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Lots of studies on large scales maize estimation was used remote sensing data at home and abroad, nevertheless, the yields estimated accuracy were poor in mountainous region due to complex surface-information such as cultivated land topography and land fragmentation. In order to improved the accuracy in mountainous region, taking spring maize in Binzhou City Weibei dryland area, Shaanxi Province as a case, we used geographic conditions data and remote sensing data, explored a set of maize yields estimation method. In this study, the classification scale of cultivated land plots was optimized by using land use data. Based on the MOD09GQ products from 2001 to 2014, MODIS NDVI ten-day synthetic cloudless time series products were reconstructed. The ten-day synthetic data of time series NDVI, ten-day temperature, ten-day cumulative precipitation and yields statistical data were analyzed by canonical method, multiple linear regression model construction and leave-one-out cross validation. The results showed that the estimated yields of the model were close to the measured yields, and the error was less, indicating that the plot data weakened the interference of same spectrum foreign matter in the process of yields-growing area identification and extraction. The yields remote sensing estimation model optimized by the plot had a certain reliability and could be used for actual yields estimation. The yields of spring maize in Binzhou were closely related to NDVI in late July, temperature and precipitation in late June, and the influence of temperature and precipitation on the yields of spring maize had an obvious lag effect. This method of maize remote sensing yields estimation model could be used for reference in the study of remote sensing yields estimation of wheat, soybean and other yields in a mountainous region in the future.
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