Multiresolution soil moisture products based on a spatially adaptive estimation model and CYGNSS data

References

• Al-Khaldi, M. M., J. T. Johnson, A. J. O’Brien, A. Balenzano, and F. Mattia. 2019. “Time-Series Retrieval of Soil Moisture Using CYGNSS.” IEEE Transactions on Geoscience and Remote Sensing 57 (7): 4322–25. https://doi.org/10.1109/TGRS.2018.2890646.
   Web of Science ®Google Scholar
• Brunsdon, C., A. S. Fotheringham, and M. Charlton. 1998. “Geographically Weighted Regression–Modeling Spatial Non-Stationary.” The Statistician 47 (3): 431–443. https://doi.org/10.1111/1467-9884.00145.
   Google Scholar
• Calabia, A., I. Molina, and S. Jin. 2020. “Soil Moisture Content from GNSS Reflectometry Using Dielectric Permittivity from Fresnel Reflection Coefficients.” Remote Sensing 12 (1): 122–143. https://doi.org/10.3390/rs12010122.
   Web of Science ®Google Scholar
• Carreno-Luengo, H., G. Luzi, and M. Crosetto. 2019. “Sensitivity of Cygnss Bistatic Reflectivity and Smap Microwave Radiometry Brightness Temperature to Geophysical Parameters Over Land Surfaces.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12 (1): 107–122. https://doi.org/10.1109/JSTARS.2018.2856588.
   Web of Science ®Google Scholar
• Chen, Y., M. Chen, B. Huang, C. Wu, and W. Shi. 2021. “Modelling the Spatiotemporal Association Between COVID‐19 Transmission and Population Mobility Using Geographically and Temporally Weighted Regression.” GeoHealth 5 (5): 1–13. https://doi.org/10.1029/2021GH000402.
   Web of Science ®Google Scholar
• Chew, C., R. Shah, C. Zuffada, G. Hajj, D. Masters, and A. J. Mannucci. 2016. “Demonstrating Soil Moisture Remote Sensing with Observations from the UK TechDemosat-1 Satellite Mission.” Geophysical Research Letters 43 (7): 3317–3324. https://doi.org/10.1002/2016GL068189.
   Web of Science ®Google Scholar
• Chew, C. C., and E. E. Small. 2018. “Soil Moisture Sensing Using Spaceborne GNSS Reflections: Comparison of CYGNSS Reflectivity to SMAP Soil Moisture.” Geophysical Research Letters 45 (9): 4049–4057. https://doi.org/10.1029/2018GL077905.
   Web of Science ®Google Scholar
• Chew, C. C., and E. E. Small. 2020. “Description of the UCAR/CU Soil Moisture Product.” Remote Sensing 12 (10): 1558–1584. https://doi.org/10.3390/rs12101558.
   Web of Science ®Google Scholar
• Cho, S., D. M. Lambert, S. G. Kim, and S. Jung. 2009. “Extreme Coefficients in Geographically Weighted Regression and Their Effects on Mapping.” GIScience & Remote Sensing 46 (3): 273–288. https://doi.org/10.2747/1548-1603.46.3.273.
   Web of Science ®Google Scholar
• Clarizia, M. P., N. Pierdicca, F. Costantini, and N. Floury. 2019. “Analysis of CyGNSS data for soil moisture retrieval.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12 (7): 2227–2235. https://doi.org/10.1109/JSTARS.2019.2895510.
   Web of Science ®Google Scholar
• Colliander, A., J. B. Fisher, G. Halverson, O. Merlin, S. Misra, R. Bindlish, T. J. Jackson, and S. Yueh. 2017. “Spatial Downscaling of SMAP Soil Moisture Using MODIS Land Surface Temperature and NDVI During SMAPVEX15.” IEEE Geoscience & Remote Sensing Letters 14 (11): 2107–2111. https://doi.org/10.1109/LGRS.2017.2753203.
   Web of Science ®Google Scholar
• Djamai, N., R. Magagi, K. Goïta, O. Merlin, Y. Kerr, and A. Roy. 2016. “A Combination of DISPATCH Downscaling Algorithm with CLASS Land Surface Scheme for Soil Moisture Estimation at Fine Scale During Cloudy Days.” Remote Sensing of Environment 184:1–14. https://doi.org/10.1016/j.rse.2016.06.010.
   Web of Science ®Google Scholar
• Eroglu, O., M. Kurum, D. Boyd, and A. C. Gurbuz. 2019. “High Spatial-Temporal Resolution CYGNSS Soil Moisture Estimates Using Artificial Neural Networks.” Remote Sensing 11 (19): 2272–2304. https://doi.org/10.3390/rs11192272.
