[en] Data from the NASA Cyclone Global Navigation Satellite System (CYGNSS) mission have shown promise for the retrieval of soil moisture, and many soil moisture products using CYGNSS data have been developed. In this work, we present a merged product that combines several CYGNSS soil moisture products using a Minimum Variance Estimator (MVE). The MVE identifies an optimal weighted averaging scheme based on the error covariance characteristics of the CYGNSS soil moisture products. The error covariance matrix is computed using two reference datasets: soil moisture data from the Soil Moisture Active Passive (SMAP) radiometer and in situ soil moisture data. The results from each of these provide insights into both the performance of the merged product and the individual input CYGNSS products. Overall, the merged product offers better performance than any individual CYGNSS product while also offering better temporal resolution than SMAP. The results of this work also demonstrate that the use of the MVE is a compelling technique for soil moisture applications.
Hodges, Erik ; Department of Electrical and Computer Engineering, Microwave Systems, Sensors, and Imaging Lab (MiXIL), University of Southern California, Los Angeles, CA, USA
Chew, Clara; Muon Space, Mountain View, CA, USA
Small, Eric E.; Department of Geological Sciences, University of Colorado, Boulder, CO, USA
Bai, Dinan ; Climate and Space Sciences and Engineering Department and the Remote Sensing Group (RSG), University of Michigan, Ann Arbor, MI, USA
Al-Khaldi, Mohammad ; Department of Electrical and Computer Engineering and ElectroScience Laboratory, The Ohio State University, Columbus, OH, USA
Ouellette, Jeffrey D. ; US Naval Research Laboratory, Washington, DC, USA
Johnson, Joel T. ; Department of Electrical and Computer Engineering and ElectroScience Laboratory, The Ohio State University, Columbus, OH, USA
Lei, Fangni ; Department of Civil and Environmental Engineering and the Hydrometeorology and Hydrologic Remote Sensing Group, University of Connecticut, Storrs, CT, USA
Kurum, Mehmet ; Department of Electrical and Computer Engineering and the Information Processing and Sensing (IMPRESS) Lab, University of Georgia, Athens, GA, USA
Gurbuz, Ali Cafer ; Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC, USA
Senyurek, Volkan ; High Performance Computing Collaboratory and the Geosystems Research Institute, Mississippi State University, Starkville, MS, USA
Nabi, M M ; School of Engineering and Applied Science, Western Kentucky University, Bowling Green, KY, USA
Xu, Xiaolan ; Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Shah, Rashmi ; Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Yueh, Simon H. ; Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Hayashi, Akiko; Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Santi, Emanuele ; Institute of Applied Physics, National Research Council, Florence, Italy
Pettinato, Simone ; Institute of Applied Physics, National Research Council, Florence, Italy
Ruf, Christopher S. ; Climate and Space Sciences and Engineering Department and the Remote Sensing Group (RSG), University of Michigan, Ann Arbor, MI, USA
Moghaddam, Mahta ; Department of Electrical and Computer Engineering, Microwave Systems, Sensors, and Imaging Lab (MiXIL), University of Southern California, Los Angeles, CA, USA
H. Carreno-Luengo, J. A. Crespo, R. Akbar, A. Bringer, A. Warnock, M. Morris, and C. Ruf, “The CYGNSS Mission: On-Going Science Team Investigations,” Remote Sensing 2021, Vol. 13, Page 1814, vol. 13, no. 9, p. 1814, 5 2021. [Online]. Available: https://www.mdpi.com/2072-4292/13/9/1814
X. Wu, W. Ma, J. Xia, W. Bai, S. Jin, and A. Calabia, “Spaceborne GNSS-R Soil Moisture Retrieval: Status, Development Opportunities, and Challenges,” Remote Sensing, vol. 13, no. 1, p. 45, 12 2020. [Online]. Available: https://www.mdpi.com/2072-4292/13/1/45
A. Melebari, J. D. Campbell, E. Hodges, and M. Moghaddam, “Analytical Assessment of GNSS-R Delay Doppler Map Sensitivity to Land Surface Variables Using a Physics-Based Model,” IEEE Transactions on Geoscience and Remote Sensing (In Review), 2024.
