Abstract
Rice is an important staple food for the billions of world population. Mapping the spatial distribution of paddy and predicting yields are crucial for food security measures. Over the last three decades, remote sensing techniques have been widely used for monitoring and management of agricultural systems. This study has employed Sentinel-based both optical (Sentinel-2B) and SAR (Sentinel-1A) sensors data for paddy acreage mapping in Sahibganj district, Jharkhand during the monsoon season in 2017. A robust machine learning Random Forest (RF) classification technique was deployed for the paddy acreage mapping. A simple linear regression yield model was developed for predicting yields. The key findings showed that the paddy acreage was about 68.3–77.8 thousand hectares based on Sentinel-1A and 2B satellite data, respectively. Accordingly, the paddy production of the district was estimated as 108–126 thousand tonnes. The paddy yield was predicted as 1.60 tonnes/hectare. The spatial distribution of paddy based on RF classifier and accuracy assessment of LULC maps revealed that the SAR-based classified paddy map was more consistent than the optical data. Nevertheless, this comprehensive study concluded that the SAR data could be more pronounced in acreage mapping and yield estimation for providing timely information to decision makers.
Similar content being viewed by others
References
DES. (2018). Directorate of Economics & Statistics, DAC&FW (DES) (2018). Agricultural Statistics at a Glance. Ministry of Agriculture, Government of India. Available online: https://eands.dacnet.nic.in. Accessed 2 Jan 2019.
Elert, E. (2014). Rice by the numbers: A good grain. Nature, 514(7524), S50–S51. https://doi.org/10.1038/514S50a.
Mosleh, M. K., Hassan, Q. K., & Chowdhury, E. H. (2016). Development of a remote sensing-based rice yield forecasting model. Spanish Journal of Agriculture Research, 14(3), e0907. https://doi.org/10.5424/sjar/2016143-8347.
Nagy, A., Fehér, J., & Tamás, J. (2018). Wheat and maize yield forecasting for the Tisza river catchment using MODIS NDVI time series and reported crop statistics. Computers and Electronics in Agriculture, 151, 41–49. https://doi.org/10.1016/j.compag.2018.05.035.
Murthy, C. S., Thiruvengadachari, S., Jonna, S., & Raju, P. V. (1997). Design of crop cutting experiments with satellite data for crop yield estimation in irrigated command areas. Geocarto International, 12(2), 5–11. https://doi.org/10.1080/10106049709354580.
You, X., Meng, J., Zhang, M., & Dong, T. (2013). Remote sensing based detection of crop phenology for agricultural zones in China using a new threshold method. Remote Sensing, 5(7), 3190–3211. https://doi.org/10.3390/rs5073190.
Blaes, X., Vanhalle, L., & Defourny, P. (2005). Efficiency of crop identification based on optical and SAR image time series. Remote Sensing of Environment, 96(3–4), 352–365. https://doi.org/10.1016/j.rse.2005.03.010.
Wan, S., Lei, T. C., & Chou, T. Y. (2010). An enhanced supervised spatial decision support system of image classification: Consideration on the ancillary information of paddy rice area. International Journal of Geographical Information Science, 24(4), 623–642. https://doi.org/10.1080/13658810802587709.
Zhao, Q., Lenz-Wiedemann, V., Yuan, F., Jiang, R., Miao, Y., Zhang, F., et al. (2015). Investigating within-field variability of rice from high resolution satellite imagery in Qixing Farm County, Northeast China. ISPRS International Journal of Geo-Information, 4(1), 236–261. https://doi.org/10.3390/ijgi4010236.
Mishra, N., Singh, R. K., Kumar, A., & Jeyaseelan, A. (2017). Rice cultivation monitoring and acreage estimation using RADARSAT SAR images in Jharkhand. SGVU Journal of ClimateChange and Water, 4, 1–8.
Mansaray, L. R., Zhang, D., Zhou, Z., & Huang, J. (2017). Evaluating the potential of temporal Sentinel-1A data for paddy rice discrimination at local scales. Remote Sensing Letters, 8(10), 967–976. https://doi.org/10.1080/2150704X.2017.1331472.
