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Land Surface Phenology, Eddy Covariance Tower Sites, North America, 2017-2021

Documentation Revision Date: 2022-12-29

Dataset Version: 1

Summary

This land surface phenology (LSP) dataset provides spatially explicit data related to the timing of phenological changes such as the start, peak, and end of vegetation activity, vegetation index metrics and associated quality assurance flags. The data are for the growing seasons of 2017-2021 for 10-km x 10-km windows centered over 104 eddy covariance towers at AmeriFlux and National Ecological Observatory Network (NEON) sites. The dataset is derived at 3-m spatial resolution from PlanetScope imagery across a range of plant functional types and climates in North America. These LSP data can be used to assess satellite-based LSP products, to evaluate predictions from land surface models, and to analyze processes controlling the seasonality of ecosystem-scale carbon, water, and energy fluxes. The data are provided in NetCDF format along with geospatial area-of-interest information and visualizations of the analysis window for each site in GeoJSON and HTML formats.

There are a total of 728 files. There are seven files for each of the 104 eddy covariance tower sites: five data files in NetCDF format (one for each year in 2017-2021), one HTML interactive map (.html), and one GeoJSON file.

Figure 1. EVI amplitude at Konza Prairie Biological Station in 2019.

Citation

Moon, M., A.D. Richardson, T. Milliman, and M.A. Friedl. 2022. Land Surface Phenology, Eddy Covariance Tower Sites, North America, 2017-2021. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/2033

Table of Contents

  1. Dataset Overview
  2. Data Characteristics
  3. Application and Derivation
  4. Quality Assessment
  5. Data Acquisition, Materials, and Methods
  6. Data Access
  7. References

Dataset Overview

This land surface phenology (LSP) dataset provides spatially explicit data related to the timing of phenological changes such as the start, peak, and end of vegetation activity, vegetation index metrics and associated quality assurance flags. The data are for the growing seasons of 2017-2021 for 10-km x 10-km windows centered over 104 eddy covariance towers at AmeriFlux and National Ecological Observatory Network (NEON) sites. The dataset is derived at 3-m spatial resolution from PlanetScope imagery across a range of plant functional types and climates in North America. These LSP data can be used to assess satellite-based LSP products, to evaluate predictions from land surface models, and to analyze processes controlling the seasonality of ecosystem-scale carbon, water, and energy fluxes. The data are provided in NetCDF format along with geospatial area-of-interest information and visualizations of the analysis window for each site in GeoJSON and HTML formats.

Related publications

Bolton, D.K., J.M. Gray, E.K. Melaas, M. Moon, L. Eklundh, and M.A. Friedl. 2020. Continental-scale land surface phenology from harmonized Landsat 8 and Sentinel-2 imagery. Remote Sensing of Environment 240:111685. https://doi.org/10.1016/j.rse.2020.111685

Moon, M., A.D. Richardson, and M.A. Friedl. 2021. Multiscale assessment of land surface phenology from harmonized Landsat 8 and Sentinel-2, PlanetScope, and PhenoCam imagery. Remote Sensing of Environment 266:112716. https://doi.org/10.1016/j.rse.2021.112716

Acknowledgments

This work was supported by NASA grants 80NSSC18K0334 and 80NSSC21K1974, and by NSF award number 1702627. The development of PhenoCam has been funded by the Northeastern States Research Cooperative, NSF's Macrosystems Biology Program (awards EF-1065029 and EF-1702697), and DOE's Regional and Global Climate Modeling Program (award DE-SC0016011). We also gratefully acknowledge NASA's Commercial Smallsat Data Acquisition (CSDA) Program, which provided access to the PlanetScope imagery used in this study.

Data Characteristics

Spatial Coverage: 104 eddy covariance towers at AmeriFlux and National Ecological Observatory Network (NEON) sites in North America

Spatial Resolution: 3 m

Temporal Coverage: 2017-01-01 to 2021-12-31

Temporal Resolution: Annual

 Study Areas: Latitude and longitude are given in decimal degrees.

