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.
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).
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
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
2021 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
(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
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
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).
Example LSP
Relevant studies
1.1 km
at nadir
Daily global LSP trends
(Bradley et al.,
2007; Wang et al.,
& Neuhaus,
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.,
375 m,
750 m
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)
TA, 2020)
1.15 km
at nadir
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)
100 m,
300 m,
1 km
regional LSP trends;
comparison with
ground phenology
Richardson, et al.,
2020; Guzmán et
al., 2019)
Geostat. 2002-Present
1 km, 3
15 min. regional LSP trends
(Sobrino et al.,
2013; Yan et al.,
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)
30 m,
80 m
LSP trends;
comparison with
ground phenology;
land cover
(Dethier et al.,
1973; Fisher et al.,
2006; Liu et al.,
2016; Melaas et al.,
10 m,
20 m,
60 m
LSP trends;
comparison with
ground phenology
(Cai, 2019; Löw &
Koukal, 2020;
Solano-Correa et
al., 2018; Vrieling
et al., 2018)
3.7 m at
LSP trends in
(Chen et al., 2019;
Cheng et al., 2020;
Myers et al., 2019;
Sadeh et al., 2019)
2020; ESA,
3 m, 5.3
LSP trends in
(Gao et al., 2020;
Herrmann et al.,
Manivasagam et
al., 2019)
GISTAM 2021 - 7th International Conference on Geographical Information Systems Theory, Applications and Management
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.,
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
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.
Details Source
Uses local pixel
denoises LSP,
preserves sharp
Automatic co-
Co-registration of
Landsat-8 to
Sentinel-2A &
Sentinel-2A to
et al.,
Landsat-8 to
(Li &
Uses convolution
neural networks
trained with
Sentinel-2 data
et al.,
A combined
e et al.,
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
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).
3.1 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.
GISTAM 2021 - 7th International Conference on Geographical Information Systems Theory, Applications and Management
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,
SPIRITS (Eerens & Dominique, 2013;
Rembold et al., 2013)
‘greenbrown’ R
(Forkel et al., 2013, 2015;
Forkel & Wutzler, 2015)
‘phenex’ R package (Lange & Doktor, 2017)
“Estimation of
phenology metrics”
(Guigoz, 2017; Nativi et al.,
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).
Duration Source Spatial
BRDF Adjusted
500 m
EVI2 from daily
500 m
from AVHRR
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.
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
Recent Advances in Land Surface Phenology Estimation with Multispectral Sensing
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).
Purpose Users Collaborations Extra information
Collect, store, distribute
phenology data
Researchers, natural
resource managers,
educators, citizen
scientists, NGO’s
-Nature’s Notebook
Standardized plant & animal
observation protocols
Collect ecological data:
in situ measurements/
observations & airborne
remote sensing surveys
Researchers -81 field sites in US 175 open access products
Open access database to
facilitate phenological
research, education,
Researchers, educators
-7 phenology network
-32 European
-Volunteer data collected
from 1868 to present;
-12 million records
International science and
education program to
promote teaching and
learning of science
Students, educators
-121 countries
Over 150 million ground
biophysical measurements
Validate terrestrial RS
products of ecology,
phenology changes
Ecologists, remote
sensing specialists,
scientists, citizens
-FluxNet, ILTER,
-38 sites worldwide,
most in Japan
Some sites measure
ecophysiological properties
For phenological model
validation, evaluation of
satellite RS products,
studies of climate change
impacts on terrestial
Researchers, remote
sensing specialists
-750 sites across
North America
Data derived from visible-
wavelength digital camera
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
( 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
Alemu, W. G., & Henebry, G. M. (2013). Land surface
phenologies and seasonalities using cool earthlight in
mid-latitude croplands. Environmental Research
Letters, 8(4).
Alemu, Woubet G., Henebry, G. M., & Melesse, A. M.
(2019). Land surface phenologies and seasonalities in
the US prairie pothole region coupling AMSR passive
microwave data with the USDA cropland data layer.
Remote Sensing, 11(21).
