作物学报2018，Vol.44Issue(5)：762-773,12.DOI:10.3724/SP.J.1006.2018.00762
基于GF-1卫星遥感数据识别京津冀冬小麦面积
Acguisition of Winter Wheat Area in the Beijing-Tianjin-Hebei Region with GF-1 Satellite Data
摘要
Abstract
Winter wheat area accurate acquisition at provincial scale is an important aspect in remote-sensing monitoring of crop area. This study aimed at estimating winter wheat area in Beijing, Tianjin, and Hebei at provincial scale using classification map units from the national standard topographic map and the winter wheat area index (WWAI) created from the wide field view (WFV) data of GF-1 satellite. A total of 984 satellite monitoring images between October 1, 2013 and June 30, 2014 were used as data sources. The major process steps were data preprocessing, NDVI synthesis of standard map units, selection of samples, crea-tion of winter wheat area index, confirmation of winter wheat crop type, provincial scale mapping, and accuracy verification. Multi-temporal GF-1/WFV data were preprocessed and NDVI value of images were calculated by using block adjustment and 6S atmospheric correction algorithm. By means of the 1:100 000 national standard topographic map framings of China, as the classi-fication map units, WWAI was equally divided at a proportion of 1 percent into 101 extraction nodes from 0 to 100%. The NDVI values of extraction nodes were compared with type confirmation samples, and the most accurate NDVI was adopted as the ex-traction threshold value of winter wheat area. This threshold was then used into WWAI image in the map units to obtain winter wheat planting distribution. The identification result showed that, by taking standard map framing as calculation units and based on the creation of WWAI of GF-1 images, we remarkably improved the wave spectrum difference between winter wheat and other ground objects, with the overall accuracy of 89.6%, user accuracy of 89.8%, mapping accuracy of 96.5%, and Kappa coefficient of 0.72. In a typical region, the algorithm proposed had a similar accuracy with supervised classification algorithm. Except for 4.77% of the difference in mapping accuracy, the differences in overall accuracy and user accuracy were less than 1.00%. These results indicate that the used in this study is of algorithm high accuracy, efficiency and consistency in classification unit identifica-tion and applicable in agricultural monitoring at provincial level.关键词
GF-1卫星/区域尺度/冬小麦/面积指数/遥感监测Key words
GF-1 satellite/region scale/winter wheat/area index/remote sensing引用本文复制引用
王利民,刘佳,杨福刚,杨玲波,姚保民,王小龙..基于GF-1卫星遥感数据识别京津冀冬小麦面积[J].作物学报,2018,44(5):762-773,12.基金项目
本研究由国家重点研发计划项目(2016YFD0300603)资助.This study was supported by the National Key Research and Development Program of China(2016YFD0300603). （2016YFD0300603）
