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1 Korean Journal of Remote Sensing, Vol.28, No.5, 2012, pp.509~520 Estimating Rice Yield Using MODIS NDVI and Meteorological Data in Korea Suk Young Hong*, Jina Hur**, Joong-Bae Ahn**, Jee-Min Lee***, Byoung-Keol Min***, Chung-Kuen Lee****, Yihyun Kim*, Kyung Do Lee*, Sun-Hwa Kim*, Gun Yeob Kim* and Kyo Moon Shim* * National Academy of Agricultural Science (NAAS), RDA, ** Division of Earth Environmental System, Pusan National University *** B&T Solutions, **** National Institutel of Crop Science (NICS), RDA Abstract : The objective of this study was to estimate rice yield in Korea using satellite and meteorological data such as sunshine hours or solar radiation, and rainfall. Terra and Aqua MODIS (The MOderate Resolution Imaging Spectroradiometer) products; MOD13 and MYD13 for NDVI and EVI, MOD15 and MYD15 for LAI, respectively from a NASA web site were used. Relations of NDVI, EVI, and LAI obtained in July and August from 2000 to 2011 with rice yield were investigated to find informative days for rice yield estimation. Weather data of rainfall and sunshine hours (climate data 1) or solar radiation (climate data 2) were selected to correlate rice yield. Aqua NDVI at DOY 233 was chosen to represent maximum vegetative growth of rice canopy. Sunshine hours and solar radiation during rice ripening stage were selected to represent climate condition. Multiple regression based on MODIS NDVI and sunshine hours or solar radiation were conducted to estimate rice yields in Korea. The results showed rice yield of kg 10a -1 and kg 10a -1 in 2011, respectively and the difference from statistics were 1.1 kg 10a -1 and 14.1 kg 10a -1, respectively. Rice yield distributions from 2002 to 2011 were presented to show spatial variability in the country. Key Words : Rice yield, Remote sensing, MODIS NDVI, Solar radiation 509

2 Korean Journal of Remote Sensing, Vol.28, No.5,

3 Estimating Rice Yield Using MODIS NDVI and Meteorological Data in Korea 511

4 Korean Journal of Remote Sensing, Vol.28, No.5, 2012 Fig. 1. Flow chart of MODIS data preprocessing. (NIR NDVI = _ RED) (-1 NDVI 1) (1) (NIR + RED) 512

5 Estimating Rice Yield Using MODIS NDVI and Meteorological Data in Korea Fig. 2. Parcel map based paddy mask(90 m). 513

Korean Journal of Remote Sensing, Vol.28, No.5, 2012 Fig. 3. Rice yield prediction schema using MODIS NDVI and climate data. Table 1. Yield statistics of Korea (Statistics Korea, http://kosis.

6 Korean Journal of Remote Sensing, Vol.28, No.5, 2012 Fig. 3. Rice yield prediction schema using MODIS NDVI and climate data. Table 1. Yield statistics of Korea (Statistics Korea, Year Unhulled rice Milled rice Year Yield(kg 10a -1 )

7 Estimating Rice Yield Using MODIS NDVI and Meteorological Data in Korea Table 2. Correlation coefficient between rice yield and MODIS products MODIS products Correlation Coefficient MODIS products 00~ 11 00~ 11 02~ 11 02~ 11 TNDVI209 (7.28) TNDVI225 (8.13) TEVI209 (7.28) TEVI225 (8.13) Terra TLAI201 (7.20) TLAI209 (7.28) TLAI217 (8.5) TLAI225 (8.13) TLAI233 (8.21) TLAI241 (8.28) ANDVI201 (7.20) ANDVI217 (8.5) 0.28 ANDVI233 (8.21) 0.62 AEVI201 (7.20) 0.50 AEVI217 (8.5) 0.18 Aqua AEVI233 (8.21) ALAI201 (7.20) ALAI209 (7.28) 0.14 ALAI217 (8.5) 0.26 ALAI225 (8.13) 0.41 ALAI233 (8.21) 0.30 ALAI241 (8.28) 0.73 rainfall (1 ) sunshine hours (1) rainfall (2 )* solar radiation (2) A: Aqua, T: Terra, 1, 2: Meteorological data 1, 2, * 00-10(n=11) or 02-10(n=9) 515