Almeida, J., Dos Santos, J. A., Alberton, B., Torres, R. da
S., & Morellato, L. P. C. (2014). Applying machine
learning based on multiscale classifiers to detect remote
phenology patterns in Cerrado savanna trees.
Ecological Informatics, 23, 49–61.
GISTAM 2021 - 7th International Conference on Geographical Information Systems Theory, Applications and Management
Aminou, D. M. A. (2002). MSG ’s SEVIRI Instrument.
ESA Bulletin, 15–17.
Belda, S., Pipia, L., Morcillo-Pallarés, P., Rivera-Caicedo,
J. P., Amin, E., De Grave, C., & Verrelst, J. (2020).
DATimeS: A machine learning time series GUI toolbox
for gap-filling and vegetation phenology trends
detection. Environmental Modelling and Software,
127(104666), 1–14.
Bolton, D., Gray, J. M., Melaas, E. K., Moon, M., Eklundh,
L., & Friedl, M. A. (2020). Continental-scale land
surface phenology from harmonized Landsat 8 and
Sentinel-2 imagery. Remote Sensing of Environment,
240(111685), 1–16.
Bórnez, K., Richardson, A. D., Verger, A., Descals, A., &
Peñuelas, J. (2020). Evaluation of VEGETATION and
PROBA-V phenology using phenocam and eddy
covariance data. Remote Sensing, 12(18).
Bradley, B. A., Jacob, R. W., Hermance, J. F., & Mustard,
J. F. (2007). A curve fitting procedure to derive inter-
annual phenologies from time series of noisy satellite
NDVI data. Remote Sensing of Environment, 106(2),
Buisson, E., Alvarado, S. T., Le Stradic, S., & Morellato, L.
P. C. (2017). Plant phenological research enhances
ecological restoration. Restoration Ecology, 25(2),
Cai, Z. (2019). Vegetation Observation in the Big Data
Era: Sentinel-2 data for mapping the seasonality of
land vegetation. Lund University, Faculty of Science.
Cai, Z., Jönsson, P., Jin, H., & Eklundh, L. (2017).
Performance of smoothing methods for reconstructing
NDVI time-series and estimating vegetation phenology
from MODIS data. Remote Sensing, 9(12), 20–22.
CBD. (2019). Convention on Biological Diversity-Aichi
Biodiversity Targets.
Chen, B., Jin, Y., & Brown, P. (2019). An enhanced bloom
index for quantifying floral phenology using multi-
scale remote sensing observations. ISPRS Journal of
Photogrammetry and Remote Sensing, 156(August),
Cheng, Y., Vrieling, A., Fava, F., Meroni, M., Marshall, M.,
& Gachoki, S. (2020). Phenology of short vegetation
cycles in a Kenyan rangeland from PlanetScope and
Sentinel-2. Remote Sensing of Environment, 248(July),
Claverie, M., Ju, J., Masek, J. G., Dungan, J. L., Vermote,
E. F., Roger, J. C., Skakun, S. V., & Justice, C. (2018).
The Harmonized Landsat and Sentinel-2 surface
reflectance data set. Remote Sensing of Environment,
219(October), 145–161.
Cota, N., Kasetkasem, T., Rakwatin, P., Chanwimaluang,
T., & Kumazawa, I. (2015). Rice phenology estimation
using SAR time-series data. 2015 6th International
Conference of Information and Communication
Technology for Embedded Systems (IC-ICTES), 1–5.
Cui, T., Martz, L., Lamb, E. G., Zhao, L., & Guo, X. (2019).
Comparison of Grassland Phenology Derived from
MODIS Satellite and PhenoCam Near-Surface Remote
Sensing in North America. Canadian Journal of
Remote Sensing, 1–16.
Cui, T., Martz, L., Zhao, L., & Guo, X. (2020).
Investigating the impact of the temporal resolution of
MODIS data on measured phenology in the prairie
grasslands. GIScience & Remote Sensing, 57(3), 395–
3.20 20.1723279
Czernecki, B., Nowosad, J., & Jabłońska, K. (2018).
Machine learning modeling of plant phenology based
on coupling satellite and gridded meteorological
dataset. International Journal of Biometeorology,
62(7), 1297–1309.
