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• W3153621657 abstract "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." @default.
• W3153621657 created "2021-04-26" @default.
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• W3153621657 date "2021-07-01" @default.
• W3153621657 modified "2023-10-15" @default.
• W3153621657 title "Spatiotemporal image fusion in Google Earth Engine for annual estimates of land surface phenology in a heterogenous landscape" @default.
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• W3153621657 cites W1987927366 @default.
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• W3153621657 cites W2027776168 @default.
• W3153621657 cites W2036627824 @default.
• W3153621657 cites W2036992772 @default.
• W3153621657 cites W2037364101 @default.
• W3153621657 cites W2042692910 @default.
• W3153621657 cites W2050225888 @default.
• W3153621657 cites W2056811372 @default.
• W3153621657 cites W2061422248 @default.
• W3153621657 cites W2068331333 @default.
• W3153621657 cites W2070660322 @default.
• W3153621657 cites W2071730984 @default.
• W3153621657 cites W2072093516 @default.
• W3153621657 cites W2075845155 @default.
• W3153621657 cites W2082263501 @default.
• W3153621657 cites W2088603520 @default.
• W3153621657 cites W2090597209 @default.
• W3153621657 cites W2095258335 @default.
• W3153621657 cites W2096682311 @default.
• W3153621657 cites W2098962835 @default.
• W3153621657 cites W2099098700 @default.
• W3153621657 cites W2099698780 @default.
• W3153621657 cites W2106811285 @default.
• W3153621657 cites W2113503197 @default.
• W3153621657 cites W2114456168 @default.
• W3153621657 cites W2115952782 @default.
• W3153621657 cites W2121981479 @default.
• W3153621657 cites W2129331467 @default.
• W3153621657 cites W2138448722 @default.
• W3153621657 cites W2138751033 @default.
• W3153621657 cites W2143659897 @default.
• W3153621657 cites W2145919800 @default.
• W3153621657 cites W2151011640 @default.
• W3153621657 cites W2165977355 @default.
• W3153621657 cites W2170787371 @default.
• W3153621657 cites W2233094088 @default.
• W3153621657 cites W2256578114 @default.
• W3153621657 cites W2280849556 @default.
• W3153621657 cites W2283248741 @default.
• W3153621657 cites W2344328155 @default.
• W3153621657 cites W2404325781 @default.
• W3153621657 cites W2509112857 @default.
• W3153621657 cites W2522055505 @default.
• W3153621657 cites W2529529423 @default.
• W3153621657 cites W2542234150 @default.
• W3153621657 cites W2548481085 @default.
• W3153621657 cites W2552805558 @default.
• W3153621657 cites W2605847660 @default.
• W3153621657 cites W2725897987 @default.
• W3153621657 cites W2758780525 @default.
• W3153621657 cites W2767410253 @default.
• W3153621657 cites W2773717538 @default.
• W3153621657 cites W2774057574 @default.
• W3153621657 cites W2793445582 @default.
• W3153621657 cites W2796980406 @default.
• W3153621657 cites W2844909617 @default.
• W3153621657 cites W2886770683 @default.
• W3153621657 cites W2891721681 @default.
• W3153621657 cites W2897285410 @default.
• W3153621657 cites W2931894371 @default.
• W3153621657 cites W2982171921 @default.
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• W3153621657 doi "https://doi.org/10.1016/j.jag.2021.102323" @default.
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