Crop phenology indicators provide essential information
on crop growth phases, which are highly required for agroecosystem
management and yield estimation. Previous crop phenology studies were mainly
conducted using coarse-resolution (e.g., 500 m) satellite data, such as the
moderate resolution imaging spectroradiometer (MODIS) data. However,
precision agriculture requires higher resolution phenology information of
crops for better agroecosystem management, and this requirement can be met
by long-term and fine-resolution Landsat observations. In this study, we
generated the first national maize phenology product with a fine spatial
resolution (30 m) and a long temporal span (1985–2020) in China, using all
available Landsat images on the Google Earth Engine (GEE) platform. First,
we extracted long-term mean phenological indicators using the harmonic
model, including the v3 (i.e., the date when the third leaf is fully
expanded) and the maturity phases (i.e., when the dry weight of maize grains
first reaches the maximum). Second, we identified the annual dynamics of
phenological indicators by measuring the difference in dates when the
vegetation index in a specific year reaches the same magnitude as its
long-term mean. The derived maize phenology datasets are consistent with
in situ observations from the agricultural meteorological stations and the
PhenoCam network. Besides, the derived fine-resolution phenology dataset
agrees well with the MODIS phenology product regarding the spatial
patterns and temporal dynamics. Furthermore, we observed a noticeable
difference in maize phenology temporal trends before and after 2000, which
is likely attributable to the changes in temperature and precipitation,
which further altered the farming activities. The extracted maize phenology
dataset can support precise yield estimation and deepen our understanding of
the future agroecosystem response to global warming. The data are available
at 10.6084/m9.figshare.16437054
(Niu et al., 2021).
Introduction
Accurate and timely crop phenology information, which contains multi-phase
growth information from sowing to harvest, is highly required for precision
agriculture management
(Gao and Zhang,
2021; Zeng et al., 2020), such as irrigation schedules and pest control. The
agriculture management schemes should be precisely scheduled according to
different growth phases, during which period the water requirements and the
possibilities of pest and disease events are different
(Yang
et al., 2021; Xiao et al., 2020). Besides, the effect of climate change on
crop phenology has been widely reported
(Abbas
et al., 2017; Zhang and Tao, 2013; Tao et al., 2012). Given that the altered
growth phases of crops will influence crop production, further
research into the response of crop phenology to global warming is necessary,
which requires long-term records of phenology change. In addition,
information on crop phenology is also helpful for crop mapping because
different crops vary in their growth phases
(Sakamoto
et al., 2014; Zhong et al., 2014; Zhang et al., 2014; Huang et al., 2019b).
Remote sensing has become a profound tool for deriving crop phenology on a
large scale (Pan et
al., 2015; Liu et al., 2018). The annual variations of crop phenology are
affected by many factors, including climate conditions, soil properties, and
anthropogenic activities (e.g., sowing dates)
(He et al., 2020). The traditional
in situ based crop phenology recording is time-consuming and focuses on
limited sites (Gao and Zhang, 2021). These
limitations have been considerably mitigated by satellite images, which
provide revisit observations of crop growth at regional and global scales
(Shanmugapriya
et al., 2019; Zhang et al., 2003; Cao et al., 2015). Different phenological
indicators (such as the start of season and the end of season) are retrieved
for crop growth monitoring using satellite observations, including the
moderate resolution imaging spectroradiometer (MODIS) data
(Sakamoto et al., 2010), the advanced
very high resolution radiometer (AVHRR) data
(Zhang
et al., 2014; Gim et al., 2020). The retrieved multiple phenological
indicators can delineate the development stages of crops from sowing to
harvest at a regional and global scale.
Fine resolution Landsat satellite data show great potential in providing
crop phenological indicators with a fine resolution and a long-term span.
Despite the fact that the coarse satellite data (such as MODIS and AVHRR) have a
fine temporal resolution, which is helpful to depict the crop growth phases,
they are limited in the spatial resolution. Recently, several attempts
have been made at deriving phenology datasets using fine resolution
satellite data, such as Landsat
(Li
et al., 2019; Senf et al., 2017), Sentinel-2
(Bolton et al., 2020), and the
harmonized Landsat8 and Sentinel-2 (HLS) data
(Claverie
et al., 2018; Bolton et al., 2020). Compared with medium-resolution
satellite data, such as MODIS, the Landsat satellite data can provide
numerous land surface records from 1985 to the present, which help to derive
the long-term crop phenology dynamics. Unfortunately, limited attempts have
been made using Landsat data to map the crop phenology with a fine
resolution and a long-term span in China due to the complex planting
patterns
(Luo et
al., 2020; Wu et al., 2010). Also, the computing resources required for such
a mapping project are a huge
challenge (Dong et al., 2016).