Site Westernmost Longitude Easternmost Longitude Northernmost Latitude Southernmost Latitude
AmeriFlux and National Ecological Observatory Network Sites in North America -155.366 -68.8209 68.70759 17.92385

Data File Information

There are a total of 728 files. There are seven files for each of the 104 eddy covariance tower sites: five data files in NetCDF format (.nc4;one for each year in 2017-2021), one HTML interactive map (.html), and one GeoJSON file (.geojson). Table 3 in Section 5 lists the 104 study sites.

Table 1. File names and descriptions.  In the file names, "Site" is a combination of the Site Code and Site Full Name for the eddy covariance tower sites listed in Table 3.

File Name Description Example File Name
Site_interactive_map.html Visualizations with the spatial extent of the files. These can be opened in a web browser. A user can zoom in and out and pan interactively. A visible satellite view is the basemap. User note: the HTML code calls in data from web links which may not be permanent. PR-GU_NEON_Guanica_Forest_interactive_map.html
Site_georeference.geojson Georeference for the site extent and location in GeoJSON format. PR-xGU_NEON_Guanica_Forest_georeference.geojson
Site_YYYY.nc Georeferenced NetCDF (.nc) files where YYYY is 2017 to 2021. PR-xGU_NEON_Guanica_Forest_PLSP_2017.nc

Table 2. Variables in the NetCDF data files.

Layer Name Description Units Scale Factor Valid Range Fill value
NumCycles Number of phenological cycles
detected in target year
Number of cycles 1 0 – 6 32767
First Vegetation Cycle: Largest EVI2 amplitude cycle
Phenology Timing Metrics
OGI Onset Greenness Increase
(Date of 15% greenness increase)
Day of year, where 1 indicates January 1 of target year 1 -181 – 548 32767
50PCGI 50 Percent Greenness Increase
(Date of 50% greenness increase)
Day of year 1 -181 – 548 32767
OGMx Onset Greenness Maximum
(Date of 90% greenness increase)
Day of year 1 -181 – 548 32767
Peak Date of Cycle Peak Day of year 1 1 – 366 32767
OGD Onset Greenness Decrease
(Date of 10% greenness decrease)
Day of year 1 -181 – 548 32767
50PCGD 50 Percent Greenness Decrease
(Date of 50% greenness decrease)
Day of year 1 -181 – 548 32767
OGMn Onset Greenness Minimum
(Date of 85% greenness decrease)
Day of year 1 -181 – 548 32767
Vegetation Indices
EVImax Maximum two-band Enhanced Vegetation Index (EVI2) during vegetation cycle - 0.0001 0 – 10000 32767
EVIamp EVI2 Amplitude during vegetation cycle - 0.0001 0 – 10000 32767
EVIarea Integrated EVI2 during vegetation cycle - 0.01 0 – 32766 32767
Second Vegetation Cycle: Second Largest EVI2 amplitude cycle
Phenology Timing Metrics
OGI_2 Onset Greenness Increase
(Date of 15% greenness increase)
Day of year 1 -181 – 548 32767
50PCGI_2 50 Percent Greenness Increase
(Date of 50% greenness increase)
Day of year 1 -181 – 548 32767
OGMx_2 Onset Greenness Maximum
(Date of 90% greenness increase)
Day of year 1 -181 – 548 32767
Peak_2 Date of Cycle Peak Day of year 1 1 – 366 32767
OGD_2 Onset Greenness Decrease
(Date of 10% greenness decrease)
Day of year 1 -181 – 548 32767
50PCGD_2 50 Percent Greenness Decrease
(Date of 50% greenness decrease)
Day of year 1 -181 – 548 32767
OGMn_2 Onset Greenness Minimum
(Date of 85% greenness decrease)
Day of year 1 -181 – 548 32767
Vegetation Indices
EVImax_2 EVI2 maximum during vegetation
cycle
- 0.0001 0 – 10000 32767
EVIamp_2 EVI2 Amplitude during vegetation
cycle
- 0.0001 0 – 10000 32767
EVIarea_2 EVI2 area during vegetation cycle - 0.01 0 – 32766 32767
Quality Assurance (QA)
QA Quality Assurance for first
vegetation cycle
- 1 1 – 4 -
QA_2 Quality Assurance for second
vegetation cycle
- 1 1 – 4 -
numObs Number of days with clear observations
in calendar year
Days 1 0 – 366 32767

