Back to Search Start Over

Potentiality of Landsat-9 for early-season mapping of winter garlic and winter wheat

Authors :
Haifeng Tian
Mengdan Yang
Fangli Wu
Yaochen Qin
Xiwang Zhang
Jiayi Liu
Weiyang Yan
Source :
Geo-spatial Information Science, Vol 27, Iss 6, Pp 2199-2210 (2024)
Publication Year :
2024
Publisher :
Taylor & Francis Group, 2024.

Abstract

Landsat-9 is the latest satellite of the Landsat program and provides useful agricultural remote sensing data. The earlier the crop distribution data is obtained, the greater the value of the data. However, whether Landsat-9 image is suitable for the early-season mapping of winter garlic and winter wheat is a problem worthy of attention. Therefore, this study evaluates the potential to use two Landsat-9 images, acquired on 13 December 2021 and 30 January 2022, for the early-season identification of winter garlic and winter wheat in China. According to J-M (Jeffries-Matusita) distances, we evaluated the separability of winter garlic, winter wheat, and other ground objects based on the two Landsat-9 images. Then, winter garlic and winter wheat were extracted by using unsupervised classification method, i.e. the IsoData and K-means clustering algorithms, and supervised classification method, i.e. the Random Forest (RF), and Support Vector Machine (SVM) algorithms. The separability between garlic and wheat in January is stronger than that in December. The classification overall accuracy based on the Landsat-9 image on 30 January 2022 is 92.03% with a kappa coefficient of 0.87 using the SVM algorithm. This period is about 4 months earlier than the crop harvest period. Landsat-9 images have good potentiality for early-season mapping of winter garlic and winter wheat.

Details

Language :
English
ISSN :
10095020 and 19935153
Volume :
27
Issue :
6
Database :
Directory of Open Access Journals
Journal :
Geo-spatial Information Science
Publication Type :
Academic Journal
Accession number :
edsdoj.6d84ce08bc644801ae456a74a4ff3324
Document Type :
article
Full Text :
https://doi.org/10.1080/10095020.2024.2311868