M. Unwin, S. Duncan, P. Jales, P. Blunt, and J. Tye, “Implementing GNSS-Reflectometry in Space on the TechDemoSat-1 Mission,” in 27th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2014), 2 2014, pp. 1222–1235.
S. Gleason, S. Hodgart, Yiping Sun, C. Gommenginger, S. Mackin, M. Adjrad, and M. Unwin, “Detection and Processing of Bistatically Reflected GPS Signals from Low Earth Orbit for the Purpose of Ocean Remote Sensing,” IEEE Transactions on Geoscience and Remote Sensing, vol. 43, no. 6, pp. 1229–1241, 6 2005. [Online]. Available: http://ieeexplore.ieee.org/document/1433022/
C. S. Ruf, S. Gleason, Z. Jelenak, S. Katzberg, A. Ridley, R. Rose, J. Scherrer, and V. Zavorotny, “The CYGNSS Nanosatellite Constellation Hurricane Mission,” in International Geoscience and Remote Sensing Symposium (IGARSS), 2012, pp. 214–216.
C. Jing, X. Niu, C. Duan, F. Lu, G. Di, and X. Yang, “Sea Surface Wind Speed Retrieval from the First Chinese GNSS-R Mission: Technique and Preliminary Results,” Remote Sensing 2019, Vol. 11, Page 3013, vol. 11, no. 24, p. 3013, 12 2019. [Online]. Available: https://www.mdpi.com/2072-4292/11/24/3013/htmhttps://www.mdpi.com/2072-4292/11/24/3013
V. Freeman, D. Masters, P. Jales, S. Esterhuizen, E. Ebrahimi, V. Irisov, and K. B. Khadhra, “Earth Surface Monitoring with Spire’s New GNSS Reflectometry (GNSS-R) CubeSats,” in EGU General Assembly, Online, 5 2020.
C. Ruf, H. Carreno-Luengo, C. Chew, M. Moghaddam, D. Posselt, J. Crespo, and Z. Pu, “The nasa cyclone global navigation satellite system smallsat constellation,” 08 2022.
P. O’Neill, D. Entekhabi, E. Njoku, and K. Kellogg, “The nasa soil moisture active passive (smap) mission: Overview,” in 2010 IEEE International Geoscience and Remote Sensing Symposium, 2010, pp. 3236–3239.
Y. H. Kerr, P. Waldteufel, J.-P. Wigneron, S. Delwart, F. Cabot, J. Boutin, M.-J. Escorihuela, J. Font, N. Reul, C. Gruhier, S. E. Juglea, M. R. Drinkwater, A. Hahne, M. Martín-Neira, and S. Mecklenburg, “The smos mission: New tool for monitoring key elements ofthe global water cycle,” Proceedings of the IEEE, vol. 98, no. 5, pp. 666–687, 2010.
M. J. Unwin, N. Pierdicca, E. Cardellach, K. Rautiainen, G. Foti, P. Blunt, L. Guerriero, E. Santi, and M. Tossaint, “An Introduction to the HydroGNSS GNSS Reflectometry Remote Sensing Mission,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 6987–6999, 2021.
D. Masters, M. Roberts, C. Chew, S. Lowe, L. Tan, I. Colwell, K. Nordstrom, and D. McCleese, “Introduction to the muon space small satellite constellation,” https://paz.ice.csic.es/documents/2ndWorkshop/DAY2_SESSION1_3_DMasters_MuonSpace.pdf, 2023, accessed: 2024–09-23.
C. Chew and E. Small, “Description of the UCAR/CU soil moisture product,” Remote Sensing, vol. 12, no. 10, 2020.
M. M. Al-Khaldi and J. T. Johnson, “Soil moisture retrievals using CYGNSS data in a time-series ratio method: Progress update and error analysis,” IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1–5, 2022.
M. M. Nabi, V. Senyurek, A. C. Gurbuz, and M. Kurum, “Deep learning-based soil moisture retrieval in CONUS using CYGNSS delay–Doppler maps,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 6867–6881, 2022.
