Improving Remote Estimation of Vegetation Phenology Using GCOM-C/SGLI Land Surface Reflectance Data
Vegetation phenology not only describes the life cycle events of periodic plants during the growing season but also acts as an indicator of biological responses to climate change. Satellite monitoring of vegetation phenology can capture the spatial patterns of vegetation dynamics at global scales. H...
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| Format: | Article |
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MDPI AG
2022-08-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/16/4027 |
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| author | Mengyu Li Wei Yang Akihiko Kondoh |
| author_facet | Mengyu Li Wei Yang Akihiko Kondoh |
| author_sort | Mengyu Li |
| collection | DOAJ |
| description | Vegetation phenology not only describes the life cycle events of periodic plants during the growing season but also acts as an indicator of biological responses to climate change. Satellite monitoring of vegetation phenology can capture the spatial patterns of vegetation dynamics at global scales. However, the existing satellite products of global vegetation phenology still show uncertainties in estimating phenological metrices, especially for dormancy onset. The Second-Generation Global Imager (SGLI) onboard the satellite Global Change Observation Mission—Climate (GCOM-C) that launched in 2017 provides a new opportunity to improve the estimation of global vegetation phenology with a spatial resolution of 250 m. In this study, SGLI land surface reflectance data were employed to estimate the green-up and dormancy dates for different vegetation types based on a relative threshold method, in which a snow-free vegetation index (i.e., the normalized difference greenness index, NDGI) was adopted. The validation results show that there are significant agreements between the trajectories of the SGLI-based NDGI and the near-surface green color coordinate index (GCC) at the PhenoCam sites with different vegetation types. The SGLI-based estimation of the green-up dates slightly outperformed that of the existing MODIS and VIIRS phenology products, with an RMSE and R<sup>2</sup> of 11.0 days and 0.71, respectively. In contrast, the estimation of the dormancy dates based on the SGLI data yielded much higher accuracies than the MODIS and VIIRS products, with an RMSE decreased from >23.8 days to 15.6 days, and R<sup>2</sup> increased from <0.51 to 0.72. These results suggest that GCOM-C/SGLI data have the potential to generate improved monitoring of global vegetation phenology in the future. |
| first_indexed | 2024-03-09T03:53:57Z |
| format | Article |
| id | doaj.art-f2f41bf9384d44238058a5532cc73fc4 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T03:53:57Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-f2f41bf9384d44238058a5532cc73fc42023-12-03T14:24:43ZengMDPI AGRemote Sensing2072-42922022-08-011416402710.3390/rs14164027Improving Remote Estimation of Vegetation Phenology Using GCOM-C/SGLI Land Surface Reflectance DataMengyu Li0Wei Yang1Akihiko Kondoh2Graduate School of Science and Engineering, Chiba University, Chiba 263-8522, JapanCenter for Environmental Remote Sensing, Chiba University, Chiba 263-8522, JapanCenter for Environmental Remote Sensing, Chiba University, Chiba 263-8522, JapanVegetation phenology not only describes the life cycle events of periodic plants during the growing season but also acts as an indicator of biological responses to climate change. Satellite monitoring of vegetation phenology can capture the spatial patterns of vegetation dynamics at global scales. However, the existing satellite products of global vegetation phenology still show uncertainties in estimating phenological metrices, especially for dormancy onset. The Second-Generation Global Imager (SGLI) onboard the satellite Global Change Observation Mission—Climate (GCOM-C) that launched in 2017 provides a new opportunity to improve the estimation of global vegetation phenology with a spatial resolution of 250 m. In this study, SGLI land surface reflectance data were employed to estimate the green-up and dormancy dates for different vegetation types based on a relative threshold method, in which a snow-free vegetation index (i.e., the normalized difference greenness index, NDGI) was adopted. The validation results show that there are significant agreements between the trajectories of the SGLI-based NDGI and the near-surface green color coordinate index (GCC) at the PhenoCam sites with different vegetation types. The SGLI-based estimation of the green-up dates slightly outperformed that of the existing MODIS and VIIRS phenology products, with an RMSE and R<sup>2</sup> of 11.0 days and 0.71, respectively. In contrast, the estimation of the dormancy dates based on the SGLI data yielded much higher accuracies than the MODIS and VIIRS products, with an RMSE decreased from >23.8 days to 15.6 days, and R<sup>2</sup> increased from <0.51 to 0.72. These results suggest that GCOM-C/SGLI data have the potential to generate improved monitoring of global vegetation phenology in the future.https://www.mdpi.com/2072-4292/14/16/4027Second-Generation Global Imagerland surface phenologynear-surface phenology observation |
| spellingShingle | Mengyu Li Wei Yang Akihiko Kondoh Improving Remote Estimation of Vegetation Phenology Using GCOM-C/SGLI Land Surface Reflectance Data Remote Sensing Second-Generation Global Imager land surface phenology near-surface phenology observation |
| title | Improving Remote Estimation of Vegetation Phenology Using GCOM-C/SGLI Land Surface Reflectance Data |
| title_full | Improving Remote Estimation of Vegetation Phenology Using GCOM-C/SGLI Land Surface Reflectance Data |
| title_fullStr | Improving Remote Estimation of Vegetation Phenology Using GCOM-C/SGLI Land Surface Reflectance Data |
| title_full_unstemmed | Improving Remote Estimation of Vegetation Phenology Using GCOM-C/SGLI Land Surface Reflectance Data |
| title_short | Improving Remote Estimation of Vegetation Phenology Using GCOM-C/SGLI Land Surface Reflectance Data |
| title_sort | improving remote estimation of vegetation phenology using gcom c sgli land surface reflectance data |
| topic | Second-Generation Global Imager land surface phenology near-surface phenology observation |
| url | https://www.mdpi.com/2072-4292/14/16/4027 |
| work_keys_str_mv | AT mengyuli improvingremoteestimationofvegetationphenologyusinggcomcsglilandsurfacereflectancedata AT weiyang improvingremoteestimationofvegetationphenologyusinggcomcsglilandsurfacereflectancedata AT akihikokondoh improvingremoteestimationofvegetationphenologyusinggcomcsglilandsurfacereflectancedata |