Spatiotemporal image fusion in Google Earth Engine for annual estimates of land surface phenology in a heterogenous landscape
Currently, quantifying phenology at landscape to regional scales is not feasible with field data or near-surface sensors. Consequently, the spatial and temporal complexity of phenology has been assessed using satellite-based estimates (land surface phenology, LSP). While estimates from Moderate Reso...
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| Format: | Article |
| Language: | English |
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Elsevier
2021-07-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0303243421000301 |
| _version_ | 1811317555457949696 |
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| author | Ty C. Nietupski Robert E. Kennedy Hailemariam Temesgen Becky K. Kerns |
| author_facet | Ty C. Nietupski Robert E. Kennedy Hailemariam Temesgen Becky K. Kerns |
| author_sort | Ty C. Nietupski |
| collection | DOAJ |
| description | Currently, quantifying phenology at landscape to regional scales is not feasible with field data or near-surface sensors. Consequently, the spatial and temporal complexity of phenology has been assessed using satellite-based estimates (land surface phenology, LSP). While estimates from Moderate Resolution Imaging Spectroradiometer (MODIS) capture intraannual patterns of phenology, they have relatively low spatial resolution. Estimates from sensors like Landsat capture finer spatial detail but are often limited by Landsat’s temporal resolution. We implemented a spatio-temporal image fusion method on the Google Earth Engine (GEE) platform and used the resulting dense time series of images to estimate intraannual LSP at 30-meter resolution. We utilized Landsat 8 surface reflectance and MODIS NBAR (Nadir BRDF-Adjusted Reflectance; MCD43A4) images from 2016 and 2017 in the interior Pacific Northwest of the United States. Images predicted from the GEE image fusion algorithm were evaluated with true Landsat observations and compared with the accuracy achieved by executing the original ESTARFM algorithm. Excluding snow and cloud obscured observations, the algorithm produced approximately 215 observations per 30-meter pixel in 2017. Root mean squared prediction error (RMSPE) of Normalized Difference Vegetation Index (NDVI) for the GEE predicted images ranged from 0.032 to 0.066, and the RMSPE for the original ESTARFM predicted images from the ranged from 0.027 to 0.064. Phenometric estimates were evaluated with near-surface sensors (PhenoCams) in shrubland, conifer, and agricultural sites and field observations of phenology in grassland, open-pine, and mixed-conifer sites. Although phenometric estimates were dissimilar at all PhenoCam sites, the general temporal pattern of the GEE image fusion and PhenoCam time series was often similar. The start of season derived from the GEE image fusion time series had closer correspondence to the PhenoCam-derived start of season at the shrubland site (13 days) than the agriculture and conifer sites. The end of season was closest at one of the conifer sites and the agriculture site (22 and 31 days, respectively). Trends of some of the field-based phenology observations aligned with phenometrics estimated from the image fusion time series. At the grassland and open-pine field sites, the phenometrics from GEE image fusion were associated with phenophase trends of dominant plant functional types. Though characterizing LSP within the interior Pacific Northwest remains a challenge, this study demonstrates that image fusion implemented in GEE can produce a densified time series capable of capturing seasonal trends in NDVI related to vegetation phenology, which can be used to estimate intraannual phenometrics. |
| first_indexed | 2024-04-13T12:10:18Z |
| format | Article |
| id | doaj.art-3499059511d14a87b22f2d8923748fc0 |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-04-13T12:10:18Z |
| publishDate | 2021-07-01 |
