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Comparative assessment of land use and land cover classification from Planet, Landsat 8, and Sentinel-2A data in the semi-arid region of Najaf, Iraq
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Iain Bye,
Jacqueline Rosette ,
Sietse Los
International Journal of Remote Sensing, Pages: 1 - 30
Swansea University Authors:
Akram Al-Qaraghuli, Peter North , Jacqueline Rosette , Sietse Los
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DOI (Published version): 10.1080/01431161.2026.2676245
Abstract
Using the Google Earth Engine platform, this study compares three datasets from the Planet, Sentinel-2A, and Landsat 8 satellites for the study of Najaf Province, Iraq, at spatial resolutions of 3.7 m, 10 m, and 30 m, respectively. Seven algorithms (smileCART, Random Forest, Gradient Tree Boost, Sup...
| Published in: | International Journal of Remote Sensing |
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| ISSN: | 0143-1161 1366-5901 |
| Published: |
Informa UK Limited
2026
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa71928 |
| Abstract: |
Using the Google Earth Engine platform, this study compares three datasets from the Planet, Sentinel-2A, and Landsat 8 satellites for the study of Najaf Province, Iraq, at spatial resolutions of 3.7 m, 10 m, and 30 m, respectively. Seven algorithms (smileCART, Random Forest, Gradient Tree Boost, Support Vector Machine, Minimum Distance, Naive Bayes, and k-Nearest Neighbour) are tested. The primary objectives were to evaluate Land Use and Land Cover (LULC) mapping, and to select a suitable algorithm and dataset. Achieving accurate mapping of LULC categories remains a major challenge, particularly within semi-arid regions characterized by complex farming systems. Supervised classification techniques, specifically the seven algorithms, were applied, and ten classes were successfully identified, namely Irrigated Cropland, Watercourses, Water Bodies, Sandy Areas and Dunes, Arable Land, Bare Areas, Wetland, Urban and Industrial Areas, Palm, and Shrubland. The overall accuracy was computed for each classifier algorithm after the collection of 28–55 samples representing the ground truth for each identified class. Among the classifiers, Planet reached the highest overall accuracy of 92% with the smileCART algorithm, followed by 88% with Gradient Tree Boost and 86% with Random Forest. On the other side, Sentinel-2A achieved a maximum accuracy of 81% (Gradient Tree Boost and Random Forest), while Landsat 8 presented an overall accuracy of 78% using Random Forest. A test using only common bands across all sensors showed that spatial resolution contributes approximately three times more to classification accuracy than spectral richness in this semi-arid environment. It is noteworthy that the Planet satellite demonstrated an ability to distinguish Shrubland from other land cover classes compared to Sentinel-2A and Landsat 8. Since the grassland/shrubland transition is critical in the ecology and degradation of arid and semi-arid regions, the study suggests a role for recently available high-resolution satellite imagery to improve monitoring of such regions. |
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| College: |
Faculty of Science and Engineering |
| Funders: |
National Centre for Earth Observation |
| Start Page: |
1 |
| End Page: |
30 |