Dannenberg, M., Wang, X., Yan, D., & Smith, W. (2020).
Phenological characteristics of global ecosystems based
on optical, fluorescence, and microwave remote
sensing. Remote Sensing, 12(4).
de Beurs, K. M., & Henebry, G. M. (2004). Land surface
phenology, climatic variation, and institutional change:
Analyzing agricultural land cover change in
Kazakhstan. Remote Sensing of Environment, 89(4),
de Beurs, K. M., & Henebry, G. M. (2005). Land surface
phenology and temperature variation in the
International Geosphere-Biosphere Program high-
latitude transects. Global Change Biology, 11(5), 779–
de Beurs, K. M., & Henebry, G. M. (2010). Chapter 9,
Spatio-Temporal Statistical Methods for Modelling
Land Surface Phenology. In Phenological Research:
Methods for Environmental and Climate Change
Analysis (pp. 177–208).
Dethier, B. E., Ashley, M. D., Blair, B., & Hopp, R. J.
(1973). Phenology satellite experiment. Symposium on
Significant Results Obtained from the Earth Resources
Technology Satellite, 157–165.
Ding, H., Xu, L., Elmore, A. J., & Shi, Y. (2020).
Vegetation phenology influenced by rapid urbanization
of the Yangtze Delta Region. Remote Sensing, 12(11).
Eerens, H., & Dominique, H. (2013). Software for the
Processing and Interpretation of Remotely sensed
Image Time Series USER ’ S MANUAL Version: 1 . 1 .
1 (Issue February).
Eklundh, L. (2017). Welcome to the TIMESAT pages!
eoPortal Directory. (2020). Satellite Missions Database.
Recent Advances in Land Surface Phenology Estimation with Multispectral Sensing
ESA. (2020). Earth Online-Current Missions.
Fisher, J. I., Mustard, J. F., & Vadeboncoeur, M. A. (2006).
Green leaf phenology at Landsat resolution: Scaling
from the field to the satellite. Remote Sensing of
Environment, 100(2), 265–279.
Forkel, M., Carvalhais, N., Verbesselt, J., Mahecha, M. D.,
Neigh, C. S. R., & Reichstein, M. (2013). Trend Change
detection in NDVI time series: Effects of inter-annual
variability and methodology. Remote Sensing, 5(5),
Forkel, M., Migliavacca, M., Thonicke, K., Reichstein, M.,
Schaphoff, S., Weber, U., & Carvalhais, N. (2015).
Codominant water control on global interannual
variability and trends in land surface phenology and
greenness. Global Change Biology, 21(9), 3414–3435.
Forkel, M., & Wutzler, T. (2015). greebrown - land surface
phenology and trend analysis. A package for the R
software. Version 2.2. http://greenbrown.r-forge.r-
Frantz, D. (2019). FORCE-Landsat + Sentinel-2 analysis
ready data and beyond. Remote Sensing, 11(9), 1–21.
Frantz, D., Stellmes, M., Röder, A., Udelhoven, T., Mader,
S., & Hill, J. (2016). Improving the Spatial Resolution
of Land Surface Phenology by Fusing Medium- and
Coarse-Resolution Inputs. IEEE Transactions on
Geoscience and Remote Sensing, 54(7), 4153–4164.
Friedl, M., Bolton, D., Moon, M., Gray, J. M., Eklundh, L.,
& Maiersperger, T. (2020). MuSLI Multi-Source Land
Surface Phenology (MS-LSP) Product User Guide
(Version 0.1, Issue May).
Gao, F., Anderson, M., Daughtry, C., Karnieli, A., Hively,
D., & Kustas, W. (2020). A within-season approach for
detecting early growth stages in corn and soybean using
high temporal and spatial resolution imagery. Remote
Sensing of Environment, 242(September 2019),
García-Feced, C., Weissteiner, C. J., Baraldi, A.,
Paracchini, M. L., Maes, J., Zulian, G., Kempen, M.,
Elbersen, B., & Pérez-Soba, M. (2014). Semi-natural
vegetation in agricultural land: European map and links
to ecosystem service supply. Agronomy for Sustainable
Development, 35(1), 273–283.
