Abstract
Ensuring food security is a long-term and arduous task. Timely and accurate grasp of grain production capacity information can provide favourable data support for the nation to formulate macroeconomic plans and food policies. With the development of remote sensing technology, it has been widely used in crop yield estimation models. In this paper, the yield of spring maize in Da’an of Jilin province was estimated based on vegetation indexes calculated from Landsat-8 images. The results have shown that the fitting degree and estimation accuracy of yield estimation models at tasselling stage are significantly better than those at milk stage. Among these vegetation indexes, the model based on GNDVI has better fitting degree and estimation accuracy. This paper can provide reference for the post construction evaluation of high standard farmland in China.
Highlights
Ensuring food security is a long-term and arduous task
Linear regression model was adopted in the study, which applied least squares to establish a statistical relationship between spring maize yield and vegetation index
The spring maize yield was concentrated at 5,000~6,500 kg/hm2, which was representative to a certain extent
Summary
Ensuring food security is a long-term and arduous task. Timely and accurate grasp of grain production capacity information can provide data support for the nation to formulate related policies. With the development of remote sensing technology, remote sensing has been widely used in crop yield estimation models. Sakamoto et al [1] used multi-temporal remote sensing data and crop phenology characteristics to establish a statistical relationship between crop yield and vegetation indexes to estimate crop yield, and high estimation accuracy was obtained. With the continuous emergence of high temporal and spatial resolution remote sensing data, remote sensing shows more and more incomparable advantages in crop yield estimation. The yield of spring maize in Da’an City of Jilin Province was estimated by using vegetation indexes from Landsat 8 satellite remote sensing data, to provide reference for the post construction evaluation of high standard farmland in China
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