Recent Advances in Land Surface Phenology Estimation with

Multispectral Sensing

Irini Soubry

, Ioannis Manakos

and Chariton Kalaitzidis

Department of Geography and Planning, University of Saskatchewan, SK S7N 5C8, Canada

Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki 57001, Greece

Department of Geoinformation in Environmental Management, Mediterranean Agronomic Institute of Chania,

73100 Crete, Greece

Keywords: Land Surface Phenology, Data Fusion, Satellite Synergies, Phenology Metrics, Global Phenology Networks,

Global Phenology Products.

Abstract: Vegetation phenology refers to changes in seasonal patterns of vegetation cycles, such as flowering and leaf

fall, influenced by annual and seasonal fluctuations of biotic and abiotic drivers. Information about phenology

is crucial for unravelling the underlying biological processes across vegetation communities in space and time.

It is also important for ecosystem and resources management, conservation, restoration, policy and decision-

making on local, national, and global scales. Numerous approaches to register Land Surface Phenology (LSP)

appeared since Earth Observation from space became possible a few decades ago. This paper attempts to

capture current progress and new capacities that arose with the advent of the free data policy, the Sentinel-

era, new multispectral satellite sensors, cloud computing, and machine learning in LSP for natural and semi-

natural environments. Spaceborne sensors’ capacity to capture LSP, data fusion, and synergies are discussed.

Information about retrieval methods through open-source tools and global LSP products and phenology

networks are presented.

1 INTRODUCTION

Vegetation phenology refers to the changes in

seasonal patterns of natural phenomena on the land,

e.g. leaf out, flowering, leaf browning and fall,

influenced by annual and seasonal fluctuations of

biotic and abiotic (e.g. temperature, day length,

precipitation) drivers (Gerstmann et al., 2016; Lieth,

1974; USA-NPN, 2020). On the other hand, Land

Surface Phenology (LSP) is the study of the spatio-

temporal vegetation development of the land surface

as measured by satellite sensors, and is different from

species-specific phenology observed on the ground

(de Beurs & Henebry, 2004, 2005). Vegetation

phenology has a pivotal function in delineating the

structure and function of ecosystems. The main

drivers of vegetation phenology are related to climate

and vary across ecoregions (Munson & Long, 2017;

Zhang et al., 2007).

https://orcid.org/0000-0001-7937-5726

https://orcid.org/0000-0001-6833-294X

https://orcid.org/0000-0001-5217-7164

Phenology is studied in various frameworks, such

as assessing urban heat islands effects on vegetation

phenology (Ding et al., 2020; Zhang et al., 2004),

vegetation phenology detection in urban areas

(Granero-Belinchon et al., 2020), and crop growth

stages detection (Gao et al., 2020). Nevertheless, this

paper focuses on its applications in natural and semi-

natural vegetation. By semi-natural vegetation, one

means vegetation that includes “extensively managed

grasslands, agro-forestry areas and all vegetated

features that are not used for crop production”

(García-Feced et al., 2014).

Knowledge of phenological cycles contributes to

the development of protection measures and

management practices to sustain ecosystems and their

services (Buisson et al., 2017). The importance of

phenology monitoring is acknowledged by the Group

on Earth Observations (GEO) (GEO-BON, 2019), the

UN (United Nations) Sustainable Development Goals

134

Soubry, I., Manakos, I. and Kalaitzidis, C.

Recent Advances in Land Surface Phenology Estimation with Multispectral Sensing.

DOI: 10.5220/0010555801340145

In Proceedings of the 7th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2021), pages 134-145

ISBN: 978-989-758-503-6

(SDGs) -towards goals 13 (climate action) and 15

(life on land) (Trondheim Conference, 2019; UN,

2019)-, and the Convention on Biological Diversity

(CBD); which has set the Aichi Biodiversity Targets

(ABTs) (target 15 – ecosystem resilience and

biodiversity contribution to carbon stocks – needs

phenological data to be resolved) (CBD, 2019).

