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ORIGINAL RESEARCH
Regional evaluation of satellite-based methods foridentifying end of vegetation growing seasonRuoque Shen1, Haibo Lu1 , Wenping Yuan1, Xiuzhi Chen1 & Bin He2
1School of Atmospheric Sciences, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Sun Yat-sen University, Zhuhai
Guangdong, 519082, China2College of Global Change and Earth System Science, Beijing Normal University, Beijing 100038, China
Keywords
Autumn phenology, end of growing season
dates, remote sensing, satellite-based
methods, vegetation index
Correspondence
Haibo Lu, School of Atmospheric Sciences,
Southern Marine Science and Engineering
Guangdong Laboratory (Zhuhai), Sun Yat-sen
University, Zhuhai, Guangdong 519082,
China. Tel: +86 0756-3668166; Fax: +860756-3668166; E-mail:
Handling Editor: Mat Disney
Associate Editor: Doreen Boyd
Received: 20 October 2020; Revised: 16 May
2021; Accepted: 25 May 2021
doi: 10.1002/rse2.223
Abstract
Autumn phenology plays an important role in regulating ecosystem carbon and
water cycling, but it has received less attention than spring phenology. Satellite-
based methods have been widely applied in monitoring autumn phenology at
large spatial scales. However, few studies have evaluated and compared the per-
formance of different satellite-based methods in autumn phenology identifica-
tion. Here, we compared the spatiotemporal variations of end of vegetation
growing season dates (EOS) as determined from eight prevailing satellite-based
methods against long-term field observations at 31 sites in China. We found
that field-based observations in forest and grassland sites, respectively, had rates
of EOS delay of 2.11 and 3.85 days per 1°C increase in mean annual tempera-
ture (MAT) during 2001–2014. However, nearly all the eight satellite-based
methods underestimated these delay rates compared with the ground observa-
tions over all sites. We also found that the eight methods weakly agreed with
the field-observed interannual variations of EOS. At the regional scale, the iden-
tified average EOS differed up to 38 and 40 days among the investigated
satellite-based methods in forest and grassland ecosystems respectively. The
delayed rate of identified EOS with the increase of MAT ranged from 0.77 to
3.51 days °C−1 for forests and from 0.41 to 2.95 days °C−1 for grasslands. The
identified EOS by most of the eight methods had delayed temporal trends in
forests during 2001–2014 while we found advanced trends in grassland ecosys-
tems. The large discrepancy in EOS identification among the prevailing
satellite-based methods highlight the need for more accurate satellite-based
methods in data gap-filling and phenometrics detection, and more extensive,
multi-species based field observations that can be used to constrain and validate
the satellite-based methods.
Introduction
A critical process during vegetation growth, autumn
phenology (i.e., leaf coloration, senescence and dor-
mancy) directly regulates the length of growing season
and has important implications for the carbon and
nutrient cycling of terrestrial ecosystem (Keenan et al.,
2014; Penuelas & Filella, 2009). The dates of end of veg-
etation growing season (EOS) have been widely shown
to be susceptible to climate change (e.g., warming and
drought) (Gallinat et al., 2015; Xie et al., 2015). In par-
ticular, numerous studies have reported that the rising
autumn temperature induced by climate warming has
delayed the EOS (Garonna et al., 2014; Ge et al., 2015;
Piao et al., 2006). Moreover, EOS has showed a larger
sensitivity to temperature than spring leaf-out phenol-
ogy, which implied that delayed EOS can contribute
more to the extension of the growing season than
advanced spring leaf-out under global warming (Fu
et al., 2018; Garonna et al., 2014). Thus, the accurate
identification of the spatiotemporal changes of EOS over
regional scale is essential for revealing the impacts of
phenological changes on regional carbon budgets and
ecosystem function.
ª 2021 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and
distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
1
Current field studies revealed the impacts of climate
change on autumn plant phenology over the past decades.
