长时间序列NOAA-NDVI数据在冬小麦区域估产中的应用

粮食产量数据对国家的宏观管理具有重要意义。对中国这个人口大国而言,粮食产量更加受到重视。在我国冬小麦主要生产基地黄淮海平原范围内选取了石家庄市、衡水市、邢台市进行了研究,利用近20年空间分辨率为8 km的月NOAA-AVHRR NDVI数据和同期冬小麦产量统计数据,以县为单位建立了冬小麦产量形成关键期内各月NDVI数据与产量的关系,同时也建立了产量形成关键期NDVI累积和数据与冬小麦产量的关系。由于NDVI值在0.2～0.8之间与植物覆盖度呈良好线性关系,因此选取该范围NDVI值来建立与产量的关系。并分别做了相应统计检验和实际模型运行精度验证,最后对各个NDVI与产量数据之间的关系做了精度比较分析。这对促进黄淮海平原冬小麦产量估计具有较重要的作用。同时,作者认为相对于分辨率较高但时间序列短的MODIS数据而言,低分辨率长时间序列的NOAA-AVHRR数据在大范围农作物估产中仍然是一种有益补充。

Yield Estimation for Winter Wheat at Regional Scale Using Long-time Series NOAA-NDVI Data

The significance of crop yield estimation is well understood in China.In this paper,the authors devised a remote-sensing-based winter wheat yield estimation model in Huanghuaihai Plain in China.The authors selected the Shijiazhuang City,Hengshui City and Xingtai City as a case study region which is located in the Plain.The monthly 8-km NOAA-NDVI MVC data and the yearly county-level statistic production data of winter wheat were used.The data lasts 20 years and is from 1981 to 2001.We selected the NDVI value between 0.20 and 0.80 and calculated the sum of NDVI value in each county to build the relations with the winter wheat production because the NDVI of terrestrial vegetations steadily increases with canopy cover in this range.In Huanghuaihai Plain,the period from March to May is critical to the winter wheat yield.The authors obtained the relations between the 3 months NDVI data and the final production respectively at the county level.The authors also provided the validation results about each statistical model and analyzed the precision.The results are satisfactory.