Five-Year Evaluation of Sentinel-2 Cloud-Free Mosaic Generation Under Varied Cloud Cover Conditions in Hawai’i
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 climat...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/16/24/4791 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846102896171548672 |
|---|---|
| author | Francisco Rodríguez-Puerta Ryan L. Perroy Carlos Barrera Jonathan P. Price Borja García-Pascual |
| author_facet | Francisco Rodríguez-Puerta Ryan L. Perroy Carlos Barrera Jonathan P. Price Borja García-Pascual |
| author_sort | Francisco Rodríguez-Puerta |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-efe5b455157344b3b9baa2b6cbc21fa0 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-efe5b455157344b3b9baa2b6cbc21fa02024-12-27T14:51:14ZengMDPI AGRemote Sensing2072-42922024-12-011624479110.3390/rs16244791Five-Year Evaluation of Sentinel-2 Cloud-Free Mosaic Generation Under Varied Cloud Cover Conditions in Hawai’iFrancisco Rodríguez-Puerta0Ryan L. Perroy1Carlos Barrera2Jonathan P. Price3Borja García-Pascual4EiFAB-iuFOR, Campus Universitario Duques de Soria s/n, Universidad de Valladolid, 42004 Soria, SpainDepartment of Geography & Environmental Science, University of Hawai’i at Hilo, Hilo, HI 96720, USAFöra Forest Technologies SLL, Campus Universitario Duques de Soria s/n, 42004 Soria, SpainDepartment of Geography & Environmental Science, University of Hawai’i at Hilo, Hilo, HI 96720, USAFöra Forest Technologies SLL, Campus Universitario Duques de Soria s/n, 42004 Soria, SpainThe 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.https://www.mdpi.com/2072-4292/16/24/4791cloud-free mosaicsSentinel-2planet imagerydeep learningGoogle Earth Enginecloud-masking algorithms |
| spellingShingle | Francisco Rodríguez-Puerta Ryan L. Perroy Carlos Barrera Jonathan P. Price Borja García-Pascual Five-Year Evaluation of Sentinel-2 Cloud-Free Mosaic Generation Under Varied Cloud Cover Conditions in Hawai’i Remote Sensing cloud-free mosaics Sentinel-2 planet imagery deep learning Google Earth Engine cloud-masking algorithms |
| title | Five-Year Evaluation of Sentinel-2 Cloud-Free Mosaic Generation Under Varied Cloud Cover Conditions in Hawai’i |
| title_full | Five-Year Evaluation of Sentinel-2 Cloud-Free Mosaic Generation Under Varied Cloud Cover Conditions in Hawai’i |
| title_fullStr | Five-Year Evaluation of Sentinel-2 Cloud-Free Mosaic Generation Under Varied Cloud Cover Conditions in Hawai’i |
| title_full_unstemmed | Five-Year Evaluation of Sentinel-2 Cloud-Free Mosaic Generation Under Varied Cloud Cover Conditions in Hawai’i |
| title_short | Five-Year Evaluation of Sentinel-2 Cloud-Free Mosaic Generation Under Varied Cloud Cover Conditions in Hawai’i |
| title_sort | five year evaluation of sentinel 2 cloud free mosaic generation under varied cloud cover conditions in hawai i |
| topic | cloud-free mosaics Sentinel-2 planet imagery deep learning Google Earth Engine cloud-masking algorithms |
| url | https://www.mdpi.com/2072-4292/16/24/4791 |
| work_keys_str_mv | AT franciscorodriguezpuerta fiveyearevaluationofsentinel2cloudfreemosaicgenerationundervariedcloudcoverconditionsinhawaii AT ryanlperroy fiveyearevaluationofsentinel2cloudfreemosaicgenerationundervariedcloudcoverconditionsinhawaii AT carlosbarrera fiveyearevaluationofsentinel2cloudfreemosaicgenerationundervariedcloudcoverconditionsinhawaii AT jonathanpprice fiveyearevaluationofsentinel2cloudfreemosaicgenerationundervariedcloudcoverconditionsinhawaii AT borjagarciapascual fiveyearevaluationofsentinel2cloudfreemosaicgenerationundervariedcloudcoverconditionsinhawaii |