F. Lei, V. Senyurek, M. Kurum, A. C. Gurbuz, D. Boyd, R. Moorhead, W. T. Crow, and O. Eroglu, “Quasi-global machine learning-based soil moisture estimates at high spatio-temporal scales using CYGNSS and SMAP observations,” Remote Sensing of Environment, vol. 276, p. 113041, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0034425722001559
V. Senyurek, F. Lei, D. Boyd, A. C. Gurbuz, M. Kurum, and R. Moorhead, “Evaluations of machine learning-based CYGNSS soil moisture estimates against SMAP observations,” Remote Sensing, vol. 12, no. 21, 2020. [Online]. Available: https://www.mdpi.com/2072-4292/12/21/3503
S. H. Yueh, R. Shah, M. J. Chaubell, A. Hayashi, X. Xu, and A. Colliander, “A semiempirical modeling of soil moisture, vegetation, and surface roughness impact on CYGNSS reflectometry data,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–17, 2022.
E. Santi, D. Comite, L. Dente, L. Guerriero, N. Pierdicca, M. P. Clarizia, and N. Floury, “Global soil moisture mapping at 5 km by combining gnss reflectometry and machine learning in view of hydrognss,” Science of Remote Sensing, vol. 10, p. 100177, 2024. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2666017224000610
T. M. Roberts, I. Colwell, C. Chew, S. Lowe, and R. Shah, “A deep-learning approach to soil moisture estimation with GNSS-R,” Remote Sensing, vol. 14, no. 14, 2022. [Online]. Available: https://www.mdpi.com/2072-4292/14/14/3299
D. Bai and C. S. Ruf, “Performance assessment of cygnss v3.2 soil moisture,” 2 2025.
P. Setti and S. Tabibi, “Comprehensive analysis of cygnss gnss-r data for enhanced soil moisture retrieval,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 18, pp. 663–679, 2025.
N. Peplinski, F. Ulaby, and M. Dobson, “Dielectric properties of soils in the 0.3-1.3-GHz range,” IEEE Transactions on Geoscience and Remote Sensing, vol. 33, no. 3, pp. 803–807, 1995.
M. P. Clarizia, C. S. Ruf, P. Jales, and C. Gommenginger, “Spaceborne GNSS-R minimum variance wind speed estimator,” IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 11, pp. 6829–6843, 2014.
J. D. Ouellette, M. M. Al-Khaldi, and J. T. Johnson, “Soil moisture retrieval using cygnss data in a time-series ratio method: Progress update and error analysis, part ii,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 17, pp. 16 426–16 432, 2024.
A. Colliander, J. Asanuma, A. Berg, T. Bongiovanni, D. Bosch, T. Caldwell, C. Holifield-Collins, K. Jensen, and et al., “SMAP/in situ core validation site land surface parameters match-up data, version 1,” 2017. [Online]. Available: https://nsidc.org/data/NSIDC-0712/versions/1
W. Dorigo, I. Himmelbauer, D. Aberer, L. Schremmer, I. Petrakovic, L. Zappa, W. Preimesberger, A. Xaver, F. Annor, J. Ardö et al., “The international soil moisture network: serving earth system science for over a decade,” Hydrology and earth system sciences, vol. 25, no. 11, pp. 5749–5804, 2021.
W. Dorigo, A. Xaver, M. Vreugdenhil, A. Gruber, A. Dostálová, A. D. Sanchis-Dufau, D. Zamojski, C. Cordes, W. Wagner, and M. Drusch, “Global automated quality control of in situ soil moisture data from the international soil moisture network,” Vadose Zone Journal, vol. 12, no. 3, p. vzj2012.0097, 2013.
T. Pellarin, J.-P. Laurent, B. Cappelaere, B. Decharme, L. Descroix, and D. Ramier, “Hydrological modelling and associated microwave emission of a semi-arid region in south-western niger,” Journal of Hydrology, vol. 375, pp. 262–272, Aug. 2009.
E. Mougin, P. Hiernaux, L. Kergoat, G. Manuela, P. Rosnay, F. Timouk, V. Le Dantec, V. Demarez, F. Lavenu, M. Arjounin, T. Lebel, N. Soumaguel, E. Ceschia, B. Mougenot, F. Baup, F. Frappart, P.L. Frison, J. Gardelle, C. Gruhier, and P. Mazzega, “The amma-catch gourma observatory site in mali: Relating climatic variations to changes in vegetation, surface hydrology, fluxes and natural resources,” Journal of Hydrology, vol. 375, Aug. 2009.