Phenology cycles can be approximated from

spaceborne time series of vegetation indices (VIs)

(Kuenzer et al., 2015). Towards this purpose, there

exist global spaceborne phenology products

(GLOBE, 2019; NEON, 2019; PEP725, 2019; USA-

NPN, 2019), which are based on LSP. Different

remote sensors can approximate LSP, i.e. LiDAR (see

the review of Salas (2020)), SAR (Synthetic Aperture

Radar) mainly related to crop phenology (Cota et al.,

2015; Mascolo et al., 2016), and passive microwave

remote sensing systems (Alemu & Henebry, 2013;.

Alemu et al., 2019; Dannenberg et al., 2020; Tong et

al., 2019). Optical remote sensing remains the

common approach in LSP estimation, since

vegetation pigments detected with multispectral

sensors relate to different phenological stages. Lastly,

a recent direction in LSP estimation is fluorescence

remote sensing; in particular the use of satellite-

derived Solar-Induced Cholophyll Fluorescence

(SIF) (Joiner et al., 2014). The coarse spatial (0.5-1°)

and temporal resolution of such systems still pose a

large barrier towards detailed monitoring of seasonal

vegetation changes (Springer et al., 2017). Recently

employed and future satellite SIF missions have

higher spatial and temporal resolutions; and will be

able to alleviate some of the current problems. One

example includes the future Fluorescence Explorer

(FLEX) (to be launched in 2022), which could

provide more accurate estimations of phenology in

heterogeneous landscapes (ESA, 2020).

Advances in sensor technology, coupled with

increasing demand for frequent, spectrally rich, and

spatially detailed satellite data, have led to the

launch of multiple satellite missions and new image

processing technologies. These allow for increased

spatial and temporal resolution of data individually,

or through fusions and synergies (Claverie et al.,

2018; Li et al., 2017; Pouliot et al., 2018). As the

science of LSP has grown dramatically over the past

two decades, there is a pressing need to report the

advances in this field. Several reviews have been

made; tackling separately LSP methods and their

limitations (de Beurs & Henebry, 2010; Zeng et al.,

2020), LSP products (Henebry & de Beurs, 2013;

Reed et al., 2009), phenology networks (Morisette et

al., 2009; Reed et al., 2009), and challenges that

arise in LSP of optical remote sensing (Helman,

2018; Henebry & de Beurs, 2013; Morisette et al.,

2009; Reed et al., 2009). This paper reviews recent

and future trend developments for LSP retrieval of

natural and semi-natural vegetation with

multispectral sensors during the Sentinel-era;

including sensors, data fusion, synergies,

workflows, products, and networks. Towards this

purpose, recent papers (up until December 2020)

from the last 5 to10 years and heavily cited papers

that fall within the aforementioned topic were

selected.

2 CURRENT AND FUTURE

PROGRES IN LSP

ESTIMATION THROUGH

MULTISPECTRAL REMOTE

SENSING

Since earth observation from space became possible,

several satellite sensors have been used for LSP

estimation. An overview of sensors and LSP

example applications is presented in Table 1. The

VIIRS LSP product follows-up the mission of the

MODIS product (Moon et al., 2019). The Project for

On-Board Autonomy-Vegetation (PROBA-V) was

developed as an improved smaller version of SPOT-

VGT to provide continuity of its 10-year archive and

to fill the gap until the launch of Sentinel-3 (in 2016

and 2018) (eoPortal Directory, 2020). As of July

2020, it can be used for experimental monitoring

over Europe and Africa up until its orbit will go into

darkness in October 2021 (eoPortal Directory,

2020). Since its data is freely available, its use in

LSP studies becomes even easier. Upcoming plans

for 2021 include the addition of a small satellite with

the same type of sensor on PROBA-V, which will

look at the same targets from a different viewing

angle so as to generate fused images (VITO, 2020).