Ge et al. (2015) conducted a meta-analysis of Chinese
phenological responses to climate change across 145 sites
and found that autumn phenology was delayed by 1.93–4.84 days decade−1 during 1960–2011 in China. Menzel
et al. (2006) reported that leaf coloring and senescence
delayed by 1.0 day °C−1 during 1971–2000 in Europe. In
North America continent, Jeong and Medvigy (2014)
found a delay trend of 0.36 day year−1 in all-species aver-
age leaf coloring date.
The availability of global remote sensing vegetation
index (VI) datasets in recent decades has yielded the abil-
ity to monitor regional and global land surface phenology
(LSP). The satellite-based methods, which are based on
the analysis of the seasonal dynamics of VI time series,
has offered a continuous and robust way to monitor plant
phenology at large spatial scales (Reed et al., 2009; Zhang
et al., 2006). Generally, three primary procedures were
conducted in the satellite-based methods to estimate the
EOS: first, cleaning and flagging the noisy data in original
VI products; second, smoothing and reconstructing VI
time series curves using curve fitting methods (e.g., logis-
tic, Gaussian-midpoint, harmonic analysis of time series
(HANTS) and polyfit maximum); third, determining the
EOS dates by identifying the inflection of the transition
point from the reconstructed VI curve or predefined
threshold of the VI (Berra & Gaulton, 2021; Zeng et al.,
2020). For example, Zhang et al. (2003) developed a
piecewise logistic function to fit time series normalized
difference vegetation index (NDVI) data and identified
the key phenological transition dates through the changes
in curvature. Garonna et al. (2014) employed the HANTS
approach to reconstruct NDVI time series and derived
the EOS dates across Europe using a local NDVI thresh-
old. Moreover, Alberton et al. (2019) deployed General-
ized Additive Mixed Models (GAMMs) to estimate the
timing and length of growing season based on phenocam-
eras networks observations. Recently, Belda et al. (2020)
applied advanced machine learning algorithms for VI
curves reconstruction and estimated phenological indica-
tors for specific crop types.
However, large discrepancies among the prevailing
satellite-based methods have been observed for identifying
phenological timing (Berra & Gaulton, 2021; Zeng et al.,
2020). A comprehensive intercomparison is still lacking,
particularly for evaluating the performance of the meth-
ods in determining EOS against long-term observations
over multiple ecosystems and geographic regions. de
Beurs and Henebry (2010) evaluated 12 state-of-the-art
remote sensing methods for modeling LSP, and they
found differences as high as 100 days in the estimated
EOS dates between individual methods. Recently, Xin
et al. (2020) compared six commonly used satellite-based
methods and two machine learning methods to retrieve
EOS, the differences among different methods ranged
from 11.60 to 44.34 days. Thus, a comprehensive evalua-
tion and interpretation of the spatiotemporal variation of
EOS derived from the satellite-based methods should be
conducted to better understand their performance and
how they differ from field-based observations.
Here, we evaluated and compared the performance of
eight prevailing satellite-based methods for determining
EOS against long-term field observations at 31 sites in
China. Our primary objectives were to (1) evaluate the
performance of the eight satellite-based methods on
reproducing the spatiotemporal variations of EOS against
field-based observations, and (2) compare the differences
among the satellite-based methods in identifying the spa-
tiotemporal pattern of regional EOS during 2001 to 2014
in China.
Materials and Methods
Study area
In this study, the temperate and deciduous broadleaf for-
ests dominated northern subtropical areas in China were
selected for autumn phenology investigation. These areas
included vegetation types of deciduous broadleaf forest
(DBF), deciduous needleleaf forest (DNF), mixed forest
(MF), and grassland. The evergreen broadleaf forest
(EBF) and evergreen needleleaf forest (ENF) regions were
excluded due to lack of seasonal dynamics. The cropland
areas of which phenology are largely affected by human
activity were also not included in this study.
