Using HJ-CCD image and PLS algorithm to estimate the yield of field-grown winter wheat

Abstract

Remote sensing has been used as an important means of estimating crop production, especially for the estimation of crop yield in the middle and late growth period. In order to further improve the accuracy of estimating winter wheat yield through remote sensing, this study analyzed the quantitative relationship between satellite remote sensing variables obtained from HJ-CCD images and the winter wheat yield, and used the partial least square (PLS) algorithm to construct and validate the multivariate remote sensing models of estimating the yield. The research showed a close relationship between yield and most remote sensing variables. Significant multiple correlations were also recorded between most remote sensing variables. The optimal principal components numbers of PLS models used to estimate yield were 4. Green normalized difference vegetation index (GNDVI), optimized soil-adjusted vegetation index (OSAVI), normalized difference vegetation index (NDVI) and plant senescence reflectance index (PSRI) were sensitive variables for yield remote sensing estimation. Through model development and model validation evaluation, the yield estimation model's coefficients of determination (R 2) were 0.81 and 0.74 respectively. The root mean square error (RMSE) were 693.9 kg ha −1 and 786.5 kg ha −1. It showed that the PLS algorithm model estimates the yield better than the linear regression (LR) and principal components analysis (PCA) algorithms. The estimation accuracy was improved by more than 20% than the LR algorithm, and was 13% higher than the PCA algorithm. The results could provide an effective way to improve the estimation accuracy of winter wheat yield by remote sensing, and was conducive to large-area application and promotion. Scientifically and accurately estimating crop yield is of significant importance for formulating plans for social and economic development, determining agricultural products import and export plans, ensuring national food security, guiding and regulating macroscopic planting structure, as well as improving the management skills of relevant agriculture-related enterprises and farmers 1-6. With the improvement of spatial, temporal and spectral resolutions of remote sensing data and the significant reduction of cost, currently remote sensing has been widely used in the estimation of production of all kinds of food crops, and it has become a research focus in the interdisciplinary field combining remote sensing and agriculture 7. At present, there were many methods and means for estimating crop yield, such as crop yield meteorological forecast, artificial sampling survey, statistical simulation model, remote sensing estimation and so on 8,9. Using a Criteria/Wofost simulation model that included the new numerical scheme for soil water balance, some researchers compared field data collected at the university of bologna's experimental farm in 1977-1987 with the median wheat yield, and the predicted value was consistent with the observed value 10. Other researches have suggested that the mars-crop ...

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