DOI QR코드

DOI QR Code

논벼 NPP 지수를 이용한 우리나라 벼 수량 추정 - MODIS 영상과 CASA 모형의 적용 -

Prediction of Rice Yield in Korea using Paddy Rice NPP index - Application of MODIS data and CASA Model -

  • 나상일 (농촌진흥청 국립농업과학원) ;
  • 홍석영 (농촌진흥청 국립농업과학원) ;
  • 김이현 (농촌진흥청 국립농업과학원) ;
  • 이경도 (농촌진흥청 국립농업과학원) ;
  • 장소영 (농촌진흥청 국립농업과학원)
  • Na, Sang Il (National Academy of Agricultural Science, Rural Development Administration) ;
  • Hong, Suk Young (National Academy of Agricultural Science, Rural Development Administration) ;
  • Kim, Yi Hyun (National Academy of Agricultural Science, Rural Development Administration) ;
  • Lee, Kyoung Do (National Academy of Agricultural Science, Rural Development Administration) ;
  • Jang, So Young (National Academy of Agricultural Science, Rural Development Administration)
  • 투고 : 2013.08.22
  • 심사 : 2013.10.10
  • 발행 : 2013.10.31

초록

CASA 모델은 작물의 순 일차생산량(NPP)을 추정하는 가장 빠르고 정확한 모델 중 하나이다. 본 연구의 목적은 (1) 2002년 ~ 2012년 동안 한국의 논지역을 대상으로 작물 NPP의 시공간적 변화 패턴을 분석하고, (2) 연간 NPP와 쌀 생산성 간의 관계를 파악하여, (3) MODIS Product와 태양 복사량을 CASA 모형에 적용하여 2012년 한국의 쌀 수량을 추정하는 것이다. 또한, (4) 통계청이 발표한 최종 수량과 비교를 통해 적용을 검토하였다. 이를 위해, 월별 또는 누적 NPP와 수량과의 상관분석을 실시하였다. 그 결과, 총 누적 NPP와 9월의 NPP가 쌀 수량과 높은 상관성을 나타내었으며, 이를 이용하여 추정한 2012년 예측 수량은 누적 NPP 적용시 526.93 kg/10a, 9월의 NPP 적용시 520.32 kg/10a로 추정되었다. 통계청의 최종 수량과의 RMSE는 각각 9.46 kg/10a, 12.93 kg/10a를 나타내었으나, 전반적으로 두 모형 모두 1:1선에 근접한 결과를 보이고 있어 NPP를 이용한 벼 수량 추정 모형이 논벼 수량의 변화특성을 잘 반영하고 있는 것으로 판단된다.

Carnegie-Ames-Stanford Approach (CASA) model is one of the most quick, convenient and accurate models to estimate the NPP (Net Primary Productivity) of vegetation. The purposes of this study are (1) to examine the spatial and temporal patterns of vegetation NPP of the paddy field area in Korea from 2002 to 2012, and (2) to investigate how the rice productivity responded to inter-annual NPP variability, and (3) to estimate rice yield in Korea using CASA model applied to MOderate Resolution Imaging Spectroradiometer (MODIS) products and solar radiation. MODIS products; MYD09 for NIR and SWIR bands, MYD11 for LST, MYD15 for FPAR, respectively from a NASA web site were used. Finally, (4) its applicability is to be reviewed. For those purposes, correlation coefficients (linear regression for monthly NPP and accumulated NPP with rice yield) were examined to evaluate the spatial and temporal patterns of the relations. As a result, the total accumulated NPP and Sep. NPP tend to have high correlation with rice yield. The rice yield in 2012 was estimated to be 526.93kg/10a by accumulated NPP and 520.32 kg/10a by Sep. NPP. RMSE were 9.46kg/10a and 12.93kg/10a, respectively, compared with the yield forecast of the National Statistical Office. This leads to the conclusion that NPP changes in the paddy field were well reflected rice yield in this study.

