Browse > Article
http://dx.doi.org/10.7745/KJSSF.2014.47.2.092

Estimation of Corn and Soybean Yields Based on MODIS Data and CASA Model in Iowa and Illinois, USA  

Na, Sangil (Climate Change & Agroecology Division, National Academy of Agricultural Science, RDA)
Hong, Sukyoung (Climate Change & Agroecology Division, National Academy of Agricultural Science, RDA)
Kim, Yihyun (Climate Change & Agroecology Division, National Academy of Agricultural Science, RDA)
Lee, Kyoungdo (Climate Change & Agroecology Division, National Academy of Agricultural Science, RDA)
Publication Information
Korean Journal of Soil Science and Fertilizer / v.47, no.2, 2014 , pp. 92-99 More about this Journal
Abstract
The crop growing conditions make accurate predictions of yield ahead of harvest time difficult. Such predictions are needed by the government to estimate, ahead of time, the amount of crop required to be imported to meet the expected domestic shortfall. Corn and soybean especially are widely cultivated throughout the world and a staple food in many regions of the world. On the other hand, the CASA (Carnegie-Ames-Stanford Approach) model is a process-based model to estimate the land plant NPP (Net Primary Productivity) based on the plant growing mechanism. In this paper, therefore, a methodology for the estimation of corn/soybean yield ahead of harvest time is developed specifically for the growing conditions particular to Iowa and Illinois. The method is based on CASA model using MODIS data, and uses Net Primary Productivity (NPP) to predict corn/soybean yield. As a result, NPP at DOY 217 (in Illinois) and DOY 241 (in Iowa) tend to have high correlation with corn/soybean yields. The corn/soybean yields of Iowa in 2013 was estimated to be 11.24/3.55 ton/ha and Illinois was estimated to be 10.09/3.06 ton/ha. Errors were 6.06/17.58% and -10.64/-7.07%, respectively, compared with the yield forecast of the USDA. Crop yield distributions in 2013 were presented to show spatial variability in the state. This leads to the conclusion that NPP changes in the crop field were well reflected crop yield in this study.
Keywords
CASA model; Predict corn/Soybean yield; NPP; MODIS; Iowa/Illinois;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
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 J. Agric. For. Meteorol., 12(1):1-10.   과학기술학회마을   DOI   ScienceOn
2 Hong, S.Y., S.K. Rim, S.J. Hwang, and S.O. Kim. 1998. Characteristics of Spectral Reflectance for Corns and Legumes at OSMI (Ocean Scanning Multi-spectral Imager) Bands, Remote Sens. Environ., 14(4):343-352.
3 Back, S.B., B.Y. Son, J.T. Kim, J.S. Lee, S.R. Kim, and W.H. Kim. 2011. RDA Interrobang, Rural Development Administration, 20:10-11.
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 Sens. Environ., 97:192-202.   DOI   ScienceOn
5 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.
6 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, Remote Sens. Environ., 28(5):509-520.
7 Kim, Y.H. and S.Y. Hong. 2011. Estimation of Soybean Growth Using Polarimetic Discrimination Ratio by Radar Scatterometer, Korean J. Soil Sci. Fert. 44(5):878-886.   DOI   ScienceOn
8 Ko, J.M., H.T. Kim, H.T. Yoon, T.J. Ha, and I.Y. Back. 2011. RDA Interrobang, Rural Development Administration, 35:7-9.
9 Lin W. et al. 2007. Vegetation NPP distribution based on MODIS data and CASA model-A case study in Haihe River basin, China, Proc. of SPIE, Vol. 6625.
10 Potter, C., S. Klooster, R. Myneni, and V. Genovese. 2003. Terrestrial Carbon Sinks Predicted from MODIS Satellite Data and Ecosystem Modeling, Geophysical Research Letters.
11 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.   과학기술학회마을   DOI   ScienceOn
12 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, Remote Sens. Environ., 29(5):461-476.
13 Noemi G. 2010. Estimating Maize Grain Yield from Crop Biophysical Parameters Using Remote Sensing, Digital Commons@University of Nebraska-Lincoln.
14 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.   DOI   ScienceOn
15 Rojas, O. 2007. Operational Maize Yield Model Development and Validation Based on Remote Sensing and Agro-meteorological Data in Kenya, Int, J. Remote Sens., 28(17):3775-3793.   DOI   ScienceOn
16 Becker-Reshef, I., E. Vermote, M. Lineman, and C. Justice. 2010. A Generalized Regression-based Model for Forecasting Winter Wheat Yields in Kansas and Ukraine using MODIS Data, Remote Sens. Environ., 114:1312-1323.   DOI   ScienceOn
17 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 Sens. Environ., 89:519-534.   DOI   ScienceOn
18 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.   DOI   ScienceOn
19 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.   DOI   ScienceOn