GEO-BON. (2019). The Group on Earth Observations
Biodiversity Observation Network - Implementation
plan for GEO Flagship (Version 2., Issue July).
Gerstmann, H., Doktor, D., Gläßer, C., & Möller, M.
(2016). PHASE: A geostatistical model for the Kriging-
based spatial prediction of crop phenology using public
phenological and climatological observations.
Computers and Electronics in Agriculture, 127, 726–
GLOBE. (2019). Overview - GLOBE.
Granero-Belinchon, C., Adeline, K., Lemonsu, A., &
Briottet, X. (2020). Phenological dynamics
characterization of alignment trees with sentinel-2
imagery: A vegetation indices time series
reconstruction methodology adapted to urban areas.
Remote Sensing, 12(4).
Gray, J., Sulla-Menasche, D., & Friedl, M. (2019). Lp Daac
- Mcd12Q2. 10.5067/MODIS/
Guigoz, Y. (2017). Estimation of phenology metrics
(PhenologyMetrics) - ECOPotential Virtual Laboratory
- ESSI Lab Documentation. https://confluence.geo
Guzmán Q., J. A., Sanchez-Azofeifa, G. A., & Espírito-
Santo, M. M. (2019). MODIS and PROBA-V NDVI
products differ when compared with observations from
phenological towers at four tropical dry forests in the
Americas. Remote Sensing, 11(19), 1–18.
Helman, D. (2018). Land surface phenology: What do we
really ‘see’ from space? Science of the Total
Environment, 618, 665–673.
Henebry, G. M., & de Beurs, K. M. (2013). Chapter 21-
Remote Sensing of Land Surface Phenology: A
prospectus. In Phenology: An Integrative
Environmental Science (pp. 483–502).
Herrmann, I., Bdolach, E., Montekyo, Y., Rachmilevitch,
S., Townsend, P. A., & Karnieli, A. (2020). Assessment
of maize yield and phenology by drone-mounted
superspectral camera. Precision Agriculture, 21(1), 51–
Joiner, J., Yoshida, Y., Vasilkov, A. P., Schaefer, K., Jung,
M., Guanter, L., Zhang, Y., Garrity, S., Middleton, E.
M., Huemmrich, K. F., Gu, L., & Belelli Marchesini, L.
(2014). The seasonal cycle of satellite chlorophyll
fluorescence observations and its relationship to
vegetation phenology and ecosystem atmosphere
carbon exchange. Remote Sensing of Environment, 152,
Karkauskaite, P., Tagesson, T., & Fensholt, R. (2017).
Evaluation of the plant phenology index (PPI), NDVI
and EVI for start-of-season trend analysis of the
Northern Hemisphere boreal zone. Remote Sensing,
9(485), 1–21.
Kopp, S., Becker, P., Doshi, A., Wright, D. J., Zhang, K.,
& Xu, H. (2019). Achieving the full vision of earth
observation data cubes. Data, 4(3).
Kuenzer, C., Dech, S., & Wagner, W. (2015). Remote
Sensing Time Series. In
Remote Sensing and Digital
Image Processing (Vol. 22, pp. 225–245).
Lange, M., & Doktor, D. (2017). Package ‘ phenex ’
(Version 1.4-5).
GISTAM 2021 - 7th International Conference on Geographical Information Systems Theory, Applications and Management
Li, Jian, & Roy, D. P. (2017). A global analysis of Sentinel-
2a, Sentinel-2b and Landsat-8 data revisit intervals and
implications for terrestrial monitoring. Remote Sensing,
Li, Jiyuan, Feng, X., Yin, J., & Chen, F. (2020). Change
Analysis of Spring Vegetation Green-Up Date in Qinba
Mountains under the Support of Spatiotemporal Data
Cube. Journal of Sensors, 2020(6413654), 1–12.
Li, S., Ganguly, S., Dungan, J. L., Wang, W., & Nemani,
R. R. (2017). Sentinel-2 MSI Radiometric
Characterization and Cross-Calibration with Landsat-8
OLI. Advances in Remote Sensing, 06(02), 147–159.
