Skip to main content
SpringerLink
Log in

Acreage estimation of kharif rice crop using Sentinel-1 temporal SAR data

  • Published:
Spatial Information Research Aims and scope Submit manuscript

Abstract

Rice is one of the most important food crop in India covering about one-fourth of the total cropped area. India is the second largest producer and consumer of rice and accounts for 21% of the world’s total rice production. Rice is fundamentally a kharif season crop and grown in mainly rainfed areas. Recently there is a considerable increase in production, area and yield of rice crop in India. Temporal monitoring of crop area under cultivation is essential for the sustainable management of agricultural activities on both national and global levels. The present study is envisaged to estimate area under kharif rice using multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) data with dual polarization (VH and VV) in Bhandara district of Maharashtra. The geographical area of Bhandara district is 4087 square kilometres and lies in between 20°64′03′' to 21°60′18′' N latitude and 79°44′93′' to 80°08′70′' E longitude. The rice area is extracted using Random Forest (RF) classification techniques available in SNAP tool and validated using the ground observation collected from the field. An area of 1760 square kilometres was found under kharif rice out of 4087 square kilometres area of entire Bhandara district. The rice is predominant crop and covered around 43% of the total geographical area of Bhandara district during kharif season. The user accuracy (omission error), producer accuracy (commission error) for rice crop, overall accuracy and Kappa coefficients were 82.7, 90.0, 91% and 0.80, respectively. The study found that SAR data can be successfully used for acreage estimation with RF classifier.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Bhatt, C. K., & Nain, A. S. (2018). Rice acreage estimation using Sentinel-1A dual polarised SAR data in Udham Singh Nagar (Uttarakhand). International Journal of Current Microbiology and Applied Sciences. https://doi.org/10.20546/ijcmas.2018.704.295.

    Article  Google Scholar 

  2. Joshi, M. (2018). Text book of field crops. New Delhi: PHI Learning Private Limited.

    Google Scholar 

  3. Department of Agriculture, Government of Maharashtra. http://krishi.maharashtra.gov.in/Site/Upload/Pdf/Final_advance_est_2018-19.pdf. Accessed 22 September 2020.

  4. Raman, M. G., Kaliaperumal, R., Pazhanivelan, S., & Kannan, B. (2019). Rice area estimation using parameterized classification of Sentinel 1A SAR data. International Archives of the Photogrammetry & Remote Sensing & Spatial Information Scienceshttps://doi.org/10.5194/isprs-archives-XLII-3-W6-141-2019.

  5. Mani, J. K., & Varghese, A. O. (2018). Remote sensing and GIS in agriculture and forest resource monitoring. In G. P. Obi Reddy & S. K. Singh (Eds.), Geospatial Technologies in Land Resources Mapping, Monitoring and Management (pp. 377–400). Switzerland: Springer Nature.

    Chapter  Google Scholar 

  6. Varghese, A. O., Suryavanshi, A., & Joshi, A. K. (2016). Analysis of different polarimetric target decomposition methods in forest density classification using C band SAR data. International Journal of Remote Sensing. https://doi.org/10.1080/01431161.2015.1136448.

    Article  Google Scholar 

  7. Varghese, A. O., & Joshi, A. K. (2015). Polarimetric classification of C-band SAR data for forest density characterization. Current Science, 108(1), 100–106.

    Google Scholar 

  8. Abdikan, S., Sekertekin, A., Ustunern, M., Sanli, F. B., & Nasirzadehdizaji, R. (2018). Backscatter analysis using multi-temporal Sentinel-1 SAR data for crop growth of maize in Konya basin, Turkey. International Archives of the Photogrammetry & Remote Sensing & Spatial Information Scienceshttps://doi.org/10.5194/isprs-archives-XLII-3-9-2018.

  9. Bazzi, H., Baghdadi, N., Hajj, M. E., Zribi, M., Minh, D. H. T., Ndikumana, E., et al. (2019). Mapping paddy rice using sentinel-1 SAR time series in Camargue. France: Remote Sensing. https://doi.org/10.3390/rs11070887.

    Book  Google Scholar 

  10. Beck, H. E., Zimmermann, N. E., McVicar, T. R., Vergopolan, N., Berg, A., & Wood, E. F. (2018). Present and future Köppen-Geiger climate classification maps at 1-km resolution. Scientific Data. https://doi.org/10.1038/sdata.2018.214.

    Article  Google Scholar 

  11. Wang, X., Ge, X., & Li, X. (2012). Evaluation of filters for Envisat ASAR speckle suppression in pasture area. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Scienceshttps://doi.org/10.5194/isprsannals-I-7-341-2012.

