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Agricultural crop growth biophysical parameters estimation by machine learning using microwave satellite data

Author Affiliations

  • 1Department of Physics, Institute of Science, Banaras Hindu University, Varanasi, India
  • 2Transport Planning and Environment Division, CSIR-Central Road Research Institute, New Delhi, India
  • 3Department of Physics, Indian Institute of Technology (BHU), Varanasi, India
  • 4Department of Physics, Indian Institute of Technology (BHU), Varanasi, India
  • 5Department of Botany, Bhupal Noble′s University Udaipur, India
  • 6Department of Civil Engineering, Indian Institute of Technology Roorkee, India
  • 7Department of Physics, Institute of Science, Banaras Hindu University, Varanasi, India

Int. Res. J. Biological Sci., Volume 8, Issue (10), Pages 1-8, October,10 (2019)


Agriculture sector is the most significant for the Indian economy. So, it becomes necessary to estimate agricultural crop growth biophysical parameters for the proper crop monitoring and forecasting of the crop yield. The objectives of the research are estimation and comparison of wheat crop biophysical parameters using Sentinel-1A images by linear kernel based support vector regression (SVR) model, radial basis function based artificial neural network regression (ANNR) machine learning model and linear regression (LR) model. The ground samples of wheat crop growth biophysical parameters like as leaf area index (LAI), plant height (PH), fresh biomass (FB), dry biomass (DB), and vegetation water content (VWC) were collected during 8 January 2015 to 29 April 2015. The estimated results are statistically analysed and compared by coefficient of determination (R2), Nash Sutcliffe efficiency (NSE), %bias and by the analysis of root mean square error (RMSE). Overall good results such as R2 = 0.919, %bias = 1.153, NSE = 0.925 and RMSE = 0.661 values were found for the estimation of VWC using ANNR model. Whereas, LR model performed poorly for the PH estimation with the R2 = 0.662, %bias = 5.638, NSE = 0.627 and RMSE = 18.347 values at C-band. The SVR and ANNR models are found more suitable for wheat crop biophysical parameters estimation in compare to the LR modeling approach. The outcomes of the present study by different models may offer the valuable information for accurate monitoring of multiple crops in future for better crop production.


