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http://dx.doi.org/10.7780/kjrs.2017.33.5.2.11

Development of a Biophysical Rice Yield Model Using All-weather Climate Data  

Lee, Jihye (Department of Environmental Science, Kangwon National University)
Seo, Bumsuk (Department of Environmental Science, Kangwon National University)
Kang, Sinkyu (Department of Environmental Science, Kangwon National University)
Publication Information
Korean Journal of Remote Sensing / v.33, no.5_2, 2017 , pp. 721-732 More about this Journal
Abstract
With the increasing socio-economic importance of rice as a global staple food, several models have been developed for rice yield estimation by combining remote sensing data with carbon cycle modelling. In this study, we aimed to estimate rice yield in Korea using such an integrative model using satellite remote sensing data in combination with a biophysical crop growth model. Specifically, daily meteorological inputs derived from MODIS (Moderate Resolution imaging Spectroradiometer) and radar satellite products were used to run a light use efficiency based crop growth model, which is based on the MODIS gross primary production (GPP) algorithm. The modelled biomass was converted to rice yield using a harvest index model. We estimated rice yield from 2003 to 2014 at the county level and evaluated the modelled yield using the official rice yield and rice straw biomass statistics of Statistics Korea (KOSTAT). The estimated rice biomass, yield, and harvest index and their spatial distributions were investigated. Annual mean rice yield at the national level showed a good agreement with the yield statistics with the yield statistics, a mean error (ME) of +0.56% and a mean absolute error (MAE) of 5.73%. The estimated county level yield resulted in small ME (+0.10~+2.00%) and MAE (2.10~11.62%),respectively. Compared to the county-level yield statistics, the rice yield was over estimated in the counties in Gangwon province and under estimated in the urban and coastal counties in the south of Chungcheong province. Compared to the rice straw statistics, the estimated rice biomass showed similar error patterns with the yield estimates. The subpixel heterogeneity of the 1 km MODIS FPAR(Fraction of absorbed Photosynthetically Active Radiation) may have attributed to these errors. In addition, the growth and harvest index models can be further developed to take account of annually varying growth conditions and growth timings.
Keywords
Rice Yield; LUE; MODIS; All-weather climate data; Crop model;
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1 Andersen, R., 2008. Modern methods for robust regression, series: Quantitative applications in the social sciences, Sage University Paper Series on Quantitative Applications in the Social Sciences 07-152. Beverly Hills, CA: Sage, USA.
2 Back, N., W. Choi, J. Ko, J. Nam, H. Park, J. Choung, S. Kim, and K. Park, 2005. Proper Transplanting Time for Improving the Rice Quality at Reclaimed Saline Land in the Southwestern Area, Korean Journal of Crop Science, 50(spc1): 41-45 (in Korean with English abstract).
3 Jang, K., S. Kang, and S. Y. Hong, 2014b. Comparisons of Collection 5 and 6 Aqua MODIS07_L2 air and dew temperature products with groundbased observation dataset, Korean Journal of Remote Sensing, 30(5): 571-586 (in Korean with English abstract).   DOI
4 Jeong, S., K. Jang, S. Hong, and S. Kang, 2011. Detection of irrigation timing and the mapping of paddy cover in Korea using MODIS images data, Korean Journal of Agricultural and Forest Meteorology, 13(2): 69-78 (in Korean with English abstract).   DOI
5 Joint-relevant authorities, 2011. Report on abnormal climate in 2011, Joint-relevant authorities, Korea.
6 Jones, C. A., J. R. Kiniry, and P. Dyke, 1986. CERESMaize: A simulation model of maize growth and development, Texas A&M University Press, College Station, USA.
7 Jones, J. W., G. Hoogenboom, C. H. Porter, K. J. Boote, W. D. Batchelor, L. A. Hunt, P. W. Wilkens, U. Singh, A. J. Gijsman, and J. T. Ritchie, 2003. The DSSAT cropping system model, European Journal of Agronomy, 18(3): 235-265.   DOI
8 Choi, W., J. Nam, S. Kim, J. Lee, J. Kim, H. Park, N. Back, M. Choi, C. Kim, and K. Jung, 2005. Optimum transplanting date for production quality rice in Honam plain area, Korean Journal of Crop Science, 50(6): 435-441 (in Korean with English abstract).
9 Becker-Reshef, I., E. Vermote, M. Lindeman, and C. Justice, 2010. A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data, Remote Sensing of Environment, 114(6): 1312-1323.   DOI
10 Bird, R. E. and R. L. Hulstrom, 1981. Simplified clear sky model for direct and diffuse insolation on horizontal surfaces, Solar Energy Research Inst., Golden, USA.
