Next Article in Journal
An Object-Based Classification Method to Detect Methane Ebullition Bubbles in Early Winter Lake Ice
Next Article in Special Issue
Smallholder Crop Area Mapped with a Semantic Segmentation Deep Learning Method
Previous Article in Journal
Machine Learning Techniques for Tree Species Classification Using Co-Registered LiDAR and Hyperspectral Data
Previous Article in Special Issue
A New CNN-Bayesian Model for Extracting Improved Winter Wheat Spatial Distribution from GF-2 imagery
Article

Mapping Winter Crops in China with Multi-Source Satellite Imagery and Phenology-Based Algorithm

by 1,2,3, 2, 2,3,*, 2,*, 2,3 and 2,3
1
College of Environment and Planning, Henan University, Kaifeng 475004, China
2
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, P.O. Box 9718, Datun Road, Chaoyang, Beijing 100101, China
3
College of Resource and Environment, University of Chinese Academy of Sciences, Yuquan Road 19, Shijingshan, Beijing 100049, China
*
Authors to whom correspondence should be addressed.
Received: 24 February 2019 / Revised: 23 March 2019 / Accepted: 1 April 2019 / Published: 5 April 2019
Timely and accurate mapping of winter crop planting areas in China is important for food security assessment at a national level. Time-series of vegetation indices, such as the normalized difference vegetation index (NDVI), are widely used for crop mapping, as they can characterize the growth cycle of crops. However, with the moderate spatial resolution optical imagery acquired by Landsat and Sentinel-2, it is difficult to obtain complete time-series curves for vegetation indices due to the influence of the revisit cycle of the satellite and weather conditions. Therefore, in this study, we propose a method for compositing the multi-temporal NDVI, in order to map winter crop planting areas with the Landsat-7 and -8 and Sentinel-2 optical images. The algorithm composites the multi-temporal NDVI into three key values, according to two time-windows—a period of low NDVI values and a period of high NDVI values—for the winter crops. First, we identify the two time-windows, according to the time-series of the NDVI obtained from daily Moderate Resolution Imaging Spectroradiometer observations. Second, the 30 m spatial resolution multi-temporal NDVI curve, derived from the Landsat-7 and -8 and Sentinel-2 optical images, is composited by selecting the maximal value in the high NDVI value period, and the minimal and median values in the low NDVI value period, using an algorithm of the Google Earth Engine. Third, a decision tree classification method is utilized to perform the winter crop classification at a pixel level. The results indicate that this method is effective for the large-scale mapping of winter crops. In the study area, the area of winter crops in 2018 was determined to be 207,641 km2, with an overall accuracy of 96.22% and a kappa coefficient of 0.93. The method proposed in this paper is expected to contribute to the rapid and accurate mapping of winter crops in large-scale applications and analyses. View Full-Text
Keywords: winter crops; NDVI; Landsat-7&8; Sentinel-2; Google Earth Engine; remote sensing winter crops; NDVI; Landsat-7&8; Sentinel-2; Google Earth Engine; remote sensing
Show Figures

Graphical abstract

MDPI and ACS Style

Tian, H.; Huang, N.; Niu, Z.; Qin, Y.; Pei, J.; Wang, J. Mapping Winter Crops in China with Multi-Source Satellite Imagery and Phenology-Based Algorithm. Remote Sens. 2019, 11, 820. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11070820

AMA Style

Tian H, Huang N, Niu Z, Qin Y, Pei J, Wang J. Mapping Winter Crops in China with Multi-Source Satellite Imagery and Phenology-Based Algorithm. Remote Sensing. 2019; 11(7):820. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11070820

Chicago/Turabian Style

Tian, Haifeng, Ni Huang, Zheng Niu, Yuchu Qin, Jie Pei, and Jian Wang. 2019. "Mapping Winter Crops in China with Multi-Source Satellite Imagery and Phenology-Based Algorithm" Remote Sensing 11, no. 7: 820. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11070820

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop