Comparative Performance of Machine Learning Classifiers for Photovoltaic Mapping in Arid Regions Using Google Earth Engine

Le Zhang, Zhaoming Wang, Hengrui Zhang, Ning Zhang, Tianyu Zhang, Hailong Bao, Haokai Chen and Qing Zhang ()
Additional contact information
Le Zhang: School of Ecology and Environment, Inner Mongolia University, Hohhot 010021, China
Zhaoming Wang: Inner Mongolia Pratacultural Technology Innovation Center Co., Ltd., Hohhot 010021, China
Hengrui Zhang: School of Ecology and Environment, Inner Mongolia University, Hohhot 010021, China
Ning Zhang: School of Ecology and Environment, Inner Mongolia University, Hohhot 010021, China
Tianyu Zhang: Meng Grass Ecological Environment (Group) Co., Ltd., Hohhot 011500, China
Hailong Bao: Meng Grass Ecological Environment (Group) Co., Ltd., Hohhot 011500, China
Haokai Chen: School of Ecology and Environment, Inner Mongolia University, Hohhot 010021, China
Qing Zhang: School of Ecology and Environment, Inner Mongolia University, Hohhot 010021, China

Energies, 2025, vol. 18, issue 17, 1-18

Abstract: With increasing energy demand and advancing carbon neutrality goals, arid regions—key areas for centralized photovoltaic (PV) station development in China—urgently require efficient and accurate remote sensing techniques to support spatial distribution monitoring and ecological impact assessment. Although numerous studies have focused on PV station extraction, challenges remain in arid regions with complex surface features to develop extraction frameworks that balance efficiency and accuracy at a regional scale. This study focuses on the Inner Mongolia Yellow River Basin and develops a PV extraction framework on the Google Earth Engine platform by integrating spectral bands, spectral indices, and topographic features, systematically comparing the classification performance of support vector machine, classification and regression tree, and random forest (RF) classifiers. The results show that the RF classifier achieved a high Kappa coefficient (0.94) and F1 score (0.96 for PV areas) in PV extraction. Feature importance analysis revealed that the Normalized Difference Tillage Index, near-infrared band, and Land Surface Water Index made significant contributions to PV classification, accounting for 10.517%, 6.816%, and 6.625%, respectively. PV stations are mainly concentrated in the northern and southwestern parts of the study area, characterized by flat terrain and low vegetation cover, exhibiting a spatial pattern of “overall dispersion with local clustering”. Landscape pattern indices further reveal significant differences in patch size, patch density, and aggregation level of PV stations across different regions. This study employs Sentinel-2 imagery for regional-scale PV station extraction, providing scientific support for energy planning, land use optimization, and ecological management in the study area, with potential for application in other global arid regions.

Keywords: photovoltaic station; random forest classifier; Google Earth Engine; Sentinel-2; landscape pattern; Yellow River Basin (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/17/4464/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/17/4464/ (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:gam:jeners:v:18:y:2025:i:17:p:4464-:d:1730148

Access Statistics for this article

Energies is currently edited by Ms. Cassie Shen

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().