Li, X., Zhou, Y., Meng, L., Asrar, G. R., Lu, C., & Wu, Q.
(2019). A dataset of 30 m annual vegetation phenology
indicators (1985-2015) in urban areas of the
conterminous United States. Earth System Science
Data, 11(2), 881–894.
Lieth, H. (1974). Purposes of a phenology book. In Ecol
Stud Anal Synth (pp. 3–19).
Liu, J., Heiskanen, J., Aynekulu, E., Maeda, E. E., &
Pellikka, P. K. E. (2016). Land cover characterization
in West Sudanian savannas using seasonal features
from annual landsat time series. Remote Sensing,
8(365), 1–18.
Löw, M., & Koukal, T. (2020). Phenology modelling and
forest disturbance mapping with sentinel-2 time series
in austria. Remote Sensing, 12(24), 1–27.
Lu, X., Liu, Z., Zhou, Y., Liu, Y., An, S., & Tang, J. (2018).
Comparison of phenology estimated from reflectance-
based indices and solar-induced chlorophyll
fluorescence (SIF) observations in a temperate forest
using GPP-based phenology as the standard. Remote
Sensing, 10(6).
Ma, X., Huete, A., Tran, N. N., Bi, J., Gao, S., & Zeng, Y.
(2020). Sun-angle effects on remote-sensing phenology
observed and modelled using himawari-8. Remote
Sensing, 12(8).
Mader, S. (2012). A Framework for the Phenological
Analysis of Hypertemporal Remote Sensing Data Based
on Polynomial Spline Models [Trier University].
Manivasagam, V. S., Kaplan, G., & Rozenstein, O. (2019).
Developing transformation functions for VENμS and
Sentinel-2 surface reflectance over Israel. Remote
Mascolo, L., Lopez-Sanchez, J. M., Vicente-Guijalba, F.,
Nunziata, F., Migliaccio, M., & Mazzarella, G. (2016).
A Complete Procedure for Crop Phenology Estimation
with PolSAR Data Based on the Complex Wishart
Classifier. IEEE Transactions on Geoscience and
Remote Sensing, 54(11), 6505–6515.
Masek, J. (2018). Harmonized Landsat Sentinel-2. NASA.
Melaas, E K, Graesser, J., & Friedl, M. A. (2017).
Multisource Imaging of Seasonal Dynamics in Land
Surface Phenology Using Harmonized Landsat and
Sentinel-2 Data. American Geophysical Union, Fall
Meeting 2017, Abstract #B34C-03, 2017, B34C-03.
Melaas, Eli K., Friedl, M. A., & Zhu, Z. (2013). Detecting
interannual variation in deciduous broadleaf forest
phenology using Landsat TM/ETM+ data. Remote
Sensing of Environment, 132, 176–185.
Meroni, M., Verstraete, M. M., Rembold, F., Urbano, F., &
Kayitakire, F. (2014). A phenology-based method to
derive biomass production anomalies for food security
monitoring in the Horn of Africa. International Journal
of Remote Sensing, 35(7), 2472–2492.
Misra, G., Buras, A., & Menzel, A. (2016). Effects of
different methods on the comparison between Land
Surface and Ground Phenology - A methodological
case study from South-Western Germany. Remote
Sensing, 8(753), 1–18.
Miura, T., Nagai, S., Takeuchi, M., Ichii, K., & Yoshioka,
H. (2019). Improved Characterisation of Vegetation
and Land Surface Seasonal Dynamics in Central Japan
with Himawari-8 Hypertemporal Data. Scientific
Reports, 9(1), 1–12.
Moon, M., Zhang, X., Henebry, G. M., Liu, L., Gray, J. M.,
Melaas, E. K., & Friedl, M. A. (2019). Long-term
continuity in land surface phenology measurements: A
comparative assessment of the MODIS land cover
dynamics and VIIRS land surface phenology products.
Remote Sensing of Environment, 226(February), 74–92.
Morisette, J. T., Richardson, A. D., Knapp, A. K., Fisher, J.
