Extraction of Crop Information in Cloudy Areas based on Optical and Radar Remote Sensing Images
Rapid and accurate extraction of crop type,spatial and temporal distribution is of great significance for agricultural structure adjustment and national food security.However,there are few optical remote sensing im-age of cloudy areas,thus crop monitoring is limited.To make up this shortage,spectral signature of winter crops and SAR time series characteristics of summer crops were proposed based on the Sentinel-2 and Sentinel-1 data for high-accuracy crop mapping.The Guanghan County,an important grain-producing region in south-west China,was studied.The object-oriented decision tree classification method was explored for spatial and temporal distribution extraction of crops in study area,and the classification accuracy was verified.The results shows that:(1)the main crops in Guanghan County are grain and oil crops,and the major crop rotation pat-terns are wheat-rice,rape-rice,potato-soybean and potato-corn;(2)the SAR time series characteristics of rice,soybean,corn show clear differences,extracting the types and distribution of winter-summer crops based on the optical-SAR remote sensing images provides a new idea for crops monitoring by remote sensing images in cloudy areas.(3)The overall accuracy and Kappa coefficient of object-oriented method reach 85.49%and 0.81,which can maintain the integrity of large area crops,and avoid salt and pepper noise.
Optical remote-sensing imageRadar remote-sensing imageCloudy areaObject orientedCrop information
NETL
NSTL
万方数据