LSP has also been estimated from geostationary

satellites. More recently, the Advanced Himawari

Imager (AHI) on the geostationary Himawari-8

satellite was used to enhance LSP estimation over

the Asian-Pacific region (Miura et al., 2019; Yan et

al., 2019), and to study the sun-angle effects on LSP

(Ma et al., 2020).

When Landsat imagery became freely available

in 2008, numerous land imaging applications and

studies were conducted. Landsat’s spatial resolution

enhances the way in which LSP variations set by

micro-climatic and topographic effects are

registered. Additionally, the heterogeneity in land

cover classes within each pixel is reduced, and a

Recent Advances in Land Surface Phenology Estimation with Multispectral Sensing

135

more detailed matching with field- Landsat’s long-

term continuity provides tremendous opportunities

for LSP time series development, especially at

present, when cloud-computing and machine learning

have set the stage for current and future trends in

image processing (see Section 2.2).

Table 1: Multispectral satellite sensor characteristics for LSP studies and example applications (spat. res.= spatial resolution;

temp. res.=temporal resolution; sun-synchr.=sun-synchronous; geostat.=geostationary).

Satellite

sensor

Orbit-

type

Operation

timespan

Spat.

res.

Temp.

res.

Example LSP

applications

Relevant studies

Data

Source

AVHRR

Sun-

synchr.

1978-Present

1.1 km

at nadir

Daily global LSP trends

(Bradley et al.,

2007; Wang et al.,

2012)

(Wunderle

& Neuhaus,

2020)

MODIS

Sun-

synchr.

1999-Present

250 m,

500 m,

1 km

Daily global LSP trends

(Cai et al., 2017;

Cui et al., 2019,

2020; Henebry &

de Beurs, 2013;

Karkauskaite et al.,

2017; Misra et al.,

2016; Wu et al.,

2017)

(ESA,

2020)

VIIRS

Sun-

synchr.

2011-Present

375 m,

250m,

750 m

Daily

global LSP trends;

comparison of global

products; comparison

with ground phenology

(Moon et al., 2019;

Zhang et al., 2017;

Zhang, Jayavelu, et

al., 2018; Zhang,

Liu, et al., 2018)

(NASA

EARTHDA

TA, 2020)

SPOT-VGT

Sun-

synchr.

1988-2014

1.15 km

at nadir

Daily

regional LSP trends;

global baseline LSP

product; comparison

with ground phenology

(Meroni et al.,

2014; Verhegghen

et al., 2014; Wu et

al., 2017)

(Wolters et

al., 2016)

PROBA-V

Sun-

synchr.

2013-2020

100 m,

300 m,

1 km

Daily

regional LSP trends;

comparison with

ground phenology

(Bórnez,

Richardson, et al.,

2020; Guzmán et

al., 2019)

(eoPortal

Directory,

2020)

SEVIRI

Geostat. 2002-Present

1 km, 3

15 min. regional LSP trends

(Sobrino et al.,

2013; Yan et al.,

2017)

(Aminou,

2002)

AHI

Geostat. 2014-Present

500 m,

1 km,

2 km

10 min. regional LSP trends

(Ma et al., 2020;

Miura et al., 2019;

Yan et al., 2019)

(eoPortal

Directory,

2020)

Landsat

Sun-

synchr.

1972-Present

30 m,

80 m

16-

days,

18-days

LSP trends;

comparison with

ground phenology;

land cover

characterization

(Dethier et al.,

1973; Fisher et al.,

2006; Liu et al.,

2016; Melaas et al.,

2013)

(eoPortal

Directory,

2020)

Sentinel-2

Sun-

synchr.

2015-Present

10 m,

20 m,

60 m

5-days,

10-days

LSP trends;

comparison with

ground phenology

(Cai, 2019; Löw &

Koukal, 2020;

Solano-Correa et

al., 2018; Vrieling

et al., 2018)

(eoPortal

Directory,

2020)

PlanetScope

Sun-

synchr.

