Winter Wheat Quality Inspection and Regionalization Based on NIR Network and Remote Sensing

In the crown of the year, inspection of wheat quality fast and accurate is very important for all of grain enterprises, farmers and governments. Governments would like to construct a fair and equitable market for grain transaction with explicit grain quality standard. Farmers would like to sell their high-quality grain at a high unit price for they have paid more attention and investment. Enterprises also would like to purchase high-quality grain with higher unit price for it can bring more profits. At the same time, generating regionalization map of wheat quality accuracy in time is very important on the grain enterprises’ purchase strategy formulating and purchase region choosing. The authors collected 1200 NIR samples in 240 points (in other words, 5 samples per point ) in 12 counties in the main wheat producing areas in China (Hebei, Henan, Jiangsu and Shandong), then analysis these samples by both GIS spatial interpolation method and RS inverse method. In contrast, RS inverse method can simulate the quality parameter more accuracy than GIS spatial interpolation method. In conclusion, RS inverse method is preferable to generate quality regionalization map with NIR network samples.