Efficiency Assessment for Crop Classification Using Multi-Sensor Data in Google Earth Engine

Farhad Ullah, Sawaid Abbas, Muhammad Usman, Aftab Ameen, Zulfiqar Ali Abbas, Syed Muhammad Irteza, Sami Ullah Khan ()
Additional contact information
Farhad Ullah, Sawaid Abbas, Muhammad Usman, Aftab Ameen, Zulfiqar Ali Abbas, Syed Muhammad Irteza, Sami Ullah Khan: Smart Sensing for Climate and Development, Center for Geographical Information System, University of the Punjab, Lahore, Pakistan. Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong SAR. Interdisciplinary Research Center for Aviation and Space Exploration, King Fahd University of Petroleum and Minerals, Dhahran,SaudiArabia. Punjab Information Technology Board, Lahore5Urban Unit, Lahore

International Journal of Innovations in Science & Technology, 2024, vol. 6, issue 6, 294-304

Abstract: Accurate mapping of agricultural lands and crop distribution is crucial for food security, sustainable development, and informed policymaking. This research classified agricultural crops in the Rahim Yar Khan district of Pakistan using multi-sensor images from Sentinel-1 and Sentinel-2 satellites. The study employed the cloud computing platform Google Earth Engine (GEE) and compared the performance of the Random Forest (RF) algorithm using Sentinel-1 (VV, HV, and HV+VV), Sentinel-2, and integrated datasets. Ground truth information obtained from field surveys and high-resolution images served as reference samples for training and validation. The fusion of Sentinel-1 and Sentinel-2 data enhanced feature extraction, leading to improved crop type classification. Post-processing procedures ensured that the maps were visually clear and free of noise, allowing for accurate crop mapping and land cover categorization. The classification results indicated high accuracy for crops such as sugarcane, cotton, rice, and water bodies. The RF classifier using fused data achieved the highest accuracy (overall accuracy of 93% and Kappa coefficient of 90%), followed by Sentinel-2 (89%), Sentinel-1 VV+VH (72%), Sentinel-1 VH (66%), and Sentinel-1 VV (62%). The study underscores the value of data integration in improving the classification accuracy of major crops (sugarcane, cotton, and rice) in the region. While some classes showed exceptional accuracy, others, such as Orchard, require further refinement in categorization methods. Overall, the study provides valuable insights into using multi-sensor remote sensing data for agricultural monitoring and decision-making.

Keywords: Machine learning; Crop classification; Sentinel-1; Sentinel-2; Google Earth Engine (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journal.50sea.com/index.php/IJIST/article/view/862/1559 (application/pdf)
https://journal.50sea.com/index.php/IJIST/article/view/862 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:abq:ijist1:v:6:y:2024:i:6:p:294-304

Access Statistics for this article

International Journal of Innovations in Science & Technology is currently edited by Prof. Dr. Syed Amer Mahmood

More articles in International Journal of Innovations in Science & Technology from 50sea
Bibliographic data for series maintained by Iqra Nazeer ().