Dense time series of Landsat 8 and Sentinel-2 imagery are creating exciting new opportunities to monitor, map, and characterize temporal dynamics in land surface properties with unprecedented spatial detail and quality. By combining imagery from the Landsat 8 Operational Land Imager and the MultiSpectral Instrument on-board Sentinel-2A and -2B, the remote sensing community now has access to moderate (10–30 m) spatial resolution imagery with repeat periods of ~3 days in the mid-latitudes. At the same time, the large combined data volume from Landsat 8 and Sentinel-2 introduce substantial new challenges for users. Land surface phenology (LSP) algorithms, which estimate the timing of phenophase transitions and quantify the nature and magnitude of seasonality in remotely sensed land surface conditions, provide an intuitive way to reduce data volumes and redundancy, while also furnishing data sets that are useful for a wide range of applications including monitoring ecosystem response to climate variability and extreme events, ecosystem modelling, crop-type discrimination, and land cover, land use, and land cover change mapping, among others. To support the need for operational LSP data sets, here we describe a continental-scale land surface phenology algorithm and data product based on harmonized Landsat 8 and Sentinel-2 (HLS) imagery. The algorithm creates high quality times series of vegetation indices from HLS imagery, which are then used to estimate the timing of vegetation phenophase transitions at 30 m spatial resolution. We present results from assessment efforts evaluating LSP retrievals, and provide examples illustrating the character and quality of information related to land cover and terrestrial ecosystem properties provided by the continental LSP dataset that we have developed. The algorithm is highly successful in ecosystems with strong seasonal variation in leaf area (e.g., deciduous forests). Conversely, results in evergreen systems are less interpretable and conclusive.

Monitoring vegetation phenology is critical for quantifying climate change impacts on ecosystems. We present an extensive dataset of 1783 site-years of phenological data derived from PhenoCam network imagery from 393 digital cameras, situated from tropics to tundra across a wide range of plant functional types, biomes, and climates. Most cameras are located in North America. Every half hour, cameras upload images to the PhenoCam server. Images are displayed in near-real time and provisional data products, including timeseries of the Green Chromatic Coordinate (Gcc), are made publicly available through the project web page (https://phenocam.sr.unh.edu/webcam/gallery/). Processing is conducted separately for each plant functional type in the camera field of view. The PhenoCam Dataset v2.0, described here, has been fully processed and curated, including outlier detection and expert inspection, to ensure high quality data. This dataset can be used to validate satellite data products, to evaluate predictions of land surface models, to interpret the seasonality of ecosystem-scale CO2 and H2O flux data, and to study climate change impacts on the terrestrial biosphere.

Land surface phenology (LSP) has been widely used as the “footprint” of urbanization and global climate change. Shifts of LSP have cascading effects on food production, carbon sequestration, water consumption, biodiversity, and public health. Previous studies mainly focused on investigating the effects of urbanization on the spatial patterns of LSP by comparing phenological metrics, e.g. start of season (SOS) and end of season (EOS), between urban center and the surrounding rural regions. However, it remains unclear how urbanization-induced land cover conversions and climate change jointly influence the temporal variations of SOS and EOS within the urban ecosystem. To fill this knowledge gap, we utilized daily two-band enhanced vegetation index, daily meteorological record, and annual land cover dataset to investigate the respective impacts of urbanization and climate change on temporal shifts of LSP between the post- and the pre-urbanization periods over 196 large cities in the northern mid-latitudes. We found 51% of the cities experienced an advanced SOS with an average of −6.39 ± 5.82 days after urbanization has occurred, while the remaining 49% of the cities had a delayed SOS with an average of 7.56 ± 5.63 days. We also found a later EOS at 53% of the cities and an earlier EOS at 47% of the cities with an average of 8.43 ± 7.59 and −5.57 ± 4.99 days between the post- and pre-urbanization periods, respectively. Multiple linear regression analysis indicates that climate variables (i.e. temperature, precipitation, and insolation) play dominant roles in regulating the temporal shifts of LSP. Furthermore, the earlier SOS and later EOS were significantly correlated with the amplitude of urbanization (i.e. increase of impervious surface area) in cities after controlling for effects of climate factors. These patterns were generally consistent across eight climate zones. Our findings provide critical information in modeling natural and anthropogenic effects on urban ecosystem, with important benefits for urban sustainability and biodiversity conservation.