키워드

참고문헌

  1. Ahn, J.B., J.N. Hur, and K.M. Shim, 2010. A Simulation of Agro-Climate Index over the Korean Peninsula Using Dynamical Downscaling with a Numerical Weather Prediction Model, Korean Journal of Agricultural and Forest Meteorology, 12(1): 1-10. https://doi.org/10.5532/KJAFM.2010.12.1.001
  2. 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.
  3. Cressman, G.P., 1959. An operational objective analysis system. Mon. Wea. Rev., 87, 367-374. https://doi.org/10.1175/1520-0493(1959)087<0367:AOOAS>2.0.CO;2
  4. Doraiswamy, P.C, T.R. Sinclair, S. Hollinger, B. Akhmedov, A. Stern, and J. Prueger, 2005. Application of MODIS derived parameters for regional crop yield assessment, Remote Sensing of Environment, 97: 192-202. https://doi.org/10.1016/j.rse.2005.03.015
  5. Falcon, W.P., and R.L. Naylor, 2005. Rethinking Food Security for the Twenty-First Century, American Journal of Agricultural Economics, 85(5): 1113-1127.
  6. 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. FerenczArkos, 2004. Crop Yield Estimation by Satellite Remote Sensing, International Journal of Remote Sensing, 25(20): 4113-4149. https://doi.org/10.1080/01431160410001698870
  7. Field, C.B., J.T. Randerson, and C.M. Malmstrom, 1995. Global net primary production: Combining ecology and remote sensing, Remote Sensing of Environment, 51: 74-88. https://doi.org/10.1016/0034-4257(94)00066-V
  8. Hong, J.H., C.S. Shim, M.J. Lee, G.H. Baek, W.K. Song, S.W. Jeon, and Y.H. Park, 2011. Net Primary Production Changes over Korea and Climate Factors, Korean Journal of Remote Sensing, 27(4): 467-480 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2011.27.4.467
  9. 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, Proc. of 2009 Korea Remote Sensing Symposium, Seoul, Korea, Mar. 25, 116-120.
  10. Hong, S.Y., J.N. Hur, J.B. Ahn, J.M. Lee, B.K. Min, C.K. Lee, Y.H. Kim, K.D. Lee, S.H. Kim, G.Y. Kim, and K.M. Shim, 2012. Estimating Rice Yield Using MODIS NDVI and Meteorological Data in Korea, Korean Journal of Remote Sensing, 28(5): 509-520 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2012.28.5.4
  11. Hong, S.Y., J.T. Lee, S.K. Rim, and J.S. Shin, 1997. Radiometric estimates of grain yields related to crop aboveground net production (ANP) in paddy rice, Proc. of 1997 International Geoscience and Remote Sensing Symposium, Singapore, Aug. 3-8, 1793-1795.
  12. 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 Journal Crop Science, 53(4): 388-393.
  13. Knorr, W., and M. Heimann, 1995. Impact of drought stress and other factors on seasonal land biosphere CO2 exchange studied through an atmospheric tracer transport model, Tellus, 47(4): 471-489. https://doi.org/10.1034/j.1600-0889.47.issue4.7.x
  14. Lobell, D.B., J.A. Hicke, G.P. Asner, C.B. Field, C.J. Tucker, and S.O. Los, 2002. Satellite estimates of productivity and light use efficiency in United States agriculture, 1982-1998. Global Change Biology, 8: 722-735. https://doi.org/10.1046/j.1365-2486.2002.00503.x
  15. Monteith, J.L., 1972. Solar radiation and productivity in tropical ecosystems, J. Appl. Ecol., 9: 747-766. https://doi.org/10.2307/2401901
  16. Na, S.I., J.K. Park, S.C. Baek, and J.H. Park, 2011. A Study on the Key Factors Extraction and Paddy Fields Mapping for the Development of Rice Yield Prediction Model using RS/GIS, Proc. of Annual Conference of Korea Society of Agricultural Engineers, Daegu, Korea, Sep. pp.22-23.
  17. Na, S.I., J.H. Park, and J.K. Park, 2012. Development of Korean Paddy Rice Yield Prediction Model (KRPM) using Meteorological Element and MODIS NDVI, Journal of the Korean Society of Agricultural Engineers, 54(3): 141-148. https://doi.org/10.5389/KSAE.2012.54.3.141
  18. Na, S.I., J.K. Park, C.S. Baek, S.Y. Oh, and J.H. Park. 2012. A Study on the Correlation between NDVI and Paddy Rice Yield by Spatial Scale, Proc. of Annual Conference of Korea Society of Agricultural Engineers, Cheonan, Korea, Sep. pp.18-19.
  19. Nayak, R.K., N.R. Patel, and V.K. Dadhwal, 2010. Estimation and analysis of terrestrial net primary productivity over India by remote-sensing-driven terrestrial biosphere model, Environ. Monit. Assess., 170: 195-213. https://doi.org/10.1007/s10661-009-1226-9
  20. Ozdogan, M., 2011. Exploring the potential contribution of irrigation to global agricultural primary productivity, Global Biogeochemical Cycles, 25(3): GB3016.
  21. Potter, C., S. Klooster, R. Myneni, and V. Genovese, 2004. Terrestrial Carbon Sinks Predicted from MODIS Satellite Data and Ecosystem Modeling, Earth Observer, 16(2): 15-20.
  22. Prince, S.D., and S.N. Goward, 1995. Global primary production: A remote sensing approach, Journal of Biogeography, 22: 815-835. https://doi.org/10.2307/2845983
  23. Raich, J.W., E.B. Rastetter, J.M. Melillo, D.W. Kicklighter, P.A. Steudler, B.J. Peterson, A. Grace , B. Moore, and C.J. Vorosmarty, 1991. Potential net primary productivity in South- America-application of a global-model, Ecological Applications, 1: 399-429. https://doi.org/10.2307/1941899
  24. Ren J.Q., Z.X. Chen, Q.B. Zhou, and H.J. Tang, 2008. Regional Yield Estimation for Winter Wheat with MODIS-NDVI Data in Shandong, China, International Journal of Applied Earth Observation and Geoinformation, 10: 403-413 https://doi.org/10.1016/j.jag.2007.11.003
  25. Ruimy, A., G. Dedieu, and B. Saugier, 1996. TURC: A diagnostic model of continental gross primary productivity and net primary productivity, Global Biogeochemical Cycles, 10(2): 269-285. https://doi.org/10.1029/96GB00349
  26. Running, S.W., R.R. Nemani, F.A. Heinsch, M.S. Zhao, M. Reeves, and H. Hashimoto, 2004. A continuous satellite-derived measure of global terrestrial primary production, Bioscience, 54(6): 547-560. https://doi.org/10.1641/0006-3568(2004)054[0547:ACSMOG]2.0.CO;2
  27. Tao, F., M. Yokozawa, Z. Zhang, Y. Xu, and Y. Hayashi, 2005. Remote sensing of crop production in China by production efficiency models: models comparisons, estimates and uncertainties, Ecological Modelling, 183: 385-396. https://doi.org/10.1016/j.ecolmodel.2004.08.023
  28. Veroustraete, F., 1994. On the use of a simple deciduous forest model for the interpretation of climate change effects at the level of carbon dynamics, Ecological Modelling, 75(76): 221-237.
  29. Wang, L., G. Hong, Z. Caiping, Z. Haitao, L. Chao, and Z. Qilin, 2008. Vegetation NPP distribution based on MODIS data and CASA model-A case study in Haihe River basin, China, Proc. of SPIE, Vol. 6625.
  30. Xiao, X.M., S. Boles, J.Y. Liu, D.F. Zhuang, and M.L. Liu, 2002. Characterization of forest types in Northeastern China, using multitemporal SPOT- 4 VEGETATION sensor data, Remote Sensing of Environment, 82: 335-348. https://doi.org/10.1016/S0034-4257(02)00051-2
  31. Xiao, X.M., D. Hollinger, J.D. Aber, M. Goltz, E.A. Davidson, and Q.Y. Zhang, 2004. Satellite-based modeling of gross primary production in an evergreen needleleaf forest, Remote Sensing of Environment, 89: 519-534. https://doi.org/10.1016/j.rse.2003.11.008
  32. Xiao, X., Q. Zh, S. Salesk, L. Hutyra, P.D. Camargo, S. Wofsy, S. Frolking, S. Boles, M. Keller, and B. Moore, 2005. Satellite-based modeling of gross primary production in a seasonally moist tropical evergreen forest, Remote Sensing of Environment, 94: 105-122. https://doi.org/10.1016/j.rse.2004.08.015
  33. Zhao, M., F.A. Heinsch, R.R. Nemani, and S.W. Running, 2005. Improvements of the MODIS terrestrial gross and net primary production global data set, Remote Sensing of Environment, 95: 164-176. https://doi.org/10.1016/j.rse.2004.12.011