   Web of Science ®Google Scholar
• Finn, M. P., M. Lewis, D. D. Bosch, M. Giraldo, K. Yamamoto, D. G. Sullivan, R. Kincaid, et al. 2011. “Remote Sensing of Soil Moisture Using Airborne Hyperspectral Data.” GIScience & Remote Sensing 48 (4): 522–540. https://doi.org/10.2747/1548-1603.48.4.522.
   Web of Science ®Google Scholar
• Fotheringham, A. S., C. Brunsdon, and M. Charlton. 2002. Geographically Weighted Regression: The Analysis of Spatial Varying Relationships. Chichester, Sussex: John Wiley.
   Google Scholar
• Huang, B., B. Wu, and M. Barrv. 2010. “Geographically and Temporally Weighted Regression for Modeling Spatial-Temporal Variation in House Prices.” International Journal of Geographical Information Science 24 (3): 383–401. https://doi.org/10.1080/13658810802672469.
   Web of Science ®Google Scholar
• Jackson, T. J., R. Bindlish, M. H. Cosh, T. J. Zhao, P. J. Starks, D. D. Bosch, and M. Seyfried, et al. 2012. “Validation of Soil Moisture and Ocean Salinity (SMOS) Soil Moisture Over Watershed Networks in the US.” IEEE Transactions on Geoscience & Remote Sensing 50 (5): 1530–1543.
   Web of Science ®Google Scholar
• Jackson, T. J., M. H. Cosh, R. Bindlish, P. J. Starks, D. D. Bosch, M. Seyfried, and C. David et al. 2010. “Validation of Advanced Microwave Scanning Radiometer Soil Moisture Products.” IEEE Transactions on Geoscience & Remote Sensing 48 (12): 4256–4272.
   Web of Science ®Google Scholar
• Jia, Y., S. Jin, H. Chen, Q. Yan, P. Savi, Y. Jin, and Y. Yuan. 2021. “Temporal-Spatial Soil Moisture Estimation from CYGNSS Using Machine Learning Regression with a Pre-Classification Approach.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14:4879–4893. https://doi.org/10.1109/JSTARS.2021.3076470.
   Web of Science ®Google Scholar
• Jia, Y., S. Jin, Q. Yan, P. Savi, R. Zhang, and W. Li. 2022. “An Effective Land Type Labeling Approach for Independently Exploiting High-Resolution Soil Moisture Products Based on CYGNSS Data.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 15:4234–4247. https://doi.org/10.1109/JSTARS.2022.3176031.
   Web of Science ®Google Scholar
• Jiang, Y., and Q. Weng. 2017. “Estimation of Hourly and Daily Evapotranspiration and Soil Moisture Using Downscaled LST Over Various Urban Surfaces.” GIScience & Remote Sensing 54 (1): 95–117. https://doi.org/10.1080/15481603.2016.1258971.
   Web of Science ®Google Scholar
• Jin, S. G., Ed. 2012. Global Navigation Satellite Systems: Signal, Theory and Applications. Rijeka, Croatia: InTech-Publisher.
   Google Scholar
• Jin, S. G., G. Feng, and S. Gleason. 2011. “Remote Sensing Using GNSS Signals: Current Status and Future Directions.” Advances in Space Research 47 (10): 1645–1653. https://doi.org/10.1016/j.asr.2011.01.036.
   Web of Science ®Google Scholar
• Kim, H., and L. Venkat. 2018. “Use of Cyclone Global Navigation Satellite System (CyGNSS) Observations for Estimation of Soil Moisture.” Geophysical Research Letters 45 (6): 8272–8282. https://doi.org/10.1029/2018GL078923.
   Google Scholar
• Lei, F., V. Senyurek, M. Kurum, A. Gurbuz, D. Boyd, R. J. Moorhead, and W. T. Crow. 2022. “Quasi-Global Machine Learning-Based Soil Moisture Estimates at High Spatio-Temporal Scales Using CYGNSS and SMAP Observations.” Remote Sensing of Environment 276:113041–113059. https://doi.org/10.1016/j.rse.2022.113041.
   Web of Science ®Google Scholar
• Leroux, D. J., Y. H. Kerr, A. Al Bitar, R. Bindlish, T. J. Jackson, B. Berthelot . 2014. “Comparison Between SMOS, VUA, ASCAT, and ECMWF Soil Moisture Products Over Four Watersheds in US.” IEEE Transactions on Geoscience & Remote Sensing 52 (3): 1562–1571.
   Web of Science ®Google Scholar
• Li, F., X. Peng, X. Chen, M. Liu, and L. Xu. 2018. “Analysis of Key Issues on GNSS-R Soil Moisture Retrieval Based on Different Antenna Patterns.” Sensors 18 (8): 2498–2514. https://doi.org/10.3390/s18082498.
