Cited by 277

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Analysis of CYGNSS Data for Soil Moisture Retrieval

Clarizia, Maria Paola, Pierdicca, Nazzareno, Costantini, Fabiano, Floury, Nicolas·2019·IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Abstract

Data from the CYGNSS mission, originally conceived to monitor tropical cyclones, are being investigated here for land applications as well. In this paper, a methodology for soil moisture (SM) retrieval from CYGNSS data is presented. The approach derives Level 3 gridded daily SM estimations, over the latitudinal band covered by CYGNSS, at a resolution of 36 km × 36 km, using the CYGNSS reflectivity over land, coupled with ancillary vegetation and roughness information from the SMAP mission. The results are compared globally with SM measurements from SMAP, which are assumed to be ground truth, showing a good agreement, and a global root-mean-square difference of 0.07 cm3/cm3. A more extensive comparison is performed over two test regions-Texas in the United States and New South Wales in Australia-where reference data from SMAP are complemented with measurements from the SMOS mission. The results over both regions are generally consistent with the global results, and a good agreement is observed between CYGNSS and reference SM measurements from SMAP and SMOS. The study demonstrates that SM can be successfully retrieved from the CYGNSS mission on a global scale and using ancillary information about the overlying vegetation and the characteristics of the soil. The results open up further future perspectives for global, high-resolution SM products from spaceborne Global Navigation Satellite System-Reflectometry data.

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Crossref · 10.1109/jstars.2019.2895510

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  • Bistatic radar data contains signatures that have been used for wide-ranging Earth Science applications, including measuring ocean surface wind speeds, soil moisture, inland water body mapping, river width and surface slope estimation.

    “The bistatic radar data contains signatures that have been used for wide-ranging Earth Science applications, including measuring ocean surface wind speeds [4], soil moisture [5][6][7], inland water body mapping [8][9][10][11][12][13][14][15], and river width [16] and surface slope [17] estimation.”

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Research works printed but not linked: 43

  1. 10.5067/ZX7YX2Y2LHEB · DOI
  2. 10.3390/rs10111856 · DOI
  3. 10.1109/JSTARS.2016.2588467 · DOI
  4. 10.3390/rs70809954 · DOI
  5. 10.1109/JSTARS.2014.2322854 · DOI
  6. 10.1109/36.841977 · DOI
  7. 10.1109/IGARSS.2018.8518405 · DOI
  8. 10.1038/s41598-018-27127-4 · DOI
  9. 10.3390/rs4082356 · DOI
  10. 10.1002/2014JD021489 · DOI
  11. 10.1016/j.earscirev.2010.02.004 · DOI
  12. 10.1029/2018GL077429 · DOI
  13. 10.1109/IGARSS.2017.8127544 · DOI
  14. 10.1029/2018GL077905 · DOI · Paper page
  15. 10.1002/2017GL074513 · DOI
  16. 10.1002/2015GL066624 · DOI
  17. nghiem, 2016, Wetland monitoring with Global Navigation Satellite System Reflectometry, Earth and Space Science, 3, 16
  18. 10.1109/TGRS.2018.2823316 · DOI
  19. martin-neira, 1993, A passive reflectometry and interferometry system (PARIS): Application to ocean altimetry, ESA J, 17, 331
  20. 10.1109/TGRS.2014.2303831 · DOI
  21. entekhabi, 2014, SMAP Handbook
  22. 10.1109/TGRS.2016.2541343 · DOI
  23. 10.1002/2015GL064204 · DOI
  24. 10.1109/TGRS.2012.2230401 · DOI
  25. 10.1109/TGRS.2018.2859191 · DOI
  26. 10.1109/TGRS.2012.2196437 · DOI
  27. 10.1175/BAMS-D-14-00218.1 · DOI
  28. 10.1109/JSTARS.2016.2602703 · DOI
  29. 10.1109/TGRS.2017.2699122 · DOI
  30. 10.1073/pnas.1204330109 · DOI
  31. 10.1175/2011JCLI3733.1 · DOI
  32. 10.1016/j.rse.2017.07.001 · DOI
  33. dorigo, 2015, [Hydrological cycle] soil moisture [in “State of the climate in 2014”], Bull Amer Meteorol Soc, 96, 28s
  34. 10.1109/JPROC.2010.2043918 · DOI
  35. 10.1109/36.942551 · DOI
  36. 10.1109/JSTARS.2018.2867773 · DOI
  37. 10.1016/j.rsase.2018.06.002 · DOI
  38. 10.1016/j.asr.2010.04.025 · DOI
  39. 10.1109/JSTARS.2018.2832981 · DOI
  40. 10.1109/JSTARS.2018.2833075 · DOI
  41. 10.1029/JC084iC09p05699 · DOI
  42. o'neill, 2015, SMAP Algorithm Theoretical Basis Document Level 2 and 3 Soil Moisture (Passive) Data Products Revision B JPL D-66480
  43. 10.1016/j.rse.2015.09.005 · DOI

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Metadata

Authors
Clarizia, M. P., Pierdicca, N., Costantini, F., & Floury, N.
Venue
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Published
2019-07
Volume
12
Issue
7
Pages
2227-2235
ISSN
  • 1939-1404
  • 2151-1535
Cited by
277
References
0
Funders
  • European Space Agency
  • Potentials of Spaceborne GNSS-R over Land (4000120299/17/NL/AF/hh)

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