Vol. 105

Front:[PDF file] Back:[PDF file]
Latest Volume
All Volumes
All Issues
2020-09-08

Performance Evaluation of a Neural Network Model and Two Empirical Models for Estimating Soil Moisture Based on Sentinel-1 SAR Data

By Yan Li, Songhua Yan, Nengcheng Chen, and Jianya Gong
Progress In Electromagnetics Research C, Vol. 105, 85-99, 2020
doi:10.2528/PIERC20071601

Abstract

The objective of this paper is to propose an inversion model of soil moisture using a neural network, and compare the performance of this method with two empirical models in soil moisture inversion. A wide dataset of backscattering coefficients extracted from Sentinel-1 images and in situ soil surface parameter measurements (moisture content and roughness) are used. Since the available backscattering models have limited performances of describing the nonlinear relationship between soil parameters and backscatter coefficient, the retrieval of soil parameters from radar backscattering coefficient remains challenging. The proposed inversion method of a neural network is used for establishing this relationship. At the same time, two empirical models are employed to estimate the soil moisture for comparison. The results show that for most of the six measuring stations the inverted soil moisture with the neural network model has higher correlation coefficient with the in-situ soil moisture than those by the empirical models. Moreover, the neural network model inversion results under multi-polarization input conditions are discussed in this paper. The results of stations 2, 4, and 5 show that R2 of multi-polarization inputs are increased by 0.1928, 0.4821, and 0.2758 respectively, compared with those of single-polarization inputs.

Citation


Yan Li, Songhua Yan, Nengcheng Chen, and Jianya Gong, "Performance Evaluation of a Neural Network Model and Two Empirical Models for Estimating Soil Moisture Based on Sentinel-1 SAR Data," Progress In Electromagnetics Research C, Vol. 105, 85-99, 2020.
doi:10.2528/PIERC20071601
http://www.jpier.org/PIERC/pier.php?paper=20071601

References


    1. Pacheco, A., et al., "The impact of national land cover and soils data on SMOS soil moisture retrieval over Canadian agricultural landscapes," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 8, No. 11, 5281-5293, 2015.
    doi:10.1109/JSTARS.2015.2417832

    2. Zhu, G., et al., "Relative soil moisture in China's farmland," Journal of Geographical Sciences, Vol. 29, 334-350, 2019.
    doi:10.1007/s11442-019-1601-6

    3. Azimi, S., et al., "Understanding the benefit of Sentinel 1 and SMAP-era satellite soil moisture retrievals for flood forecasting in small basins: Effect of revisit time and the spatial resolution," Journal of Hydrology, Vol. 581, 2019.

    4. Ma, S.-C., et al., "Effects of controlling soil moisture regime based on root-sourced signal characteristics on yield formation and water use efficiency of winter wheat," Agricultural Water Management, Vol. 221, 486-492, 2019.
    doi:10.1016/j.agwat.2019.05.019

    5. Dari, J., et al., "Spatial-temporal variability of soil moisture: Addressing the monitoring at the catchment scale," Journal of Hydrology, Vol. 570, 2019.

    6. Gao, Q., et al., "Synergetic use of Sentinel-1 and Sentinel-2 data for soil moisture mapping at 100 m resolution," Sensors, Vol. 17, 1966, 2017.
    doi:10.3390/s17091966

    7. Paloscia, S., et al., "Soil moisture mapping using Sentinel-1 images: Algorithm and preliminary validation," Remote Sensing of Environment, Vol. 134, 234, 2013.
    doi:10.1016/j.rse.2013.02.027

    8. Ulaby, F., et al., "Microwave Radar and Radiometric Remote Sensing," Artech House, 2014.

    9. Dubois, P. C., J. V. Zyl, and T. Engman, "Measuring soil moisture with imaging radars," IEEE Transactions on Geoscience and Remote Sensing, Vol. 33, No. 4, 915-926, 1995.
    doi:10.1109/36.406677

    10. Fung, A. K., Microwave Scattering and Emission Models and Their Applications, Artech House, Norwood, MA, 1994.

    11. Oh, Y., K. Sarabandi, and F. Ulaby, "Semi-empirical model of the ensemble-averaged differential Mueller matrix for microwave backscattering from bare soil surfaces," IEEE Transactions on Geoscience and Remote Sensing, Vol. 40, 1348-1355, 2002.
    doi:10.1109/TGRS.2002.800232

