Rapid Estimation of Decameter FPAR from Sentinel-2 Imagery on the Google Earth Engine
As a direct indicator of vegetation photosynthesis, the fraction of absorbed photosynthetically active radiation (FPAR) serves as a critical input in a series of land surface models. While existing satellite FPAR products are generally at coarse resolutions ranging from 250 m to 1 km, operational FP...
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MDPI AG
2022-12-01
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| Series: | Forests |
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| Online Access: | https://www.mdpi.com/1999-4907/13/12/2122 |
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| author | Yiting Wang Yinggang Zhan Donghui Xie Jinghao Liu Haiyang Huang Dan Zhao Zihang Xiao Xiaode Zhou |
| author_facet | Yiting Wang Yinggang Zhan Donghui Xie Jinghao Liu Haiyang Huang Dan Zhao Zihang Xiao Xiaode Zhou |
| author_sort | Yiting Wang |
| collection | DOAJ |
| description | As a direct indicator of vegetation photosynthesis, the fraction of absorbed photosynthetically active radiation (FPAR) serves as a critical input in a series of land surface models. While existing satellite FPAR products are generally at coarse resolutions ranging from 250 m to 1 km, operational FPAR products at fine resolution are urgently needed in studying land surface processes at the plot scale. However, existing methods for estimating fine-resolution FPAR were mainly designed for Landsat data, and few studies have attempted to develop algorithms for Sentinel-2 data. In particular, the operational estimation of decameter FPAR has a higher requirement for the algorithms in terms of generalizability, efficiency, accuracy, and adaptability to Sentinel-2 data. In this paper, we developed a retrieval chain on the Google Earth Engine (GEE) platform to estimate FPAR by learning the relationship between MODIS FPAR and Sentinel-2 surface reflectance. Scale-consistent multilinear models were used to model the relationship between MODIS FPAR and Sentinel-2 surface reflectance, and the model coefficients were regressed from the selected training samples. To account for the spectral and spatial characteristics of the Sentinel-2 data, we designed criteria for selecting training samples and compared different band combinations. Three strategies for band combination were used: (1) green, red, and near infrared (NIR) bands at 10 m resolution (i.e., three bands); (2) green, red, NIR, and red edge (RE) 1, RE2, and RE3 bands at 20 m resolution (i.e., five bands); and (3) green, red, NIR, RE1, RE2, RE3, shortwave infrared1 (SWIR1) and SWIR2 bands at 20 m resolution (i.e., eight bands). Meanwhile, the official Sentinel Application Platform (SNAP) method has also been implemented to estimate the Sentinel FPAR at 10 m and 20 m resolutions for comparison. Both methods were applied to the western Guanzhong area, Shaanxi Province, China, for FPAR estimation of all cloud-free Sentinel-2 images in 2021. The results show that the scaling-based method using five bands at 20 m resolution was the most accurate compared to the in situ measurements (RMSE = 0.076 and R² = 0.626), which outperformed the SNAP method at 10 m and 20 m resolutions and the scaling-based method using other strategies. The results of the scaling-based method using all three strategies were highly consistent with the MODIS FPAR product, while the SNAP method systematically underestimated FPAR values compared to the MODIS FPAR products. The proposed method is more ready-to-use and more efficient than SNAP software. Considering that the service of the MODIS sensor is overdue, the proposed method can be extended to alternatives to MODIS products, such as VIIRS and Sentinel-3 data. |
| first_indexed | 2024-03-09T16:37:44Z |
| format | Article |
| id | doaj.art-f8608d9ff63f400982500c89d8b95639 |
| institution | Directory Open Access Journal |
| issn | 1999-4907 |
| language | English |
| last_indexed | 2024-03-09T16:37:44Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forests |
