Difference analysis of peanut remote sensing extraction based on Sentinel-2 and Landsat-8 of GEE

Zihao Yu; Weiheng Zheng

doi:10.1117/12.3010404

15 November 2023 Difference analysis of peanut remote sensing extraction based on Sentinel-2 and Landsat-8 of GEE

Zihao Yu, Weiheng Zheng

Proceedings Volume 12815, International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023); 128150I (2023) https://doi.org/10.1117/12.3010404
Event: International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023), 2023, Kaifeng, China

Abstract

Peanut ranks among the foremost cash crops in China. Accurate and timely understanding of peanut planting is very important for governments at all levels to manage production and formulate related policies. In this study, Pingtan was selected as the research area and Sentinel-2 and Landsat-8 data were acquired through the GEE cloud platform. The peanut planting areas in the study site were extracted using a random forest algorithm, and differences between the two datasets were compared. The experimental results show that: (1) The peanut planting area of Pingtan is all over the county, mainly concentrated in Haitan Island. There is a large peanut planting area in the northern part of Haitan Island, and other peanut planting areas are scattered in the southern part of Haitan Island and other islands; (2) The overall accuracy of Sentinel-2 data extraction was 94.44%, and the Kappa coefficient was 0.93; The overall accuracy of Landsat-8 data was 87.44%, and the Kappa coefficient was 0.84. (3) Remote sensing image data can be quickly used using GEE cloud platform, and crops can be quickly extracted based on the powerful processing capacity of GEE cloud platform.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zihao Yu and Weiheng Zheng "Difference analysis of peanut remote sensing extraction based on Sentinel-2 and Landsat-8 of GEE", Proc. SPIE 12815, International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023), 128150I (15 November 2023); https://doi.org/10.1117/12.3010404

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Earth observing sensors

Landsat

Remote sensing

Clouds

Random forests

Feature extraction

Vegetation

Show All Keywords

Keywords/Phrases

Search In:

Publication Years