All Issue

2022 Vol.64, Issue 1 Preview Page
Classification of Summer Paddy and Winter Cropping Fields Using Sentinel-2 Images

Sentinel-2 위성영상을 이용한 하계 논벼와 동계작물 재배 필지 분류 및 정확도 평가

Joo-Pyo Honga
,
Seong-Ju Jangb
,
Jin-Seok Parkc
,
Hyung-Jin Shind
,
In-Hong Songe†
홍 주표a
,
장 성주b
,
박 진석c
,
신 형진d
,
송 인홍e†
aUndergraduate Student, Department of Rural Systems Engineering, Seoul National University
bMS Student, Department of Rural Systems Engineering, Global Smart Farm Convergence Major, Seoul National University
cPh.D. Student, Department of Rural Systems Engineering, Global Smart Farm Convergence Major, Seoul National University
dAssociate Researcher, Rural Research Institute, Korea Rural Community Corporation
eAssociate Professor, Department of Rural Systems Engineering, Global Smart Farm Convergence Major, Research Institute of Agriculture and Life Sciences, Seoul National University
a서울대학교 농촌시스템공학과 학부생
b서울대학교 글로벌스마트팜 융합전공 농촌시스템공학과 석사
c박사 서울대학교 글로벌스마트팜 융합전공 농촌시스템공학과 학생
d한국농어촌공사 농촌연구원 부연구원
e서울대학교 농업생명과학연구원 글로벌스마트팜 융합전공 농촌시스템공학과 부교수
† Corresponding author Tel.: +82-2-880-4581, Fax: +82-2-873-2087 E-mail: inhongs@snu.ac.kr
2022. pp. 51-63
PDF XML Citation
Abstract

Up-to-date statistics of crop cultivation status is essential for farm land management planning and the advancement in remote sensing technology allows for rapid update of farming information. The objective of this study was to develop a classification model of rice paddy or winter crop fields based on NDWI, NDVI, and HSV indices using Sentinel-2 satellite images. The 18 locations in central Korea were selected as target areas and photographed once for each during summer and winter with a eBee drone to identify ground truth crop cultivation. The NDWI was used to classify summer paddy fields, while the NDVI and HSV were used and compared in identification of winter crop cultivation areas. The summer paddy field classification with the criteria of –0.195<NDWI<0.15 was 90.9% accurate on average when compared with ground truth. Model performance for the winter crop field classification was also excellent showing the accuracy greater than 98.2% on average for both models. However, cloud and greenhouses appeared to demonstrate optical similarity with paddy in the NDWI values, implying additional caution should be taken when removing cloud and greenhouse effects. Overall the developed methods for paddy and winter crop classification performed reasonably well at the irrigation district level and further studies are needed to investigate model performance at regional or national scales.