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| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-3499059511d14a87b22f2d8923748fc02022-12-22T02:47:29ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322021-07-0199102323Spatiotemporal image fusion in Google Earth Engine for annual estimates of land surface phenology in a heterogenous landscapeTy C. Nietupski0Robert E. Kennedy1Hailemariam Temesgen2Becky K. Kerns3Department of Forest Engineering, Resources and Management, Oregon State University, 140 Peavy Hall, 3100 SW Jefferson Way, Corvallis, OR 97333, USA; Corresponding author.College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, 104 CEOAS Admin Bldg., Corvallis, OR 97331, USADepartment of Forest Engineering, Resources and Management, Oregon State University, 140 Peavy Hall, 3100 SW Jefferson Way, Corvallis, OR 97333, USAUSDA Forest Service, Pacific Northwest Research Station, 3200 SW Jefferson Way, Corvallis, OR 97331, USACurrently, quantifying phenology at landscape to regional scales is not feasible with field data or near-surface sensors. Consequently, the spatial and temporal complexity of phenology has been assessed using satellite-based estimates (land surface phenology, LSP). While estimates from Moderate Resolution Imaging Spectroradiometer (MODIS) capture intraannual patterns of phenology, they have relatively low spatial resolution. Estimates from sensors like Landsat capture finer spatial detail but are often limited by Landsat’s temporal resolution. We implemented a spatio-temporal image fusion method on the Google Earth Engine (GEE) platform and used the resulting dense time series of images to estimate intraannual LSP at 30-meter resolution. We utilized Landsat 8 surface reflectance and MODIS NBAR (Nadir BRDF-Adjusted Reflectance; MCD43A4) images from 2016 and 2017 in the interior Pacific Northwest of the United States. Images predicted from the GEE image fusion algorithm were evaluated with true Landsat observations and compared with the accuracy achieved by executing the original ESTARFM algorithm. Excluding snow and cloud obscured observations, the algorithm produced approximately 215 observations per 30-meter pixel in 2017. Root mean squared prediction error (RMSPE) of Normalized Difference Vegetation Index (NDVI) for the GEE predicted images ranged from 0.032 to 0.066, and the RMSPE for the original ESTARFM predicted images from the ranged from 0.027 to 0.064. Phenometric estimates were evaluated with near-surface sensors (PhenoCams) in shrubland, conifer, and agricultural sites and field observations of phenology in grassland, open-pine, and mixed-conifer sites. Although phenometric estimates were dissimilar at all PhenoCam sites, the general temporal pattern of the GEE image fusion and PhenoCam time series was often similar. The start of season derived from the GEE image fusion time series had closer correspondence to the PhenoCam-derived start of season at the shrubland site (13 days) than the agriculture and conifer sites. The end of season was closest at one of the conifer sites and the agriculture site (22 and 31 days, respectively). Trends of some of the field-based phenology observations aligned with phenometrics estimated from the image fusion time series. At the grassland and open-pine field sites, the phenometrics from GEE image fusion were associated with phenophase trends of dominant plant functional types. Though characterizing LSP within the interior Pacific Northwest remains a challenge, this study demonstrates that image fusion implemented in GEE can produce a densified time series capable of capturing seasonal trends in NDVI related to vegetation phenology, which can be used to estimate intraannual phenometrics.http://www.sciencedirect.com/science/article/pii/S0303243421000301Vegetation phenologySpatio-temporal image fusionNDVIGoogle Earth EngineTime seriesLandsat |
| spellingShingle | Ty C. Nietupski Robert E. Kennedy Hailemariam Temesgen Becky K. Kerns Spatiotemporal image fusion in Google Earth Engine for annual estimates of land surface phenology in a heterogenous landscape International Journal of Applied Earth Observations and Geoinformation Vegetation phenology Spatio-temporal image fusion NDVI Google Earth Engine Time series Landsat |
| title | Spatiotemporal image fusion in Google Earth Engine for annual estimates of land surface phenology in a heterogenous landscape |
| title_full | Spatiotemporal image fusion in Google Earth Engine for annual estimates of land surface phenology in a heterogenous landscape |
| title_fullStr | Spatiotemporal image fusion in Google Earth Engine for annual estimates of land surface phenology in a heterogenous landscape |
| title_full_unstemmed | Spatiotemporal image fusion in Google Earth Engine for annual estimates of land surface phenology in a heterogenous landscape |
| title_short | Spatiotemporal image fusion in Google Earth Engine for annual estimates of land surface phenology in a heterogenous landscape |
| title_sort | spatiotemporal image fusion in google earth engine for annual estimates of land surface phenology in a heterogenous landscape |
| topic | Vegetation phenology Spatio-temporal image fusion NDVI Google Earth Engine Time series Landsat |
| url | http://www.sciencedirect.com/science/article/pii/S0303243421000301 |
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