中文摘要：

粮食产量估测对于国家粮食生产宏观调控具有重要意义。以河南省为例，利用区域NDVI数据进行冬小麦产量监测研究。基于2000～2010年冬小麦生长关键期3～5月的MO—DIS～NDVI数据集，结合河南省18地市冬小麦生产数据，分析了研究区小麦产量和播种面积的时间动态变化特征，建立了基于区域NDVI的冬小麦产量估算模型。结果表明：自2000至2010年，研究区冬小麦产量呈上升趋势，播种面积基本保持稳定；利用单月NDVI建立的冬小麦产量线性模型，平均相对误差分别为12．02％、10．70％和9．27％；利用不同月份组合的NDVI累积和建立的冬小麦产量模型，平均相对误差分别为11．13％、10．38％、8．37％和9．41％；利用多个月份组合的NDVI建立的多元线性回归模型，平均相对误差分别为11．00％、9．32％、9．04％和9．58％；将小麦播种面积作为限制因素引入多元线性模型后，估算精度得到了很大提升，平均相对误差分别为5．65％、5．34％、6．76％和5．47％。通过误差对比后发现，在模型中引入播种面积后，利用区域NDVI可以有效、快速、准确地对冬小麦进行估产。

英文摘要：

Grain production estimates has an important significance in national macro-control of food production. In this paper, winter wheat yield estimation models were devised based on regional NDVI and planting area of winter wheat in Henan province. The annual variation of NDVI and wheat planting status were investigated based on MODIS-NDVI data in March, April and May from 2000 to 2010 and winter wheat yield data in this province, and estimation models was established based on the above-mentioned data. The results showed that wheat production had a significant growth from 2000 to 2010 in the stud- y region when wheat planting area kept stable. Through the statistical calculation and error comparison in models, it is showed that average relative errors were 12.02%, 10.70% and 9.27% respectively based on linear models built with NDVI of March, April, May and winter wheat production. Average relative errors were 11.13%, 10.38%, 8.37% and 9. 41% respectively based on linear models built with NDVI accumulation and yield of winter wheat. Average relative errors were 11.00%, 9. 32%, 9.04% and 9. 58% respectively based on multiple linear prediction models built with NDVI of March, April, May and winter wheat production. When we took the planting area of winter wheat as an independ- ent variable, the overall prediction accuracy of winter wheat production were 5.65%, 5. 34%, 6.76% and 5.47% respectively. Through comparing, the yield of winter wheat can be quickly and efficiently estimated with regional NDVI, but the accuracy and stability needs to be further improved. It is very difficult to predict winter wheat production accurately due to many influencing factors, but the accuracy can be gradually improved with various methods