    12. Oh, Y., K. Sarabandi, and F. T. Ulaby, "An empirical-model and an inversion technique for radar scattering from bare soil surfaces," IEEE Transactions on Geoscience and Remote Sensing, Vol. 30, 370-381, 1992.
    doi:10.1109/36.134086

    13. Kumar, A., et al., "Study of empirical approaches for retrieval of soil moisture in Solani river catchment area of Uttarakand, India," 2016 11th International Conference on Industrial and Information Systems (ICIIS), 481-485, 2016.
    doi:10.1109/ICIINFS.2016.8262988

    14. Baghdadi, N., et al., "Evaluation of radar backscattering models IEM, Oh, and Dubois for SAR data in X-band over bare soils," IEEE Geoscience and Remote Sensing Letters, Vol. 8, 1160-1164, 2011.
    doi:10.1109/LGRS.2011.2158982

    15. Baghdadi, N., et al., "A new empirical model for radar scattering from bare soil surfaces," Remote Sensing, Vol. 8, 920, 2016.
    doi:10.3390/rs8110920

    16. Sekertekin, A., A. Marangoz, and S. Abdikan, "ALOS-2 and Sentinel-1 SAR data sensitivity analysis to surface soil moisture over bare and vegetated agricultural fields," Computers and Electronics in Agriculture, Vol. 171, 1-11, 2020.

    17. Santi, E., et al., "Remote sensing of forest biomass using GNSS reflectometry," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020.

    18. Yuan, Q., et al., "Estimating surface soil moisture from satellite observations using a generalized regression neural network trained on sparse ground-based measurements in the continental U.S.," Journal of Hydrology, Vol. 580, 124351, 2020.

    19. Achieng, K. O., "Modelling of soil moisture retention curve using machine learning techniques: Artificial and deep neural networks vs support vector regression models," Computers & Geosciences, Vol. 133, 104320, 2019.

    20. Mirsoleimani, H., et al., "Bare soil surface moisture retrieval from Sentinel-1 SAR data based on the calibrated IEM and dubois models using neural networks," Sensors, Vol. 19, 3209, 2019.

    21. Kweon, S. and Y. Oh, "Estimation of soil moisture and surface roughness from single-polarized radar data for bare soil surface and comparison with dual- and quad-polarization cases," IEEE Transactions on Geoscience and Remote Sensing, Vol. 52, No. 7, 4056-4064, 2014.

    22. Picard, G., T. Le Toan, and F. Mattia, "Understanding C-band radar backscatter from wheat canopy using a multiple-scattering coherent model," IEEE Transactions on Geoscience and Remote Sensing, Vol. 41, 1583-1591, 2003.

    23. Beckmann, P. and A. Spizzichino, The Scattering of Electromagnetic Waves From Rough Surfaces, 511, Artech House, Inc., Norwood, MA, 1987.

    24. Engman, E. and N. Chauhan, "Status of microwave soil moisture measurements with remote sensing," Remote Sensing of Environment, Vol. 51, 189-198, 1995.

    25. Hallikainen, et al., "Microwave dielectric behavior of wet soil — Part 1: Empirical models and experimental observations," IEEE Transactions on Geoscience and Remote Sensing, Vol. 23, No. 1, 25-34, 1985.

    26. Beauchemin, M., K. Thomson, and G. Edwards, "Modelling forest stands with MIMICS: Implications for calibration," Canadian Journal of Remote Sensing, Vol. 21, 518-526, 1995.

    27. Baghdadi, N., M. Bernier, and R. Neeson, "Evaluation of C-band SAR data for wetlands mapping," International Journal of Remote Sensing, Vol. 22, 71-88, 2001.

    28. Baghdadi, N., N. Holah, and M. Zribi, "Soil moisture estimation using multi-incidence and multi-polarization ASAR SAR data," International Journal of Remote Sensing, Vol. 27, 2006.

    29. Santi, E., et al., "Application of artificial neural networks for the soil moisture retrieval from active and passive microwave spaceborne sensors," International Journal of Applied Earth Observation and Geoinformation, Vol. 48, 2015.