| Agriculture has changed from traditional agriculture to high-tech agriculture. Remote sensing technology as one high-tech has become more important to agriculture. With the development of remote sensing technology to provide fast, accurate, large area, and high precision data, remote sensing based growing stage monitoring, growing status, yield estimation have become more and more important to fine agriculture, especially in China with a large pressure of population. This study researched the relationship of winter wheat growth and EVI data by using the MODIS-EVI data and the field data of winter wheat. Based on the detected growing stages, this study built the model of growth monitoring and yield prediction of winter wheat, which differs from traditional methods in that the EVI data in same stages was applied to monitor growth and estimate yield of winter wheat,.Savitzky-Golay Filter as an effective method was adopted to build high-quality EVI time series data. Then, the maximum variation of slope method, the window of extreme value method, and the turning point method were used to monitor planting time, heading time, and harvest time. The result shows that mean absolute deviation (ADE) for three growing stages was11.2day and RMSE was13.94days for all samples of2029winter wheat data. In addition, the error statistics in planting time had an ADE of10.59days and an RMSE of12.84days with a sample number of548. An ADE of11.05days and an RMSE of14.07days were obtained in heading time with a sample number of766. An error of ADE-9.26days and RMSE-14.57days was obtained in harvest time with a sample number of715data. The evaluation demonstrated that it was feasible to monitor growing stages by using remote sensing data.Based on the detected crop stage data, crop growth monitoring was carried out by using remote sensing data from2006to2010. Further evaluation by using field leaf erea index (LAI) shows that the new monitoring method by using the same stage’EVI data was better than traditional method by using same Julian day’EVI data. The new method gave an improved statistics with correlation coefficients ranging from0.51to0.86compared with field LAI data. However, traditional method had an wide variation of correlation coefficient of-0.96～0.61. Further monitoring result given by the new method showed that winter wheat in2010grew better than that in2009, which agrees well with field LAI data. But the traditional method showed that winter wheat grew in2010worse than that in2009, which contradicts with field LAI data. It can be concluded that compared with the traditional method, the new growth monitoring method, based on same growing stage, was better.As growth of winter wheat in planting time, heading time, and harvest time was important to the yield forming of winter wheat, this study built yield prediction models of winter wheat based on three key stages and all stages. The evaluation for six years shows that the yield prediction error changed from-6.96%to5.84%for planting time model, from-6.73%to6.89%for heading time model, and from-7.87%to4.7%for harvest time model, respectively. The error of yield prediction for all stage model varied from-1.04to1.57for six years, which indicates that the all stage model was the best. However, the traditional prediction model based on julian day of70,115, and165had an error of-9.1～10.74%,-7.17%to10.51%, and-11.34%to6.27%, respectively. The three time model based on julian date had an error of-11.34%6.27%for six years. Above comparison indicates that yield prediction models of winter wheat based on the same growing stage had a better performance than that based on the same julian day. |
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