Satellite-based EOS identification methods
We evaluated the performance of eight prevailing
satellite-based methods for determining EOS dates. Each
satellite-based EOS detection method consists of a curve
fitting method for reconstructing NDVI time series and a
phenological metrics extraction method to derivate the
EOS dates. In total, there are eight NDVI curve fitting
methods and four phenological metrics extraction meth-
ods being applied in the eight investigated satellite-based
methods. All the eight methods were described in Table 1.
To explore the effects of different NDVI curve fitting
and phenological metrics detection methods on the per-
formance of the eight satellite-based methods, the eight
curve fitting methods and four phenological metrics
detection methods were recombined. We generated 30
method combinations, including 22 new methods in addi-
tion to the eight original methods (Table 2). The
2 ª 2021 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London
Regional Evaluation of Satellite-based EOS Methods R. Shen et al.
performances of all the 30 methods on estimating the
spatiotemporal variations of EOS were evaluated and
intercompared against the ground observations.
Data
Phenological observations in China
In this study, the ground observations of leaf senescence
date were defined as observed EOS. The long-term, field-
observed records of leaf senescence date in China used in
this study were sourced from two datasets: the Chinese
Phenological Observation Network (CPON) for woody
plants and the Chinese Ecosystem Research Network
(CERN) Phenology Dataset for herbaceous plants (Song
et al., 2017). At both CPON and CERN observation sites,
the same standardized criteria for field measurements
were followed (Wan & Liu, 1979). Most of these observa-
tion sites locate at long-term ecological observation sta-
tions in China and the rest locate at botany gardens and
natural parks. To represent the phenology of multiple
species, only when the observations covered at least 20
tree or 5 herbaceous species during 2001–2014, could the
observation sites be included in this study. In total, we
Table 1. Description of the eight satellite-based methods for EOS identification.
Methods NDVI curve fitting methods
Phenological metrics detection
methods References
Simple logistic
method
NDVI tð Þ¼ a1þ a21þexp a3þa4tð Þ Change rate of curvature K¼ dα
ds
� �Zhang et al. (2003)
Double logistic
method
NDVI tð Þ¼ a1þ a2�a7tð Þ 1
1þexp a�ta4
� �� 1
1þexp a�ta6
� �24
35 Change rate of curvature Elmore et al. (2012)
Generalized
double
logistic
method
NDVI tð Þ¼ a1tþb1ð Þþ a2t2þb2tþc
� �1
½1þq1e�h1 t�n1ð Þ �v1
1
½1þq2e�h2 t�n2ð Þ �v2
� �Change rate of curvature Klosterman et al.
(2014)
Gaussian-
midpoint
method
NDVI tð Þ¼ aþb�e�t�cdð Þ2 0.5 of the NDVI amplitude
NDVIðtÞratio ¼ NDVI tð Þ�Min NDVIð ÞMax NDVIð Þ�Min NDVIð Þ
� � White et al. (2009)
Spline-
midpoint
method
NDVI tð Þ¼ att3þjbtt2þcttþdt 0.5 of the NDVI amplitude White et al. (2009)
HANTS-
maximum
method
NDVI tð Þ¼ a0þ∑n
i¼1
aicos ωi t�φið Þ Local NDVI ratio
NDVIratio tð Þ¼ NDVI tþ1ð Þ�NDVI tð ÞNDVI tð Þ
� � Jakubauskas et al.
(2001) and Garonna
et al. (2014)
Polyfit-
maximum
method
NDVI tð Þ¼ a0þa1tþa2t2þa3t
3þa4t4þa5t
5þa6t6 Local NDVI ratio Piao et al. (2006)
Timesat SG
method
Savitzky-Golay (SG) filtering method 0.2 of the NDVI amplitude Savitzky and Golay
(1964)
t is the day of year; NDVI(t) indicates the NDVI value on tth day; a0 to a6, a, b, b1, b2, c, d, h1, h2, n1, n2, q1, q2, v1 and v2 are curve fitting param-
eters; K indicates the curvature.