피인용 문헌

  1. A Study on Estimating Rice Yield in DPRK Using MODIS NDVI and Rainfall Data vol.31, pp.5, 2015, https://doi.org/10.7780/kjrs.2015.31.5.8
  2. 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 - vol.32, pp.5, 2016, https://doi.org/10.7780/kjrs.2016.32.5.7
  3. Effects of Climate Change and Ozone Concentration on the Net Primary Productivity of Forests in South Korea vol.9, pp.3, 2018, https://doi.org/10.3390/f9030112
  4. MODIS와 기상자료 기반 회선신경망 알고리즘을 이용한 남한 전역 쌀 생산량 추정 vol.34, pp.5, 2016, https://doi.org/10.7848/ksgpc.2016.34.5.525
  5. MODIS 전천후 기상자료 기반의 생물리학적 벼 수량 모형 개발 vol.33, pp.5, 2013, https://doi.org/10.7780/kjrs.2017.33.5.2.11
  6. SSAE 알고리즘을 통한 2003-2016년 남한 전역 쌀 생산량 추정 vol.33, pp.5, 2017, https://doi.org/10.7780/kjrs.2017.33.5.2.3
  7. MODIS NDVI와 기상요인을 고려한 마늘·양파 주산단지 단수예측 모형 개발 vol.33, pp.5, 2013, https://doi.org/10.7780/kjrs.2017.33.5.2.5
  8. Selection of Optimal Vegetation Indices and Regression Model for Estimation of Rice Growth Using UAV Aerial Images vol.50, pp.5, 2013, https://doi.org/10.7745/kjssf.2017.50.5.409
  9. UAV를 이용한 농경지 분광특성 및 식생지수 분석 vol.22, pp.4, 2019, https://doi.org/10.11108/kagis.2019.22.4.086
  10. 장기미집행공원 개발에 따른 도시 식생 탄소 흡수량에 미치는 영향 및 경제적 가치 평가 vol.21, pp.10, 2020, https://doi.org/10.5762/kais.2020.21.10.361
  11. 시계열 마스크 맵이 논벼 NDVI와 단수와의 관계에 미치는 영향 vol.36, pp.5, 2013, https://doi.org/10.7780/kjrs.2020.36.5.1.6
  12. 벼 수량 자료의 추세분석을 통한 MODIS NDVI 및 기상자료 기반의 벼 수량 추정 모형 개선 vol.37, pp.2, 2013, https://doi.org/10.7780/kjrs.2021.37.2.2