The verification was based on ground observation data from the International SM Network (ISMN), considering both static factors (such as climate zone, land cover type, and soil type) and dynamic factors (including SM, leaf area index, and land surface temperature). Our goal was to assess the accuracy and applicability of these products. We analyzed the spatial and temporal distribution characteristics of global SM and discussed the vegetation effect on SM products. Additionally, we examined the global high-frequency fluctuations in the SMAP L-VOD product, along with their correlation with the normalized difference vegetation index, leaf area index, and vegetation water content. Our findings revealed that product quality was higher in regions located in tropical and arid zones, closed shrubs, loose rocky soil, and gray soil with low soil moisture, low leaf area index, and high average land surface temperature. Among the evaluated products, SMAP-IB, SMAP DCA, SMAP SCA-H, SMAP SCA-V, and ERA5-Land consistently performed better, demonstrating a good ability to capture the spatial and temporal variations in SM and showing a correlation of approximately 0.60 with ISMN. SMOS IC and SMOS L3 followed in performance, while LPRM_C1, LPRM_C2, and LPRM_X exhibited relatively poor results in SM inversion. These findings serve as a valuable reference for improving satellite/reanalysis SM products and conducting global-scale SM studies.

Suggested Citation

  • Peng Zhang & Hongbo Yu & Yibo Gao & Qiaofeng Zhang, 2023. "Evaluation of Remote Sensing and Reanalysis Products for Global Soil Moisture Characteristics," Sustainability, MDPI, vol. 15(11), pages 1-27, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:11:p:9112-:d:1164261
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/11/9112/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/11/9112/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cao, Meng & Chen, Min & Liu, Ji & Liu, Yanli, 2022. "Assessing the performance of satellite soil moisture on agricultural drought monitoring in the North China Plain," Agricultural Water Management, Elsevier, vol. 263(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liu, Cong & Li, Kaiwei & Zhang, Jiquan & Guga, Suri & Wang, Rui & Liu, Xingpeng & Tong, Zhijun, 2023. "Dynamic risk assessment of waterlogging disaster to spring peanut (Arachis hypogaea L.) in Henan Province, China," Agricultural Water Management, Elsevier, vol. 277(C).
    2. Zhang, Siyao & Li, Jianzhu & Zhang, Ting & Feng, Ping & Liu, Weilin, 2024. "Response of vegetation to SPI and driving factors in Chinese mainland," Agricultural Water Management, Elsevier, vol. 291(C).

    More about this item

    Keywords

    microwave remote sensing; soil moisture; SMAP; SMOS; LPRM; ERA5-Land;
    All these keywords.

    JEL classification:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:11:p:9112-:d:1164261. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.