Table 2. Recombination of the eight NDVI curve fitting methods and
four phenological metrics detection methods.
NDVI curve
fitting
methods
Phenological metrics detection methods
Change
rate of
curvature
Local
NDVI
ratio
0.2 of the
NDVI
amplitude
0.5 of the
NDVI
amplitude
Simple logistic *Sim Sim2 Sim3 Sim4
Double logistic *Dou Dou2 Dou3 Dou4
Generalized
double
logistic
*Gen Gen2 Gen3 Gen4
Gaussian Gau1 Gau2 Gau3 *GauSpline Spl1 Spl2 Spl3 *SplHANTS HAN1 *HAN HAN3 HAN4
Polyfit Pol1 *Pol Pol3 Pol4
Timesat SG / / *SG SG4
*The asterisks indicate the eight original methods and the other indi-
cate the 22 new combinations. For example, *Sim indicates the origi-
nal method of the simple logistical curve fitting method combining
with the change rate of curvature method; Sim2, Sim3, and Sim4
indicate three new generated methods, that is, the simple logistical
curve fitting method combining with the local NDVI ratio, the 0.2 of
the NDVI amplitude and the 0.5 of the NDVI amplitude method
respectively.
ª 2021 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London 3
R. Shen et al. Regional Evaluation of Satellite-based EOS Methods
included observations of 1035 plant species (737 tree and
298 herbaceous species) at 31 sites ranging from 25 to
53°N (Fig. 1, Table S1). To examine the temporal trend
of autumn plant phenology, we further selected 25 sites
that had observations longer than 10 years during 2001–2014, eventually there were 6226 observations from 266
tree and 58 herbaceous species remained for temporal
variation analysis (Table S1). In addition, the leaf senes-
cence date in this study was defined as the end of leaf fall,
which represents the date on which 95% of the leaves on
the monitored plants had fallen (Wan & Liu, 1979).
Satellite and meteorology data
The land surface EOS dates during 2001–2014 were
derived from the NDVI time series data (MOD13C1)
from the Moderate Resolution Imaging Spectroradiometer
(MODIS) Version 6 datasets (https://e4ftl01.cr.usgs.gov//
MODV6_Cmp_C/MOLT/MOD13C1.006/). The temporal
and spatial resolution of the NDVI data we used were
16 days and 5 km. The daily surface air temperature data-
set at a spatial resolution of 0.1° during 2001–2014 was
from the China Meteorological Forcing Dataset (CMFD)
product (He et al., 2020), which was used for calculating
mean annual temperature (MAT). The vegetation types of
study area were derived from the MODIS land cover pro-
duct (MOD12Q1, V004) at a spatial resolution of 1 km
(Ran et al., 2010). The air temperature and land cover
datasets were resampled to the spatial resolution of 0.05°to match the NDVI dataset.
Data analysis
In order to compare satellite-based EOS with ground
observed leaf senescence dates (i.e., observed EOS) at each
observation site, we selected 20 pixels which had the same
vegetation type and were nearest to each observation site
to retrieve EOS. These pixels were all around the observa-
tions site within 100 km range. NDVI time series of each
pixel was extracted and EOS was determined using the
eight satellite-based methods. Then, the averaged EOS
from the 20 pixels was used to compare against the
ground observation.
To examine the spatial variations of EOS, linear regres-
sion analysis was conducted between MAT and the
satellite-based and ground observed mean annual EOS
over all the 31 sites. At the 25 observation sites which
had long-term (>10 years) ground observations, we
Figure 1. Descriptions of the study area and the location of ground phenology observation sites.
4 ª 2021 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London
Regional Evaluation of Satellite-based EOS Methods R. Shen et al.
detected the temporal trends of satellite-based and ground
observed EOS during 2001–2014.