2009-Present

3.7 m at

nadir

Daily

LSP trends in

agriculture

(Chen et al., 2019;

Cheng et al., 2020;

Myers et al., 2019;

Sadeh et al., 2019)

(eoPortal

Directory,

2020; ESA,

2020)

VENμS

Sun-

synchr.

2017-Present

3 m, 5.3

2-days

LSP trends in

agriculture

(Gao et al., 2020;

Herrmann et al.,

2020;

Manivasagam et

al., 2019)

(eoPortal

Directory,

2020)

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136

The Sentinel-2 MultiSpectral Instrument (MSI)

improves the temporal and spatial coverage of

existing satellite sensors and has recently been used

for LSP extraction (Cai, 2019; Löw & Koukal, 2020;

Vrieling et al., 2018). Sentinel-2 data have spatial and

spectral complementarity with Landsat data, which

could enable integration (Storey et al., 2016),

allowing for an average temporal overpass of 2.9 days

(Li & Roy, 2017), providing higher chances of cloud-

free surface data use for LSP studies.

LSP can also been retrieved with the use of very

high spatial (<10 m) and temporal resolution data.

The potential use of PlanetScope for phenology

estimation in semi-arid rangelands showed promising

results (Cheng et al., 2020). Additionally, Vegetation

and Environment monitoring on a New Micro-

Satellite (VENμS) has also been used for LSP studies

(Gao et al., 2020; Herrmann et al., 2020), and

transformation functions between Sentinel-2 and

VENμS surface reflectance have been developed in

order to combine these data into one dense time-series

for vegetation monitoring (Manivasagam et al.,

2019).

Upcoming satellite generations will be able to

support data continuity for LSP monitoring through

optical remote sensing and improve data quality.

More specifically, the JPSS mission, carrying -among

others- the VIIRS instrument, is scheduled to launch

three spacecrafts between 2021 and 2031 (Trenkle &

Driggers, 2019). Furthermore, commercial solutions,

such as the Planetscope nanosatellite constellation,

with continuous launches every three to six months,

will eventually allow for daily imaging of the entire

globe at very high spatial resolution (3m

approximately) (eoPortal Directory, 2020; ESA,

2020). Lastly, UrtheDaily will be launched by

UrtheCast in 2022, providing daily medium

resolution global images with 9 spectral bands that

will be cross calibrated to Sentinel-2 and will be

analysis ready through a constellation of six satellites

(UrtheCast, 2020).

2.1 Multi-Source Satellite Data

Integration Methods for LSP

Estimation

The use of composite images has been frequently

applied for AVHRR, MODIS, and SPOT data in

order to account for cloud cover. However, this

technique reduces the temporal frequency of the data,

which is important for LSP. Data fusion or blending

of satellite data from different sensors to create

synthetic information of high spatio-temporal

resolution has introduced a way that optimizes the

capacity to monitor land surface changes (Zhu et al.,

2010). Similarly, synergies between satellite products

(e.g. Sentinel-2 and Landsat-8) are used to densify

time series; here the individual products that make up

the synergy remain the same. These methods are

particularly important for LSP estimations, since both

high temporal and high spatial resolution are needed

to derive detailed phenology cycles. Examples of

recent types of data integration methods are provided

in Table 2.

Table 2: Examples of satellite data integration methods (i.e.

data fusion & synergies) that are useful for LSP estimation.

Method

Sensor

combination

Details Source

FORCE

ImproPhe

MODIS,

Landsat,

Sentinel

Uses local pixel

neighborhood,

denoises LSP,

preserves sharp

edges

(Frantz,

2019)

Automatic co-

registration

Landsat,

Sentinel

Co-registration of

Landsat-8 to

Sentinel-2A &

Sentinel-2A to

Sentinel-2B

(Skakun

et al.,

2017)

Assisted

downscaling

Landsat,

Sentinel

Downscales

Landsat-8 to

Sentinel-2

resolution

(Li &

Roy,

2017)

Super-

resolution

enhancement

Landsat,

Sentinel

Uses convolution

neural networks

trained with

Sentinel-2 data

(Pouliot

et al.,

2018)

HLS

Landsat,

Sentinel

A combined

Landsat/Sentinel

product

(Claveri

e et al.,

2018)

Scientists of the NASA Multi-source Land

Imaging (MuSLI) program combined Sentinel-2 and

Landsat-8 data (Li et al., 2017) towards the

production of the Harmonized Landsat Sentinel-2

(HLS) dataset. This currently covers the entire North

America and other globally distributed test sites.