The advent of the Google Earth Engine (GEE) platform relieves the huge
stress of data storage and computing at regional and global scales. The GEE
platform has included petabyte-scale remote sensing data with
high-performance computing capabilities and powerful algorithm libraries
(Gorelick et al., 2017). Presently, many successful
studies have been conducted using the GEE platform, such as mapping forest
dynamics (Xiong et al., 2020), terrace
(Cao
et al., 2021), and surface water (Pekel et al., 2016). It is
convenient to obtain and process satellite data using the GEE platform. The
combination of massive satellite observations and a flexible development
environment makes it possible to derive annual dynamics of crop phenology
with fine resolution in China.
In this study, we extracted spatial and temporal patterns of maize phenology
indicators in China from Landsat observations using the GEE platform. The
derived phenology indicators include v3 (the date when the third leaf is
fully expanded) and maturity (i.e., when the dry weight of maize grains
first reaches the maximum) phases. We mapped annual phenological indicators
of maize at a fine resolution (30 m) from 1985 to 2020, using the full archive of
Landsat images. The remainder of this paper is organized as follows: Sect. 2
introduces the study area and datasets, Sect. 3 presents the method used
in this study, Sects. 4 and 5 describe the results with discussion
and the derived dataset, respectively, and a conclusion is provided in
Sect. 6.
Study areas and datasets
We selected China's main maize producing area as our study area (Fig. 1).
Maize is one of the major crops in China and is planted over a wide region,
the sown area and production accounted for 36 % and 39 % of food crops in
2019 (National Bureau of Statistics of China, 2021), respectively. The
planting pattern and phenology of maize are highly heterogeneous due to the
influence of climate conditions, soil properties, and anthropogenic
activities (e.g., sowing date) (Wu et al.,
2010). The spring maize is mainly distributed in Northeast China, dominated
by the single cropping type. However, summer maize is mainly planted in the
Huang-Huai-Hai Plain (Fig. 1b), where the double cropping system (rotation
between winter wheat and summer maize) is commonly seen
(Luo et al., 2020). In addition,
there is also a certain amount of maize in other provinces (e.g., Xinjiang
province). The growth period of summer maize spans roughly from June (after
the harvest of winter wheat) to October compared with that of spring maize
from May to October. Furthermore, the maize in Northeast China is mainly
rain-fed. In contrast, irrigation is needed for maize and commonly exists in the Huang-Huai-Hai Plain and Northwest China (arid and semi-arid
areas) (Wu et al., 2010). Under these diverse
cropping systems, phenology dates (such as v3 and maturity) of maize varied
significantly between locations.
Distribution of maize within the study area (a), which
contains 17 provincial level administrative regions (b). The green polygons,
transformed from pixel-form classification results into vectors in (a),
indicate maize cover under a zoomed view in one specific site. Subplot (b)
also shows the agricultural zones in China, and the data are from the
Institute of Geographic Sciences and Natural Resources Research, Chinese
Academy of Sciences. In addition, the base map of figures is from ESRI
(https://www.arcgis.com/apps/mapviewer/index.html, last access: 13 August 2021).
We used the Landsat satellite data as the primary data source to
characterize the phenological changes of maize in China. We used all available
Landsat surface reflectance data in this study, including images obtained
from Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and
Operational Land Imager (OLI), from 1985 to 2020. The Landsat surface
reflectance data have been corrected for the radiometric and topographic
effects. The atmospheric effect has also been corrected using the Landsat
ecosystem disturbance adaptive processing system (LEDAPS)
(Masek et al., 2006).
Clouds and shadows were removed using the function of the mask procedure
(Zhu and Woodcock, 2012). Therefore,
all available clear-sky pixels of Landsat observations over the past three
decades were used in our study.
Distribution of agricultural meteorological stations (AMS) with
phenology records of spring (a) and summer (b) maize. The blue and yellow
areas are provinces with available AMS in this study.
Maize maps from multiple resources were adopted to constrain the region of
crop phenology mapping. The distribution map of maize in Northeast China was
derived using Sentinel-2 data (You et
al., 2021), and the maize map used was the classification result in 2019.
You et al. (2021) derived the crop maps in Northeast China using a random
forest classifier, with optimized features including spectral, temporal, and
textural characteristics (gray-level co-occurrence matrix). In other
provinces, the maize maps were obtained using the temporal similarity
assessment approach proposed by Dong et al. (2020). The distribution of
maize is mainly determined by comparing the similarity of the vegetation
index series of unknown pixels with a referred curve derived from maize
fields. The retrieved maize datasets have been validated with survey data
with reliable performance (Fig. S1 in the Supplement). The accuracy of the maize map in
Northeast China is 0.85 (more than 8000 samples for cross-validation in
2019), and that of maize maps in other provinces is 0.79 (about 2000 samples
for validation). Given that the original resolutions of these two
classification maps are 10 m (i.e., Northeast China) and 30 m (i.e., other
provinces), we aggregated the 10 m maize map to 30 m in our study. It is worth
noting that the maize distribution map is consistent across different years in our
study due to the relatively stable planting situation as one of the major
crops (Sun
et al., 2007; Li et al., 2008). Of course, we also admit that certain
dynamics in maize distribution exist due to the changing maize price,
climate conditions, and choice of farmers across different years. Mapping
the maize dynamics at the national scale in China is challenging because of
the scarcity of massive ground samples. There is also no publicly available
maize dynamic product with fine spatial resolution and a long temporal span.
So we kept the maize distribution map consistent and derived dynamics of
maize phenology indicators with tolerable errors.
In addition, we also collected massive datasets to validate our results,
such as the agricultural meteorological stations (AMS), PhenoCam network,
and the MODIS phenology product (MCD12Q2). First, the records in the AMS include
phenology information of major crops (such as maize, wheat, and rice) in
China, with large spatial and temporal ranges
(Luo
et al., 2020; Huang et al., 2019a). Crucial phases during the maize growth periods,
including v3 (i.e., the date when the third leaf is fully expanded), and
maturity (when the dry weight of maize grains first reaches the maximum)
phases, were recorded in the AMS. Thus, this dataset can validate the mapped
phenological indicators from remote sensing, and we collected AMS phenology
records of the spring and summer maize (Fig. 2). Second, the in situ PhenoCam
observation was derived from digital cameras using the green chromatic
coordinate (GCC) index, which is composited by visible wavebands and able to
characterize the dynamic greenness of vegetation. Third, the MODIS phenology
product (MCD12Q2) was also employed in our study to validate the results
derived from Landsat observations. The phenological indicators (e.g., dates
of green-up and dormancy) in the MODIS product were mainly derived from the
two-band enhanced vegetation index (EVI2) time series data (Gray
et al., 2019). The multiple cycles (up to two) of crop rotations were also
recorded in the MODIS phenology product, which is suitable for validation
with our phenology results of maize in this study.
Methodology
We extracted the phenology indicators of maize using the full archive of
Landsat images in GEE. The adopted framework includes three components (Fig. 3). First, we collected all available Landsat images during 1985–2020 in our
study area and used the collected maize map as a mask. After the cloud
removal, we constructed the long-term time series data of EVI for each
pixel. Second, we fitted the long-term mean EVI curve using the harmonic
model, which can delineate multiple cycles of crop rotations and identify
the number of cycles (i.e., used to distinguish spring and summer maize).
Thus, two phenological indicators, the v3 (the date when the third leaf is
fully expanded) and the maturity (when the dry weight of maize grains first
reaches the maximum) phases, were determined from the long-term mean curve of
spring and summer maize. Finally, we identified the annual dynamics of these
two phenological indicators by measuring the difference in dates when the
vegetation index in a specific year reaches the same magnitude as its
long-term mean. Details of each component are given in the following
sections.
The adopted framework for deriving annual phenology dynamics
(1985–2020) from Landsat time series data, including data preprocessing (a),
mapping the long-term mean phenology indicators (b), and identifying the
annual dynamics (c).
Illustration of mapping long-term mean phenology of spring
maize (a) and summer maize (b). The blue shaded areas represent the growing
period of maize. All acronyms are as follows: g: the EVI amplitude of spring
maize; g1 and g2 are the EVI amplitudes of green-up and green-down segments,
respectively; a1 and a2 are the EVI amplitudes of the first and second
cycles, respectively; DOY: day of year.
Illustration of detecting annual variabilities of phenological
indicators (taking the spring maize as an example). The blue shaded areas
represent the growth period of maize. In addition, green and orange shaded
areas indicate the reasonable change range of v3 and maturity. The solid and
empty circles are long-term and year-specific enhanced vegetation index
(EVI) observations. The definitions of all the acronyms are as follows: EOP:
end of peak; SOP: start of peak.
Data preprocessing
We implemented the data preprocessing step in the GEE platform. First, we
used the quality layer in the Landsat surface reflectance data to remove
clouds and shadows. Thus, all available clear-sky pixels can be used to
enrich the Landsat observations. Second, we excluded non-maize areas using
the maize map, which can significantly reduce the computational requirement.
Third, we calculated the EVI indicator using Eq. (1) to minimize the impact
of soil and clouds; meanwhile, it is sensitive to vegetation growth and
dormancy
(Huang
et al., 2019a; Li et al., 2019).
EVI=G×NIR-REDNIR+C1×RED-C2×BLUE+L,
where NIR, RED, and BLUE represent surface reflectance of the corresponding
spectral bands in Landsat. The parameters G, L, C1, C2 were used to
correct the disturbance of aerosols and soil background, as suggested with
values of 2.5 (G), 1 (L), 6 (C1), and 7.5 (C2) in Huang et al. (2019a).