Ground validation of satellite-based vegetation phenology has been challenging because ground phenology data are sparsely distributed and mostly observed from limited numbers of plant species at discrete phenophases. The recently developed PhenoCam network has measured continuous growth of vegetation canopy greenness that can be used to validate satellite-based vegetation phenology across a variety of plant functional types. In this study, we used PhenoCam green chromatic coordinate (GCC) in North America to evaluate grassland phenology derived from three types of MODIS vegetation indices: the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and a per-pixel GCC (GCCpp) which was computed to describe the average vegetation color at the pixel level. The start of greenness (SOG), end of greenness (EOG), and length of greenness (LOG), and the dates for detailed seasonal dynamics for each site-year were compared. Our results indicate that MODIS VIs can be used to predict phenological metrics and seasonal dynamics in grassland greenness measured from PhenoCam GCC. More importantly, we quantified the difference between SOG, EOG, and LOG and seasonality estimated from satellite and near-surface remote sensing and discovered that GCCpp may be more suitable than NDVI and EVI at estimating dynamics in grassland greenness during senescence.

Drylands account for approximately 40% of the global land surface and play a dominant role in the trend and variability of terrestrial carbon uptake and storage. Gross ecosystem photosynthesis – termed gross primary productivity (GPP) – is a critical driver of terrestrial carbon uptake and remains challenging to be observed directly. Currently, vegetation indices that largely capture changes in greenness are the most commonly used datasets in satellite-based GPP modeling. However, there remains significant uncertainty in the spatiotemporal relationship between greenness indices and GPP, especially for relatively heterogeneous dryland ecosystems. In this paper, we compared vegetation greenness indices from PhenoCam and satellite (Landsat and MODIS) observations against GPP estimates from the eddy covariance technique, across three representative ecosystem types of the southwestern United States. We systematically evaluated the changes in the relationship between vegetation greenness indices and GPP: i) across spatial scales of canopy-level, 30-meter, and 500-meter resolution; and ii) across temporal scale of daily, 8-day, 16-day, and monthly resolution. We found that greenness-GPP relationships were independent of spatial scales as long as land cover type and composition remained relatively constant. We also found that the greenness-GPP relationships became stronger as the time interval increased, with the strongest relationships observed at the monthly resolution. We posit that the greenness-GPP relationship breaks down at short timescales because greenness changes more slowly than plant physiological function, which responds rapidly to changes in key biophysical drivers. These findings provide insights into the potential for and limitations of modeling GPP using remotely sensed greenness indices across dryland ecosystem types.

Observations of vegetation phenology at regional-to-global scales provide important information regarding seasonal variation in the fluxes of energy, carbon, and water between the biosphere and the atmosphere. Numerous algorithms have been developed to estimate phenological transition dates using time series of remotely sensed spectral vegetation indices. A key challenge, however, is that different algorithms provide inconsistent results. This study provides a comprehensive comparison of start of season (SOS) and end of season (EOS) phenological transition dates estimated from 500 m MODIS data based on two widely used sources of such data: the TIMESAT program and the MODIS Global Land Cover Dynamics (MLCD) product. Specifically, we evaluate the impact of land cover class, criteria used to identify SOS and EOS, and fitting algorithm (local versus global) on the transition dates estimated from time series of MODIS enhanced vegetation index (EVI). Satellite-derived transition dates from each source are compared against each other and against SOS and EOS dates estimated from PhenoCams distributed across the Northeastern United States and Canada. Our results show that TIMESAT and MLCD SOS transition dates are generally highly correlated (r = 0.51-0.97), except in Central Canada where correlation coefficients are as low as 0.25. Relative to SOS, EOS comparison shows lower agreement and higher magnitude of deviations. SOS and EOS dates are impacted by noise arising from snow and cloud contamination, and there is low agreement among results from TIMESAT, the MLCD product, and PhenoCams in vegetation types with low seasonal EVI amplitude or with irregular EVI time series. In deciduous forests, SOS dates from the MLCD product and TIMESAT agree closely with SOS dates from PhenoCams, with correlations as high as 0.76. Overall, our results suggest that TIMESAT is well-suited for local-to-regional scale studies because of its ability to tune algorithm parameters, which makes it more flexible than the MLCD product. At large spatial scales, where local tuning is not feasible, the MLCD product provides a readily available data set based on a globally consistent approach that provides SOS and EOS dates that are comparable to results from TIMESAT.