B. Cappelaere, L. Descroix, T. Lebel, N. Boulain, D. Ramier, J.-P. Laurent, G. Favreau, S. Boubkraoui, M. Boucher, I. Moussa, V. Chaffard, P. Hiernaux, H. B.-A. Issoufou, E. Breton, I. Mamadou, Y. Nazoumou, M. Oi, C. Ottle, and G. Quantin, “The amma-catch experiment in the cultivated sahelian area of south-west niger, investigating water cycle response to a fluctuating climate and changing environment,” Journal of Hydrology, vol. 375, pp. 34–51, Aug. 2009.
P. Rosnay, C. Gruhier, F. Timouk, F. Baup, E. Mougin, P. Hiernaux, L. Kergoat, and V. LeDantec, “Multi-scale soil moisture measurements at the gourma meso-scale site in mali,” Journal of Hydrology, vol. 375, pp. 241–252, Aug. 2009.
T. Lebel, B. Cappelaere, S. Galle, N. Hanan, L. Kergoat, S. Levis, B. Vieux, L. Descroix, M. Gosset, E. Mougin, C. Peugeot, and L. Seguis, “Amma-catch studies in the sahelian region of west-africa: an overview,” Journal of Hydrology, vol. 375, pp. 3–13, Aug. 2009.
S. Galle, M. Grippa, C. Peugeot, I. Bouzou Moussa, B. Cappelaere, J. Demarty, E. Mougin, T. Lebel, and V. Chaffard, “AMMA-CATCH a Hydrological, Meteorological and Ecological Long Term Observatory on West Africa: Some Recent Results,” in AGU Fall Meeting Abstracts, vol. 2015, Dec. 2015, pp. GC42A–01.
D. R. Cook, “Soil temperature and moisture profile (stamp) system handbook,” 11 2016, last accessed 01 November 2021. [Online]. Available: https://www.osti.gov/biblio/1332724
D. Cook, “Surface energy balance system (sebs) instrument handbook,” 4 2018, last accessed 01 November 2021. [Online]. Available: https://www.osti.gov/biblio/1004944
M. Zreda, D. Desilets, T. Ferré, and R. Scott, “Measuring soil moisture content non-invasively at intermediate spatial scale using cosmic-ray neutrons,” Geophysical Research Letters, vol. 35, no. 21, Nov. 2008.
M. Zreda, W. J. Shuttleworth, X. Zeng, C. Zweck, D. Desilets, T. Franz, and R. Rosolem, “Cosmos: the cosmic-ray soil moisture observing system,” Hydrology and Earth System Sciences, vol. 16, no. 11, pp. 4079–4099, 2012.
C. Mattar, A. Santamaría-Artigas, C. Durán-Alarcón, L. Olivera-Guerra, R. Fuster, and D. Borvarán, “The lab-net soil moisture network: Application to thermal remote sensing and surface energy balance,” Data, vol. 1, no. 1, 2016.
C. Mattar, A. Santamaría-Artigas, C. Durán-Alarcón, L. Olivera-Guerra, and R. Fuster, “Lab-net the first chilean soil moisture network for remote sensing applications,” in Quantitative Remote Sensing Symposium (RAQRS), 2014, pp. 22–26.
Z. Su, J. Wen, L. Dente, R. Velde, L. Wang, Y. Ma, K. Yang, and Z. Hu, “The tibetan plateau observatory of plateau scale soil moisture and soil temperature (tibet-obs) for quantifying uncertainties in coarse resolution satellite and model products,” Hydrology and earth system sciences, vol. 15, no. 7, pp. 2303–2316, 2011.
L. Dente, Z. Su, and J. Wen, “Validation of smos soil moisture products over the maqu and twente regions,” Sensors, vol. 12, no. 8, pp. 9965–9986, 2012.
