Generation of spatio-temporal information such as land use system and management practices is one of the key ingredients for carrying out the regional level agro-ecosystem modelling. However, at the regional level availability of such data is scarce, where analysis of a cropping system is essential and a pre-requisite for studying the overall sustainability of the agricultural production system. The present investigation was carried out to identify the actually practised cropping pattern and their mapping in Hisar district of Haryana (India) using Multi-Data Approach (MDA). Multi-date sentinel-1 for the rabi season of 2019 was classified using multi-phase unsupervised classification approach and classes of interest were identified. Finally, classified images were subjected to logical combinations which helped in generating crop classification maps and statistics. Results showed that cropping pattern of the district exhibited huge variations and area under wheat was observed to be highest (204.76 thousand ha) in comparison to mustard crop (64.42 thousand ha) and least was under the sugarcane crop (0.97 thousand ha). Some other crops like vegetables and horticultural crops were also identified during this period, but the major crops that were identified during rabi 2019 were wheat and mustard. Hence, regional crop classification using sentinel-1 data appears to be a valuable tool for predicting a specific regions cropping pattern, which is considered to be the most significant aspect of an agricultural production system.
Crop classification, Mapping, Multi-data approach, Remote sensing, Sentinel-1
Badarinath, K.V.S., Kiran Chand, T.R., Prasad and K.V. (2006). Agriculture crop residue burning in Indo-Gangetic Plains – A study using IRS-P6 AWiFS satellite data. Current Science,91(8): 1085–1089.
Bhatta, B. (2008). Remote Sensing and GIS, Oxford University Press.
Blackmore, S., Godwin, R.J. and Fountas, S. (2003). The analysis of spatial and temporal trends in yield map data over six years. Biosystems Engineering, 84(4): 455-466. doi:10.1016/S1537-5110(03)00038-2.
Bramley, R.G.V. (2009). Lessons from nearly 20 years of Precision Agriculture research, development, and adoption as a guide to its appropriate application. Crop and Pasture Science,60: 197- 217. doi:10.1071/CP08304.
Casa, R., Cavalieri, A. and Lo Cascio, B. (2011). Nitrogen fertilization management in precision agriculture: A preliminary application example on maize. Italian Journal of Agronomy, 6(e5): 23-27. doi:10.4081/ija.2011.e5.
Delin, S. and Berglund, K. (2005). Management zones classified with respect to drought and waterlogging. Precision Agriculture, 6: 321- 340. doi:10.1007/s11119-005-2325-4.
Gopalasundaram, P., Bhaskaran A. andRakkiyappan, P. (2012). Integrated nutrient management in sugarcane.Sugar Tech,14: 3–20. doi:10.1007/s12355-011-0097-x.
Lopes, H.L., Candeias, A.L.B., Accioly, L.J.O., Sobral, M. doC. M. and Pacheco, A.P. (2010). Parametros biofísicos na detecçao de mudanças na cobertura e uso do solo em bacias hidrograficas. Revista Brasileira de Engenharia Agrícola e Ambiental, 14 (11): 1210-1219. doi:10.1590/S1415-43662010001100011.
Satyawan, Yadav, M. and Hooda, R.S. (2014). Cropping system analysis using Geospatial Approach: A case study of Sirsa district in Haryana, India.International Journal of Science and Research, 3(9): 113-132.
Sheoran, H.S., Phogat, V.K., Dahiya, R. andKakar, R. (2018). Soil organic carbon stocks and carbon dynamics under organic and conventional farming systems in Indo-Gangetic Plains. Indian Journal of Agricultural Science, 89 (5): 813-20.
Singh, J., Mahapatra, A., Basu, S. and Banerjee, B. (2019). Assessment of Sentinel-1 and Sentinel-2 Satellite Imagery for Crop Classification in Indian Region During Kharif and Rabi Crop Cycles. International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 3720-3723. doi:10.1109/IGARSS.2019.8900491.
Wang, M., Son, S. and Shi, W. (2009). Evaluation of MODIS SWIR and NIR-SWIR atmospheric correction algorithms using SeaBASS data. Remote Sensing of Environment, 113(3): 635–644. doi:10.1016/j.rse.2008.11.005.
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