Application and Derivation

Vegetation phenology is a key control on water, energy, and carbon fluxes in terrestrial ecosystems. Because vegetation canopies are heterogeneous, spatially explicit information related to seasonality in vegetation activity provides valuable information for studies that use eddy covariance measurements to study land-atmosphere interactions. These land surface phenology (LSP) data can be used to assess satellite-based LSP products, to evaluate predictions from land surface models, and to analyze processes controlling the seasonality of ecosystem-scale carbon, water, and energy fluxes.

Quality Assessment

Exhaustive technical validation for phenometrics from PlanetScope imagery has been reported in Bolton et al. (2020), suggesting phenological metrics from PlanetScope time series show strong agreement with other independent LSP records and provide fine-scale variations in LSP that are not captured in moderate to coarse resolution of LSP datasets. The previous approach was extended using the most up-to-date independent data sets. Specifically, first, mid-greenup dates (i.e., 50PCGI) and EVI2 seasonal amplitude images for three representative sites were provided. Second, mid- greenup and mid-greendown dates (i.e., 50PCGI and 50PCGD, respectively) from PlanetScope imagery were compared to the independent LSP dataset from HLS time series (i.e., MSLSP30NA V011 (Chu et al., 2021)). Lastly, 50PCGI and 50PCGD from PlanetScope were compared against corresponding values estimated from PhenoCam GCC time series to provide a ground-based basis for assessing the realism and quality of phenometrics from PlanetScope.

Quality assurance metrics for each pixel are included with this dataset.

Data Acquisition, Materials, and Methods

A total of 104 AmeriFlux Core Sites were selected covering a range of geographical extents across the US (Novick et al., 2018). Among these, 44 sites are co-registered in the National Ecological Observatory Network (NEON).

Table 3. List of sites.