Phenology is a valuable diagnostic of ecosystem health, and has applications to environmental monitoring and management. Here, we conduct an intercomparison analysis using phenological transition dates derived from near-surface PhenoCam imagery and MODIS satellite remote sensing. We used approximately 600 site-years of data, from 128 camera sites covering a wide range of vegetation types and climate zones. During both “greenness rising” and “greenness falling” transition phases, we found generally good agreement between PhenoCam and MODIS transition dates for agricultural, deciduous forest, and grassland sites, provided that the vegetation in the camera field of view was representative of the broader landscape. The correlation between PhenoCam and MODIS transition dates was poor for evergreen forest sites. We discuss potential reasons (including sub-pixel spatial heterogeneity, flexibility of the transition date extraction method, vegetation index sensitivity in evergreen systems, and PhenoCam geolocation uncertainty) for varying agreement between time series of vegetation indices derived from PhenoCam and MODIS imagery. This analysis increases our confidence in the ability of satellite remote sensing to accurately characterize seasonal dynamics in a range of ecosystems, and provides a basis for interpreting those dynamics in the context of tangible phenological changes occurring on the ground.

Near surface (i.e., camera) and satellite remote sensing metrics have become widely used indicators of plant growing seasons. While robust linkages have been established between field metrics and ecosystem exchange in many land cover types, assessment of how well remotely-derived season start and end dates depict field conditions in arid ecosystems remain unknown. We evaluated the correspondence between field measures of start (SOS; leaves unfolded and canopy greenness >0) and end of season (EOS) and canopy greenness for two widespread species in southwestern U.S. ecosystems with those metrics estimated from near-surface cameras and MODIS NDVI for five years (2012–2016). Using Timesat software to estimate SOS and EOS from the phenocam green chromatic coordinate (GCC) greenness index resulted in good agreement with ground observations for honey mesquite but not black grama. Despite differences in the detectability of SOS and EOS for the two species, GCC was significantly correlated with field estimates of canopy greenness for both species throughout the growing season. MODIS NDVI for this arid grassland site was driven by the black grama signal although a mesquite signal was discernable in average rainfall years. Our findings suggest phenocams could help meet myriad needs in natural resource management.

Vegetation phenology controls the seasonality of many ecosystem processes, as well as numerous biosphere-atmosphere feedbacks. Phenology is also highly sensitive to climate change and variability. Here we present a series of datasets, together consisting of almost 750 years of observations, characterizing vegetation phenology in diverse ecosystems across North America. Our data are derived from conventional, visible-wavelength, automated digital camera imagery collected through the PhenoCam network. For each archived image, we extracted RGB (red, green, blue) colour channel information, with means and other statistics calculated across a region-of-interest (ROI) delineating a specific vegetation type. From the high-frequency (typically, 30 min) imagery, we derived time series characterizing vegetation colour, including “canopy greenness”, processed to 1- and 3-day intervals. For ecosystems with one or more annual cycles of vegetation activity, we provide estimates, with uncertainties, for the start of the “greenness rising” and end of the “greenness falling” stages. The database can be used for phenological model validation and development, evaluation of satellite remote sensing data products, benchmarking earth system models, and studies of climate change impacts on terrestrial ecosystems.

Scientists gathered at a workshop in Cambridge, Mass., last June to identify opportunities and challenges associated with integrating multiscale, multiplatform streams of data to produce higher-level phenological data products (e.g., models) and applications at a variety of spatial and temporal resolutions.