   PubMed Web of Science ®Google Scholar
• Ma, H., X. Li, J. Zeng, X. Zhang, J. Dong, N. Chen, L. Fan, et al. 2023. “An Assessment of L-Band Surface Soil Moisture Products from SMOS and SMAP in the Tropical Areas.” Remote Sensing of Environment 284:113344–113403. https://doi.org/10.1016/j.rse.2022.113344.
   Web of Science ®Google Scholar
• Mardan, M., and S. Ahmadi. 2021. “Soil Moisture Retrieval Over Agricultural Fields Through Integration of Synthetic Aperture Radar and Optical Images.” GIScience & Remote Sensing 58 (8): 1276–1299. https://doi.org/10.1080/15481603.2021.1974276.
   Web of Science ®Google Scholar
• Ma, H., J. Zeng, N. Chen, X. Zhang, and W. Wang. 2019. “Satellite Surface Soil Moisture from SMAP, SMOS, AMSR2 and ESA CCI: A Comprehensive Assessment Using Global Ground-Based Observations.” Remote Sensing of Environment 231C:111215–111259. https://doi.org/10.1016/j.rse.2019.111215.
   Google Scholar
• Nabi, M., V. Senyurek, A. Gurbuz, and M. Kurum. 2022. “Deep Learning-Based Soil Moisture Retrieval in CONUS Using CYGNSS Delay-Doppler Maps.” Journal of Selected Topics in Applied Earth Observations and Remote Sensing, IEEE 15:6876–6881. https://doi.org/10.1109/JSTARS.2022.3196658.
   Web of Science ®Google Scholar
• Owe, M., R. A. M. de Jeu, and T. R. H. Holmes. 2008. “Multisensor historical climatology of satellite-derived global land surface moisture.” Journal of Geophysical Research: Earth Surface 113 (F01002): 1–17. https://doi.org/10.1029/2007JF000769.
   Google Scholar
• Padhee, S. K., B. R. Nikam, S. Dutta, and S. P. Aggarwal. 2017. “Using Satellite-Based Soil Moisture to Detect and Monitor Spatiotemporal Traces of Agricultural Drought Over Bundelkhand Region of India.” GIScience & Remote Sensing 54 (2): 144–166. https://doi.org/10.1080/15481603.2017.1286725.
   Web of Science ®Google Scholar
• Rodriguez-Alvarez, N., E. Podest, K. Jensen, and K. C. McDonald. 2019. “Classifying Inundation in a Tropical Wetlands Complex with GNSS-R.” Remote Sensing 11 (9): 1053–1077. https://doi.org/10.3390/rs11091053.
   Web of Science ®Google Scholar
• Ruf, C. S., A. Robert, P. Chang, P. Clarizia, and V. Zavorotny. 2016. “New Ocean Winds Satellite Mission to Probe Hurricanes and Tropical Convection.” Bulletin of the American Meteorological Society 97 (3): 385–395. https://doi.org/10.1175/BAMS-D-14-00218.1.
   Web of Science ®Google Scholar
• Senyurek, V., F. Lei, D. Boyd, A. C. Gurbuz, R. Moorhead, and R. Moorhead. 2020a. “Evaluations of a Machine Learning-Based CYGNSS Soil Moisture Estimates Against SMAP Observations.” Remote Sensing 12 (21): 3503–3525. https://doi.org/10.3390/rs12213503.
   Web of Science ®Google Scholar
• Senyurek, V., F. Lei, D. Boyd, M. Kurum, A. C. Gurbuz, and R. Moorhead. 2020b. “Machine Learning-Based CYGNSS Soil Moisture Estimates Over ISMN Sites in CONUS.” Remote Sensing of Environment 12 (7): 1168–1192. https://doi.org/10.3390/rs12071168.
   Google Scholar
• Shibata, A., K. Imaoka, and T. Koike. Feb 2003. “AMSR/AMSR-E Level 2 and 3 Algorithm Developments and Data Validation Plans of NASDA.” IEEE Transactions on Geoscience and Remote Sensing 41 (2): 195–203. https://doi.org/10.1109/TGRS.2002.808320.
   Web of Science ®Google Scholar
• Su, Z., P. de Rosnay, J. Wen, L. Wang, and Y. Zeng. 2013. “Evaluation of Ecmwf’s Soil Moisture Analyses Using Observations on the Tibetan Plateau.” Journal of Geophysical Research 118 (11): 5304–5318. http://dx.doi.org/10.1002/jgrd.50468.
   Web of Science ®Google Scholar
• Su, Z., J. Wen, L. Dente, R. van der Velde, L. Wang, and Y. Ma. 2011. “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 15 (7): 2303–2316.
   Web of Science ®Google Scholar
• Tang, F., and S. Yan. 2022. “CYGNSS Soil Moisture Estimations Based on Quality Control.” IEEE Geoscience and Remote Sensing Letters 19:1–5. https://doi.org/10.1109/LGRS.2021.3119850.
