Detection of rice and soybean grown fields and their related cultivation area using Sentinel-2 satellite images in summer cropping patterns to analyze temporal changes in their cultivation area (Case study: four watershed basins of Golestan Province)

Document Type : Complete scientific research article


1 عضو هیات علمی

2 دانشگاه علوم کشاورزی و منابع طبیعی گرگان


Background and Objectives: In Golestan province, the suitability of climatic condition to produce most of the agricultural products has led to high diversity in crop production, so this province has the first rank in terms of cultivating and producing oilseeds, especially soybean, in Iran. This research was carried out at four major watershed basins of Golestan province, Mohammad Abad, Qaresoo, Zaringol, and Gharnabad. This study was aimed to estimate the area under rice- and soybeans-cultivation in the aforementioned watershed basins. For this, Sentinel2 satellite images were used for the first time using different supervised classification methods (Maximum likelihood, the minimum distance of average and the Mahalanobis distance).
Materials and Methods:In this study, two Sentinel-2 satellite images of August and September of 2016 were used to identify, detect and evaluate the cultivated area of rice and soybean as two summer crops. This research was carried out at four watershed basins of Golestan Province (Mohammad Abad, Qaresoo, Zaringol, and Gharnabad). Radiometric, atmospheric, and geometric corrections were made after downloading the images of the study area. Then, band compounds, pseducolor combinations, image mosaics and rational band calculations were carried out, and the NDVI vegetation index was used to detect vegetation cover from other land uses, and finally, a land use map and crop layer was produced.
Results: results of this study showed that the soybean cultivation area which is an alternative plant for rice in summer cropping, has decreased compared to past years. In the present study, two Sentinel-2 satellite images of August and September of 2016 were used to identify, detect and evaluate the cultivated area of rice and soybean as two summer crops in four watershed basins of Golestan province. To compare the outputs of the three classification methods, training and test samples were used. In order to evaluate the accuracy of the classification results, the generated map was analyzed using the GPS-registered ground control point .The Maximum likelihood classification with kappa coefficient and overall accuracy of 92% and 95.5% was selected as the superior method for rice. In this method, the rice cultivation area was estimated 32911 hectares with a 18% bias compared to the Agricultural Jihad statistics (27839 hectares). Whereas for soybean, the minimum distance method with kappa coefficient and the overall accuracy of 88% and 95.2% was selected as superior classification method. Based on the results, the soybean cultivation area was estimated as 28359 hectares, with a bias of 13%, compared to the Agricultural Jihad statistics (25083 hectares).
Conclusion: Sentinel2 satellite images have a high potential for quick land detection and providing crops cultivation area maps in a regional scale. Also, the rice cultivation area has been increased compared to past years, while has been decreased for soybean.


