한국농공학회논문집 (Journal of The Korean Society of Agricultural Engineers)

  • 제54권3호
  • /
  • Pages.141-148
  • /
  • 2012
  • /
  • 1738-3692(pISSN)
  • /
  • 2093-7709(eISSN)
한국농공학회 (The Korean Society of Agricultural Engineers)
권호 표지
DOI QR코드

DOI QR Code

기상요소와 MODIS NDVI를 이용한 한국형 논벼 생산량 예측모형 (KRPM)의 개발

Development of Korean Paddy Rice Yield Prediction Model (KRPM) using Meteorological Element and MODIS NDVI

  • 나상일 (충북대학교 농업생명환경대학 지역건설공학과) ;
  • 박종화 (충북대학교 농업생명환경대학 지역건설공학과) ;
  • 박진기 (충북대학교 농업생명환경대학 지역건설공학과)
  • 투고 : 2012.03.16
  • 심사 : 2012.05.16
  • 발행 : 2012.05.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Food policy is considered as the most basic and central issue for all countries, while making efforts to keep each country's food sovereignty and enhance food self-sufficiency. In the case of Korea where the staple food is rice, the rice yield prediction is regarded as a very important task to cope with unstable food supply at a national level. In this study, Korean paddy Rice yield Prediction Model (KRPM) developed to predict the paddy rice yield using meteorological element and MODIS NDVI. A multiple linear regression analysis was carried out by using the NDVI extracted from satellite image. Six meteorological elements include average temperature; maximum temperature; minimum temperature; rainfall; accumulated rainfall and duration of sunshine. Concerning the evaluation for the applicability of the KRPM, the accuracy assessment was carried out through correlation analysis between predicted and provided data by the National Statistical Office of paddy rice yield in 2011. The 2011 predicted yield of paddy rice by KRPM was 505 kg/10a at whole country level and 487 kg/10a by agroclimatic zones using stepwise regression while the predicted value by KOrea Statistical Information Service was 532 kg/10a. The characteristics of changes in paddy rice yield according to NDVI and other meteorological elements were well reflected by the KRPM.

키워드

참고문헌

  1. Allen J. D., 1990. A Look at the Remote Sensing Applications Program of the National Agricultural Statistics Service. Journal of Official Statistics 6(4): 393-409.
  2. Eidenshink J. C., and J. L. Faundeen, 1994. The 1km AVHRR Global Land Data Set: First Stagesof Implementation. International Journal of Remote Sensing 15(17): 443-462. https://doi.org/10.1080/01431169408954085
  3. Ferencz Cs., P. Bognar, J. Lichtenberger, D. Hamar, G. Tarcsai, G. Timar, G. Molnar, SZ. Pasztor, P. Steinbach, B. Szekely, O.E. Ferencz, and I. Ferencz-Arkos, 2004. Crop Yield Estimation by Satellite Remote Sensing. International Journal of Remote Sensing 25(20): 4113-4149. https://doi.org/10.1080/01431160410001698870
  4. Hong S. Y., E. Y. Choe, G. Y. Kim, S. K. Kang, Y. H. Kim, and Y. S. Zhang, 2009. A Study on Estimating Rice Yield of North Korea using MODIS NDVI. Korea Society of Remote Sensing (in Korean).
  5. Jung Y. R., K. S. Jo, and B. W. Lee, 2006. Evaluation of Site-specific Potential for Rice Production in Korea under the Changing Climate. Korean Journal of Agricultural and Forest Meteorology 8(4): 229-241 (in Korean).
  6. Kim B. S., and S. H. Song, 2009. Satellite Imagery for Agricultural Outlook. Korea Rural Economic Institute (in Korean).
  7. Kim M. H., C. K. Lee, H. K. Park, J. E. Lee, B. C. Koo, and J. C. Shin, 2008. A Study on Rice Growth and Yield Monitoring Using Medium Resolution Landsat Imagery. Korean J. Crop Sci. 53(4): 388-393 (in Korean).
  8. Korean Statistical Information Service Homepage. http://www.kosis.kr/. Accessed 23 Oct. 2011.
  9. Lee J. W., G. A. Park, H. K. Joh, K. H. Lee, S. I. Na, J. H. Park, and S. J. Kim, 2011. Analysis of Relationship between Vegetation Indices and Crop Yield using KOMPSAT (KOreaMulti-Purpose SATellite)-2 Imagery and Field Investigation Data. Journal of the Korean Society of Agricultural Engineers, 53(3): 75-82 (in Korean). https://doi.org/10.5389/KSAE.2011.53.3.075
  10. Lee Y, S., 2008. The Impacts of Climate Change on Rice Yield, Thesis for Master. Konkuk University, Seoul.
  11. Montgomery D. C., and E. A. Peck, 1992. Introduction to Linear Regression Analysis. 2nd ed., New York.: Wiley.
  12. Oguro Y., C. Imamoto, Y. Suga, and S. Takeuchi, 2001. Monitoring of Rice Field by LANDSAT-7 ETM+ and LANDSAT-5 TM Data. 22nd Asian Conference on Remote Sensing (be held 5-9 November 2001 in Singapore), Asian Association on Remote Sensing.
  13. Soria R. J., and O. Y. Fernandez, 2002. Maize Crop Yield Prediction through Satellite Images and Mathematical Models. 17th WCSS symposium (be held in 14-21 August 2002 in Tailand), Poster # 1393.
  14. Yoo S. H., and C. Y. Yang, 2006. The Causal Analysis of Electricity Consumption and Economic Growth in Indonesia. The Korean Association of Asian Studies 9(2): 197-218 (in Korean).

피인용 문헌

  1. Estimation of Corn and Soybean Yields Based on MODIS Data and CASA Model in Iowa and Illinois, USA vol.47, pp.2, 2014, https://doi.org/10.7745/KJSSF.2014.47.2.092
  2. Assessment of Multimodel Ensemble Seasonal Hindcasts for Satellite-Based Rice Yield Prediction vol.72, pp.3-4, 2016, https://doi.org/10.2480/agrmet.D-15-00019
  3. Applicability of Vegetation Indices from Terra MODIS and COMS GOCI Imageries vol.55, pp.6, 2013, https://doi.org/10.5389/KSAE.2013.55.6.047
  4. Prediction of Rice Yield in Korea using Paddy Rice NPP index - Application of MODIS data and CASA Model - vol.29, pp.5, 2013, https://doi.org/10.7780/kjrs.2013.29.5.2