Version 1.4 is available for 120 pilot regions, which

correspond to 4090 MGRS (Military Grid Reference

System) tiles (Masek, 2018; Skakun et al., 2018).

This data is tested for several applications, including

LSP (Claverie et al., 2018). A project targeting an

enhanced LSP product was created (Melaas et al.,

2017), and further developed towards an operational

LSP product (Bolton et al., 2020; Friedl et al., 2020).

The integration and combined use of these satellite

sensors provide a chance of developing time series

with unprecedented frequency. Nevertheless, the

combined use of different constellations introduces

various theoretical and technical hurdles.

Recent Advances in Land Surface Phenology Estimation with Multispectral Sensing

137

2.2 New Trends in LSP Retrieval and

Recent Discoveries

The twinned potential of cloud computing (CC) and

machine learning provides new pathways for enhanced

LSP retrieval. The current big volume of satellite data

requires high-performance processing methods, which

are hard to obtain through just a single computer. CC

represents a paradigm shift to next-generation studies

of plant phenology, since it allows for processing and

analysis of previously unmanageable volumes of data,

shifting the processing burden from a scientist’s

personal computer to an external server that is accessed

through the cloud. Since Landsat data have long-term

data continuity, CC has made it possible to assemble

time series from all available Landsat scenes. Cloud

solutions for data storage and LSP processing include

freely accessible platforms, such as Google Earth

Engine, Amazon Web Services (AWS) Open Data,

TerraScope Virtual Machine, and the ‘Phenology

Metrics’ algorithm (see Section 3.1). For instance,

Google Earth Engine (GEE) has allowed for online LSP

calculation and analysis in recent studies (Bórnez et al.,

2020a; Li et al., 2019; Venkatappa et al., 2019; Workie

& Debella, 2018), facilitating processing burden.

Moreover, data cube technologies are gaining

popularity in the earth observation society for remote

sensing data processing. Image data cubes are defined

as “large collections of temporal, multivariate datasets

typically consisting of analysis ready multispectral

Earth observation data” (Kopp et al., 2019). The

Committee of Earth Observation Satellites (CEOS)

created Open Data Cube to facilitate the creation of

such cubes. LSP processing can hugely benefit from

such technology. For instance, Li et al. (2020) used

data cube processing to analyse changes in vegetation

green-up dates over various dimensions to reveal

greater spatiotemporal discrimination. Overall,

researchers can incorporate all available imagery over

much larger extents, leading to phenology results that

are either temporally detailed, geographically

expansive, or both.

Similarly, machine-learning techniques deserve a

mention, given that the large volume of available data

has made it possible to apply increasing sophisticated

analysis approaches that depend on massive data

inputs. Machine learning could be applied to data

cubes and multi-source earth observation data. For

now, it has been used to predict ground-based

phenophases or LSP from daily pheno-tower data.

Examples include its use to learn phenological patterns

and detect them in a large number of ground digital

imagery (Almeida et al., 2014; Ryu et al., 2018), or in

filling spatiotemporal ground-based LSP observations

and forecasting phenophases with remote sensing and

meteorological data (Czernecki et al., 2018). Recently,

the DATimeS software was launched to specifically

incorporate twelve different machine learning fitting

algorithms for time series analysis of phenology data

(see Section 3.1). Overall, the use of machine learning

techniques to enhance phenological models has not

been fully explored until now.