A. Smith, J. Walker, A. Western, R. Young, K. Ellett, R. Pipunic, R. Grayson, L. Siriwardena, F. Chiew, and H. Richter, “The murrumbidgee soil moisture monitoring network data set,” Water Resources Research, vol. 48, no. 7, Jul. 2012.
R. Young, J. Walker, N. Yeoh, A. Smith, K. Ellett, O. Merlin, and A. Western, “Soil moisture and meteorological observations from the murrumbidgee catchment,” Department of Civil and Environmental Engineering, The University of Melbourne, 2008.
G. Schaefer, M. Cosh, and T. Jackson, “The usda natural resources conservation service soil climate analysis network (scan),” Journal of Atmospheric and Oceanic Technology - J ATMOS OCEAN TECHNOL, vol. 24, no. 12, pp. 2073 – 2077, 12 2007.
J. Ardö, “A 10-year dataset of basic meteorology and soil properties in central sudan,” Dataset Papers in Geosciences [data set], vol. 2013, Jan. 2013.
G. H. Leavesley, O. David, D. C. Garen, J. Lea, J. K. Marron, T. C. Pagano, T. R. Perkins, and M. L. Strobel, “A Modeling Framework for Improved Agricultural Water Supply Forecasting,” in AGU Fall Meeting Abstracts, vol. 2008, Dec. 2008, pp. C21A–0497.
G. H. Leavesley, O. David, D. C. Garen, N. Nrcs-Usda, A. G. Goodbody, J. K. Lea, J. K. Marron, and M. Strobel, “A modeling framework for improved agricultural water-supply forecasting,” 2010. [Online]. Available: https://api.semanticscholar.org/CorpusID:129416417
T. Bongiovanni and T. G. Caldwell, “Texas Soil Observation Network (TxSON),” 2019. [Online]. Available: https://doi.org/10.18738/T8/ JJ16CF
J. Bell, M. Palecki, B. Baker, W. Collins, J. Lawrimore, R. Leeper, M. Hall, J. Kochendorfer, T. Meyers, T. Wilson, and H. Diamond, “U.s. climate reference network soil moisture and temperature observations,” Journal of Hydrometeorology, vol. 14, pp. 977–988, Jun. 2013.
P. E. O’Neill, S. Chan, E. G. Njoku, T. Jackson, and R. Bindlish, “SMAP L3 radiometer global daily 36 km EASE-Grid soil moisture, version 4,” 2016. [Online]. Available: https://nsidc.org/data/SPL3SMP/versions/4
A. Colliander, R. H. Reichle, W. T. Crow, M. H. Cosh, F. Chen, S. Chan, N. N. Das, R. Bindlish, J. Chaubell, S. Kim, Q. Liu, P. E. O’Neill, R. S. Dunbar, L. B. Dang, J. S. Kimball, T. J. Jackson, H. K. Al-Jassar, J. Asanuma, B. K. Bhattacharya, A. A. Berg, D. D. Bosch, L. Bourgeau-Chavez, T. Caldwell, J.-C. Calvet, C. H. Collins, K. H. Jensen, S. Livingston, E. Lopez-Baeza, J. Martínez-Fernández, H. McNairn, M. Moghaddam, C. Montzka, C. Notarnicola, T. Pellarin, I. Greimeister-Pfeil, J. Pulliainen, J. G. Ramos Hernández, M. Seyfried, P. J. Starks, Z. Su, R. van der Velde, Y. Zeng, M. Thibeault, M. Vreugdenhil, J. P. Walker, M. Zribi, D. Entekhabi, and S. H. Yueh, “Validation of soil moisture data products from the nasa smap mission,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 364–392, 2022.
M. Abdelkader, M. Temimi, A. Colliander, M. H. Cosh, V. R. Kelly, T. Lakhankar, and A. Fares, “Assessing the spatiotemporal variability of smap soil moisture accuracy in a deciduous forest region,” Remote Sensing, vol. 14, no. 14, 2022. [Online]. Available: https://www.mdpi.com/2072-4292/14/14/3329
M. Friedl and D. Sulla-Menashe, “MCD12C1 MODIS/Terra+Aqua land cover type yearly L3 global 0.05deg CMG v006,” 2015.
