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Extracting Lotus Fields Using the Spectral Characteristics of GF-1 Satellite Data

Dongping Zha1,2, Haisheng Cai1,*, Xueling Zhang1, Qinggang He1, Liting Chen1, Chunqing Qiu1, Shufang Xia2

1 The Key Laboratory of Po-Yang Lake Watershed Agricultural Resources and Ecology of Jiangxi Province, Jiangxi Agricultural University, Nanchang, 330045, China
2 Jiangxi Tourism and Commerce Vocational College, Nanchang, 330100, China

* Corresponding Author: Haisheng Cai. Email:

(This article belongs to this Special Issue: Integrating Agronomy and Plant Physiology for Improving Crop Production)

Phyton-International Journal of Experimental Botany 2022, 91(10), 2297-2311.


The lotus (Nelumbo nucifera Gaertn.) is an aquatic plant that grows in shallow water and has long been cultivated in South China. It can improve the incomes of farmers and plays an important role in alleviating poverty in rural China. However, a modern method is required to accurately estimate the area of lotus fields. Lotus has spectral characteristics similar to those of rice, grassland, and shrubs. The features surrounding areas where it is grown are complex, small, and fragmented. Few studies have examined the remote sensing extraction of lotus fields, and automatic extraction and mapping are still challenging methods. Here, we compared the spectral characteristics of lotus fields and other ground objects and devised a remote sensing method for the rapid extraction of lotus fields. Using this method, the extraction accuracy of lotus was 96.3%. The Kappa coefficient was 0.926, which is higher than those of the unsupervised K-means classification, Mahalanobis distance, and support vector machine supervised classification, and demonstrates the potential of this method for extracting and mapping lotus fields by remote sensing.


Cite This Article

Zha, D., Cai, H., Zhang, X., He, Q., Chen, L. et al. (2022). Extracting Lotus Fields Using the Spectral Characteristics of GF-1 Satellite Data. Phyton-International Journal of Experimental Botany, 91(10), 2297–2311.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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