Keywords
Sentienl-2
winter crop
paddy
NDWI
NDVI
HSV
References

REFERENCES

1
F. P. Alex, Good stuff international, Aplicación de técnicas de teledetección para apoyar el uso sostenible del agua en un contexto de cambio climático en la cuenca del río Barbate (2019)
2
S. H. Chae and K. K. Jun, Journal of Korea Multimedia Society, HSV color model based hand contour detector robust to noise, 18(10); 1149-1156, in Korean (2015)10.9717/kmms.2015.18.10.1149
3
Y. W. Choi, W. M. Yook and G. S. Cho, Journal of the Korean Society for Geospatial Information System, Development of building 3D spatial information extracting system using HSI color model, 21(4); 151-159, in Korean (2013)10.7319/kogsis.2013.21.4.151
4
B. C. Gao, Remote Sensing of Environment, NDWI-A normalized difference water index for remote sensing of vegetation liquid water from space, 58(3); 257-266 (1996)10.1016/50034-4257(96)00067-3
5
E. Hamuda, B. M. Ginley, M. Glavin and E. Jones, Computers and Electronics in Agriculture, Automatic crop detection under field conditions using the HSV colour space and morphological operations, 133; 97-107 (2017)10.1016/j.compag.2016.11.021
6
S. Y. Hong, S. I. Na, K. D. Lee, Y. S. Kim and S. C. Baek, Korean Journal of Remote Sensing, A study on estimating rice yield in DPRK using MODIS NDVI and rainfall data, 31(5); 441-448, in Korean (2015)10.7780/kjrs.2015.31.5.8
7
S. Y. Hong, S. K. Rim, K. S. Lee, I. S. Jo and K. U. Kim, NIAST, Estimation of rice-planted area using Landsat TM imagery in Dangjin-gun area, 3(1); 5-15, in Korean (2000)10.5389/KSAE.2012.54.2.047
8
X. Hu and F. Li, Fisheries, Study on classification corn seedling and weed based on RGB model and HSV model, 6(1); 49-54 (2017)10.11648/j.aff.20170601.17
9
A. R. Huete, Geography Compass, Vegetation indices, remote sensing and forest monitoring, 6(9); 513-532 (2012)10.1111/j.1749-8198.2012.00507.x
10
A. Kern, Z. Barcza, H. Marjanović, T. Árendás, N. Fodor, P. Bónis, P. Bognár and J. Lichtenberger, Agricultural and Forest Meteorology, Statistical modelling of crop yield in Central Europe using climate data and remote sensing vegetation indices, 260; 300-320 (2018)10.1016/ j.agrformet.2018.06.009
11
J. H. Kim, C. K. Lee, W. G. Sang, P. Shin, H. S. Cho and M. C. Seo, Korean Journal of Remote Sensing, Introduction to empirical approach to estimate rice yield and comparison with remote sensing approach, 33(5); 733-740, in Korean (2017)10.7780/kjrs.2017.33.5.2.12
12
K. S. Kim, Y. K. Hong, H. J. Kim, G. H. Kim, K. C. Kim and M. H. Lee, The Jounral of Korean Institute of Communications and Information Sciences, Tomato growth rate measurement system using image processing, 45(8); 1460-1471, in Korean (2020)10.7840/kics. 2020.45.8.1460
13
KOSTAT, Development of yield estimation method for major crops using remote sensing technique, in Korean (2011)
14
D. H. Lee, A. K. Kim, C. H. Choi and Y. J. Kim, Journal of Korea Institute of Information, Electronics, and Communication, Study on image-based flock density evaluation of broiler chicks, 12(4); 373-379, in Korean (2019)10.17661/jkiiect.2019.12.4.373
15
H. J. Lee, W. H. Nam, D. H. Yoon, M. W. Jang, E. M. Hong, T. G. Kim and D. E. Kim, The Korean Society of Agricultural Engineers, Estimation of water storage in small agricultural reservoir using Sentinel-2 satellite imagery, 62(6); 1-9, in Korean (2020)10.5389/ KSAE.2020.62.6.001
16
K. D. Lee, S. G. Kim, H. Y. Ahn, K. H. So and S. I. Na, Korean Journal of Remote Sensing, Early estimation of rice cultivation in Gimje-si using Sentinel-1 and UAV imagery, 37(3); 503-514, in Korean (2021)10.7780/ KJRS.2021.37.3.11
17
K. D. Lee, S. Y. Hong, S. G. Kim, C. W. Park, H. Y. Ahn, S. I. Na and K. H. So, Korean Journal of Soil Science and Fertilize, Estimation of rice cultivation area by threshold method using Sentinel-1 imagery in South Korea, 53(3); 345-354, in Korean (2020)10.7745/ KJSSF.2020.53.3.345