Site Code Site Full Name Site Code Site Full Name
PR-xGU NEON Guanica Forest US-Var Vaira Ranch
PR-xLA NEON Lajas Experimental Station US-Vcm Valles Caldera Mixed Conifer
US-ALQ Allequash Creek Site US-Vcp Valles Caldera Ponderosa Pine
US-ARM ARM Southern Great Plains site US-Vcs Valles Caldera Sulphur Springs Mixed Conifer
US-Bi1 Bouldin Island Alfalfa US-WCr Willow Creek
US-Bi2 Bouldin Island corn US-Whs Walnut Gulch Lucky Hills Shrub
US-BMM Bangtail Mountain Meadow US-Wjs Willard Juniper Savannah
US-BRG Bayles Road Grassland Tower US-Wkg Walnut Gulch Kendall Grasslands
US-CF1 CAF-LTAR Cook East US-xAB NEON Abby Road
US-CF2 CAF-LTAR Cook West US-xAE NEON Klemme Range Research Station
US-CF3 CAF-LTAR Boyd North US-xBL NEON Blandy Experimental Farm
US-CF4 CAF-LTAR Boyd South US-xBN NEON Caribou Creek - Poker Flats Watershed
US-Ha1 Harvard Forest EMS Tower US-xBR NEON Bartlett Experimental Forest
US-Ha2 Harvard Forest Hemlock Site US-xCL NEON LBJ National Grassland
US-HB1 North Inlet Crab Haul Creek US-xCP NEON Central Plains Experimental Range
US-HB2 Hobcaw Barony Mature Longleaf Pine US-xDC NEON Dakota Coteau Field School
US-HB3 Hobcaw Barony Longleaf Pine Restoration US-xDJ NEON Delta Junction
US-Ho1 Howland Forest US-xDL NEON Dead Lake
US-ICs Imnavait Creek Watershed Wet Sedge Tundra US-xDS NEON Disney Wilderness Preserve
US-KFS Kansas Field Station US-xGR NEON Great Smoky Mountains National Park
US-Me2 Metolius mature ponderosa pine US-xHA NEON Harvard Forest
US-Me6 Metolius Young Pine Burn US-xHE NEON Healy
US-MMS Morgan Monroe State Forest US-xJE NEON Jones Ecological Research Center
US-Mpj Mountainair Pinyon-Juniper Woodland US-xJR NEON Jornada LTER
US-Myb Mayberry Wetland US-xKA NEON Konza Prairie Biological Station-Relocatable
US-NC2 NC_Loblolly Plantation US-xKZ NEON Konza Prairie Biological Station
US-NC3 NC_Clearcut#3 US-xLE NEON Lenoir Landing
US-NC4 NC_AlligatorRiver US-xMB NEON Moab
US-Ne1 Mead-irrigated continuous maize site US-xML NEON Mountain Lake Biological Station
US-Ne2 Mead-irrigated maize-soybean rotation site US-xNG NEON Northern Great Plains Research Laboratory
US-Ne3 Mead-rainfed maize-soybean rotation site US-xNQ NEON Onaqui-Ault
US-NR1 Niwot Ridge Forest US-xNW NEON Niwot Ridge Mountain Research Station
US-PFa Park Falls US-xPU NEON Pu'u Maka'ala Natural Area Reserve
US-Rms RCEW Mountain Big Sagebrush US-xRM NEON Rocky Mountain National Park
US-Ro4 Rosemount Prairie US-xRN NEON Oak Ridge National Lab
US-Ro5 Rosemount I18_South US-xSB NEON Ordway-Swisher Biological Station
US-Ro6 Rosemount I18_North US-xSC NEON Smithsonian Conservation Biology Institute
US-Rws Reynolds Creek Wyoming big sagebrush US-xSE NEON Smithsonian Environmental Research Center
US-Seg Sevilleta grassland US-xSJ NEON San Joaquin Experimental Range
US-Ses Sevilleta shrubland US-xSL NEON North Sterling
US-Sne Sherman Island Restored Wetland US-xSP NEON Soaproot Saddle
US-Snf Sherman Barn US-xSR NEON Santa Rita Experimental Range
US-SRG Santa Rita Grassland US-xST NEON Steigerwaldt Land Services
US-SRM Santa Rita Mesquite US-xTA NEON Talladega National Forest
US-Syv Sylvania Wilderness Area US-xTE NEON Lower Teakettle
US-Ton Tonzi Ranch US-xTL NEON Toolik
US-Tw1 Twitchell Wetland West Pond US-xTR NEON Treehaven
US-Tw3 Twitchell Alfalfa US-xUK NEON The University of Kansas Field Station
US-Tw4 Twitchell East End Wetland US-xUN NEON Univ. of Notre Dame Environmental Research Center
US-Tw5 East Pond Wetland US-xWD NEON Woodworth
US-UMB Univ. of Mich. Biological Station US-xWR NEON Wind River Experimental Forest
US-UMd UMBS Disturbance US-xYE NEON Yellowstone Northern Range

Image download

A Python script was created to interact with Planet’s RESTful API interface (https://developers.planet.com/docs/apis/data/) to search, request, and download the dataset. For each site, an area of interest (AOI) was defined by a 10-km x 10-km polygon centered at the flux tower for each site. This AOI window was chosen because 80% of monthly footprint climatologies at eddy covariance towers range from 103 to 107 m2 (Chu et al., 2021). The polygon was loaded as a GeoJson (included with this dataset) and used to submit search requests to the API. The following additional search filters were applied: (1) quality category classified as standard; (2) cloud cover less than or equal to 50%; and (3) ground control as true. Searches were done on a per-year basis between 2016 and 2021. The resulting data set included over 1.8 million unique files with a total volume of 62.2 TB.

