Optimization of Open-Access Optical and Radar Satellite Data in Google Earth Engine for Oil Palm Mapping in the Muda River Basin, Malaysia

Ju Zeng, Mou Leong Tan (), Yi Lin Tew, Fei Zhang, Tao Wang, Narimah Samat, Fredolin Tangang and Zulkifli Yusop
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Ju Zeng: GeoInformatic Unit, Geography Section, School of Humanities, Universiti Sains Malaysia, Penang 11800, Malaysia
Mou Leong Tan: GeoInformatic Unit, Geography Section, School of Humanities, Universiti Sains Malaysia, Penang 11800, Malaysia
Yi Lin Tew: GeoInformatic Unit, Geography Section, School of Humanities, Universiti Sains Malaysia, Penang 11800, Malaysia
Fei Zhang: College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830017, China
Tao Wang: College of Geography and Land Engineering, Yuxi Normal University, Yuxi 653100, China
Narimah Samat: GeoInformatic Unit, Geography Section, School of Humanities, Universiti Sains Malaysia, Penang 11800, Malaysia
Fredolin Tangang: Department of Earth Sciences and Environment, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
Zulkifli Yusop: Centre for Environmental Sustainability and Water Security (IPASA), Universiti Teknologi Malaysia (UTM), Johor Bharu 81310, Malaysia

Agriculture, 2022, vol. 12, issue 9, 1-19

Abstract: Continuous oil palm distribution maps are essential for effective agricultural planning and management. Due to the significant cloud cover issue in tropical regions, the identification of oil palm from other crops using only optical satellites is difficult. Based on the Google Earth Engine (GEE), this study aims to evaluate the best combination of open-source optical and microwave satellite data in oil palm mapping by utilizing the C-band Sentinel-1, L-band PALSAR-2, Landsat 8, Sentinel-2, and topographic images, with the Muda River Basin (MRB) as the test site. The results show that the land use land cover maps generated from the combined images have accuracies from 95 to 97%; the best combination goes to Sentinel-1 and Sentinel-2 for the overall classification. Meanwhile, the best combination for oil palm classification is C5 (PALSAR-2 + Landsat 8), with the highest producer accuracy (96%) and consumer accuracy (100%) values. The combination of C-band radar images can improve the classification accuracy of oil palm, but compared with the combination of L-band images, the oil palm area was underestimated. The oil palm area had increased from 2015 to 2020, ranging from 10% to 60% across all combinations. This shows that the selection of optimal images is important for oil palm mapping.

Keywords: Google Earth Engine; land use; Muda River Basin; oil palm; Malaysia; sentinel; Landsat; PALSAR; climate adaptation (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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