一种基于趋势单产和遥感修正模型的混合估产模型

doi:10.3864/j.issn.0578-1752.2017.10.005

Abstract

Abstract: 【Objective】 This paper analyzed the advantages and disadvantages of current crop yield estimation methods and proposed a novel hybrid yield estimation model which combines statistical yield estimation and yield estimation methods. 【Method】The model consists of three parts, trend yield estimate (Yt), remote sensing correction yield (Ys) and random error. The trend yield estimation was firstly calculated by using the polynomial regression method based on a long time series data of historical yield and then corrected by ARIMA model, which was set up by using the bias between the trend yield estimates and the historical yields. After that, a multiple linear regression model was set up to further reduce the estimation errors by using the bias between the trend yield estimates (Yt) and the reference yields as dependent variable and NDVI in critical growth period of crop as independent variables. In order to verify the feasibility and accuracy of the new hybrid estimation model, this paper estimated the yield of winter wheat in Beijing in 2015 based on three HJ Imagery obtained in winter wheat growing season, winter wheat yield of 30 sampling fields in 2015, and a nearly 30 years time series data of winter wheat yield (1985-2014) of Beijing. The estimation results from the hybrid yield estimation model was then compared with the true yield (2015 statistic winter wheat yield).【Result】The accuracy of winter wheat yield by using novel hybrid yield estimation model was 98.7% at city level and above 90% at country level. Except Fangshan(90.3%), the relative accuracy of yield estimation at the other countries was above 95%. The accuracy of winter wheat yield by using traditional trend yield model in Beijing was 94.75%, but the accuracy by using traditional trend yield model at country level was low, especially was lower 80% in Fangshan. ARIMA model was used for improving the accuracy of the traditional trend yield model. The accuracy of winter wheat yield improved in average by introducing the ARIMA model. For the remote sensing correction model established in this paper, using three remote sensing images for improving the accuracy was better, and this method improved the accuracy of winter wheat yield by 3.55%, especially the accuracy had a significant ascension in Fangshan and Pinggu.【Conclusion】The accuracy of winter wheat yield by using the novel hybrid estimation model is good at city level and county level. The model considers the change of time and spatial and can be used in crop yield estimation.

Key words: yield estimation model, winter wheat, ARIMA model, NDVI

CHEN ChangWei, ZHU XiuFang, CAI Yi, GUO Hang. A Hybrid Yield Estimation Model Based on the Trend Yield Model and Remote Sensing Correction Yield Model[J].Scientia Agricultura Sinica, 2017, 50(10): 1792-1801.

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A Hybrid Yield Estimation Model Based on the Trend Yield Model and Remote Sensing Correction Yield Model

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