Image processing

Low quality pixels were excluded for all 4 bands (i.e., blue, green, red, and near-infrared). Specifically, pixels were excluded if the value of Unusable Data Mask was not 0, which represents the pixel is cloud contaminated or non- images area, or the value of Usable Data Mask is 0, which represents that the pixel is not clear due to snow, shadow, haze, or cloud. Next, all images were cropped using the AOI. For dates with multiple images for the AOI, those images merged into single mosaicked image based on mean surface reflectance values for that date. The resulting database of daily surface reflectance images were sorted in chronological order, sub-divided into 200 sub-areas at each site (i.e., 0.5 km2 each), and then stored as image stacks to facilitate parallel processing to estimate LSP metrics, where each image stack included all images from 2016 to January 2022.

Retrieving phenological metrics

Land surface phenological (LSP) metrics were estimated using an algorithm adapted from Bolton et al. (2020), which extracts the timing of key phenological transition dates during the growing season (Moon et al., 2021). Prior to LSP estimation, daily images of the two-band Enhanced Vegetation Index (EVI2) data were generated from PlanetScope imagery. EVI2 was used rather than other vegetation indices such as Enhanced Vegetation Index (EVI) or Normalized Difference Vegetation Index (NDVI) because EVI2 is less prone to saturation over dense canopy cover and soil background influences (Jiang, et al., 2008). Therefore, phenological metrics derived from the EVI2 time series tend to have better agreement with PhenoCam observations than those from NDVI time series (Klosterman et al., 2014).

Daily images of EVI2 data were generated from the PlanetScope imagery and then interpolated to create smooth time series of daily EVI2 values at each pixel. Sources of variation related to clouds, atmospheric aerosols, and snow were detected and removed from the EVI2 time series at each pixel based on both data mask layers and outlier detection criteria (i.e., de-spiking and removing negative EVI2 values). Second, the ‘background’ EVI2 value (i.e., the minimum EVI2 value outside of the growing season) was identified based on the 10th percentile of snow-free EVI2 values at each pixel. Any dates with EVI2 values below the background value were replaced with the background EVI2. Third, penalized cubic smoothing splines were used to create daily EVI2 time series across all years of available data for gap filling and smoothing the time series. Complete details on these steps are given in Bolton et al. (2020).

LSP metrics are estimated for each pixel in up to two growth cycles in each year. If no growth cycles are detected, the algorithm returns NA values. If more than two growth cycles are detected, which is rare but does occur (e.g., alfalfa), the algorithm records seven LSP metrics corresponding to the two growth cycles with the largest EVI2 amplitude. The resulting data set includes seven ‘timing’ metrics that identify the timing of: greenup onset; mid-greenup; maturity; peak EVI2; greendown onset; mid-greendown, and dormancy. These metrics record the day of year (DOY) when the EVI2 time series exceeds 15%, 50%, and 90% of EVI2 amplitude during the greenup phase, reaches its maximum, and goes below 90%, 50%, and 15% of EVI2 amplitude during the greendown phase. In addition, three additional metrics were recorded that characterize the magnitude of seasonality and total ‘greenness’ at each pixel in each growth cycle: the EVI2 amplitude, the maximum EVI2, and the growing season integral of EVI2, which is calculated as the sum of daily EVI2 values between the growth cycle start- and end-dates (i.e., from greenup onset to dormancy). See variables listed in Table 2.