Results
Evaluation on the spatial variations of theidentified EOS
We found that the eight remote sensing methods failed to
reproduce the spatial variations of EOS in both forest and
grassland ecosystems (Fig. 2). For each of the eight
satellite-based methods, the coefficient of determination
(R2) between identified and field-observed EOS was less
than 0.5 of over the forest sites, except for the double
logistic method (0.62) (Fig. 2I). The regression slopes
were also quite low, ranging from 0.13 to 0.46 (Fig. 2K).
For the grassland sites, the R2 between the identified and
observed EOS were all lower than 0.3, and the regression
slopes were lower than 0.4 (Fig. 2J and 2L). These low
slopes indicated that there was less spatial variation in the
satellite-based EOS than field-observed EOS. According to
the field observations, the EOS were delayed 2.11 and
3.85 days per 1°C increase in mean annual temperature
(MAT) for forest and grassland sites respectively (Fig. 3).
However, all the eight methods significantly underesti-
mated the delay rate of EOS for forest sites, ranging from
0.45 to 1.96 days °C−1 (Fig. 3B). For the grassland sites,
the estimated delay rates of the eight methods ranged
from 0.07 to 2.25 days °C−1 (Fig. 3D). The polyfit maxi-
mum method had the best estimates of the delay rate of
EOS with MAT in both forest and grassland sites (Fig. 3).
Evaluation on the temporal trends of theidentified EOS
The interannual variations of identified EOS by satellite-
based methods were evaluated against the observations at
15 forest and 10 grassland sites where observations were
recorded for more than 10 years. For each site, the liner
regression analysis between the field-observed and
Figure 2. Observed and satellite-based methods identified averaged EOS during 2001–2014 over all sites (A)–(H). The green dots and lines
indicate the observed and identified EOS for forest sites and the yellow dots and lines indicate those for grassland sites. Comparison of R2 and
slope of the linear regression between observed and identified EOS derived from the eight satellite-based methods (I)–(L). The green (I and K) and
yellow (J and L) columns represent the R2 and slope for forest and grassland sites respectively.
ª 2021 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London 5
R. Shen et al. Regional Evaluation of Satellite-based EOS Methods
identified EOS was employed to examine the performance
of the remote sensing methods. Generally, the eight meth-
ods showed poor performance in capturing the interan-
nual variations of EOS across the forest and grassland
sites (Fig. 4). The mean R2 values across all sites were
quite low, ranging from 0.05 to 0.11 for forest and 0.06
to 0.18 for grassland sites among the eight satellite-based
methods respectively (Fig. 4A and B). The corresponding
slopes of the regression lines also deviated broadly from 1
at both forest and grassland sites (Fig. 4C and D).
The temporal trends of identified EOS by satellite-
based methods at each site were also compared with the
observations. Large variations in the trend of field-
observed EOS existed among the observation sites. The
eight methods performed poorly at estimating the tempo-
ral trends in both forest and grassland sites (Fig. 5).
Compared to the field-observed trends, the generalized
double logistic and the timesat SG methods significantly
underestimated the trends at 60% forest sites whereas the
Logistic, the HANTS maximum and the polyfit maximum
methods overestimated the trends at more than 40% for-
est sites (Fig. 5B). For grasslands sites, each of the eight
methods significantly overestimated the trends. The
HANTS maximum and the timesat SG methods showed
significantly higher estimates in more than 40% grass-
lands sites (Fig. 5D).
Comparisons of the method structure
We found that most of the 22 newly recombined methods
did not perform better than the eight original methods in
reproducing the spatial and temporal variations of the
EOS. That implied that the eight original methods were
the best among the potential combinations (Fig. 6). How-
ever, the Gaussian method with the NDVI ratio approach
improved the performance in capturing the temporal
Figure 3. Spatial variations of the observed and satellite-based methods identified EOS with the mean annual temperature (MAT) increasing for
forest (A) and grassland (C) sites. (B) and (D) showed the slope of regression lines which indicates the delay rate of EOS with the rising MAT. The
letters indicate the statistically significant (P < 0.05) difference among observations and estimates.
