The generation of cloud-free satellite mosaics is essential for a range of remote sensing applications, including land use mapping, ecosystem monitoring, and resource management. This study focuses on remote sensing across the climatic diversity of Hawai’i Island, which encompasses ten Köppen climate zones from tropical to Arctic: periglacial. This diversity presents unique challenges for cloud-free image generation. We conducted a comparative analysis of three cloud-masking methods: two Google Earth Engine algorithms (CloudScore+ and s2cloudless) and a new proprietary deep learning-based algorithm (L3) applied to Sentinel-2 imagery. These methods were evaluated against the best monthly composite selected from high-frequency Planet imagery, which acquires daily images. All Sentinel-2 bands were enhanced to a 10 m resolution, and an advanced weather mask was applied to generate monthly mosaics from 2019 to 2023. We stratified the analysis by cloud cover frequency (low, moderate, high, and very high), applying one-way and two-way ANOVAs to assess cloud-free pixel success rates. Results indicate that CloudScore+ achieved the highest success rate at 89.4% cloud-free pixels, followed by L3 and s2cloudless at 79.3% and 80.8%, respectively. Cloud removal effectiveness decreased as cloud cover increased, with clear pixel success rates ranging from 94.6% under low cloud cover to 79.3% under very high cloud cover. Additionally, seasonality effects showed higher cloud removal rates in the wet season (88.6%), while no significant year-to-year differences were observed from 2019 to 2023. This study advances current methodologies for generating reliable cloud-free mosaics in tropical and subtropical regions, with potential applications for remote sensing in other cloud-dense environments.
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