Quality assurance flags

Quality Assurance (QA) layers were estimated at each pixel based on the density of observations and the quality of cubic spline fits during each phase of the growing season. QA value 1 (high quality) was assigned if the correlation coefficient of the relationship between observation versus splined was greater than 0.75 and the length of maximum gap over the segment was less than 30 days; QA value 2 (moderate quality) was assigned if the correlation coefficient of the relationship between observation versus splined is less than 0.75 or the length of maximum gap over the segment was greater than 30 days; QA value 3 (low quality) was assigned if the correlation coefficient of the relationship between observation versus splined was less than 0.75 and the length of maximum gap over the segment was less than 30 days; and QA value 4 was assigned if no cycles detected or the algorithm was not run.

Additional details on methods are provided in Moon et al. (2021).

Data Access

These data are available through the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).

Land Surface Phenology, Eddy Covariance Tower Sites, North America, 2017-2021

Contact for Data Center Access Information:

References

Bolton, D.K., J.M. Gray, E.K. Melaas, M. Moon, L. Eklundh, and M.A. Friedl. 2020. Continental-scale land surface phenology from harmonized Landsat 8 and Sentinel-2 imagery. Remote Sensing of Environment 240:111685. https://doi.org/10.1016/j.rse.2020.111685

Chu, H., X. Luo, Z. Ouyang, W.S. Chan, S. Dengel, S.C. Biraud, M.S. Torn, S. Metzger, J. Kumar, M.A. Arain, T.J. Arkebauer, D. Baldocchi, C. Bernacchi, D. Billesbach, T.A. Black, P.D. Blanken, G. Bohrer, R. Bracho, S. Brown, N.A. Brunsell, J. Chen, X. Chen, K. Clark, A.R. Desai, T. Duman, D. Durden, S. Fares, I. Forbrich, J.A. Gamon, C.M. Gough, T. Griffis, M. Helbig, D. Hollinger, E. Humphreys, H. Ikawa, H. Iwata, Y. Ju, J.F. Knowles, S.H. Knox, H. Kobayashi, T. Kolb, B. Law, X. Lee, M. Litvak, H. Liu, J.W. Munger, A. Noormets, K. Novick, S.F. Oberbauer, W. Oechel, P. Oikawa, S.A. Papuga, E. Pendall, P. Prajapati, J. Prueger, W.L. Quinton, A.D. Richardson, E.S. Russell, R.L. Scott, G. Starr, R. Staebler, P.C. Stoy, E. Stuart-Haëntjens, O. Sonnentag, R.C. Sullivan, A. Suyker, M. Ueyama, R. Vargas, J.D. Wood, and D. Zona. 2021. Representativeness of eddy-covariance flux footprints for areas surrounding AmeriFlux sites. Agricultural and Forest Meteorology 301–302:108350. https://doi.org/10.1016/j.agrformet.2021.108350

Jiang, Z., A. Huete, K. Didan, and T. Miura. 2008. Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment 112:3833–3845. https://doi.org/10.1016/j.rse.2008.06.006

Klosterman, S.T., K. Hufkens, J.M. Gray, E. Melaas, O. Sonnentag, I. Lavine, L. Mitchell, R. Norman, M.A. Friedl, and A.D. Richardson. 2014. Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery. Biogeosciences 11:4305–4320. https://doi.org/10.5194/bg-11-4305-2014

Moon, M., A.D. Richardson, and M.A. Friedl. 2021. Multiscale assessment of land surface phenology from harmonized Landsat 8 and Sentinel-2, PlanetScope, and PhenoCam imagery. Remote Sensing of Environment 266:112716. https://doi.org/10.1016/j.rse.2021.112716

Novick, K.A., J.A. Biederman, A.R. Desai, M.E. Litvak, D.J. P. Moore, R.L. Scott, and M.S. Torn. 2018. The AmeriFlux network: A coalition of the willing. Agricultural and Forest Meteorology 249:444–456. https://doi.org/10.1016/J.AGRFORMET.2017.10.009