1.Ahmad, A., and Quegan, S. 2013.Comparative analysis of supervised andunsupervised classification onmultispectral data. Applied MathematicalSciences, 7: 74. 3681-3694.
2.Ahmadpour, A., Soleimani, K., Shokri,M., and Qarbati, J. 2011 Comparison ofThree Commonly Used SupervisoryClassifications of Satellite Data inVegetation, Rem. Sens. Appl. Mag. GIS
Natur. Resour. J. 2: 2. 69-81. (In Persian)
3.Alipur, F., Aghkhani, M., Abbaspourfard,M., and Sepehr, AS. 2014. Separationof agricultural products by ETM+satellite images (Case study: AstanQuds Razavi sample farm). Agric.Machin. J. 4: 2. 244-254. (In Persian)
4.Allen, R.G., Tasumi, M., Trezza, R.,Waters, R., and Bastiaanssen, W.2002. SEBAL (Surface Energy BalanceAlgorithms for Land). Advance Trainingand User's Manual-Idaho Implementation,version1.
5.Bani Aghil, A.S., Rahemi Karizaki, A.,Bayatani, A., and Faramarzi, H. 2015.Investigation of susceptible soybeanregions based on climate indicators inGolestan province. J. Appl. Res. Plant
Ecophysiol. 2: 2. 19-32. (In Persian)
6.Bani Aghil, A.S., Rahimi Karizaki, A.,Bayatani, A., and Faramarzi, H. 2015.Study of Soybean Susceptible Areas inGolestan Province Based on ClimaticIndices. J. Appl. Veg. Eco Physiol.
Second Course Second Issue. Pp: 19-32.
7.Baret, F., and Guyot, G. 1991. Potentialsand limits of vegetation indices for LAIand APAR assessment. Remote sensingof environment. 35: 3. 161-173.
8.Firozinejad, M., Torahi, A., andAbdolkhani, A. 2012. Comparison ofclassification algorithms in land usemapping (Case study: Woodlands ofMaroon in Behbahan). The First NationalConference on Sustainable DevelopmentStrategies. Tehran. (In Persian)
9.Kamusoko, C., and Aniya, M. 2007. Landuse cover change and landscapefragmentation analysis in the BinduraDistrict, Zimbabwe. Land degradationand development. 18: 2. 221-233.
10.Kazemi, H., Tahmasebi Sarvestani, Z.,Kamkar, B., Shataee, Sh., and Sadeghi,S. 2012. Agro ecological zoning ofagricultural lands in Golestan provincefor canola cultivation by Geographic
Information System (GIS) andAnalytical Hierarchy Process (AHP).Elec. J. Crop Prod. 5: 123-139.
(In Persian)
11.Kazemi, H., Tahmasebi Sarvestani, Z.,Kamkar, B., Shataei, Sh., and Sadeghi,S. 2013. Agro-ecological zoning ofGolestan province Lands for Soybeancultivation using geographical informationsystem (GIS). J. Agr. Know Sustain.Prod. 23: 4. 22-40. (In Persian)
12.Khajehpour, M.R. 2012. Industrial Plants.Jihad University Press (Isfahan University
of Technology). 580p. (In Persian)
13.Khajehpour, M.R. 2009. Principlesand Fundamentals of Crop Production.Third edition, Jihad University Press(Isfahan University of Technology).386p. (In Persian)
14.Li, C., Wang, J., Wang, L., Hu, L., andGong, P. 2014. Comparison ofclassification algorithms and trainingsample sizes in urban land classificationwith Landsat thematic mapper imagery.
Remote Sensing, 6: 2. 964-983.
15.Li, P., Jiang, L., and Feng, Z. 2013.Cross-comparison of vegetation indicesderived from Landsat-7 enhancedthematic mapper plus (ETM+) andLandsat-8 operational land imager (OLI)
sensors. Remote Sensing. 6: 1. 310-329.
16.Marry L. McHugh. 2012. Interraterreliability: the kappa statistic. BiochemiaMedica; 22: 3. 276-82.
17.Rahimzadegan, M., and Pourgholam, M.2014. Identification of the area undercultivation of Saffron using Landsat-8temporal satellite images (Case study:Torbat Heydarieh). J. RS GIS Natur.
Resour. Seventh Year. 4: 115-97.(In Persian)
18.Rasouli, A.A. 2008. Principles ofapplied remote sensing with emphasison satellite images processing, TabrizUniversity Press. 806p. (In Persian)
19.Saadat, H., Adamowski, J., Bonnell, R.,Sharifi, F., Namdar, M., andAle-Ebrahim, S. 2011. Land use andland cover classification over a largearea in Iran based on single date analysisof satellite imagery. ISPRS J.Photogram. Rem. Sens. 66: 5. 608-619.
20.Summary of Rural Land Conditions ofGolestan province in 2009-2010.
21.Mather, P., and Tso, B. 2009. Classification methods for remotely sensed data, Second
Edition. CRC press. 376p.
22.Yousefi, S., Tazeh, M., Mirzaee, S.,Moradi, H.R., and Tavangar, S.H. 2011.Comparison of different classificationalgorithms in satellite imagery to produceland use maps (Case study: Noor city). J.
Appl. RS GIS Techniq. Natur. Resour.Sci. 2: 15-25. (In Persian)
23.Zhang, H., Li, Q., Liu, J., Shang, J., Du,X., and Zhao, L., Wang, N., and Dong,T. 2017. Crop classification and acreageestimation in North Korea usingphenology features. GIS science and
Remote Sensing. 54: 3. 381-406.
24.Zeaiean Firouzabadi, P., Sayadbidhendi,L., and Eskandarinoudeh, M. 2009.Mapping and acreage estimation of riceagricultural land using Radar satelliteimages. Physical Geography Research
Quarterly. 68: 45-58. (In Persian)
25.Zobeiry, M., and Majd, A.R. 2008.An introduction to remote sensingtechnology and its application in naturalresources. Tehran University Press,Seventh Book. 317p. (In Persian)