| With the development of satellite remote sensing technology, remote sensing data resource with high time phase and high spatial resolution is growing, making the accuracy of crop growth monitoring obtain favorable technical support.Taking the Huang-Huai-Hai region as the studied area, this paper calculated NDVI time series function through the pretreatment methods such as image mosaicing, projection transformation, and image clipping, by using the MODIS remote sensing image at a spatial resolution of 250 meters of land product data in the epoch of 2009-2013, then used the methods of dynamic process monitoring and real-time monitoring of crop growth, combining with the corresponding meteorological observation data of the research area, and thus analyzed winter wheat growth in Huang-Huai-Hai region. Through research and analysis, the main conclusions are as follows:(1) Through the process monitoring of winter wheat growth between 2009 and 2013, namely analyzing NDVI time series curve, we find that the trend of the curve of provinces is basically consistent, taking on the shape of two peaks and a valley. We observe that the winter wheat NDVI in Anhui and Henan region is higher than the average level in Huang-Huai-Hai region, through contrastive analysis of the regional difference of provinces in Huang-Huai-Hai region. According to NDVI time-series curves for five years, we calculate the maximum rising rate of NDVI and cumulative NDVI in growth period. The rising rate has close correlation with growth of winter wheat, and the cumulative value is also closely connected with single yield of winter wheat. The rising rate of Henan province is largest for 13.2, and Anhui province take second place at the rate of 12.3, then is followed by Jiangsu, Shandong and Hebei province, at the rising rate of 9.7,8.4 and 7.1 respectively. The rank of the mean cumulative NDVI for five years during growth period is Anhui (12.44), Henan (11.80), Jiangsu (10.88), Shandong (9.32), Hebei (7.10), and the cumulative NDVI decrease from the south to the north with the latitude.(2) Real-time monitoring of growth can reflect the differences between seedling stage and the corresponding period and spatial distribution of crop at a specific point in time, through comparison with the same period by using difference model, and classify according to the growing level. The results of the real-time monitoring show that the growth level of winter wheat of 2013 is basically in line with perennial in Huang-Huai-Hai region starting from the seedling stage, but on February 2, growth is obviously poor comparing with perennial in eastern Henan and northern Anhui, and the ratio of poorer growth and poor growth in the studied area is 48.39% and 9.21% respectively. But as time goes on, starting from the double ridge stage, growth of winter wheat in poor region gradually recover, and the number of region of well growth increase gradually, and on April 6, the ratio of poorer growth and poor growth has been reduced to 21.47% and 8.74% before heading period, while the ratio of well growth and better growth has also reached 34.4%.(3) Through the analysis of the two meteorological factors of accumulated temperature and precipitation, we find that NDVI of winter wheat has close correlation with the accumulated temperature and precipitation. The correlation coefficient of precipitation and NDVI before overwintering in 2012 and 2013 is 0.433 and 0.243 respectively, and the correlation coefficient is 0.366 and 0.384 after overwintering. The correlation coefficient of accumulated temperature and NDVI in whole growing period is 0.415 and 0.366. In the case of P= 0.01, all pass the significance test, and we can find that the influence of precipitation and accumulated temperature on winter wheat growth is very obvious.This study can reflect the growth information during main phenological period of winter wheat in Huang-Huai-Hai region more objectively, and can make guidance for agricultural activities based on the real-time and dynamic monitoring of growth in growing season of winter wheat, combining with the meteorological information, thus can evaluate the crop yield trend effectively. |
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