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2020-10-07
Biophysical Parameters Retrieval and Sensitivity Analysis of Rabi Crops (Mustard and Wheat) from Structural Perspective
By
Progress In Electromagnetics Research C, Vol. 106, 61-75, 2020
Abstract
The sensitivity of dual-polarized Sentinel-1 backscatter towards biophysical parameters (height and biomass) of wheat and mustard crop was investigated. The plant height and biomass observations categorized into three groups, were useful in understanding the sensitivity across a particular biomass and height range whose significance was determined using a statistical measure (student's t-test). The crop parameters were retrieved only for the C-band sensitive biomass (< 5 Kg m-2) and height (< 160 cm for mustard and < 80 cm for wheat) range considering the saturation of signals at advanced crop stages and based on the detailed investigation. The sensitivity towards the mustard plant height becomes very weak as the crop proceeds to a height > 190 cm. A low RMSE (11.50 cm) was observed when the retrieval was done for height < 160 cm. The cross-polarized responses were more sensitive to crop biomass than co-polarized responses mainly due to the dominant depolarization of the transmitted power. An early saturation was found at co-polarized VV (4 Kg m-2) as compared to cross-polarized VH (6 Kg m-2) particularly for planophiles like mustard and little later in the case of erectophile such as wheat. The backscatter response was found to be sensitive at early crop stages for both the crop geometry, and hence retrieval of biophysical parameters at these stages can yield better accuracy than the overall retrieval. The retrieval of wheat height resulted in a low RMSE of 9.25 cm when the retrieval was carried out for crop height < 80 cm. Retrieval was attempted using the simplistic logarithmic model which can find ways in the operational application using wide swath dual-polarized datasets.
Citation
Dipanwita Haldar Abhinav Verma Om Pal , "Biophysical Parameters Retrieval and Sensitivity Analysis of Rabi Crops (Mustard and Wheat) from Structural Perspective," Progress In Electromagnetics Research C, Vol. 106, 61-75, 2020.
doi:10.2528/PIERC20053001
http://www.jpier.org/PIERC/pier.php?paper=20053001
References

1. Ainsworth, T. L., J. P. Kelly, and J. S. Lee, "Classification comparisons between dual-pol, compact polarimetric and quad-pol SAR imagery," ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 64, No. 5, 464-471, 2009.
doi:10.1016/j.isprsjprs.2008.12.008

2. Verma, A. and D. Haldar, "SAR polarimetric analysis for major land covers including pre-monsoon crops," Geocarto International, 1-17, 2019, https://doi.org/10.1080/10106049.2019.1695957.

3. Ballester-Berman, J. D., J. M. Lopez-Sanchez, and J. Fortuny-Guasch, "Retrieval of biophysical parameters of agricultural crops using polarimetric SAR interferometry," IEEE Transactions on Geoscience and Remote Sensing, Vol. 43, No. 4, 683-694, 2005.
doi:10.1109/TGRS.2005.843958

4. Battude, M., A. Al Bitar, D. Morin, J. Cros, M. Huc, C. M. Sicre, V. Le Dantec, and V. Demarez, "Estimating maize biomass and yield over large areas using high spatial and temporal resolution Sentinel-2 like remote sensing data," Remote Sensing of Environment, Vol. 184, 668-681, 2016.
doi:10.1016/j.rse.2016.07.030

5. Chen, K. S., W. P. Huang, D. H. Tsay, and F. Amar, "Classification of multifrequency polarimetric SAR imagery using a dynamic learning neural network," IEEE Transactions on Geoscience and Remote Sensing, Vol. 34, No. 3, 814-820, 1996.
doi:10.1109/36.499786

6. Colombo, R., D. Bellingeri, D. Fasolini, and C. M. Marino, "Retrieval of leaf area index in different vegetation types using high resolution satellite data," Remote Sensing of Environment, Vol. 86, No. 1, 120-131, 2003.
doi:10.1016/S0034-4257(03)00094-4

7. Chakraborty, M., K. R. Manjunath, S. Panigrahy, N. Kundu, and J. S. Parihar, "Rice crop parameter retrieval using multi-temporal, multi-incidence angle Radarsat SAR data," ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 59, No. 5, 310-322, 2005.
doi:10.1016/j.isprsjprs.2005.05.001

8. Chen, J., H. Lin, and Z. Pei, "Application of ENVISAT ASAR data in mapping rice crop growth in Southern China," IEEE Geoscience and Remote Sensing Letters, Vol. 4, No. 3, 431-435, 2007.
doi:10.1109/LGRS.2007.896996

9. Del Frate, F., G. Schiavon, D. Solimini, M. Borgeaud, D. H. Hoekman, and M. A. Vissers, "Crop classification using multiconfiguration C-band SAR data," IEEE Transactions on Geoscience and Remote Sensing, Vol. 41, No. 7, 1611-1619, 2003.
doi:10.1109/TGRS.2003.813530

10. Hoekman, D. H. and B. A. M. Bouman, "Interpretation of C-and X-band radar images over an agricultural area, the Flevoland test site in the Agriscatt-87 campaign," Remote Sensing, Vol. 14, No. 8, 1577-1594, 1993.
doi:10.1080/01431169308953987

11. Haldar, D., A. Das, S. Mohan, O. Pal, R. S. Hooda, and M. Chakraborty, "Assessment of L-band SAR data at different polarization combinations for crop and other landuse classification," Progress In Electromagnetics Research B, Vol. 36, 303-321, 2012.
doi:10.2528/PIERB11071106

12. Haldar, D., C. Patnaik, and M. Chakraborty, "Jute crop discrimination and biophysical parameter monitoring using multi-parametric SAR data in West Bengal, India," Open Access Library Journal, Vol. 1, No. 6, 1, 2014.

