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A Study on Estimating Rice Yield in DPRK Using MODIS NDVI and Rainfall Data
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  • Journal title : Korean Journal of Remote Sensing
  • Volume 31, Issue 5,  2015, pp.441-448
  • Publisher : The Korean Society of Remote Sensing
  • DOI : 10.7780/kjrs.2015.31.5.8
 Title & Authors
A Study on Estimating Rice Yield in DPRK Using MODIS NDVI and Rainfall Data
Hong, Suk Young; Na, Sang-Il; Lee, Kyung-Do; Kim, Yong-Seok; Baek, Shin-Chul;
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 Abstract
Lack of agricultural information for food supply and demand in Democratic People`s republic Korea(DPRK) make people sometimes confused for right and timely decision for policy support. We carried out a study to estimate paddy rice yield in DPRK using MODIS NDVI reflecting rice growth and climate data. Mean of MODIS in paddy rice over the country acquired and processed from 2002 to 2014 and accumulated rainfall collected from 27 weather stations in September from 2002 to 2014 were used to estimated paddy rice yield in DPRK. Coefficient of determination of the multiple regression model was 0.44 and Root Mean Square Error(RMSE) was 0.27 ton/ha. Two-way analysis of variance resulted in 3.0983 of F ratio and 0.1008 of p value. Estimated milled rice yield showed the lowest value as 2.71 ton/ha in 2007, which was consistent with RDA rice yield statistics and the highest value as 3.54 ton/ha in 2006, which was not consistent with the statistics. Scatter plot of estimated rice yield and the rice yield statistics implied that estimated rice yield was higher when the rice yield statistics was less than 3.3 ton/ha and lower when the rice yield statistics was greater than 3.3 ton/ha. Limitation of rice yield model was due to lower quality of climate and statistics data, possible cloud contamination of time-series NDVI data, and crop mask for rice paddy, and coarse spatial resolution of MODIS satellite data. Selection of representative areas for paddy rice consisting of homogeneous pixels and utilization of satellite-based weather information can improve the input parameters for rice yield model in DPRK in the future.
 Keywords
MODIS NDVI;Rainfall data;Rice;Yield;DPRK;
 Language
Korean
 Cited by
1.
Machine Learning Approaches to Corn Yield Estimation Using Satellite Images and Climate Data: A Case of Iowa State,;;

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