8 Korean Journal of Remote Sensing, Vol.28, No.5, 2012 Table 3. Rice yield prediction model based on Aqua NDVI 233 (x 1 ) and sunshine hours 1(x 2 ) or rainfall 1(x 2 ) of ripening stage( 02~ 10, n=9) Yield Prediction Model 2011 Rice Yield predicted(kg/10a) Rice Yield = (NDVI 233 (x 1 ), sunshine hours 1(x 2 )) P= RSq=0.70 * RMSE=17.9 kg 10a Rice Yield = (NDVI 233 (x 1 ), rainfall 1(x 2 )) P= RSq=0.82 * RMSE=13.7 kg 10a Table 4. Rice yield prediction model based on Aqua NDVI 233 (x 1 ) and insolation 2(x 2 ) or rainfall 2(x 2 ) of ripening stage( 02~ 10, n=9) Yield Prediction Model 2011 Rice Yield predicted(kg/10a) Rice Yield = (NDVI 233 (x 1 ), solar radiation 2(x 2 )) P= RSq=0.78 * RMSE=15.2 kg 10a Rice Yield = (NDVI 233 (x 1 ), rainfall 2(x 2 )) P= RSq=0.80 * RMSE=14.5 kg 10a (a) Fig. 4. Comparision of predicted rice yield based on MODIS NDVI and sunshine hours 1 (a) and MODIS NDVI and solar radiation 2 (b) and rice yield statistics from 2002 to (b) 516

9 Estimating Rice Yield Using MODIS NDVI and Meteorological Data in Korea Fig. 5. Rice yield map of Korea from 2002 to

10 Korean Journal of Remote Sensing, Vol.28, No.5, Fig. 5. Continued

11 Estimating Rice Yield Using MODIS NDVI and Meteorological Data in Korea Ahn, J.B., C.K. Park, and E.S. Im, Reproduction of regional scale surface air temperature by estimating systematic bias of mesoscale numerical model, Journal of Korean Meteorological Society, 38(1): Becker-Reshef, I., E. Vermote, M. Lineman, and C. Justice, A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data, Remote Sensing of Environment, 114: Chang, J., M.C. Hansen, K. Pittman, M. Carroll, and C. DiMiceli, Corn and soybean mapping in the United States using MODIS time-series data sets, Agronomy Journal, 99: Cock, J.H. and S. Yoshida, Accumulation of 519

12 Korean Journal of Remote Sensing, Vol.28, No.5, C-labelled carbohydrate before flowering and its subsequent redistribution and respiration in the rice plant, Proceedings of the Crop Science Society of Japan, 41: Cressman, G.P., 1959: An operational objective analysis system. Mon. Wea. Rev., 87, Doraiswamy, P.C, T.R. Sinclair, S. Hollinger, B. Akhmedov, A. Stern. and J. Prueger, Application of MODIS derived parameters for regional crop yield assessment, Remote Sensing of Environment, 97: FAO (GIEW), fao.org/giews/english/index.htm Hong, S.Y., J.T. Lee, S.K. Rim, and J.S. Shin, 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, pp Jordan, C.F., Derivation of leaf area index from quality of light on the forest floor, Ecology, 50: Myneni, R.B., G. Asrar, and F.G. Hall, A three dimensional radiative transfer modelsalgorithms-experiments. Remote Sensing of Environment, 51: Narongrit, C. and K. Chankao, Development and validation of rice evapotranspiration model based on Terra/MODIS remotely sensed data, Journal of Food, Agriculture & Environment, 7: Rasmussen M.S., Operational yield forecast using AVHRR NDVI data: reduction of environmental and inter-annual variability, International Journal of Remote Sensing, 18(5): Rouse, J.W, R.H. Haas, J.A. Schell, and D.W. Deering, Monitoring vegetation systems in the great plains with ETRA. In third ETRS Symposium, NASA SP-353. U.S. Govt. Printing Office, Washington D.C., 1: Ren, J., Z. Chen, Q. Zhou, and H. Tang, Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China, International Journal of Applied Earth Observation and Geoinformation, 10: Sun, J., Dynamic monitoring and yield estimation of crops by mainly using the remote sensing technique in China, Photogrammetric Engineering and Remote Sensing, 66(5):

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09È«¼®¿µ5~152s Korean Journal of Remote Sensing, Vol.23, No.2, 2007, pp.45~52 Measurement of Backscattering Coefficients of Rice Canopy Using a Ground Polarimetric Scatterometer System Suk-Young Hong*, Jin-Young Hong**,