Exploring the temporal density of Landsat observations for cropland mapping: experiments from Egypt, Ethiopia, and South Africa. Issue 21 (2nd November 2018)

• Record Type: Journal Article
• Authors: Xu, Yidi; Yu, Le; Peng, Dailiang; Cai, Xueliang; Cheng, Yuqi; Zhao, Jiyao; Zhao, Yuanyuan; Feng, Duole; Hackman, Kwame; Huang, Xiaomeng; Lu, Hui; Yu, Chaoqing; Gong, Peng
• Is Part Of: International journal of remote sensing. Volume 39:Issue 21(2018)
• Page Start: 7328
• Page End: 7349
• Publication Date: 2018-11-02
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2018.1468115
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: Accurate land-use/land-cover mapping based on remote-sensing images depends on clear and frequent observations. This study aimed to explore how many Landsat images were needed within a year and when they should be acquired, for cropland mapping in Africa. Three Landsat footprints in Egypt (Path/Row: 177/039, 127 images), Ethiopia (Path/Row: 168/054, 98 images), and South Africa (Path/Row: 170/078, 207 images) from 1984 to 2016 were used together with spectral indices and a 30-m digital elevation model in a random forest-based supervised classification. Detailed exploration was conducted into the number and temporal distribution of Landsat images required. Our results indicated that average cropland mapping accuracies for these three sites ranged from 81.17% to 87.59% (Egypt), 54.43% to 79.72% (Ethiopia), and 28.11% to 59.35% (South Africa) using different numbers of images within a year. The overall cropland accuracies were improved with an increase in available Landsat images within a year and reached a relatively stable stage when more than five images were acquired in all three sites. Growing season images played a key role in identifying cropland, accounting for a 13.22% average accuracy improvement compared with non-growing season images. Therefore, at least five images are recommended from a computational efficiency perspective, although fewer images, as low as two growing season images, can also achieve good results in specific regions.