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http://dx.doi.org/10.7780/kjrs.2021.37.2.2

Detrending Crop Yield Data for Improving MODIS NDVI and Meteorological Data Based Rice Yield Estimation Model  

Na, Sang-il (Climate Change Assessment Division, National Institute of Agricultural Sciences, Rural Development Administration)
Hong, Suk-young (Rural Environment & Resources Division, National Institute of Agricultural Sciences, Rural Development Administration)
Ahn, Ho-yong (Climate Change Assessment Division, National Institute of Agricultural Sciences, Rural Development Administration)
Park, Chan-won (Research Policy Bureau, Rural Development Administration)
So, Kyu-ho (Climate Change Assessment Division, National Institute of Agricultural Sciences, Rural Development Administration)
Lee, Kyung-do (Climate Change Assessment Division, National Institute of Agricultural Sciences, Rural Development Administration)
Publication Information
Korean Journal of Remote Sensing / v.37, no.2, 2021 , pp. 199-209 More about this Journal
Abstract
By removing the increasing trend that long-term time series average of rice yield due to technological advancement of rice variety and cultivation management, we tried to improve the rice yield estimation model which developed earlier using MODIS NDVI and meteorological data. A multiple linear regression analysis was carried out by using the NDVI derived from MYD13Q1 and weather data from 2002 to 2019. The model was improved by analyzing the increasing trend of rime-series rice yield and removing it. After detrending, the accuracy of the model was evaluated through the correlation analysis between the estimated rice yield and the yield statistics using the improved model. It was found that the rice yield predicted by the improved model from which the trend was removed showed good agreement with the annual change of yield statistics. Compared with the model before the trend removal, the correlation coefficient and the coefficient of determination were also higher. It was indicated that the trend removal method effectively corrects the rice yield estimation model.
Keywords
Rice Yield; Detrending; MODIS NDVI; Weather Data; Remote Sensing;
Citations & Related Records
Times Cited By KSCI : 6  (Citation Analysis)
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1 Mishra, V. and K. Cherkauer, 2010. Retrospective droughts in the crop growing season: Implications to corn and soybean yield in the Midwestern United States, Agricultural and Forest Meteorology, 150: 1030-1045.   DOI
2 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 (in Korean with English abstract).   DOI
3 Na, S.I., S.Y. Hong, Y.H. Kim, and K.D. Lee, 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
4 Rojas, O., 2007. Operational maize yield model development and validation based on remote sensing and agro-meteorological data in Kenya, International Journal of Remote Sensing, 28(17): 3775-3793.   DOI
5 Sadras V.O., K.G. Cassman, P. Grassini, A.J. Hall, W.G.M. Bastiaanssen, A.G. Laborte, A.E. Milne, G. Sileshi, and P. Steduto, 2015. Yield Gap Analysis of Field Crops: Methods and Case Studies, Food and Agriculture Organization of the United Nations, Rome, IT.
6 Wu, Z., N.E. Huang, S.R. Long, and C.K. Peng, 2007. On the trend, detrending, and variability of nonlinear and nonstationary time series, Proceeded of National Academy of Sciences, 104(38): 14889-14894.   DOI
7 Cressman, G.P., 1959. An operational objective analysis system, Monthly Weather Review, 87: 367-374.   DOI
8 Ahn, J.B., J. 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 (in Korean with English abstract).   DOI
9 Campbell, J.B., 1996. Introduction to Remote Sensing 2nd ed, The Gilford Press, 4(5): 550-551.
10 Cockm J.H. and S. Yoshida, 1972. Accumulation of 14C-labelled carbohydrate before flowering and its subsequent redistribution and respiration in the rice plant, Proceeded of the Crop Science Society of Japan, 41: 226-234.
11 Data Portal Homepage, http://www.data.go.kr/, Accessed on Sep. 25, 2020.
12 Goldblum, D., 2009. Sensitivity of corn and soybean yield in Illinois to air temperature and precipitation: the potential impact of future climate change, Physical Geography, 30(1): 27-42.   DOI
13 Hong, S.Y., 1999. Analysis on rice growth information and estimation of paddy field area by using remotely sensed data, Dissertation, Kyungpook National University, Daegu, KOR (in Korean with English abstract).
14 Lee, K.D., S.Y. Hong, S.G. Kim, C.W. Park, H.Y. Ahn, S.I. Na, and K.H. So, 2020. Estimation of rice cultivation area using Sentinel-1 imagery in South Korea, Korean Journal of Soil Science and Fertilizer, 53(3): 345-354. (in Korean with English abstract).   DOI
15 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, SG, Aug. 3-8, Vol. 4, pp. 1793-1795.
16 Hong, S.Y., J. Hur, J.B. Ahn, J.M. Lee, B.K. Min, C.K. Lee, Y. 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).   DOI
17 Hong, S.Y., S.I. Na, K.D. Lee, Y.S. Kim, and S.C. Baek, 2015. A study on estimating rice yield in DPRK using MODIS NDVI and rainfall data, Korean Journal of Remote Sensing, 31(5): 441-448 (in Korean with English abstract).   DOI
18 Korean Statistical Information Service Homepage, http://www.kosis.kr/, Accessed on Sep. 12, 2020.
19 Kim, Y.H. and, S.Y. Hong, 2008. Estimation of rice grain protein content using optical ground sensor, Korean Journal of Remote Sensing, 24(6): 551-558 (in Korean with English abstract).   DOI
20 Lizumi, T., M. Kotoku, W. Kim, P.C. West, J.S. Gerber, and M.E. Brown, 2018. Uncertainties of potentials and recent changes in global yields of major crops resulting from census- and satellite-based yield datasets at multiple resolutions, PLoS ONE, 13(9): 1-15.
21 Lu, J., G.J. Carbone, and P. Gao, 2017. Detrending crop yield data for spatial visualization of drought impacts in the United States, 1895-2014, Agricultural and Forest Meteorology, 237-238: 196-208.   DOI