中文摘要：

准确掌握农作物的空间种植分布情况,对于国家宏观指导农业生产、制定农业政策有重要意义。针对黑龙江省玉米与大豆生育期接近、光谱特征相似,较难区分的问题,以多时相16 m空间分辨率高分一号（GF-1）卫星宽覆盖（wide field of view,WFV）影像为数据源,选择归一化植被指数（normalized difference vegetation index,NDVI）、增强植被指数（enhanced vegetation index,EVI）、宽动态植被指数（wide dynamic range vegetation index,WDRVI）、归一化水指数（normalized difference water index,NDWI）4个特征,结合实地调查样本点,采用随机森林分类算法,提取黑龙江省黑河市嫩江县玉米与大豆种植面积。研究表明,区分玉米与大豆的最佳时段为9月下旬至10月上旬,即大豆已收获而玉米未收获的时段,在4个待选特征中,NDVI、NDWI与WDRVI指数组合表现最佳;随机森林算法与最大似然算法、支持向量机算法相比,分类精度更高,其总体分类精度为84.82%,Kappa系数为77.42%。玉米制图精度为91.49%,用户精度为93.48%;大豆制图精度为91.14%,用户精度为82.76%。该方法为大区域农作物的分类提供重要参考和借鉴价值。

英文摘要：

Planting area and spatial distribution information of crops are vital for guiding agricultural production, taking effective management measurements, and monitoring crop growth conditions. Numerous crop classification algorithms have been developed with rapid development of different remote sensing data. However, distinguishing of corn and soybean cropping areas still remains a difficult challenge due to their similar growth calendar and spectral characteristics. In this study, we tried to identify corn and soybean cropping area using random forest（RF） classifier which has been proved to be an effective method in land cover classification based on multi-temporal GF-1 WFV（wide field of view） imagery. We selected Nenjiang County, Heilongjiang Province in China as the study area which was called the Town of Soybean. Seven GF-1 WFV time-series images（April 14 th, May 20 th, June 26 th, July 16 th, August 26 th, September 4th, and September 29th）, from which the key growth stages could be extracted and the effects of clouds could be avoided, were chosen to classify main crops. First, we conducted atmospheric and geometric corrections on multi-temporal GF-1 imagery. In order to improve the accuracy of distinguishing corn and soybean cropping area, the parameters of RF classifier were input, which included normalized difference vegetation index（NDVI）, wide dynamic range vegetation index（WDRVI）, enhanced vegetation index（EVI）, and normalized difference water index（NDWI）, and hundreds of field sample points were collected in the field survey. Also, it＇s necessary to evaluate the importance of different combination of these indices. The results showed that the combination of NDVI, WDRVI and NDWI achieved the best accuracy with the producer accuracy of 91.14% for soybean and 91.49% for corn, and with the user accuracy of 82.76% for soybean and 93.48% for corn. Then, the support vector machine（SVM） and maximum likelihood（ML） supervised classifiers were also used to map corn and soybean cr