  12. Yommy, A. S., Liu, R., & Wu, A. S. (2015). SAR image despeckling using refined lee filter. IEEE Xplore. https://doi.org/10.1109/IHMSC.2015.236.

    Article  Google Scholar 

  13. Breiman, L. (2001). Random forests. Machine Learning. https://doi.org/10.1023/A:1010933404324.

    Article  Google Scholar 

  14. Le Maire, G., Marsden, C., Nouvellon, Y., Grinand, C., Hakamada, R., Stape, J. L., & Laclau, J. P. (2011). MODIS NDVI time-series allow the monitoring of Eucalyptus plantation biomass. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2011.05.017.

    Article  Google Scholar 

  15. Mutanga, O., Adam, E., & Cho, M. A. (2012). High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm. International Journal of Applied Earth Observation and Geoinformation. https://doi.org/10.1016/j.jag.2012.03.012.

    Article  Google Scholar 

  16. Adam, E., Mutanga, O., Odindi, J., & Abdel-Rahman, E. M. (2014). Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: Evaluating the performance of random forest and support vector machines classifiers. International Journal of Remote Sensing. https://doi.org/10.1080/01431161.2014.903435.

    Article  Google Scholar 

  17. Coulston, J. W., Moisen, G. G., Wilson, B. T., Finco, M. V., Cohen, W. B., & Brewer, C. K. (2012). Modeling percent tree canopy cover: a pilot study. Photogrammetric Engineering & Remote Sensing. https://doi.org/10.14358/PERS.78.7.715.

    Article  Google Scholar 

  18. Gessner, U., Machwitz, M., Conrad, C., & Dech, S. (2013). Estimating the fractional cover of growth forms and bare surface in savannas. A multi-resolution approach based on regression tree ensembles. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2012.10.026.

    Article  Google Scholar 

  19. Vuolo, F., & Atzberger, C. (2014). Improving Land Cover Maps in Areas of Disagreement of Existing Products using NDVI Time Series of MODIS-Example for Europe Verbesserung von Landbedeckungskarten in GebietenwidersprüchlicherGrundlagenmitHilfe der NDVI-Zeitreihe von MODIS-Beispielfür Europa. Photogrammetrie-Fernerkundung-Geoinformation. https://doi.org/10.1127/1432-8364/2014/0232.

    Article  Google Scholar 

  20. Li, H., Leung, K. S., Wong, M. H., & Ballester, P. J. (2014). Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: Cyscore as a case study. BMC Bioinformatics. https://doi.org/10.1186/1471-2105-15-291.

    Article  Google Scholar 

  21. Verrelst, J., Rivera, J. P., Veroustraete, F., Muñoz-Marí, J., Clevers, J. G. P. W., Camps-Valls, G., & Moreno, J. (2015). Experimental Sentinel-2 LAI estimation using parametric, non-parametric and physical retrieval methods-A comparison. ISPRS Journal of Photogrammetry and Remote Sensing. https://doi.org/10.1016/j.isprsjprs.2015.04.013.

    Article  Google Scholar 

  22. Dave, R., Haldar, D., Manjunath, K., Dave, V., Chakraborty, M., & Pandey, V. (2019). Identification of cotton crop in Gujarat using multi date RISAT-1 SAR data. Journal of Agrometeorology, 21((Special issue-“NASA 2014” part-III)), 1–6.

    Google Scholar 

  23. McNairn, H., Champagne, C., Shang, J., Holmstrom, D., & Reichert, G. (2009). Integration of optical and synthetic aperture radar (SAR) imagery for delivering operational annual crop inventories. ISPRS Journal of Photogrammetry & Remote Sensing. https://doi.org/10.1016/j.isprsjprs.2008.07.006.

    Article  Google Scholar 

  24. Wang, C. Z., Wu, J. P., Zhang, Y., Pan, G. D., Qi, J. G., & Salas, W. A. (2009). Characterizing L-band scattering of paddy rice in Southeast China with radiative transfer model and multitemporal ALOS/PALSAR imagery. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/TGRS.2008.2008309.

    Article  Google Scholar 

  25. Dong, Y., Sun, G., & Pang, Y. (2006). Monitoring of rice crop using ENVISAT ASAR data. Science in China Series D. https://doi.org/10.1007/s11430-006-0755-0.

    Article  Google Scholar 

  26. Jia, M., Tong, L., Zhang, Y., & Chen, Y. (2013). Multitemporal radar back scattering measurement of wheat fields using multifrequency (L, S, C and X) and full-polarization. Radio Science. https://doi.org/10.1002/rds.20048.

