Garlic and Winter Wheat Identification Based on Active and Passive Satellite Imagery and the Google Earth Engine in Northern China

Remote Sensing

Boyan Zhou¹  Haifeng Tian¹  Yaochen Qin¹  Xuecao Li²  Jianxi Huang²  Jian Wang³  Jie Pei⁴  Li Wang⁵ 

[1] College of Environment and Planning/National Demonstration Center for Environment and Planning, Henan University, Kaifeng 475004, China;College of Land Science and Technology, China Agricultural University, Beijing 100083, China;Department of Geography, The Ohio State University, Columbus, OH 43210, USA;School of Geospatial Engineering and Science, Sun Yat-Sen University, Zhuhai 519000, China;State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing Normal University, Beijing 100101, China;

关键词: garlic;    winter wheat;    Sentinel;    Landsat;    Google Earth Engine;   

DOI  :  10.3390/rs12213539

来源: DOAJ

【 摘 要 】

Garlic and winter wheat are major economic and grain crops in China, and their boundaries have increased substantially in recent decades. Updated and accurate garlic and winter wheat maps are critical for assessing their impacts on society and the environment. Remote sensing imagery can be used to monitor spatial and temporal changes in croplands such as winter wheat and maize. However, to our knowledge, few studies are focusing on garlic area mapping. Here, we proposed a method for coupling active and passive satellite imagery for the identification of both garlic and winter wheat in Northern China. First, we used passive satellite imagery (Sentinel-2 and Landsat-8 images) to extract winter crops (garlic and winter wheat) with high accuracy. Second, we applied active satellite imagery (Sentinel-1 images) to distinguish garlic from winter wheat. Third, we generated a map of the garlic and winter wheat by coupling the above two classification results. For the evaluation of classification, the overall accuracy was 95.97%, with a kappa coefficient of 0.94 by eighteen validation quadrats (3 km by 3 km). The user’s and producer’s accuracies of garlic are 95.83% and 95.85%, respectively; and for the winter wheat, these two accuracies are 97.20% and 97.45%, respectively. This study provides a practical exploration of targeted crop identification in mixed planting areas using multisource remote sensing data.

【 授权许可】

Unknown