摘要
GF-1号卫星是中国2013年4月26日发射的一颗高分辨率遥感卫星,为解决该新型卫星数据在农作物对地抽样遥感调查中的应用技术方法问题,该文针对GF-1号卫星数据的特点,研究了基于GF-1号卫星16m WFV传感器和2m/8m PMS传感器卫星数据的农作物种植面积遥感抽样调查方法。根据研究区物候历,选择农作物识别关键期的16m WFV传感器数据进行多时相农作物种植面积的中分辨率遥感提取;在中分辨率农作物面积遥感分类图基础上,计算研究区域的MORAN I指数,确定格网抽样单元的大小,进行多目标农作物的MPPS(multivariate probability proportional to size)抽样;对抽样单元采用2m/8 m PMS传感器卫星数据进行高分辨率农作物面积制图;最后根据MPPS抽样方法进行总体农作物种植面积的推断,并计算CV值,评价抽样精度。以江苏省东台市为研究区对GF-1号卫星数据进行了应用研究。研究结果表明,GF-1号卫星数据完全可以应用于县级农作物种植面积的提取,农作物种植面积提取精度优于90%。
The Chinese GF-1 satellite is a new high spatial resolution satellite launched on April 26, 2013. It was equipped with two types of sensors. One is the wide field view sensor （WFV sensor）; the other is the panchromatic and multispectral sensor （PMS sensor）. The WFV sensor can acquire multispectral image in blue, green, red, and near-infrared bands with 16 meters spatial resolution and 4 days temporal resolution. The PMS sensor can acquire a panchromatic and multispectral image with 41 days temporal resolution. The spatial resolution of a panchromatic image acquired by the PMS sensor is 2 meters, while the spatial resolution of a multispectral image acquired by the PMS sensor in blue, green, red, and near-infrared bands is 8 meters. According to the characteristics of a GF-1 satellite image, a method for mapping crops using remote sensing and sampling technology was proposed. There are four kinds of summer harvest crops in our study area of Dongtai county. There are winter wheat, barley, rapeseed and vegetables. According to the crop’s phenology calendar information of this study area, there are three key periods for the identification of crops. In later March, winter wheat and barley are in the growing season, while rapeseed is in the flowering period. In early April, barley and rapeseed are in the flowering period, while winter wheat is in the growing season. In early and middle May, winter wheat is in the flowering stage, while canola and barley are at maturity. So the 16 meters resolution WFV sensor data acquired in those periods were used to classify those crops. First, that data was preprocessed for ortho rectification, geometric correction, and atmospheric correction. Then multi-days NDVI were calculated and was used to generate a false color composite image. In the false-color composite image, we found that those kinds of crops exhibited distinctly different colors. Vegetables were yellow, canola was light red, grain crop including winter wheat and barley is dark red. So those crops can be easily
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2015年第5期160-166,共7页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家自然科学基金(41301390)
高分辨率国家科技重大专项
国家高技术研究发展计划(863计划)(2014AA06A511)
重庆市教委科学技术研究资助项目(KJ100415)
重庆市科委自然科学基金资助项目(CSTC
2014jcyj A0915
2007BB2395)
关键词
农作物
遥感
抽样
GF-1号
种植面积
crops
remote sensing
sampling
GF-1 satellite
planting area
