위성영상과 기후자료를 이용한 미중서부 옥수수와 콩의 수확량 추정
Estimation of Corn and Soybeans Yields of the US Midwest using Satellite Imagery and Climate Dataset
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This paper described the estimation of corn and soybeans yields of four states in the USMidwest using time-series satellite imagery and climate dataset between 2001 and 2012. We firstconstructed a database for (1) satellite imagery acquired from Terra MODIS (Moderate ResolutionImaging Spectroradiometer) including NDVI (Normalized Difference Vegetation Index), EVI (EnhancedVegetation Index), LAI (Leaf Area Index), FPAR (Fraction of Photosynthetically Active Radiation), andGPP (Gross Primary Productivity), (2) climate dataset created by PRISM (Parameter-Elevation Regressionson Independent Slopes Model) such as precipitation and mean temperature, and (3) US yield statistics ofcorn and soybeans. Then we built OLS (Ordinary Least Squares) regression models for corn and soybeansyields between 2001 and 2010 after correlation analyses and multicollinearity tests. These regressionmodels were used in estimating the yields of 2011 and 2012. Comparisons with the US yield statisticsshowed the RMSEs (Root Mean Squared Errors) of 0.892 ton/ha and 1.095 ton/ha for corn yields in 2011and 2012 respectively, and those of 0.320 ton/ha and 0.391 ton/ha for soybeans yields. This result can bethought of as a good agreement with the in-situ statistics because the RMSEs were approximately 10%of the usual yields: 9 ton/ha for corn and 3 ton/ha for soybeans. Our approach presented a possibility forextending to more advanced statistical modeling of crop yields using satellite imagery and climate dataset.
Abstract
This paper described the estimation of corn and soybeans yields of four states in the USMidwest using time-series satellite imagery and climate dataset between 2001 and 2012. We firstconstructed a database for (1) satellite imagery acquired from Terra MODIS (Moderate ResolutionImaging Spectroradiometer) including NDVI (Normalized Difference Vegetation Index), EVI (EnhancedVegetation Index), LAI (Leaf Area Index), FPAR (Fraction of Photosynthetically Active Radiation), andGPP (Gross Primary Productivity), (2) climate dataset created by PRISM (Parameter-Elevation Regressionson Independent Slopes Model) such as precipitation and mean temperature, and (3) US yield statistics ofcorn and soybeans. Then we built OLS (Ordinary Least Squares) regression models for corn and soybeansyields between 2001 and 2010 after correlation analyses and multicollinearity tests. These regressionmodels were used in estimating the yields of 2011 and 2012. Comparisons with the US yield statisticsshowed the RMSEs (Root Mean Squared Errors) of 0.892 ton/ha and 1.095 ton/ha for corn yields in 2011and 2012 respectively, and those of 0.320 ton/ha and 0.391 ton/ha for soybeans yields. This result can bethought of as a good agreement with the in-situ statistics because the RMSEs were approximately 10%of the usual yields: 9 ton/ha for corn and 3 ton/ha for soybeans. Our approach presented a possibility forextending to more advanced statistical modeling of crop yields using satellite imagery and climate dataset.
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