基于MODIS时序NDVI主要农作物种植信息提取研究

农业在黄河三角洲地区占有重要地位,及时、准确地掌握该地区农作物分布信息对政府有关部门制定农业政策、指导农业生产具有十分重要的意义。时序植被指数能够反映农作物物候特征,帮助识别农作物类型,在农作物种植信息提取方面具有明显优势。论文选取2014年MOD09Q1时序遥感数据集,以黄河三角洲主要农作物为研究对象,利用Harmonic Analysis of NDVI Time-Series（Hants）滤波重构NDVI时序曲线,通过对比待分像元NDVI时序曲线与参考时序曲线的相似性,实现农作物种植信息提取。对分类结果进行面积统计和空间分布精度检验：冬小麦、棉花、玉米的面积提取精度分别达到96.8%、95.5%、85.1%,空间匹配总体精度达86.9%。结果表明该方法有效可行,能够为该地区农业监测提供技术基础。

Extraction of Main Crops in Yellow River Delta Based on MODIS NDVI Time Series

Agriculture plays a key role in the Yellow River Delta, which is one of the greatest granaries of China. Timely and accurately understanding the crop distribution information is very important for related government departments to make reasonable decisions and guide agricultural production. Traditional methods based on field investigation and statistic data were time consuming and labor consuming. Time series of vegetation indices based on remote sensing images have obvious advantages and great application potentials in the extraction of crop planting information. This paper aimed at extracting the main crops in the Yellow River Delta, including winter wheat, maize and cotton. MODIS images with 250 m spatial resolution were used in this study. Dezhou City, Binzhou City and Dongying City were chosen as the study area for the convenience. The 250 m MOD09Q1 8 d time series remote sensing images in 2014 were acquired from the website of NASA. To avoid the disturbance from orchards and grasslands, firstly, the non-crop areas were masked out with the 1:100 000 land use map of the study area in 2014. Considering that there were some irregular fluctuations of the NDVI time series caused by the influence of clouds and atmosphere, we secondly reconstructed the NDVI time series with Hants filters. Then, the main crops planting information was extracted by comparing the NDVI time series with the reference NDVI time series which were the average NDVI of the sampling points collected in May, 2014 and November, 2014. Finally, the threshold value of each crop was determined and the planting information was extracted according to the thresholds. Two precision validation methods, spatial distribution and areal statistics, were adopted. The results showed that the accuracies of wheat and cotton in area were high (96.8%, 95.5%), while the accuracy of maize in area is a little lower (85.1%). The overall spatial consistency was 86.9% according to spatial distribution cooperation. The result suggests that the method in this paper is effective and practicable.