  1. Jiao X., Kovacs J.M., Shang J., McNairn H., Walters D., Ma B. and Geng X. (2014)., Object-oriented crop mapping and monitoring using multi-temporal polarimetric RADARSAT-2 data., ISPRS Journal of Photogrammetry and Remote Sensing, 96, 38-46.
  2. Kumar P., Gupta D.K., Mishra V.N. and Prasad R. (2015)., Comparison of support vector machine, artificial neural network, and spectral angle mapper algorithms for crop classification using LISS IV data., International Journal of Remote Sensing, 36(6), 1604-1617.
  3. Kumar P., Prasad R., Choudhary A., Mishra V.N., Gupta D. K. and Srivastava P.K. (2017)., A statistical significance of differences in classification accuracy of crop types using different classification algorithms., Geocarto International, 32(2), 206-224.
  4. McNairn H., Ellis J., Van Der Sanden J.J., Hirose T. and Brown R.J. (2002)., Providing crop information using RADARSAT-1 and satellite optical imagery., International Journal of Remote Sensing, 23(5), 851-870.
  5. Blaes X., Vanhalle L. and Defourny P. (2005)., Efficiency of crop identification based on optical and SAR image time series., Remote sensing of environment, 96(3-4), 352-365.
  6. Haldar D., Chakraborty M., Manjunath K.R. and Parihar J. S. (2014)., Role of polarimetric SAR data for discrimination/biophysical parameters of crops based on canopy architecture., The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 8, 737-744.
  7. Mishra V.N., Kumar P., Gupta D.K. and Prasad R. (2014)., Classification of various land features using RISAT-1 dual polarimetric data., The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(8), 833.
  8. Kumara P., Prasad R., Gupta D.K., Vishwakarma A.K. and Choudhary A. (2017)., Retrieval of rice crop growth variables using multi-temporal RISAT-1 remotely sensed data., Russian agricultural sciences, 43(6), 461-465.
  9. Prevot L., Dechambre M., Taconet O., Vidal-Madjar D., Normand M. and Gallej S. (1993)., Estimating the characteristics of vegetation canopies with airborne radar measurements., International Journal of Remote Sensing, 14(15), 2803-2818.
  10. Maity S., Patnaik C., Chakraborty M. and Panigrahy S. (2004)., Analysis of temporal backscattering of cotton crops using a semiempirical model., IEEE Transactions on Geoscience and Remote Sensing, 42(3), 577-587.
  11. Cuizhen W., Jiaping W., Yuan Z., Guangdong P. and Jiaguo Q. (2009)., Characterizing L-band scattering of paddy rice in southeast China with radiative transfer model and multi-Temporal ALOS/PALSAR imagery., IEEE Trans Geosci Remote Sens., 47, 988-998.
  12. Oh Y., Hong S.Y., Kim Y., Hong J.Y. and Kim Y.H. (2009)., Polarimetric backscattering coefficients of flooded rice fields at L-and C-bands: Measurements, modeling, and data analysis., IEEE Transactions on Geoscience and Remote Sensing, 47(8), 2714-2721.
  13. Kim Y., Jackson T., Bindlish R., Hong S., Jung G. and Lee K. (2013)., Retrieval of wheat growth parameters with radar vegetation indices., IEEE Geoscience and Remote Sensing Letters, 11(4), 808-812.
  14. Picard G., Le Toan T. and Mattia F. (2003)., Understanding C-band radar backscatter from wheat canopy using a multiple-scattering coherent model., IEEE Transactions on Geoscience and Remote Sensing, 41(7), 1583-1591.
  15. Inoue Y., Kurosu T., Maeno H., Uratsuka S., Kozu T., Dabrowska-Zielinska K. and Qi J. (2002)., Season-long daily measurements of multifrequency (Ka, Ku, X, C, and L) and full-polarization backscatter signatures over paddy rice field and their relationship with biological variables., Remote Sensing of Environment, 81(2-3), 194-204.
  16. Brakke T.W., Kanemasu E.T., Steiner J.L., Ulaby F.T. and Wilson E. (1981)., Microwave response to canopy moisture, leaf area index, and dry weight of wheat, corn and sorghum., Remote Sens Environ., 11, 207-220.
  17. Kumar P., Prasad R., Gupta D.K., Mishra V.N. and Choudhary A. (2015)., Support vector machine for classification of various crop using high resolution LISS-IV imagery., Bull. Environ. Sci. Res., 4, 1-5.
  18. Vapnik V.N. (1995)., The Nature of Statistical Learning Theory., Springer-Verlag, New York.
  19. Durbha S.S., King R.L. and Younan N.H. (2007)., Support vector machines regression for retrieval of leaf area index from multiangle imaging spectroradiometer., Remote Sensing of Environment, 107(1-2), 348-361.
  20. Wang L.A., Zhou X., Zhu X., Dong Z. and Guo W. (2016)., Estimation of biomass in wheat using random forest regression algorithm and remote sensing data., The Crop Journal, 4(3), 212-219.
  21. Kumar P., Prasad R., Mishra V.N., Gupta D.K., Choudhary A. and Srivastava P.K. (2015)., Artificial neural network with different learning parameters for crop classification using multispectral datasets., International conference on microwave, optical and communication engineering, December 18-20, IIT Bhubaneswar, India.