   Web of Science ®Google Scholar
• Tobler, W. 1970. “A Computer Movie Simulating Urban Growth in the Detroit Region.” Economic Geography 46:234. https://doi.org/10.2307/143141.
   Web of Science ®Google Scholar
• Wang, X., N. Chen, Z. Chen, X. Yang, and J. Li. 2016. “Earth Observation Metadata Ontology Model for Spatiotemporal-Spectral Semantic-Enhanced Satellite Observation Discovery: A Case Study of Soil Moisture Monitoring.” GIScience & Remote Sensing 53 (1): 22–44. https://doi.org/10.1080/15481603.2015.1092490.
   Web of Science ®Google Scholar
• Wang, Z., T. Zhao, J. Qiu, X. Zhao, R. Li, and S. Wang. 2021. “Microwave-Based Vegetation Descriptors in the Parameterization of Water Cloud Model at L-Band for Soil Moisture Retrieval Over Croplands.” GIScience & Remote Sensing 58 (1): 48–67. https://doi.org/10.1080/15481603.2020.1857123.
   Web of Science ®Google Scholar
• Wan, W., R. Ji, B. Liu, H. Li, and S. Zhu. 2020. “A Two-Step Method to Calibrate CYGNSS-Derived Land Surface Reflectivity for Accurate Soil Moisture Estimations.” IEEE Geoscience and Remote Sensing Letters 19:1–5. https://doi.org/10.1109/LGRS.2020.3023650.
   Web of Science ®Google Scholar
• Xu, L., N. Chen, Z. Chen, C. Zhang, and H. Yu. 2021. “Spatiotemporal Forecasting in Earth System Science: Methods, Uncertainties, Predictability and Future Directions.” Earth Science Review 222:103828–103846. https://doi.org/10.1016/j.earscirev.2021.103828.
   Web of Science ®Google Scholar
• Yan, Q., S. Gong, S. Jin, W. Huang, and C. Zhang. 2022. “Near Real-Time Soil Moisture in China Retrieved from CyGnss Reflectivity.” IEEE Geoscience and Remote Sensing Letters 19:1–5. https://doi.org/10.1109/LGRS.2020.3039519.
   Web of Science ®Google Scholar
• Yang, T., W. Wan . 2020. “Comprehensive Evaluation of Using TechDemosat-1 and CYGNSS Data to Estimate Soil Moisture Over Mainland China.” Remote Sensing 12 (11): 1699.
   Web of Science ®Google Scholar
• Yang, T., W. Wan, Z. Sun, B. Liu, S. Li, and X. Chen. 2020. “Comprehensive Evaluation of Using TechDemosat-1 and CYGNSS Data to Estimate Soil Moisture Over Mainland China.” Remote Sensing 12 (11): 1699–1721. https://doi.org/10.3390/rs12111699.
   Web of Science ®Google Scholar
• Yan, Q., W. Huang, S. Jin, and Y. Jia. 2020. “Pan-Tropical Soil Moisture Mapping Based on a Three-Layer Model from CYGNSS GNSS-R Data.” Remote Sensing of Environment 247:111944–111954. https://doi.org/10.1016/j.rse.2020.111944.
   Web of Science ®Google Scholar
• Zeng, J., K. S. Chen, H. Bi, and Q. Chen. 2016. “A Preliminary Evaluation of the Smap Radiometer Soil Moisture Product Over United States and Europe Using Ground-Based Measurements.” IEEE Transactions on Geoscience and Remote Sensing 54 (8): 4929–4940. https://doi.org/10.1109/TGRS.2016.2553085.
   Web of Science ®Google Scholar
• Zeng, J., Z. Li, Q. Chen, H. Bi, J. Qiu, and P. Zou. 2015. “Evaluation of Remotely Sensed and Reanalysis Soil Moisture Products Over the Tibetan Plateau Using in-Situ Observations.” Remote Sensing of Environment 163:91–110. https://doi.org/10.1016/j.rse.2015.03.008.
   Web of Science ®Google Scholar
• Zhao, T. J., L. X. Zhang, J. C. Shi, and L. M. Jiang. 2011b. “A Physically Based Statistical Methodology for Surface Soil Moisture Retrieval in the Tibet Plateau Using Microwave Vegetation Indices.” Journal of Geophysical Research: Atmospheres 116 (D8): D08116.
   Google Scholar
• Zribi, M., D. Guyon, E. Motte, S. Dayau, J. P. Wigneron, N. Baghdadi, and N. Pierdicca. 2019. “Performance of GNSS-R GLORI Data for Biomass Estimation Over the Land Forest.” International Journal of Applied Earth Observation and Geoinformation 74:150–158. https://doi.org/10.1016/j.jag.2018.09.010.
   Web of Science ®Google Scholar