Paddy acreage mapping and yield prediction using sentinel-based optical and SAR data in Sahibganj district, Jharkhand (India)

Avinash Kumar Ranjan1, Bikash Ranjan Parida1
1Department of Land Resource Management, School of Natural Resource Management, Central University of Jharkhand, Ranchi, India

Tóm tắt

Từ khóa


Tài liệu tham khảo

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 .