6 ª 2021 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London
Regional Evaluation of Satellite-based EOS Methods R. Shen et al.
variation of EOS, compared to the original Gaussian mid-
point method. The polyfit method with a local NDVI
threshold of 0.5 performed better than the original polyfit
maximum method in identifying the EOS temporal varia-
tions (Fig. 6).
Regional differences in identified EOSamong satellite-based methods
Large divergences were observed in the identification of
the regional EOS in forest and grassland ecosystems
among the eight satellite-based methods. The Timesat SG
method identified the latest average EOS (331 days for
forest and 325 days for grassland), and the Gaussian mid-
point method had the earliest average EOS (293 days for
forest and 285 days for grassland). Thus, there were dif-
ferences of 38 and 40 days between the two methods for
forest and grassland ecosystems respectively (Figs. 7 and
8). However, the eight methods reproduced the similar
spatial pattern in both of forest and grassland ecosystems
in that the EOS advanced with increased MAT (Figs. 7–9). Nevertheless, the delayed rate of identified EOS with
increased MAT substantially differed among methods.
The generalized double logistic method generated the
lowest estimates of 0.77 and 0.41 days °C−1 for forest and
grassland respectively. The highest estimates were derived
from the polyfit maximum method (3.51 days °C−1 for
forest and 2.95 days °C−1 for grassland) (Fig. 9).
The temporal trends of identified EOS during 2000–2014 over the entire study area were examined and
Figure 4. Comparison of the interannual variations between the identified and observed EOS from 2001 to 2014 over all sites. (A) and (C) show
the R2 and slope of the linear regression between identified and observed interannual variations of EOS for forest sites, respectively; (B) and (D)
show those for grassland sites. Different letters indicate significant differences (P < 0.05) of R2 and slope among the eight methods.
ª 2021 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London 7
R. Shen et al. Regional Evaluation of Satellite-based EOS Methods
substantial divergences of identified trends were found
among the eight methods. Most of the methods showed
the delayed temporal trends of EOS for forests with the
averaged rates from 0.32 to 0.52 day year−1 over the study
area, except for the generalized double logistic and the
timesat SG method which identified averaged advanced
trends of −0.26 and −0.41 day year−1 respectively (Fig. 10
A). On the contrary, most of the eight methods generated
advanced temporal trends for grassland, ranging from
0.05 to 0.44 day year−1. Only the HANTS maximum
(0.17 day year−1) and the polyfit maximum method
(0.02 day year−1) showed delayed temporal trends
(Fig. 10B).
Discussion
As one of the most reliable and robust approach to moni-
tor LSP at large spatial scales, satellite-based methods are
widely employed to monitor phenology dynamics and its
responses to climate changes at global and regional scales
(Reed et al., 2009; Zeng et al., 2020; Zhang et al., 2003).
However, our study showed that the prevailing eight
satellite-based methods performed poorly at identifying
the temporal and spatial variations of EOS against the
ground observations at the 31 sites (Figs. 2–5). Thus, one
should be cautious that when interpreting the temporal
and spatial variations of satellite-based EOS using the
eight methods we investigated. The discrepancy between
the identified EOS by the remote sensing methods and
the ground observations was also reported in the previous
studies. Zhao et al. (2020) reported that the autumn phe-
nology dates derived from satellite data was consistently
earlier than that from direct observations in a northern
mixed forest. Such a discrepancy may be attributed to the
satellite data having had a relatively coarse spatial resolu-
tion and thus integrated a broader vegetation signal
across a heterogeneous landscape than the field individual
species observation. The incompatibility of spatial scales
and mixed pixel effects might induce uncertainty when
evaluating and constraining the performance of satellite-
based EOS against ground in situ observations (Chen
et al., 2018; Donnelly et al., 2018; Liang et al., 2011; Zeng
et al., 2020). Hence, it is necessary to extend the field
observations of autumn phenology covering multiple
plant species and various ecosystem types to reduce the
uncertainty in benchmarking and improving the satellite-
based LSP algorithms.