    Article  Google Scholar 

  27. Tan, L., Chen, Y., Jia, M., Tong, L., Li, X., & He, L. (2015). Rice biomass retrieval from advanced synthetic aperture radar image based on radar backscattering measurement. Journal of Applied Remote Sensing. https://doi.org/10.1117/1.JRS.9.097091.

    Article  Google Scholar 

  28. He, Z., Li, S., Wang, Y., Dai, L., & Lin, S. (2018). Monitoring rice phenology based on backscattering characteristics of multi-temporal RADARSAT-2 datasets. Remote Sensing. https://doi.org/10.3390/rs10020340.

    Article  Google Scholar 

  29. FAO. (2018). Rice Market Monitor. Volume XXI(I). Available online: http://www.fao.org/3/I9243EN/i9243en.pdf. Accessed 23 September 2020.

  30. Nguyen, D. B., Gruber, A., & Wagner, W. (2016). Mapping rice extent and cropping scheme in the Mekong Delta using Sentinel-1A data. Remote Sensing Letters. https://doi.org/10.1080/2150704X.2016.1225172.

    Article  Google Scholar 

  31. Ranjan, A. K., & Parida, B. R. (2019). Paddy acreage mapping and yield prediction using sentinel—based optical and SAR data in Sahibganj district. Jharkhand (India): Spatial Information Research. https://doi.org/10.1007/s41324-019-00246-4.

    Book  Google Scholar 

  32. Onojeghuo, A. O., Blackburn, G. A., Wang, Q., Atkinson, P. M., Kindred, D., & Miao, Y. (2018). Mapping paddy rice fields by applying machine learning algorithms to multi-temporal Sentinel-1A and Landsat data. International Journal of Remote Sensing. https://doi.org/10.1080/01431161.2017.1395969.

    Article  Google Scholar 

  33. Son, N.-T., Chen, C.-F., Chen, C.-R., & Minh, V.-Q. (2017). Assessment of Sentinel-1A data for rice crop classification using random forests and support vector machines. Geocarto International. https://doi.org/10.1080/10106049.2017.1289555.

    Article  Google Scholar 

Download references

Acknowledgement

Authors extend their sincere gratitude to Regional Remote Sensing Centre-Central, National Remote Sensing Centre (NRSC), Indian Space Research Organisation (ISRO) and Jawaharlal Nehru Technological University Kakinada for providing opportunity and facility to conduct the study. The technical support provided by RRSC-Central staff is duly acknowledged. We are also thankful to Sentinel Copernicus Science Hub maintained by the European Space Agency (ESA) for online accessibility of Sentinel-1 SAR data. Authors are also thankful to the anonymous reviewers.

Funding

Not applicable.

Author information

Authors and Affiliations

  1. School of Spatial Information Technology, Jawaharlal Nehru Technological University, Kakinada, Andhra Pradesh, 533003, India

    Nandepu V. V. S. S. Teja Subbarao & K. Srinivas

  2. Regional Remote Sensing Centre-Central, National Remote Sensing Centre, Indian Space Research Organisation, Amravati Road, Nagpur, Maharashtra, 440033, India

    Jugal Kishore Mani, Ashish Shrivastava & A. O. Varghese

Authors
  1. Nandepu V. V. S. S. Teja Subbarao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jugal Kishore Mani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ashish Shrivastava
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. K. Srinivas
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. A. O. Varghese
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Nandepu.V. V. S. S. Teja Subbarao, Jugal Kishore Mani and A. O. Varghese formulated and designed this research. Nandepu.V. V. S. S. Teja Subbarao, Jugal Kishore Mani and Ashish Shrivastava carried out the data processing and analysis. Nandepu.V. V. S. S. Teja Subbarao and K. Srinivas wrote the paper. A. O. Varghese provided suggestions and modified the paper. Nandepu.V. V. S. S. Teja Subbarao, Jugal Kishore Mani and A. O. Varghese revised the manuscript draft.

Corresponding author

Correspondence to Nandepu V. V. S. S. Teja Subbarao.

Ethics declarations

Conflicts of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Subbarao, N.V.V.S.S.T., Mani, J.K., Shrivastava, A. et al. Acreage estimation of kharif rice crop using Sentinel-1 temporal SAR data. Spat. Inf. Res. 29, 495–505 (2021). https://doi.org/10.1007/s41324-020-00374-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41324-020-00374-2

Keywords

Search

Navigation