  22. Kumar P., Prasad R., Mishra V.N., Gupta D.K. and Singh S.K. (2016)., Artificial neural network for crop classification using C-band RISAT-1 satellite datasets., Russian agricultural sciences, 42(3-4), 281-284.
  23. Gupta D.K., Kumar P., Mishra V.N., Prasad R., Dikshit P.K.S., Dwivedi S.B., Ohri A., Singh R.S. and Srivastava V. (2015)., Bistatic measurements for the estimation of rice crop variables using artificial neural network., Adv. Space Res., 55, 1613-1626.
  24. Gupta D.K., Prasad R., Kumar P. and Mishra V.N. (2015)., Estimation of crop variables using bistatic scatterometer data and artificial neural network trained by empirical models., Comput Electron Agric., 123, 64-73.
  25. Pandey A., Jha S.K. and Prasad R. (2010)., Retrieval of crop parameters of spinach by radial basis neural network approach using X-band scatterometer data., Russian Agri Sci., 36, 312-315.
  26. Prasad R., Kumar R. and Singh D. (2009)., A radial basis function approach to retrieve soil moisture and crop variables from X-band scatterometer observations., Progress In Electromagnetics Research, 12, 201-217.
  27. Pandey A., Thapa K.B., Prasad R. and Singh K.P. (2012)., General regression neural network and radial basis neural network for the estimation of crop variables of lady finger., Journal of the Indian Society of Remote Sensing, 40(4), 709-715.
  28. Pandey A. and Mishra A. (2017)., Application of artificial neural networks in yield prediction of potato crop., Russian Agricultural Sciences, 43(3), 266-272.
  29. Kussul N., Lemoine G., Gallego F.J., Skakun S.V., Lavreniuk M. and Shelestov A.Y. (2016)., Parcel-based crop classification in ukraine using landsat-8 data and sentinel-1A data., IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(6), 2500-2508.
  30. Navarro A., Rolim J., Miguel I., Catalão J., Silva J., Painho M. and Vekerdy Z. (2016)., Crop monitoring based on SPOT-5 take-5 and Sentinel-1A data for the estimation of crop water requirements., Remote Sensing, 8(6), 525.
  31. Kumar P., Prasad R., Choudhary A., Gupta D.K., Mishra V. N., Vishwakarma A.K. and Srivastava P.K. (2019)., Comprehensive evaluation of soil moisture retrieval models under different crop cover types using C-band synthetic aperture radar data., Geocarto International, 34(9), 1022-1041. doi.org/10./10106049.2018.1464601. pp. 1-20.
  32. Vapnik V., Golowich S.E. and Smola A.J. (1997)., Support vector method for function approximation, regression estimation and signal processing., In Advances in neural information processing systems, 281-287.
  33. Anandhi V. and Chezian R.M. (2013)., Support vector regression to forecast the demand and supply of pulpwood., International journal of future computer and communication, 2, 266-269.
  34. Powell M.J.D. (1987)., Radial basis functions for multivariable interpolation: A review, in Algorithms for Approximarion., J.C. Mason and M.G. Cox, Eds. Oxford, 143-167.
  35. CHEN S.C.C.F.N. and Grant P.M. (1991)., Orthogonal Least Squares Learning Algorithm for Radial Basis Function Networks., IEEE Transactions on Neural Networks, 2(2), 302-309.
  36. Powell M.J.D. (1987)., Radial basis function approximations to polynomials., in Proc. 12th Biennial Numerical Analysis Conf. (Dundee), 223-241.
  37. Gupta D.K., Prasad R., Kumar P. and Vishwakarma A.K. (2017)., Soil moisture retrieval using ground based bistatic scatterometer data at X-band., Adv. Space Res., 59, 996-1007.
  38. Shataee S., Kalbi S., Fallah A. and Pelz D. (2012)., Forest attribute imputation using machine-learning methods and ASTER data: comparison of k-NN, SVR and random forest regression algorithms., International journal of remote sensing, 33(19), 6254-6280.
  39. Nash J.E. and Sutcliffe J.V. (1970)., River flow forecasting through conceptual models part I—A discussion of principles., Journal of hydrology, 10(3), 282-290.
  40. McRoberts R.E. (2009)., Diagnostic tools for nearest neighbors techniques when used with satellite imagery., Remote Sensing of Environment, 113(3), 489-499.
  41. Moser G. and Serpico S.B. (2009)., Automatic parameter optimization for support vector regression for land and sea surface temperature estimation from remote sensing data., IEEE Transactions on Geoscience and Remote Sensing, 47(3), 909-921.
  42. Ali I., Greifeneder F., Stamenkovic J., Neumann M. and Notarnicola C. (2015)., Review of machine learning approaches for biomass and soil moisture retrievals from remote sensing data., Remote Sensing, 7(12), 16398-16421.
  43. Inoue Y., Kurosu T., Maeno H., Uratsuka S., Kozu T., Dabrowska-Zielinska K. and Qi J. (2002)., Season-long daily measurements of multifrequency (Ka, Ku, X, C, and L) and full-polarization backscatter signatures over paddy rice field and their relationship with biological variables., Remote Sensing of Environment, 81(2-3), 194-204.