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Automatic Cotton Mapping Using Time Series of Sentinel-2 Images

by 1,2, 3 and 1,4,*
Aerospace Information Research Institute, Chinese Academy of Science, Beijing 100101, China
Beijing Key Laboratory of Urban Spatial Information Engineering, Beijing Institute of Surveying and Mapping, Beijing 100038, China
Water Conservancy and Civil Engineering College, Inner Mongolia Agricultural University, Hohhot 010018, China
The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Group, Shihezi University, Shihezi 832003, China
Author to whom correspondence should be addressed.
Academic Editors: Liangpei Zhang, Lefei Zhang, Qian Shi and Yanni Dong
Received: 1 March 2021 / Revised: 25 March 2021 / Accepted: 29 March 2021 / Published: 1 April 2021
(This article belongs to the Special Issue Recent Advances in Hyperspectral Image Processing)
Large-scale crop mapping is essential for agricultural management. Phenological variation often exists in the same crop due to different climatic regions or practice management, resulting in current classification models requiring sufficient training samples from different regions. However, the cost of sample collection is more time-consuming, costly, and labor-intensive, so it is necessary to develop automatic crop mapping models that require only a few samples and can be extended to a large area. In this study, a new white bolls index (WBI) based on the unique canopy of cotton at the bolls opening stage was proposed, which can characterize the intensity of bolls opening. The value of WBI will increase as the opening of the bolls increases. As a result, the white bolls index can be used to detect cotton automatically from other crops. Four study areas in different regions were used to evaluate the WBI performance. The overall accuracy (OA) for the four study sites was more than 82%. Additionally, the dates when the opening stage of bolls begins can be determined based on the time series of WBI. The results of this research demonstrated the potential of the proposed approach for cotton mapping using sentinel-2 time series of remotely sensed data. View Full-Text
Keywords: cotton mapping; Sentinel-2 time series; white bolls’ index; automatic crop mapping cotton mapping; Sentinel-2 time series; white bolls’ index; automatic crop mapping
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MDPI and ACS Style

Wang, N.; Zhai, Y.; Zhang, L. Automatic Cotton Mapping Using Time Series of Sentinel-2 Images. Remote Sens. 2021, 13, 1355.

AMA Style

Wang N, Zhai Y, Zhang L. Automatic Cotton Mapping Using Time Series of Sentinel-2 Images. Remote Sensing. 2021; 13(7):1355.

Chicago/Turabian Style

Wang, Nan, Yongguang Zhai, and Lifu Zhang. 2021. "Automatic Cotton Mapping Using Time Series of Sentinel-2 Images" Remote Sensing 13, no. 7: 1355.

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