Choudhury, I., & Chakraborty, M. (2006). SAR signature investigation of rice crop using RADARSAT data. International Journal of Remote Sensing, 27(3), 519–534. https://doi.org/10.1080/01431160500239172.
Shen, S., Yang, S., Li, B., Tan, B., Li, Z., & Le Toan, T. (2009). A scheme for regional rice yield estimation using ENVISAT ASAR data. Science in China, Series D: Earth Sciences, 52(8), 1183–1194. https://doi.org/10.1007/s11430-009-0094-z.
Haldar, D., & Patnaik, C. (2010). Synergistic use of multi-temporal Radarsat SAR and AWiFS data for Rabi rice identification. Journal of the Indian Society of Remote Sensing, 38(1), 153–160. https://doi.org/10.1007/s12524-010-0006-x.
Kastens, J., Kastens, T., Kastens, D., Price, K., Martinko, E., & Lee, R. (2005). Image masking for crop yield forecasting using AVHRR NDVI time series imagery. Remote Sensing of Environment, 99(3), 341–356. https://doi.org/10.1016/j.rse.2005.09.010.
Huang, J., Wang, X., Li, X., Tian, H., & Pan, Z. (2013). Remotely sensed rice yield prediction using multi-temporal NDVI data derived from NOAA’s-AVHRR. PLoS ONE, 8(8), e70816. https://doi.org/10.1371/journal.pone.0070816.
Mkhabela, M. S., Bullock, P., Raj, S., Wang, S., & Yang, Y. (2011). Crop yield forecasting on the Canadian Prairies using MODIS NDVI data. Agricultural and Forest Meteorology, 151(3), 385–393. https://doi.org/10.1016/j.agrformet.2010.11.012.
Nuarsa, I. W., Nishio, F., Nishio, F., Hongo, C., & Hongo, C. (2011). Relationship between rice spectral and rice yield using modis data. Journal of Agricultural and Science. https://doi.org/10.5539/jas.v3n2p80.
Panda, S. S., Hoogenboom, G., & Paz, J. O. (2010). Remote sensing and geospatial technological applications for site-specific management of fruit and nut crops: A review. Remote Sensing, 2(8), 1973–1997. https://doi.org/10.3390/rs2081973.
Johnson, D. M. (2014). An assessment of pre- and within-season remotely sensed variables for forecasting corn and soybean yields in the United States. Remote Sensing of Environment, 141, 116–128. https://doi.org/10.1016/j.rse.2013.10.027.
Haldar, A. K., Srivastava, R., Thampi, C. J., Sarkar, D., Singh, D. S., Sehgal, J., et al. (1996). Soils of Bihar for optimizing land use (Soils of India Series) (pp. 1–70). Nagpur: National Bureau of Soil Survey and Land Use Planning.
GARMIN Manuals for eTrex® 30. (2018). Available online: https://static.garmin.com/pumac/eTrex_10_20x_30x_OM_EN.pdf. Accessed 2 Jan 2019.
NRSC. (2014). Land use/land cover database on 1:50,000 scale, Natural Resources Census Project, LUCMD, LRUMG, RSAA, National Remote Sensing Centre, ISRO, Hyderabad (NRSC), Hyderabad.
Friedl, M. A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N., Sibley, A., et al. (2010). MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sensing of Environment, 114(1), 168–182. https://doi.org/10.1016/j.rse.2009.08.016.
Parida, B. R., & Ranjan, A. K. (2019). Wheat acreage mapping and yield prediction using Landsat 8-OLI satellite data: A case study in Sahibganj province, Jharkhand (India). Remote Sensing and Earth Systems Science (under review).
Ho, T. K. (1995). Random Decision Forests. In Proceedings of the 3rd international conference on document analysis and recognition (pp. 278–282). Montreal, QC, 14–16 August 1995.
Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1), 35–46. https://doi.org/10.1016/0034-4257(91)90048-B.
Wang, Wenjie, Li, Weidong, Zhang, Chuanrong, & Zhang, Weixing. (2018). Improving object-based land use/cover classification from medium resolution imagery by Markov chain geostatistical post-classification. Land, 7(1), 31. https://doi.org/10.3390/land7010031.
Ranjan, A. K., Anand, A., Vallisree, S., & Singh, K. R. (2016). LU/LC change detection and forest degradation analysis in Dalma Wildlife Sanctuary using 3S technology: A case study in Jamshedpur-India. AIMS Geosciences, 2(4), 273–285. https://doi.org/10.3934/geosci.2016.4.273.
Rao, P. P. N., & Rao, V. R. (1987). Rice crop identification and area estimation using remotely-sensed data from Indian cropping patterns. International Journal of Remote Sensing, 8(4), 639–650. https://doi.org/10.1080/01431168708948670.
Nuarsa, I. W., Nishio, F., & Hongo, C. (2011). Rice yield estimation using landsat ETM+ data and field observation. Journal of Agricultural Science. https://doi.org/10.5539/jas.v4n3p45.
Son, N. T., Chen, C. F., Chen, C. R., Minh, V. Q., & Trung, N. H. (2014). A comparative analysis of multi-temporal MODIS EVI and NDVI data for large-scale rice yield estimation. Agricultural and Forest Meteorology, 197, 52–64. https://doi.org/10.1016/j.agrformet.2014.06.007.
Ok, A. O., Akar, O., & Gungor, O. (2012). Evaluation of random forest method for agricultural crop classification. European Journal of Remote Sensing, 45(1), 421–432. https://doi.org/10.5721/EuJRS20124535.
Sonobe, R., Tani, H., Wang, X., Kobayashi, N., & Shimamura, H. (2014). Random forest classification of crop type using multi-temporal TerraSAR-X dual-polarimetric data. Remote Sensing Letters, 5(2), 157–164. https://doi.org/10.1080/2150704X.2014.889863.
Tatsumi, K., Yamashiki, Y., Canales Torres, M. A., & Taipe, C. L. R. (2015). Crop classification of upland fields using Random forest of time-series Landsat 7 ETM+ data. Computers and Electronics in Agriculture, 115, 171–179. https://doi.org/10.1016/j.compag.2015.05.001.
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.
Lasko, K., Vadrevu, K. P., Tran, V. T., & Justice, C. (2018). Mapping double and single crop paddy rice with Sentinel-1A at varying spatial scales and polarizations in Hanoi, Vietnam. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(2), 498–512. https://doi.org/10.1109/jstars.2017.2784784.
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, 39(4), 1042–1067. https://doi.org/10.1080/01431161.2017.1395969.
Karila, K., Nevalainen, O., Krooks, A., Karjalainen, M., & Kaasalainen, S. (2014). Monitoring changes in rice cultivated area from SAR and optical satellite images in Ben Tre and Tra Vinh Provinces in Mekong Delta, Vietnam. Remote Sensing, 6(5), 4090–4108. https://doi.org/10.3390/rs6054090.
Acknowledgements
This research was supported by the Science and Engineering Research Board (SERB), Department of Science & Technology (DST) project grant no. YSS/2015/000801. Authors thanks to USGS and ASF for providing Sentinel-2B and Sentinel-1A satellite data. Authors also thank to anonymous reviewers for their constructive comment and suggestions.
Author information
Authors and Affiliations
Contributions
Conceived, designed research, analyzed data, and wrote the manuscript: AKR and BRP.
Corresponding author
Ethics declarations
Conflict of interest
Authors declare no potential conflict of interest.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Ranjan, A.K., Parida, B.R. Paddy acreage mapping and yield prediction using sentinel-based optical and SAR data in Sahibganj district, Jharkhand (India). Spat. Inf. Res. 27, 399–410 (2019). https://doi.org/10.1007/s41324-019-00246-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s41324-019-00246-4