Furthermore, large divergences in detecting the spa-
tiotemporal variations of EOS were also observed among
the different methods (Figs. 7–9), which was supported
Figure 5. Temporal trends of identified and observed EOS for forest (A, B) and grassland (C, D) sites. The observations are presented as hollow
dots with an error bar which indicates the standard deviation of the trend of each plant species, and the colorful dots/stars indicate the estimated
trends by the satellite-based methods (A, C). (B) and (D) show the percentage of sites in which the identified trends lower or higher than the
observations (significance level at P < 0.05).
8 ª 2021 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London
Regional Evaluation of Satellite-based EOS Methods R. Shen et al.
by previous studies. de Beurs and Henebry (2010) con-
ducted a comparison of 10 common remote sensing
methods for detecting phenology, and the differences in
identified growing season length were as much as 64 to
190 days among methods. We found that the polyfit max-
imum method performed best at estimating EOS when
compared to field observations. This better match could
be because the polyfit-maximum method is a high-order
Fourier transforms, which reconstructs the VI curve
through the entire growing season with the highest tem-
poral resolution (i.e., daily) (Piao et al., 2006). The other
seven methods reconstruct the VI curve at the original
temporal resolution (i.e., 16-day). The wide applications
of machine learning approaches open a new path to the
development of satellite-based phenology models.
Recently, Belda et al. (2020) blended machine learning
algorithms (e.g., Gaussian Process Regression, GPR) using
leaf area index (LAI) curve reconstruction in satellite-
based phenology methods, the new methods successfully
reconstructed LAI curves and retrieved reliable
phenological indicators. Leaf falling phase is a transient
process, the curve fitting method with higher resolution
can better capture the phenological information (Her-
mance, 2007). Moreover, the VI trajectory during the leaf
coloring and senescence phase could appear as a two-
stage decline, that is, before a rapid drop-off, there may
exist a gradual decrease in the VI, which could last several
weeks to months (Elmore et al., 2012; Guyon et al., 2011;
Zeng et al., 2020). In this study, we also observed the
gradual decrease of NDVI before EOS dates, such a two-
stage decline can complicate the detection of the autumn
phenological transition dates. In addition, our results
showed that nearly all the investigated satellite-based
methods identified advanced temporal trend of regional
EOS for grassland ecosystem while generated delayed tem-
poral trend for forest ecosystems (Fig. 10). The advanced
EOS trend of grasslands was also reported by Ren et al.
(2017) who found that 65% of Inner Mongolia plateau in
China showed earlier EOS trend during 2000–2011. Theadvanced EOS trends for grasslands can attribute to a
Figure 6. Comparison of the various combinations of the eight NDVI curve fitting methods and four transition point detection methods in
reproducing the spatial (A, B) and temporal (C, D) trends of identified EOS against observations. The R2 and slope derive from the linear
regression of identified and observed EOS. The white bars indicate the original combinations of the eight methods.
ª 2021 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London 9
R. Shen et al. Regional Evaluation of Satellite-based EOS Methods
decline in precipitation from mid-1990s onward and soil
water deficit induced by climate warming (Bao et al.,
2021; Guo et al., 2021; Wang et al., 2019).
