##plugins.themes.bootstrap3.article.main##

Chinnu Raju K Ajith B Ajithkumar S Anitha V Divya Vijayan

Abstract

Proper calculation of rice cultivation area well before harvest is critical for projecting rice yields and developing policies to assure food security. This research looks at how Remote Sensing (RS) and Geographic Information System (GIS) can be used to map rice fields in Palakkad district of Kerala. The area was delineated using three multi-temporal cloud free Sentinel-2 data with 10 m spatial resolution, matching to crop's reproductive stage during mundakan season (September-October to December-January), 2020-21. To make classification easier, the administrative boundary of district was placed over the mosaicked image. The rice acreage estimation and land use classification of the major rice tract of Palakkad district comprising five blocks was done using Iterative Self-Organisation Data Analysis Technique (ISODATA) unsupervised classification provision in ArcGIS 10.1 software, employing False Colour Composite (FCC) including Blue (B2), Green (B3), Red (B4) and Near-infrared (B8) Bands of Sentinel-2 images. The classification accuracy was determined by locating a total of 60 validation points throughout the district, comprising 30 rice and 30 non-rice points. The total estimated area was 24742.76 ha, with an average accuracy of 88.33% and kappa coefficient 0.766 in five blocks of Palakkad district. The information generated will be helpful in assessing the anticipated production as well as the water demand of the rice fields.

##plugins.themes.bootstrap3.article.details##

##plugins.themes.bootstrap3.article.details##

Keywords

Area mapping, Remote sensing, Rice, Sentinel-2 images, Unsupervised classification

References
Abraham, M.P. (2019). Paddy cultivation in Kerala: A trend analysis of area, production and productivity at district level (1980-81 to 2012-13). Retrieved January 2, 2022. https://keralaeconomy.com.
Ajith, K., Geethalakshmi, V., Raghunath, K.P., Pazhanivelan, S. & Panneerselvam, S. (2017). Rice acreage estimation in Thanjavur, Tamil Nadu using Landsat 8 OLIIMAGES and GIS techniques. International Journal of Current Microbiology and Applied Sciences, 6(7), 2327-2335. doi.org/10.20546/ijcmas.2017.607.275.
Chen, C. F., Son, N.T. & Chen, C.R. (2019). Rice crop mapping using time-series Sentinel-2 data. Geophysical Research Abstracts, 21, EGU2019-7974. https://ui.adsabs.harvard.edu/abs/2019EGUGA..21.7974C
Cohen, J. (1960). A coefficient of agreement for nominal scale. Educational and Psychological Measurement, 20, 37-46. doi:10.1177/001316446002000104
Dadhwal, V.K. & Ray, S.S. (2000). Crop assessment using remote sensing - Part II: Crop condition and yield assessment. Indian Journal of Agricultural Economics, 55(2), 54-67. doi.10.22004/ag.econ.297744
Ferrant, S., Selles, A., Le Page, M., Herrault, P.A., Pelletier, C., Al-Bitar, A., Mermoz, S., Gascoin, S., Bouvet, A., Saqalli, M., Dewandel, B., Caballero, Y., Ahmed, S., Maréchal, J.C. & Kerr, Y. (2017). Detection of irrigated crops from Sentinel-1 and Sentinel-2 data to estimate seasonal groundwater use in south India. Remote Sensing, 9(11), 1119. doi.org/10.3390/rs9111119
Kamal, M., Schulthess, U., Krupnik, T.J. (2020). Identification of mung bean in a smallholder farming setting of coastal south Asia using manned aircraft photography and Sentinel-2 images. Remote Sensing, 12, 3688. doi:10.3390/rs12223688.
Mosleh, M.K., Hassan, Q.K. & Chowdhury, E.H. (2015). Application of remote sensors in mapping rice area and forecasting its production: A review. Sensors, 15, 769-791. doi: 10.3390/s150100769.
Mostafa, K.K.M. (2015). Use of GIS and Remote sensing in mapping rice areas and forecasting it’s production at large geographical extent. Ph.D. thesis, University of Calgary, Canada. doi.org/10.11575/PRISM/28608
Munghemezulu, Z.M., Chirima, G.J. & Munghemezulu, C. (2021). Delineating smallholder maize farms from Sentinel-1 coupled with Sentinel-2 data using machine learning. Sustainability, 13, 4728. doi.org/10.3390/su130947.
Persello, C., Tolpekin, V.A., Bergado, J.R. & de By, R.A. (2019). Delineation of agricultural fields in smallholder farms from satellite images using fully convolutional networks and combinatorial grouping. Remote Sensing of Environment, 231, 111253-111267. doi.org/10.1016/j.rse.2019.111253.
Raza, S.M.H., Mahmood, S.A. & Khan, A.A. (2018). Delineation of potential sites for rice cultivation through multi-criteria evaluation (MCE) using remote sensing and GIS. International Journal of Plant Production, 12, 1-11. doi.org/10.1007/s42106-017-0001-z.
Sethi, R.R., Sahu, A.S., Kaledhonkar, M.J., Sarangi, A., Rani, P., Kumar, A. & Mandal, K.G. (2014). Quantitative determination of rice cultivated areas using geospatial techniques. IOSR Journal of Environmental Science, Toxicology and Food Technology, 8(4), 76-81. http://www.iosrjournals.org/iosr-jestft/pages/8(4)Version-2.html
Stephen, V.S. (1997). Selecting and interpreting measures of thematic classification accuracy. Remote Sensing of Environment, 62(1), 77-89. doi.org/10.1016/S0034-4257(97)00083-7.
Xiao, W., Xu, S. & He, T. (2021). Mapping paddy rice with Sentinel-1/2 and phenology-object-based algorithm-A implementation in Hangjiahu plain in China using GEE platform. Remote Sensing, 13, 990. doi.org/10.3390/rs13050990.
Section
Research Articles

How to Cite

Rice area mapping in Palakkad district of Kerala using Sentinel-2 data and Geographic information system technique. (2022). Journal of Applied and Natural Science, 14(4), 1360-1366. https://doi.org/10.31018/jans.v14i4.3898