I., Graham, E. A., Abatzoglou, J., Wilson, B. E.,
Breshears, D. D., Henebry, G. M., Hanes, J. M., &
Liang, L. (2009). Tracking the rhythm of the seasons in
the face of global change: Phenological research in the
21 st century. Frontiers in Ecology and the
Environment, 7(5), 253–260.
Munson, S. M., & Long, A. L. (2017). Climate drives shifts
in grass reproductive phenology across the western
USA. New Phytologist
, 213(4), 1945–1955.
Myers, E., Kerekes, J., Daughtry, C., & Russ, A. (2019).
Assessing the impact of satellite revisit rate on
estimation of corn phenological transition timing
through shape model fitting. Remote Sensing, 11(21),
NASA EARTHDATA. (2020). Visible Infrared Imaging
Radiometer Suite (VIIRS).
Nasahara, K. N., & Nagai, S. (2015). Review: Development
of an in situ observation network for terrestrial
Recent Advances in Land Surface Phenology Estimation with Multispectral Sensing
ecological remote sensing: the Phenological Eyes
Network (PEN). Ecological Research, 30(2), 211–223.
Nativi, S., Mazzetti, P., & Santoro, M. (2016). Deliverable
No: D10.1 Design of the ECOPOTENTIAL Virtual
Laboratory, Version V1.0 (Final Draft) (Issue July).
NEON. (2019). NEON Science-About.
PEN. (2020). Phenological Eyes Network (PEN).
PEP725. (2019). About the Pan European Phenology
Project PEP725.
PHENOCAM. (2020). About the PhenoCam Data.
Pouliot, D., Latifovic, R., Pasher, J., & Duffe, J. (2018).
Landsat super-resolution enhancement using
convolution neural networks and Sentinel-2 for
training. Remote Sensing, 10(394), 1–18.
Reed, B. C., Schwartz, M. D., & Xiao, X. (2009). Remote
Sensing Phenology: Status and the Way Forward. In A.
Noormets (Ed.), Phenology of Ecosystem Processes:
Applications in Global Change Research (pp. 231–
246). Springer Science + Business Media.
Rembold, F., Tote, C., Eerens, H., Haesen, D., Gilliams, S.,
& Byderkerke, L. (2013). SPIRITS (AfricaGIS 2013 /
GSDI 14).
Rodrigues, A., Marcal, A. R. S., & Cunha, M. (2013).
Monitoring Vegetation Dynamics Inferred by Satellite
Data Using the PhenoSat Tool. IEEE Transactions on
Geoscience and Remote Sensing, 51(4), 2096–2104.
Ryu, D., Kim, T. K., Won, M. S., Kim, S. K., Lim, J., &
Kim, H. S. (2018). Developing a Machine Learning
Based Automatic Plant Phenology Observation System.
AGU Fall Meeting Abstracts, 2018, B51H-2020.
Sadeh, Y., Zhu, X., Chenu, K., & Dunkerley, D. (2019).
Sowing date detection at the field scale using CubeSats
remote sensing. Computers and Electronics in
Agriculture, 157(October 2018), 568–580.
Salas, E. A. L. (2020). Waveform LiDAR concepts and
applications for potential vegetation phenology
monitoring and modeling: a comprehensive review.
Geo-Spatial Information Science, 00(00), 1–22.
Skakun, P. S., Ju, J., Claverie, M., Roger, J., Vermote, E.,
Franch, B., Dungan, J. L., & Masek, J. (2018).
Harmonized Landsat Sentinel-2 ( HLS ) Product User ’
s Guide (Vol. 2).
Skakun, S., Roger, J. C., Vermote, E. F., Masek, J. G., &
Justice, C. O. (2017). Automatic sub-pixel co-
registration of Landsat-8 Operational Land Imager and
Sentinel-2A Multi-Spectral Instrument images using
phase correlation and machine learning based mapping.
International Journal of Digital Earth, 10(12), 1253–
Sobrino, J. A., Julien, Y., & Soria, G. (2013). Phenology
estimation from meteosat second generation data. IEEE
Journal of Selected Topics in Applied Earth
Observations and Remote Sensing, 6(3), 1653–1659.