The remote sensing methods have been widely used to
generate LSP products, such as the MODIS Global Vege-
tation Phenology product (MCD12Q2) (Friedl et al.,
2019) and the Visible Infrared Imaging Radiometer Suite
(VIIRS) LSP product (Zhang et al., 2018). Our results
implied that the prevailing LSP products derived from
satellite data may have large uncertainty in EOS identifi-
cation, which can further impede our understanding on
the responses of autumn phenology to climate change at
regional and global scale. Hmimina et al. (2013) evaluated
the MODIS phenology product against field observations
and found that the biases were more than two weeks dur-
ing the autumn phenological transitions between the
Figure 7. Identified averaged end of growing season (EOS) dates during 2001–2014 by the eight satellite-based methods over forest ecosystems
in China.
10 ª 2021 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London
Regional Evaluation of Satellite-based EOS Methods R. Shen et al.
MODIS product and field observations. The low temporal
resolution of the current satellite-based data which are
derived from polar-orbiting satellites might be the main
reason for this discrepancy. For a single source satellite-
derived data, there is a trade-off between spatial and tem-
poral resolution. However, the relatively coarse temporal
resolution and frequent cloudy conditions can greatly
reduce the accuracy of phenophases determination
(Fensholt et al., 2007; Zhang et al., 2017). Geostationary
satellites shed light on a more accurate determination of
LSP, they can offer sub-hourly observations and are more
likely to obtain cloud-free observations (Fensholt et al.,
2007; Yan et al., 2019). As a result, geostationary satellites
showed higher sensitivity than polar-orbiting satellites in
capturing the seasonal dynamic of vegetation at large
scale, especially in cloud-prone regions, thereby the data
Figure 8. Identified averaged end of growing season (EOS) dates during 2001–2014 by the eight satellite-based methods over grassland
ecosystems in China.
ª 2021 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London 11
R. Shen et al. Regional Evaluation of Satellite-based EOS Methods
Figure 9. Spatial variations of identified regional EOS for forest (A) and grassland (B) ecosystems with the rising mean annual temperature (MAT).
(C) Shows the delayed rate of EOS with the rising MAT.
Figure 10. Long-term trends of identified EOS derived from the eight satellite-based methods for forest (A) and grassland (B) ecosystems.
12 ª 2021 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London
Regional Evaluation of Satellite-based EOS Methods R. Shen et al.
from geostationary satellites can be a better choice for
determining LSP (Fensholt et al., 2007).
Conclusions
Satellite-based methods were widely applied to monitor
phenology at regional and global scale. In this study, we
evaluated the performance of eight prevailing satellite-
based methods for EOS identification against long-term
field observations at 31 sites in China. Our results indi-
cated that nearly all the eight investigated methods under-
estimated the variations of EOS with increasing MAT for
both forest and grassland ecosystems when compared to
the field observations. We also found that the eight meth-
ods weakly agreed with the field-observed interannual
variations of EOS, which implied large uncertainty of
satellite-based EOS in response to climate change. More-
over, substantial discrepancies of estimated regional EOS
existed among the eight satellite-based methods. The
identified EOS by most of the eight methods had delayed
temporal trends in forests during 2001-2014 while we
found advanced trends in grassland ecosystems. Our
study highlighted that the prevailing satellite-based meth-
ods had large uncertainty in identifying autumn phenol-
ogy, and cautions should be used when drawing
conclusion about the changes of autumn phenology based
on the eight methods. This study also suggested a need
for more accurate satellite-based methods to determine
EOS, and more extensive, species-based field observations
that can be used to constrain and validate the satellite-
based methods.
Acknowledgments
This study was funded by the National Key Basic Research
Program of China (2018YFA0606104, 2016YFA0602701),
National Science Fund for Distinguished Young Scholars
(41925001), Guangdong Basic and Applied Basic Research
Foundation (2020A1515111145), Innovation Group Project
of Southern Marine Science and Engineering Guangdong
Laboratory (Zhuhai) (No. 311021009), Fundamental
Research Funds for the Central Universities, Sun Yat-sen
University (19lgjc02), and Natural Science Foundation of
Tianjin (18JCQNJC78100).
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Supporting Information
Additional supporting information may be found online
in the Supporting Information section at the end of the
article.
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