Lastly, many studies note that LSP of end of season

(EOS) is more difficult to estimate because canopy

greenness of plants changes gradually during autumn.

To avoid being based on just one method for EOS

estimation, Yuan et al. (2020) recently calculated EOS

by taking the average of two methods (i.e. the midpoint

and double logistical fitting). Furthermore, recent

studies revealed that the estimat-ion of autumn

phenology is a gradual process that requires a

combination of sensors and satellite data for accurate

depiction. Lu et al. (2018) found that autumn

phenology defined by fluorescence satellite data

agreed better with gross primary production (GPP)

autumn phenology than that derived from VIs. Their

findings agree with those of Wang et al. (2020). They

support that photosynthetic activity senesces before

changes in leaf color, and that the decrease in

vegetation water content occurs at the end. This was

consistent globally, providing a better understanding of

the underlying structural and functional processes

behind autumn senescence, being a longer and more

gradual process than start of season (SOS).

3 LSP SOFTWARE TOOLS,

GLOBAL PRODUCTS &

NETWORKS

3.1 Open-source LSP Software Tools

There is an abundance of LSP data processing and

extraction software from Earth observation time-series.

All of these use a variety of fitting functions to extract

a number of LSP metrics. Exemplary open-source

tools are presented in Table 3. They all provide the

advantage of being freely available, but might have

limitations regarding the nature of the time series,

algorithm verification, lack of a graphical user

interface, or demand for advanced knowledge. The use

of cloud processing with an online workflow for

“Estimation of phenology metrics” by the Centre for

Research and Technology - Hellas (CERTH), and the

incorporation of next-generation machine learning

regression algorithms for LSP time series by DATimeS

are promising.

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138

Table 3: Open-source software tools for LSP extraction.

Software tool Source

TIMESAT (Eklundh, 2017)

PhenoSat (Rodrigues et al., 2013)

BFAST (Verbesselt et al., 2010)

SpliTS (Frantz et al., 2016; Mader,

2012)

SPIRITS (Eerens & Dominique, 2013;

Rembold et al., 2013)

‘greenbrown’ R

package

(Forkel et al., 2013, 2015;

Forkel & Wutzler, 2015)

‘phenex’ R package (Lange & Doktor, 2017)

“Estimation of

phenology metrics”

by CERTH

(Guigoz, 2017; Nativi et al.,

2016)

DATimeS (Belda et al., 2020)

3.2 Global LSP Products

Some of the most important global LSP product are

listed in Table 4. One of the benefits of the

MCD12Q2 product is that it can be used for regions

that have two growing seasons (Henebry & de Beurs,

2013). Similarly, the VIIRS GLSP product can

separate phenological phases in a wide variety of

vegetation types and climate systems, with high

quality (Zhang et al., 2018). The MEaSUREs VIP

product has the advantage of combining AVHRR and

MODIS data and provides 26-year LSP metrics.

Lastly, the HLS surface reflectance dataset (Bolton et

al., 2020), which currently covers several pilot sites

Table 4: Global LSP products: MODIS Land Cover

Dynamics product (MCD12Q2), VIIRS Global Land

Surface Phenology product (GLSP), Making Earth System

Data Records for Use in Research Environments

(MEaSUREs) Vegetation Index and Phenology (VIP)

global dataset. Information retrieved from Gray et al.

(2019), USGS (2019), and Zhang et al. (2018).

Global

LSP

products

Duration Source Spatial

Resolution

MCD12Q2

2001-

2017

EVI2 from MODIS

BRDF Adjusted

Reflectance

(NBAR)

500 m

VIIRS

GLSP

2012-

Present

EVI2 from daily

VIIRS BRDF

NBAR

500 m

MEaSUREs

VIP

1981-

2014

NDVI and EVI2

from AVHRR

1981-1999;

MODIS MOD09

2000-2014

5600 m

around the world, can be used to derive LSP time

series, and should also be mentioned here, as future

plans envision for it to have global cover.

