Time-Series Retrieval of Soil Moisture Using CYGNSS

被引:149
|
作者
M-Khaldi, Mohammad M. [1 ,2 ]
Johnson, Joel T. [1 ,2 ]
O'Brien, Andrew J. [1 ,2 ]
Balenzano, Anna [3 ]
Mattia, Francesco [3 ]
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] Ist Rilevamento Elettromagnet Ambiente, I-70126 Bari, Italy
来源
关键词
Global Navigation Satellite System-Reflectometry (GNSS-R); remote sensing; soil moisture; time-series algorithms; DIELECTRIC MODEL; VEGETATION; SCATTERING; REFLECTIVITY; SENSITIVITY; SIGNALS; WATER;
D O I
10.1109/TGRS.2018.2890646
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
Time-series retrievals of soil moisture obtained from the Cyclone Global Navigation Satellite System (CYGNSS) constellation are presented. The retrieval approach assumes that vegetation and roughness changes occur on timescales longer than those associated with soil moisture changes to allow soil moisture sensing in the presence of vegetation and surface roughness contributions as well as the varying incidence angles associated with spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) systems. The approach is focused on incoherent scattering from land surfaces due to the expectation that coherent land surface returns arise primarily from inland water body contributions that are not directly representative of soil moisture. An approach for discarding coherent CYGNSS measurements is therefore developed and described. Because the approach requires the retrieval of N temporal soil moisture samples at a given location but uses only N - 1 ratios of CYGNSS measured quantities, ancillary information is incorporated in the retrieval through the use of maximum and minimum monthly soil moisture maps obtained from the Soil Moisture Active Passive (SMAP) mission. Retrieved soil moistures are presented for the 6-month period December 2017-May 2018 and are compared against values reported by the SMAP mission. The comparisons suggest that there exists the potential for using spaceborne GNSS-R systems for global soil moisture retrievals with an rms error on the order of 0.04 cm(3)/cm(3) over varied terrain.
引用
收藏
页码:4322 / 4331
页数:10
相关论文
共 50 条
  • [31] Soil Moisture Sensing Using Spaceborne GNSS Reflections: Comparison of CYGNSS Reflectivity to SMAP Soil Moisture
    Chew, C. C.
    Small, E. E.
    [J]. GEOPHYSICAL RESEARCH LETTERS, 2018, 45 (09) : 4049 - 4057
  • [32] Time-series prediction of organomineral fertilizer moisture using machine learning
    Korkmaz, Cem
    Kacar, Ilyas
    [J]. APPLIED SOFT COMPUTING, 2024, 165
  • [33] An Extension of the Alpha Approximation Method for Soil Moisture Estimation Using Time-Series SAR Data Over Bare Soil Surfaces
    He, Lian
    Qin, Qiming
    Panciera, Rocco
    Tanase, Mihai
    Walker, Jeffrey P.
    Hong, Yang
    [J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2017, 14 (08) : 1328 - 1332
  • [34] Daily Soil Moisture Retrieval by Fusing CYGNSS and Multi-Source Auxiliary Data Using Machine Learning Methods
    Yang, Ting
    Wang, Jundong
    Sun, Zhigang
    Li, Sen
    [J]. SENSORS, 2023, 23 (22)
  • [35] Evaluation of Long Time-Series Soil Moisture Products Using Extended Triple Collocation and In Situ Measurements in China
    Zhang, Liumeng
    Yang, Yaping
    Liu, Yangxiaoyue
    Yue, Xiafang
    [J]. ATMOSPHERE, 2023, 14 (09)
  • [36] Using Robust Regression to Retrieve Soil Moisture from CyGNSS Data
    Liu, Qi
    Zhang, Shuangcheng
    Li, Weiqiang
    Nan, Yang
    Peng, Jilun
    Ma, Zhongmin
    Zhou, Xin
    [J]. REMOTE SENSING, 2023, 15 (14)
  • [37] A modified version of the SMAR model for estimating root-zone soil moisture from time-series of surface soil moisture
    Faridani, Farid
    Farid, Alireza
    Ansari, Hossein
    Manfreda, Salvatore
    [J]. WATER SA, 2017, 43 (03) : 492 - 498
  • [38] Robust time-series retrieval using probabilistic adaptive segmental alignment
    Shariat, Shahriar
    Pavlovic, Vladimir
    [J]. KNOWLEDGE AND INFORMATION SYSTEMS, 2016, 49 (01) : 91 - 119
  • [39] Robust time-series retrieval using probabilistic adaptive segmental alignment
    Shahriar Shariat
    Vladimir Pavlovic
    [J]. Knowledge and Information Systems, 2016, 49 : 91 - 119
  • [40] GEOSTATISTICAL ANALYSIS OF SURFACE TEMPERATURE AND IN-SITU SOIL MOISTURE USING LST TIME-SERIES FROM MODIS
    Sohrabinia, M.
    Rack, W.
    Zawar-Reza, P.
    [J]. XXII ISPRS CONGRESS, TECHNICAL COMMISSION VII, 2012, 39 (B7): : 17 - 21