Abstract: Abstract: 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 classified using the maximum likelihood method. Then we converted the classification map to vector files and calculated a MORAN index in this study area using ARCGIS. The MORAN index in this study area was 0.78, and the distance threshold was 5480 meters. The resolution of WFV data was 16meters, so 5440meters (340pixels of WFV) was set to the size of the sampling units. Then 10 sampling units were selected using the MPPS method based on the proportional information of each crop for each unit which was calculated using the block statistics function of ARCGIS. After that, the high spatial PMS sensor data of those 10 sampling units were resized out. In the high spatial false-color composite PMS sensor image, we found that those crops also had different colors. Rapeseed was light red, wheat was dark red, and vegetables were gray. So the crops in each of the sampling units were classified with the maximum likelihood method using the field investigation data as the training sample. Finally, according to the MPPS method, the total areas of each crop in the study area and CV were calculated. The area of crops, rapeseed, and Vegetables of 2014 in Dongtai using temporal WFV data was 582.74km2, 226.7873km2, and 271.288km2. Compared with the data published by the Dongtai County Farm Bureau, the errors of crops, rapeseed, and vegetables using temporal WFV data were -9.9%, -2.8% and -15.2%. The area of crops, rapeseed, and vegetables of 2014 in Dongtai County using remote sensing and sampling methods was 638.6318km2, 244.8km2, and 322.9601km2. Compared with the data published by the Dongtai County Farm Bureau, the errors of crops, rapeseed, and vegetables using remote sensing and sampling methods were 3%, 5%, and 1%. Those results showed that this method could classify crops, rapeseed, and vegetable areas effectively. High mapping precision of 90% was acquired.