An Analysis of a Commercial GNSS-R Soil Moisture Dataset

被引:1
|
作者
Al-Khaldi, Mohammad M. [1 ,2 ]
Johnson, Joel T. [1 ,2 ]
Horton, Dustin [1 ,2 ]
McKague, Darren S. [3 ]
Twigg, Dorina [4 ]
Russel, Anthony [4 ]
Policelli, Frederick S. [5 ]
Ouellette, Jeffrey D. [6 ]
Bindlish, Rajat [5 ]
Park, Jeonghwan [5 ]
机构
[1] Ohio State Univ, Dept Elect & Comp Engn, Columbus, OH 43210 USA
[2] Ohio State Univ, ElectroSci Lab, Columbus, OH 43210 USA
[3] Univ Michigan, Dept Climate & Space Sci & Engn, Ann Arbor, MI 48109 USA
[4] Univ Michigan, Space Phys Res Lab, Ann Arbor, MI 48109 USA
[5] NASA Goddard Space Flight Ctr, Greenbelt, MD 20771 USA
[6] US Naval Res Lab, Washington, DC 20375 USA
关键词
Soil moisture; Surface roughness; Rough surfaces; Receivers; Reflectivity; Scattering; Surface treatment; Bistatic radar systems; CubeSats; global navigation satellite systems reflectometry (GNSS-R); rough surface scattering; SmallSats; soil moisture; SIGNALS; PREDICTABILITY; REFLECTIONS; SCATTERING; DYNAMICS; SYSTEM; OCEAN; SMOS;
D O I
10.1109/JSTARS.2024.3449773
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
An analysis of a Level-2 (L2) soil moisture record extending from 1 May 2021 to 1 January 2024 derived from Spire, Inc.'s Global Navigation Satellite System Reflectometry (GNSS-R) observatories is presented. The product's sensitivity to large scale soil moisture variability is demonstrated using an example of a 2022 flood in Pakistan. Product consistency among the constellation's multiple satellites is also investigated; no clear evidence of intersatellite biases is observed. Further comparisons are performed with soil moisture datasets from the Soil Moisture Active Passive (SMAP) and Cyclone Global Navigation Satellite System (CYGNSS) missions, from the European Center for Medium-Range Weather Forecasts Reanalysis v5 (ERA5), and from in situ International Soil Moisture Network (ISMN) sites. Although an overall product correlation with SMAP soil moisture of approximately 85$\%$ is determined, per-pixel correlations vary significantly and per-pixel root-mean-square errors (RMSE) can range from 0.02 to 0.09 (cm(3)/cm(3)) depending on land class. The importance of applying the product's quality flags is also demonstrated. The influence of other calibration effects and inland water body contamination on these results is also discussed.
引用
收藏
页码:15480 / 15493
页数:14
相关论文
共 50 条
  • [1] Review of GNSS-R Technology for Soil Moisture Inversion
    Yang, Changzhi
    Mao, Kebiao
    Guo, Zhonghua
    Shi, Jiancheng
    Bateni, Sayed M.
    Yuan, Zijin
    REMOTE SENSING, 2024, 16 (07)
  • [2] Comprehensive Analysis of CYGNSS GNSS-R Data for Enhanced Soil Moisture Retrieval
    Setti, Paulo
    Tabibi, Sajad
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2025, 18 : 663 - 679
  • [3] Spaceborne GNSS-R Soil Moisture Retrieval: Status, Development Opportunities, and Challenges
    Wu, Xuerui
    Ma, Wenxiao
    Xia, Junming
    Bai, Weihua
    Jin, Shuanggen
    Calabia, Andres
    REMOTE SENSING, 2021, 13 (01) : 1 - 24
  • [4] Enhancing GNSS-R Soil Moisture Accuracy with Vegetation and Roughness Correction
    Dong, Zhounan
    Jin, Shuanggen
    Chen, Guodong
    Wang, Peng
    ATMOSPHERE, 2023, 14 (03)
  • [5] Sensing soil moisture and vegetation using GNSS-R polarimetric measurement
    Jia, Yan
    Savi, Patrizia
    ADVANCES IN SPACE RESEARCH, 2017, 59 (03) : 858 - 869
  • [6] The Study of Soil Moisture Retrieval Algorithm from GNSS-R
    Mao Kebiao
    Zhang Mengyang
    Wang Jianming
    Tang Huajun
    Zhou Qingbo
    2008 INTERNATIONAL WORKSHOP ON EDUCATION TECHNOLOGY AND TRAINING AND 2008 INTERNATIONAL WORKSHOP ON GEOSCIENCE AND REMOTE SENSING, VOL 1, PROCEEDINGS, 2009, : 438 - +
  • [7] POLARIMETRIC GNSS-R MEASUREMENTS FOR SOIL MOISTURE AND VEGETATION SENSING
    Jia, Yan
    Savi, Patrizia
    2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2016, : 5260 - 5263
  • [8] The Sensitivity Analysis on GNSS-R Soil Moisture Retrieval
    Jia, Yan
    Jin, Shuanggen
    Yan, Qingyun
    Savi, Patrizia
    2021 PHOTONICS & ELECTROMAGNETICS RESEARCH SYMPOSIUM (PIERS 2021), 2021, : 2307 - 2311
  • [9] Effective Surface Roughness Impact in Polarimetric GNSS-R Soil Moisture Retrievals
    Munoz-Martin, Joan Francesc
    Rodriguez-Alvarez, Nereida
    Bosch-Lluis, Xavier
    Oudrhiri, Kamal
    REMOTE SENSING, 2023, 15 (08)
  • [10] An Analytical Formulation of the Correlation of GNSS-R Signals
    Di Martino, Gerardo
    Di Simone, Alessio
    Iodice, Antonio
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2022, 60