Solano-Correa, Y. T., Bovolo, F., Bruzzone, L., &
Fernández-Prieto, D. (2018). Automatic derivation of
cropland phenological parameters by adaptive non-
parametric regression of Sentinel-2 NDVI time series.
International Geoscience and Remote Sensing
Symposium (IGARSS), 2018-July, 1946–1949.
Springer, K. R., Wang, R., & Gamon, J. A. (2017). Parallel
seasonal patterns of photosynthesis, fluorescence, and
reflectance indices in boreal trees. Remote Sensing,
9(7), 1–18.
Storey, J., Roy, D. P., Masek, J., Gascon, F., Dwyer, J., &
Choate, M. (2016). A note on the temporary
misregistration of Landsat-8 Operational Land Imager
(OLI) and Sentinel-2 Multi Spectral Instrument (MSI)
imagery. Remote Sensing of Environment, 186(2016),
Templ, B., Koch, E., Bolmgren, K., Ungersböck, M., Paul,
A., Scheifinger, H., Rutishauser, T., Busto, M.,
Chmielewski, F. M., Hájková, L., Hodzić, S., Kaspar,
F., Pietragalla, B., Romero-Fresneda, R., Tolvanen, A.,
Vučetič, V., Zimmermann, K., & Zust, A. (2018). Pan
European Phenological database (PEP725): a single
point of access for European data. International
Journal of Biometeorology, 62(6), 1109–1113.
Tong, X., Tian, F., Brandt, M., Liu, Y., Zhang, W., &
Fensholt, R. (2019). Trends of land surface phenology
derived from passive microwave and optical remote
sensing systems and associated drivers across the dry
tropics 1992–2012. Remote Sensing of Environment,
232(July), 111307.
Trenkle, T., & Driggers, P. (2019). Joint polar satellite
system. Sensors, Systems, and Next-Generation
Satellites XV.
Trondheim Conference, B. (2019). Biodiversity and the
Sustainable Development Goals (Ninth Trondheim
Conference on Biodiversity: Background Document,
Issues 2-5 July).
UN. (2019). Sustainable Development Goals-Knowledge
UrtheCast. (2020). UrtheDaily. https://www.urthe
USA-NPN. (2019). USA National Phenology Network.
USA-NPN. (2020). Phenology Networks around the World.
USGS. (2019). VIPPHEN_NDVI v004.
Venkatappa, M., Sasaki, N., Shrestha, R. P., Tripathi, N. K.,
& Ma, H. O. (2019). Determination of vegetation
thresholds for assessing land use and land use changes
GISTAM 2021 - 7th International Conference on Geographical Information Systems Theory, Applications and Management
in Cambodia using the Google Earth Engine cloud-
computing platform. Remote Sensing, 11(1514), 1–30.
Verbesselt, J., Hyndman, R., Newnham, G., & Culvenor, D.
(2010). Detecting trend and seasonal changes in
satellite image time series. Remote Sensing of
Environment, 114(1), 106–115.
Verhegghen, A., Bontemps, S., & Defourny, P. (2014). A
global NDVI and EVI reference data set for land-
surface phenology using 13 years of daily SPOT-
VEGETATION observations. International Journal of
Remote Sensing, 35(7), 2440–2471.
VITO. (2020). proba VEGETATION: News. https://proba-
Vrieling, A., Meroni, M., Darvishzadeh, R., Skidmore, A.
K., Wang, T., Zurita-Milla, R., Oosterbeek, K.,
O’Connor, B., & Paganini, M. (2018). Vegetation
phenology from Sentinel-2 and field cameras for a
Dutch barrier island. Remote Sensing of Environment,
215(March 2018), 517–529.
Wang, X., Dannenberg, M. P., Yan, D., Jones, M. O.,
Kimball, J. S., Moore, D. J. P., van Leeuwen, W. J. D.,
Didan, K., & Smith, W. K. (2020). Globally Consistent
Patterns of Asynchrony in Vegetation Phenology
Derived From Optical, Microwave, and Fluorescence
Satellite Data. Journal of Geophysical Research:
Biogeosciences, 125(7), 1–15.
