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http://dx.doi.org/10.7780/kjrs.2021.37.3.11

Early Estimation of Rice Cultivation in Gimje-si Using Sentinel-1 and UAV Imagery  

Lee, Kyung-do (Climate Change Assessment Division, National Institute of Agricultural Sciences, Rural Development Administration)
Kim, Sook-gyeong (Climate Change Assessment Division, National Institute of Agricultural Sciences, Rural Development Administration)
Ahn, Ho-yong (Climate Change Assessment Division, National Institute of Agricultural Sciences, Rural Development Administration)
So, Kyu-ho (Climate Change Assessment Division, National Institute of Agricultural Sciences, Rural Development Administration)
Na, Sang-il (Climate Change Assessment Division, National Institute of Agricultural Sciences, Rural Development Administration)
Publication Information
Korean Journal of Remote Sensing / v.37, no.3, 2021 , pp. 503-514 More about this Journal
Abstract
Rice production with adequate level of area is important for decision making of rice supply and demand policy. It is essential to grasp rice cultivation areas in advance for estimating rice production of the year. This study was carried out to classify paddy rice cultivation in Gimje-si using sentinel-1 SAR (synthetic aperture radar) and UAV imagery in early July. Time-series Sentinel-1A and 1B images acquired from early May to early July were processed to convert into sigma naught (dB) images using SNAP (SeNtinel application platform, Version 8.0) toolbox provided by European Space Agency. Farm map and parcel map, which are spatial data of vector polygon, were used to stratify paddy field population for classifying rice paddy cultivation. To distinguish paddy rice from other crops grown in the paddy fields, we used the decision tree method using threshold levels and random forest model. Random forest model, trained by mainly rice cultivation area and rice and soybean cultivation area in UAV image area, showed the best performance as overall accuracy 89.9%, Kappa coefficient 0.774. Through this, we were able to confirm the possibility of early estimation of rice cultivation area in Gimje-si using UAV image.
Keywords
Rice; Cultivation area; Sentinel-1; UAV; RandomForest; Decision Tree;
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1 Rudiyanto, B. Minasny, R.M. Shah, N.C. Soh, C. Arif, and B.U. Setiawan, 2019. Automated near-realtime mapping and monitoring of rice extent, cropping patterns, and growth stages in southeast asia using sentinel-1 time series on google earth engine platform, Remote Sensing, 11: 1666.   DOI
2 Filipponi, F., 2019. Sentinel-1 GRD preprocessing workflow, Proc. of the 3rd International Electronic Conference on Remote Sensing, https://sciforum.net/conference/ecrs-3, Accessed on May. 22-June. 5.
3 Hong, S.Y., Y.H. Kim, E.Y. Choe, Y.S. Zhang, Y.K. Sonn, C.W. Park, K.H. Jung, B.K. Hyun, S.K. Ha, and K.C. Song, 2010. Geographic Information System and Remote Sensing in Soil Science, Korean Journal of Soil Science and Fertilizer, 43(5): 684-695 (in Korean with English abstract).
4 Hong, S.Y., B.K. Min, J.M. Lee, Y.H. Kim, and K.D. Lee. 2012. Estimation of paddy field area in North Korea using RapidEye images, Korean Journal of Soil Science and Fertilizer, 45(6): 1194-1202 (in Korean with English abstract).   DOI
5 Jeong S., S. Kang, K. Jang, H. Lee, S. Hong, and D. Ko, 2012. Development of variable threshold models for detection of irrigated paddy rice fields and irrigation timing in heterogeneous land cover, Agricultural Water Management, 115: 83-91 (in Korean with English abstract).   DOI
6 KOSIS(Korean Statistical Information Service), 2021. Agricultural area survey, Statistics Korea, http://kosis.kr/, Accessed on Jun. 1, 2021.
7 Lee, K.D., S.Y. Hong, S.G. Kim, C.W. Park, H.Y. An, S.I. Na, and K.H. So, 2020. Estimation of Rice Cultivation Area by Threshold method using Sentinel-1 Imagery in South Korea, Korean Journal of Soil Science and Fertilizer, 53(3): 345-354 (in Korean with English abstract).   DOI
8 ME(Ministry of Environment), 2013. Land cover map by ministry of environment, https://egis.me.go.kr, Accessed on May. 23, 2021.
9 Nguyen, D.B. and W. Wagner, 2017. European rice cropland mapping with Sentinel-1 data: the mediterranean region case study, Water, 9: 392.   DOI
10 Bazzi, H., N. Baghdadi, M.E. Hajj, M. Zribi, D.H.T. Minh, E. Ndikumana, D. Courault, and H. Belhouchette, 2019. Mapping paddy rice using Sentinel-1 SAR time series in Camargue, France, Remote Sensing Letters, 11: 887.   DOI
11 Jensen, J.R., 2014. Remote sensing of the environment an earth resource perspective, Prentice-Hall, Upper Saddle River, NJ, USA.
12 Campbell, J.B., 1996. Introduction to remote sensing, 2nded., TheGilfordPress, NewYork, NY, USA. pp.4-5, pp.550-551.
13 European Space Agency, 2021. Sentinel-1 SAR user guide introduction, http://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar, Accessed on Apr. 7, 2021.
14 Hong, S.Y., S.K. Rim, K.S. Lee, I.S. Jo, and K.K. Kim, 2001. Estimation of rice-planted area using Landsat TM imagery in Dangjin-gun area, Korean Journal of Agricultural and Forest Meteorology, 3(1): 5-15 (in Korean with English abstract).
15 Lee, H.Y., 2011. Application of SAR imagery, Korean Journal of Elecromagnetic Engineering and Science, 22(6): 55-67 (in Korean with English abstract).
16 Hong, S.Y., S.H. Hong, and S.K. Rim, 2000. Relationship between RADARSAT backscattering coefficient and rice growth, Korean Journal of Remote Sensing, 16(2): 109-116 (in Korean with English abstract).   DOI
17 MAFRA(Ministry of Agriculture Food and Rural Affairs), 2021. Farm map by ministry of agriculture, food and rural affairs, https://data.mafra.go.kr, Accessed on May. 2, 2021.