3.3 Ground Phenology Networks for

LSP Validation

To link LSP estimations with ground phenology

observations, it is advised to gain complete

understanding of the species composition in the study

area (Misra et al., 2016). Therefore, simultaneous

field-based and remote sensing data has to be

obtained along various stages of multiple growing

seasons. The downside of in situ phenological data

collection is that it is labor-intensive, localized, and

includes only a small sample of species (Misra et al.,

2016). Therefore, many countries operate ground

phenology based on crowd-sourced information and

transboundary networks. It has been suggested by the

Society of Biometeorology Phenology Commission

(ISB-PC) and the World Meteorological

Organization Commission for Agricultural

Meteorology (WMO-CAgM) to build a Global

Alliance of Phenological Observation Networks

(GAPON) (USA-NPN, 2020). The phenology

networks that are included into this alliance are up to

date 52 in number, and include –among others-

nationwide approaches. Examples of some major

phenological networks are provided in Table 5.

4 CONCLUSIONS

It has become obvious that a new era with time series

at higher spatial and temporal resolution brings

enormous opportunities and challenges to LSP

research. The big volume of data requires high-

performance processing methods. To tackle this

issue, cloud solutions for data storage and processing

are freely accessible along with machine learning

workflows; and data cube processing techniques are

being developed. All of this will facilitate the role that

phenology has to play in the UN SDGs and ABTs

together with the development of EBVs (essential

biodiversity variables) in line with the GEO

initiatives. Through this review it is made clear that

the use of satellite constellations might reduce the

problems associated with the spatial and temporal

resolution of LSP data (e.g. HLS product). Lastly, the

variety of open-source tools, global products, and

ground phenology networks gives opportunity for

LSP estimation by diverse science teams and

capacities.

Recent Advances in Land Surface Phenology Estimation with Multispectral Sensing

139

Table 5: Major existing phenology networks. Information retrieved from GLOBE (2019), Nasahara & Nagai (2015), NEON

(2019), PEN (2020), PEP725 (2019), Templ et al. (2018), USA-NPN (2019), PHENOCAM (2020).

Phenology

Networks

Purpose Users Collaborations Extra information

USA-NPN

Collect, store, distribute

phenology data

Researchers, natural

resource managers,

policy-makers,

educators, citizen

scientists, NGO’s

-NEON;

-Nature’s Notebook

Standardized plant & animal

observation protocols

NEON

Collect ecological data:

in situ measurements/

observations & airborne

remote sensing surveys

Researchers -81 field sites in US 175 open access products

PEP725

Open access database to

facilitate phenological

research, education,

environmental

monitoring

Researchers, educators

-7 phenology network

partners;

-32 European

meteorological

services

-Volunteer data collected

from 1868 to present;

-12 million records

GLOBE

International science and

education program to

promote teaching and

learning of science

Students, educators

-NASA, NSF,

NOAA;

-121 countries

Over 150 million ground

biophysical measurements

PEN

Validate terrestrial RS

products of ecology,

phenology changes

Ecologists, remote

sensing specialists,

scientists, citizens

-FluxNet, ILTER,

AsiaFlux

-38 sites worldwide,

most in Japan

Some sites measure

environmental

ecophysiological properties

PhenoCam

For phenological model

validation, evaluation of

satellite RS products,

studies of climate change

impacts on terrestial

ecosystems

Researchers, remote

sensing specialists

-750 sites across

North America

Data derived from visible-

wavelength digital camera

imagery

ACKNOWLEDGEMENTS

The authors acknowledge valuable suggestions and

support from Giorgos Kordelas and George Kazakis.

This review study has been partially funded and

supported by the European Union’s Horizon 2020

Coordination and Support Action under Grant

Agreement No. 952111, EOTiST

(https://cordis.europa.eu/project/id/952111). The

authors declare no conflict of interest. All authors

certify that they have no affiliations with or

involvement in any organization or entity with any

financial interest or non-financial interest in the

subject matter or materials discussed in this

manuscript.

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