Wang, Y., Zhao, J., Zhou, Y., & Zhang, H. (2012).
Variation and trends of landscape dynamics, land
surface phenology and net primary production of the
Appalachian Mountains. Journal of Applied Remote
Sensing, 6, 061708.
Wolters, E., Swinnen, E., Toté, C., & Sterckx, S. (2016).
SPOT-VGT Collection 3 - Product User Manual (1).
Workie, T. G., & Debella, H. J. (2018). Climate change and
its effects on vegetation phenology across ecoregions of
Ethiopia. Global Ecology and Conservation, 13, 1–13.
Wu, C., Peng, D., Soudani, K., Siebicke, L., Gough, C. M.,
Arain, M. A., Bohrer, G., Lafleur, P. M., Peichl, M.,
Gonsamo, A., Xu, S., Fang, B., & Ge, Q. (2017). Land
surface phenology derived from normalized difference
vegetation index (NDVI) at global FLUXNET sites.
Agricultural and Forest Meteorology, 233, 171–182.
Wunderle, S., & Neuhaus, C. (2020). AVHRR Master Data
Set Handbook - Deliverable 16 (WP 7) (ESA-IPL-POE-
SBo-sp-RFP-2016-502; 1, Vol. 16). https://earth.
Yan, D., Zhang, X., Nagai, S., Yu, Y., Akitsu, T., Nasahara,
K. N., Ide, R., & Maeda, T. (2019). Evaluating land
surface phenology from the Advanced Himawari
Imager using observations from MODIS and the
Phenological Eyes Network. International Journal of
Applied Earth Observation and Geoinformation,
79(May 2018), 71–83.
Yan, D., Zhang, X., Yu, Y., & Guo, W. (2017).
Characterizing land cover impacts on the responses of
land surface phenology to the rainy season in the congo
basin. Remote Sensing, 9(5).
Yuan, H., Wu, C., Gu, C., & Wang, X. (2020). Evidence for
satellite observed changes in the relative influence of
climate indicators on autumn phenology over the
Northern Hemisphere. Global and Planetary Change,
187(July 2019), 103131.
Zeng, L., Wardlow, B. D., Xiang, D., Hu, S., & Li, D.
(2020). A review of vegetation phenological metrics
extraction using time-series, multispectral satellite data.
Remote Sensing of Environment, 237(November 2018),
Zhang, X., Friedl, M. A., Schaaf, C. B., Strahler, A. H., &
Schneider, A. (2004). The footprint of urban climates
on vegetation phenology. Geophysical Research
Letters, 31(12), 10–13.
Zhang, X., Jayavelu, S., Liu, L., Friedl, M. A., Henebry, G.
M., Liu, Y., Schaaf, C. B., Richardson, A. D., & Gray,
J. (2018). Evaluation of land surface phenology from
VIIRS data using time series of PhenoCam imagery.
Agricultural and Forest Meteorology, 256257, 137–
Zhang, X., Liu, L., Liu, Y., Jayavelu, S., Wang, J., Moon,
M., Henebry, G. M., Friedl, M. A., & Schaaf, C. B.
(2018). Generation and evaluation of the VIIRS land
surface phenology product. Remote Sensing of
Environment, 216(July), 212–229.
Zhang, X., Liu, L., & Yan, D. (2017). Comparisons of
global land surface seasonality and phenology derived
from AVHRR, MODIS, and VIIRS data. Journal of
Geophysical Research: Biogeosciences, 122(6), 1506–
Zhang, X., Tarpley, D., & Sullivan, J. T. (2007). Diverse
responses of vegetation phenology to a warming
climate. Geophysical Research Letters, 34(19), 1–5.
Zhu, X., Chen, J., Gao, F., Chen, X., & Masek, J. G. (2010).
An enhanced spatial and temporal adaptive reflectance
fusion model for complex heterogeneous regions.
Remote Sensing of Environment, 114(11), 2610–2623.
Recent Advances in Land Surface Phenology Estimation with Multispectral Sensing