| spelling | doaj.art-f8608d9ff63f400982500c89d8b956392023-11-24T14:55:49ZengMDPI AGForests1999-49072022-12-011312212210.3390/f13122122Rapid Estimation of Decameter FPAR from Sentinel-2 Imagery on the Google Earth EngineYiting Wang0Yinggang Zhan1Donghui Xie2Jinghao Liu3Haiyang Huang4Dan Zhao5Zihang Xiao6Xiaode Zhou7College of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, ChinaState Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Aerospace Information Research Institute of Chinese Academy of Sciences, Beijing 100875, ChinaCollege of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, ChinaState Key Laboratory of Eco-Hydraulics in Northwest Arid Region, Xi’an University of Technology, Xi’an 710048, ChinaAs a direct indicator of vegetation photosynthesis, the fraction of absorbed photosynthetically active radiation (FPAR) serves as a critical input in a series of land surface models. While existing satellite FPAR products are generally at coarse resolutions ranging from 250 m to 1 km, operational FPAR products at fine resolution are urgently needed in studying land surface processes at the plot scale. However, existing methods for estimating fine-resolution FPAR were mainly designed for Landsat data, and few studies have attempted to develop algorithms for Sentinel-2 data. In particular, the operational estimation of decameter FPAR has a higher requirement for the algorithms in terms of generalizability, efficiency, accuracy, and adaptability to Sentinel-2 data. In this paper, we developed a retrieval chain on the Google Earth Engine (GEE) platform to estimate FPAR by learning the relationship between MODIS FPAR and Sentinel-2 surface reflectance. Scale-consistent multilinear models were used to model the relationship between MODIS FPAR and Sentinel-2 surface reflectance, and the model coefficients were regressed from the selected training samples. To account for the spectral and spatial characteristics of the Sentinel-2 data, we designed criteria for selecting training samples and compared different band combinations. Three strategies for band combination were used: (1) green, red, and near infrared (NIR) bands at 10 m resolution (i.e., three bands); (2) green, red, NIR, and red edge (RE) 1, RE2, and RE3 bands at 20 m resolution (i.e., five bands); and (3) green, red, NIR, RE1, RE2, RE3, shortwave infrared1 (SWIR1) and SWIR2 bands at 20 m resolution (i.e., eight bands). Meanwhile, the official Sentinel Application Platform (SNAP) method has also been implemented to estimate the Sentinel FPAR at 10 m and 20 m resolutions for comparison. Both methods were applied to the western Guanzhong area, Shaanxi Province, China, for FPAR estimation of all cloud-free Sentinel-2 images in 2021. The results show that the scaling-based method using five bands at 20 m resolution was the most accurate compared to the in situ measurements (RMSE = 0.076 and R² = 0.626), which outperformed the SNAP method at 10 m and 20 m resolutions and the scaling-based method using other strategies. The results of the scaling-based method using all three strategies were highly consistent with the MODIS FPAR product, while the SNAP method systematically underestimated FPAR values compared to the MODIS FPAR products. The proposed method is more ready-to-use and more efficient than SNAP software. Considering that the service of the MODIS sensor is overdue, the proposed method can be extended to alternatives to MODIS products, such as VIIRS and Sentinel-3 data.https://www.mdpi.com/1999-4907/13/12/2122fraction of absorbed photosynthetically active radiation (FPAR)MODISSentinel-2scalingremote sensing trend surfaceGoogle Earth Engine (GEE) |
| spellingShingle | Yiting Wang Yinggang Zhan Donghui Xie Jinghao Liu Haiyang Huang Dan Zhao Zihang Xiao Xiaode Zhou Rapid Estimation of Decameter FPAR from Sentinel-2 Imagery on the Google Earth Engine Forests fraction of absorbed photosynthetically active radiation (FPAR) MODIS Sentinel-2 scaling remote sensing trend surface Google Earth Engine (GEE) |
| title | Rapid Estimation of Decameter FPAR from Sentinel-2 Imagery on the Google Earth Engine |
| title_full | Rapid Estimation of Decameter FPAR from Sentinel-2 Imagery on the Google Earth Engine |
| title_fullStr | Rapid Estimation of Decameter FPAR from Sentinel-2 Imagery on the Google Earth Engine |
| title_full_unstemmed | Rapid Estimation of Decameter FPAR from Sentinel-2 Imagery on the Google Earth Engine |
| title_short | Rapid Estimation of Decameter FPAR from Sentinel-2 Imagery on the Google Earth Engine |
| title_sort | rapid estimation of decameter fpar from sentinel 2 imagery on the google earth engine |
| topic | fraction of absorbed photosynthetically active radiation (FPAR) MODIS Sentinel-2 scaling remote sensing trend surface Google Earth Engine (GEE) |
| url | https://www.mdpi.com/1999-4907/13/12/2122 |
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