18
S. B. Lee, Y. M. Kim, J. Y. Kim and Y. J. Park, Journal of the Korean Society of Hazard Mitigation, Detection of alteration in river area using Landsat satellite imagery, 17(3); 391-398, in Korean (2017)10.9798/kosham.2017.17.3.391
19
S. H. Lee, Y. G. Oh, N. Y. Park, S. H. Lee and J. Y. Choi, Journal of the Korean Society of Agricultural Engineers, Extraction of paddy field in Jaeryeong, North Korea by object-oriented classification with RapidEye NDVI imagery, 56(3); 55-64, in Korean (2014)10.5389/ksae.2014.56.3.055
20
S. K. McFeeters, International Journal of Remote Sensing, The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features, 17(7); 1425-1432 (1996)10.1080/01431169608948714
21
S. I. Na, C. W. Park, K. H. So, J. M Park and K. D. Lee, Korean Journal of Remote Sensing, Satellite imagery based winter crop classification mapping using hierarchical classification, 33(5); 677-687, in Korean (2017)10.7780/KJRS.2017.33.5.2.7
22
S. I. Na, C. W. Park, Y. J. Kim and K. D. Lee, Korean Journal of Soil Science and Fertilizer, Mapping the spatial distribution of IRG growth based on UAV, 49(5); 495-502, in Korean (2016)10.7745/kjssf.2016.49.5.495
23
S. I. Na, C. W. Park, Y. K. Cheong, C. S. Kang, I. B. Choi and K. D. Lee, Korean Journal of Remote Sensing, Development of biomass evaluation model of winter crop using RGB imagery based on unmanned aerial vehicle, 32(5); 483-497, in Korean (2016)10.7780/kjrs. 2016.32.5.7
24
S. I. Na, Korean Journal of Remote Sensing, Selection of optimal vegetation indices for estimation of barley & wheat growth based on remote sensing -An application of unmanned aerial vehicle and field investigation data-, 32(5); 483-497, in Korean (2016)10.7780/kjrs.2016.32.5.7
25
A. Pandya and R. S. Priya, International Journal of Research in IT, Management and Engineering, Classification of vegetation area from satellite images using image processing techniques, 5(3); 2249-1619 (2015)10.1080/01431160050505829
26
J. K. Park and J. H. Park, The Korean Society of Agricultural Engineers, Analysis of rice field drought area using unmanned aerial vehicle (UAV) and geographic information system (GIS) methods, 59(3); 21-28 (2017)10.5389/ksae.2017.59.3.021
27
J. S. Park, S. J. Jang, H. J. Kim, R. G. Hong and I. H. Song, Journal of the Korean Society of Agricultural Engineers, Application of drone images to investigate biomass management practices and estimation of CH4 emissions from paddy fields, 62(3); 39-49, in Korean (2020)
28
J. Xue and B. Su, Significant remote sensing vegetation indices: A review of developments and applications, Article ID 1353691 (2017)10.1155/2017/1353691
29
H. Y. Yoo, K. D. Lee, S. I. Na, C. W. Park and N. W. Park, Korean Journal of Remote Sensing, Field crop classification using multi-temporal high-resolution satellite imagery: A case study on garlic/onion field, 33(5); 621-630, in Korean (2017)10.7780/kjrs.2017.33.5.2.2
30
W. Zhang, H. Liu, W. Wu, L. Zhan and J. Wei, Remote Sensing, Mapping rice paddy based on machine learning with Sentinel-2 multi-temporal data: Model comparison and transferability, 12(10); 1620 (2020)10.3390/rs12101620
Information
  • Publisher :The Korean Society of Agricultural Engineers
  • Publisher(Ko) :한국농공학회
  • Journal Title :Journal of Korean Society of Agricultural Engineers
  • Journal Title(Ko) :한국농공학회논문집
  • Volume : 64
  • No :1
  • Pages :51-63
  • Received Date : 2021-09-16
  • Revised Date : 2021-10-29
  • Accepted Date : 2021-12-10
  • DOI :https://doi.org/10.5389/KSAE.2022.64.1.051
Journal Informaiton Journal of Korean Society of Agricultural Engineers Journal of Korean Society of Agricultural Engineers
  • NRF
  • KOFST
  • crossref crossmark
  • crossref cited-by
  • crossref funder-registry
  • crosscheck
  • orcid
  • open access
Journal Informaiton Journal Informaiton - close