13. Haldar, D., M. Chakraborty, K. R. Manjunath, and J. S. Parihar, "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, Vol. 8, 737-744, 2014.
doi:10.5194/isprsarchives-XL-8-737-2014

14. Haldar, D., V. Dave, A. Misra, and B. Bhattacharya, "Radar vegetation index for assessing cotton crop condition using RISAT-1 data," Geocarto International, 1-12, 2018, https://doi.org/10.1080/10106049.2018.1516249.

15. Inoue, Y., T. Kurosu, H. Maeno, S. Uratsuka, T. Kozu, K. Dabrowska-Zielinska, and J. Qi, "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, Vol. 81, No. 2–3, 194-204, 2002.
doi:10.1016/S0034-4257(01)00343-1

16. Kurosu, T., M. Fujita, and K. Chiba, "Monitoring of rice crop growth from space using the ERS-1 C-band SAR," IEEE Transactions on Geoscience and Remote Sensing, Vol. 33, No. 4, 1092-1096, 1995.
doi:10.1109/36.406698

17. Kogan, F., N. Kussul, T. Adamenko, S. Skakun, O. Kravchenko, O. Kryvobok, A. Shelestov, A. Kolotii, O. Kussul, and A. Lavrenyuk, "Winter wheat yield forecasting in Ukraine based on Earth observation, meteorological data and biophysical models," International Journal of Applied Earth Observation and Geoinformation, Vol. 23, 192-203, 2013.
doi:10.1016/j.jag.2013.01.002

18. Kussul, N., S. Skakun, A. Shelestov, and O. Kussul, "The use of satellite SAR imagery to crop classification in Ukraine within JECAM project," 2014 IEEE Geoscience and Remote Sensing Symposium, 1497-1500, IEEE, 2014.
doi:10.1109/IGARSS.2014.6946721

19. Le Toan, T., F. Ribbes, L. F. Wang, N. Floury, K. H. Ding, J. A. Kong, M. Fujita, and T. Kurosu, "Rice crop mapping and monitoring using ERS-1 data based on experiment and modeling results," IEEE Transactions on Geoscience and Remote Sensing, Vol. 35, No. 1, 41-56, 1997.
doi:10.1109/36.551933

20. Li, Y., Q. Liao, X. Li, S. Liao, G. Chi, and S. Peng, "Towards an operational system for regional-scale rice yield estimation using a time-series of Radarsat ScanSAR images," International Journal of Remote Sensing, Vol. 24, No. 21, 4207-4220, 2003.
doi:10.1080/0143116031000095970

21. McNairn, H., K. Hochheim, and N. Rabe, "Applying polarimetric radar imagery for mapping the productivity of wheat crops," Canadian Journal of Remote Sensing, Vol. 30, No. 3, 517-524, 2004.
doi:10.5589/m03-068

22. Nguyen, D. B., A. Gruber, and W. Wagner, "Mapping rice extent and cropping scheme in the Mekong Delta using Sentinel-1A data," Remote Sensing Letters, Vol. 7, No. 12, 1209-1218, 2016.
doi:10.1080/2150704X.2016.1225172

23. Skriver, H., M. T. Svendsen, and A. G. Thomsen, "Multitemporal C-and L-band polarimetric signatures of crops," IEEE Transactions on Geoscience and Remote Sensing, Vol. 37, No. 5, 2413-2429, 1999.
doi:10.1109/36.789639

24. Shao, Y., X. Fan, H. Liu, J. Xiao, S. Ross, B. Brisco, R. Brown, and G. Staples, "Rice monitoring and production estimation using multitemporal RADARSAT," Remote Sensing of Environment, Vol. 76, No. 3, 310-325, 2001.
doi:10.1016/S0034-4257(00)00212-1

25. Salehi, M., A. Mohammadzadeh, and Y. Maghsoudi, "Agricultural crop monitoring using polarimetric interferometric synthetic aperture radar images," Journal of Geomatics Science and Technology, Vol. 8, No. 2, 1-11, 2018.

26. Wu, F., C. Wang, H. Zhang, B. Zhang, and Y. Tang, "Rice crop monitoring in South China with RADARSAT-2 quad-polarization SAR data," IEEE Geoscience and Remote Sensing Letters, Vol. 8, No. 2, 196-200, 2010.
doi:10.1109/LGRS.2010.2055830

27. Yang, H., X. Yang, X. Xu, Z. Gao, C. Li, J. Wang, and C. Zhao, "Potential of fully polarimetric SAR data for crops biophysical parameters retrieval," 2012 First International Conference on Agro- Geoinformatics (Agro-Geoinformatics), 1-5, IEEE, 2012.

28. Zhang, Y., B. Yang, X. Liu, and C. Wang, "Estimation of rice grain yield from dual-polarization Radarsat-2 SAR data by integrating a rice canopy scattering model and a genetic algorithm," International Journal of Applied Earth Observation and Geoinformation, Vol. 57, 75-85, 2017.
doi:10.1016/j.jag.2016.12.014