Soil moisture retrieval using CYGNSS/SMAP data fusion semi-empirical model

被引：0
作者：
Zhang Y. [1 ,2 ]
Zhang D. [1 ,2 ]
Meng W. [3 ]
Gu J. [4 ]
Han Y. [1 ,2 ]
Yang S. [1 ,2 ]
机构：
[1] College of Information Technology, Shanghai Ocean University, Shanghai
[2] Shanghai Marine Intelligent Information and Navigation Remote Sensing Engineering Technology Research Center, Shanghai
[3] Shanghai Spaceflight Institute of TT&C and Telecommunication, Shanghai
[4] Shanghai SastSpace Technology Co.,Ltd., Shanghai
来源：
Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics | 2023年 / 49卷 / 11期
基金：
中国国家自然科学基金;
关键词：
CYGNSS; GNSS reflected signal; SMAP; soil moisture; soil surface rough; vegetation attenuation;
D O I：
10.13700/j.bh.1001-5965.2022.0044
中图分类号：
学科分类号：
摘要：
Soil moisture retrieval of the satellite-borne GNSS reflected signal (GNSS-R) is more susceptible to the influence of the changing environmental factors on the land. At present, there is little research on error correction and extrapolation performance analysis in the satellite-borne GNSS-R soil moisture retrieval model. The accuracy of the reflectivity of land points is enhanced by correction in this article, which synthesizes a number of error correction models, including the GNSS satellite transmit power deviation, the attenuation of the reflected signal strength by vegetation and surface roughness, and these factors. A semi-empirical retrieval model of CYGNSS/SMAP data fusion of reflectance-soil moisture was established. One-year high-precision extrapolation retrieval is achieved, retrieval bias is -0.003 7 cm3/cm3, the root mean square error (RMSE) is 0.026 4 cm3/cm3 and the correlation coefficient is 0.963 6. At the same time, a seasonal extrapolation model is proposed to improve the extrapolation accuracy in low water content seasons. The longitude of the experimental area is 90̊E-130̊E, the latitude is 20̊N-38̊N, and the CYGNSS/SMAP data from October 2019 to September 2020 are used for training. Push the soil moisture from October 2020 to September 2021. After the reflectivity is corrected by the error model, the semi-empirical model retrieval bias is improved by 6.80%, and and RMSE is improved by 3.30%. Aiming at the problem that the winter and spring soil moisture content low in the experimental area affects the retrieval accuracy, proposes a seasonal training model for extrapolation in the same season,compared with the training model with one-year data volume, the RMSE of winter retrieval can be increased by 21.58%, and the spring can be increased by 21.05%. Comparing the retrieval results from June 1 to 10, 2021 with the soil moisture measured on the ground by CLDAS, the Bias is 0.005 8 cm3/cm3 and the RMSE is 0.085 4 cm3/cm3, which can maintain good accuracy. This paper’s research results have proved the effectiveness of using the reflectivity-soil moisture semi-empirical model and the reflectivity error correction, which are of significance for the promotion of the commercial application of the on-board GNSS-R soil moisture retrieval. © 2023 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.
引用
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页码：2873 / 2882
页数：9
相关论文
共 23 条
  • [1] TU J S., Soil moisture retrieve using GNSS-R observation data, pp. 1-2, (2019)
  • [2] ZHAO X, CHEN S N., Comparative study and error analysis of soil moisture products from SMAP satellite, Journal of Gansu Sciences, 33, 2, pp. 27-32, (2021)
  • [3] KERR Y H, WALDTEUFEL P, WIGNERON J P, Et al., Soil moisture retrieval from space: The soil moisture and ocean salinity (SMOS) mission, IEEE Transactions on Geoscience and Remote Sensing, 39, 8, pp. 1729-1735, (2001)
  • [4] ENTEKHABI D, NJOKU E G, O'NEILL P E, Et al., The soil moisture active passive (SMAP) mission, Proceedings of the IEEE, 98, 5, pp. 704-716, (2010)
  • [5] HANG S J, ZHANG Y, LI B B, Et al., Feasibility of coastal phase altimetry using BeiDou IGSO satellite reflected signal, Remote Sensing Information, 35, 1, pp. 73-81, (2020)
  • [6] ZHANG Y, ZHANG Y Y, MENG W T, Et al., Research on sea surface altimetry model of airborne GNSS reflected signal, Haiyang Xuebao, 42, 3, pp. 149-156, (2020)
  • [7] ZHANG Y, MA D H, MENG W T, Et al., Sea surface height retrieval of GPS reflected signal based on TechDemoSat-1 satellite, Journal of Beijing University of Aeronautics and Astronautics, 47, 10, pp. 1941-1948, (2021)
  • [8] ZHANG Y, CHEN X, MENG W T, Et al., Wind direction retrieval using support vector machine from CYGNSS sea surface data, Remote Sensing, 13, 21, (2021)
  • [9] CHEW C C, SHAH R, ZUFFADA C, Et al., Demonstrating soil moisture remote sensing with observations from the UK TechDemoSat-1 satellite mission, Geophysical Research Letters, 43, 7, pp. 3317-3324, (2016)
  • [10] CHEW C C, SMALL E E., Soil moisture sensing using spaceborne GNSS reflections: Comparison of CYGNSS reflectivity to SMAP soil moisture, Geophysical Research Letters, 45, 9, pp. 4049-4057, (2018)
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