11 Diepen, C. v., J. Wolf, H. v. Keulen, and C. Rappoldt, 1989. WOFOST: a simulation model of crop production, Soil Use and Management, 5(1): 16-24.   DOI
12 Do, N., S. Kang, S. Myeong, T. Chun, J. Lee, and C. Lee, 2012. The estimation of gross primary productivity over North Korea using MODIS FPAR and WRF meteorological data, Korean Journal of Remote Sensing, 28(2): 215-226 (in Korean with English abstract).   DOI
13 Lobell, D. B., M. J. Roberts, W. Schlenker, N. Braun, B. B. Little, R. M. Rejesus, and G. L. Hammer, 2014. Greater sensitivity to drought accompanies maize yield increase in the U.S. Midwest, Science, 344(6183): 516-519.   DOI
14 Kang, S., Y. Kim, and Y. Kim, 2005. Errors of MODIS product of Gross Primary Production by using Data Assimilation Office Meteorological Data, Korean Journal of Agricultural and Forest meteorology, 7(2): 171-183 (in Korean with English abstract).
15 Kastens, J. H., T. L. Kastens, D. L. Kastens, K. P. Price, E. A. Martinko, and R. Lee, 2005. Image masking for crop yield forecasting using AVHRR NDVI time series imagery, Remote Sensing of Environment, 99(3): 341-356.   DOI
16 Kiniry, J. R., B. Bean, Y. Xie, and P. Chen, 2004. Maize yield potential: critical processes and simulation modeling in a high-yielding environment, Agricultural Systems, 82(1): 45-56.   DOI
17 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(4): 385-396.   DOI
18 Yao, F., Y. Tang, P. Wang, and J. Zhang, 2015. Estimation of maize yield by using a processbased model and remote sensing data in the Northeast China Plain, Physics and Chemistry of the Earth, Parts A/B/C, 87: 142-152.
19 Lee, J., S. Kang, K. Jang, J. Ko, and S. Hong, 2011. The evaluation of meteorological inputs retrieved from MODIS for estimation of gross primary productivity in the US corn belt region, Korean Journal of Remote Sensing, 27(4): 481-494 (in Korean with English abstract).   DOI
20 Lee, J., S. Kang, K. Jang, and S. Y. Hong, 2016. A comparative study for reconstructing a highquality NDVI time series data derived from MODIS surface reflectance, Korean Journal of Remote Sensing, 31(2) (in Korean with English abstract).   DOI
21 McCree, K. and I. SETLIIK, 1970. An equation for the rate of respiration of white clover grown under controlled conditions, Prediction and measurement of photosynthetic productivity. Proceedings of the IBP/PP Technical Meeting, Trebon, [Czechoslovakia], Center for Agricultural Publishing and Documentation, Wageningen, Netherland.
22 Mu, Q., F. A. Heinsch, M. Zhao, and S. W. Running, 2007. Development of a global evapotranspiration algorithm based on MODIS and global meteorology data, Remote Sensing of Environment, 111(4): 519-536.   DOI
23 Fritsch, S., M. Machwitz, A. Ehammer, C. Conrad, and S. Dech, 2012. Validation of the collection 5 MODIS FPAR product in a heterogeneous agricultural landscape in arid Uzbekistan using multitemporal RapidEye imagery, International Journal of Remote Sensing, 33(21): 6818-6837.   DOI
24 Na, S. I., S. Y. Hong, Y. H. Kim, K. D. Lee, and S. Y. Jang, 2013. Prediction of rice yield in Korea using paddy rice NPP index-Application of MODIS data and CASA model, Korean Journal of Remote Sensing, 29(5): 461-476 (in Korean with English abstract).   DOI
25 Running, S. and M. Zhao, 2015. User's guide daily GPP and annual NPP (MOD17A2/A3) products NASA earth observing system MODIS land algorithm, Version 3 for Collection, University of Montana, Missoula, USA.
26 Stockle, C. O., M. Donatelli, and R. Nelson, 2003. CropSyst, a cropping systems simulation model, European Journal of Agronomy, 18(3): 289-307.   DOI
27 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(2): 192-202.   DOI
28 Ewert, F., R. P. Rotter, M. Bindi, H. Webber, M. Trnka, K. C. Kersebaum, J. E. Olesen, M. K. van Ittersum, S. Janssen, and M. Rivington, 2015. Crop modelling for integrated assessment of risk to food production from climate change, Environmental Modelling & Software, 72(1): 287-303.   DOI
29 Hong, S. Y., J. Hur, J. Ahn, J. Lee, B. Min, C. Lee, Y. Kim, K. D. Lee, S. Kim, and G. Y. Kim, 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).   DOI
30 Jang, K., S. Kang, H. Kim, and H. Kwon, 2009. Evaluation of shortwave irradiance and evapotranspiration derived from Moderate Resolution Imaging Spectroradiometer (MODIS), Asia-Pacific Journal of Atmospheric Sciences, 45(2): 233-246.
31 Jang, K., S. Kang, J. S. Kimball, and S. Y. Hong, 2014a. Retrievals of all-weather daily air temperature using MODIS and AMSR-E data, Remote Sensing, 6(9): 8387-8404.   DOI
32 Jang, K., S. Kang, Y. Lim, S. Jeong, J. Kim, J. S. Kimball, and S. Y. Hong, 2013. Monitoring daily evapotranspiration in Northeast Asia using MODIS and a regional Land Data Assimilation System, Journal of Geophysical Research: Atmospheres, 118(23): 12927-12940.   DOI