Long-term mean phenological indicators
We derived the long-term mean maize phenological indicators including v3 and
maturity. First. we sorted all available EVI observations according to the
day of the year (DOY) and fitted the annual crop cycle using the harmonic
model (Eq. 2). Compared with other fitting approaches, the harmonic model
can easily delineate multiple seasonal cycles of crops within 1 year, with
clear physical meaning for each parameter
(de
Beurs and Henebry, 2010; Chen et al., 2018; Lee et al., 2020).
ft=a0+a1tT+∑i=1nbicosi2πtT+cisini2πtT,
where f(t) is the fitted EVI value, t is the Julian date of a particular
observation, and T is the maximum value of the time variable, bi and ci are
coefficients for intra-annual change of the EVI time series data, a1 and a0
represent the slope and intercept of EVI change among different seasonal
cycles, n represents the maximum number of harmonic components, and it needs
to be calibrated according to different situations. Considering the
double crop (winter wheat-summer maize rotation system) and the planting
patterns of winter wheat (planted in autumn of the first year and harvested
in the second year), we set n as 6 due to the good fitting performance in
our study after trial and error tests using multiple sites in different
regions.
Then, we identified spring and summer maize according to the cycles of the
fitted curve (Fig. 4). Spring and summer maize can be identified using the
information of EVI cycles. For instance, summer maize always has two crop
cycles, notably different from spring maize, with only one cycle. To
identify maize with different cycles, we calculated the derivative of the
fitted harmonic model and identified the peaks of these cycles. When the EVI
peak value before the maize part exceeded 40 % of the maximum EVI value of
the maize cycle (Gray et al., 2019; Wu
et al., 2010), we regarded it as double cropping, and the second part of it
is summer maize (Fig. 4b); otherwise, it is a single crop (i.e., spring
maize) (Fig. 4a).
Finally, we adopted the dynamic threshold approach to derive the v3 and
maturity dates from the green-up and green-down segments (shadowed blue
areas in Fig. 4). These two segments were derived from the cycle of maize
growth, separated by the point with peak values of EVI. Given that the EVI
amplitudes of green-up and green-down are different for the spring (g in
Fig. 4a) and summer maize (g1 and g2 in Fig. 4b), we determined the v3 and
maturity dates according to their EVI amplitudes accordingly. For the spring
maize, the v3 and maturity dates were defined as the dates with 10 % EVI
amplitude (g in Fig. 4a) during the green-up segment and 50 % EVI
amplitude (g in Fig. 4a) during the green-down segment
(Huang et al., 2019a),
respectively. Similarly, for the summer maize, the EVI amplitude during the
green-up and green-down segments was referred to g1 and g2 in Fig. 4b to
determine v3 and maturity, respectively.
Annual dynamics of phenological indicators
We adopted a similar approach to Li et al. (2017) to estimate the annual
dynamics of phenological indicators. First, we adopted a self-adjusting
strategy to determine the rational range of EVIs during the green-up and
green-down periods (shaded areas in Fig. 5). These ranges were determined
using the derived long-term mean curve, and they can be used to filter
outliers in individual years. Then, we measured the difference in dates when
the EVI in a specific year reaches the same magnitude as its long-term mean
(Fig. 5). The mean value of the date difference between the observations and
the long-term mean was adopted as the annual variability of phenological
indicators.
Results and discussionPerformance of the harmonic model
The harmonic model can easily delineate the seasonal dynamics of EVI for
spring and summer maize. Spring maize belongs to the single cropping type
and has one rotation cycle. In contrast, summer maize is mainly distributed
in the double cropping system area (i.e., the second rotation cycle is
summer maize). These crop growth cycles can be detected by the EVI time
series data from Landsat observations (Fig. 6). The fitting performance from
the harmonic model suggests that the fitted line can easily delineate the growth
phase of crops across different regions and types.
Comparison with records from the AMS
The derived long-term mean maize phenological indicators from Landsat
observations are consistent with the records from the AMS (Fig. 7). We
compared results derived from Landsat and AMS from 2001 to 2010, during
which period the AMS observations can be maximally used. Due to the lack of
accurate locations of observed crops in AMS (i.e., only station locations),
we measured the uncertainties of phenological indicators of maize within the
range of 5 km to the station. The adopted approach performed well in
extracting summer maize phenology. The correlations of v3 and maturity dates
of summer maize are 0.60 and 0.80, respectively (Fig. 7c–d). Besides, the
RMSE of v3 and maturity dates are 5.20 d and 6.38 d, respectively. Nevertheless, the
correlation of derived maturity dates of spring maize and corresponding
records from AMS is relatively low, likely attributed to the discrepancies
in the definitions between remotely sensed results and AMS. For instance,
the v3 phase in AMS is defined as the date when the third leaf is exposed
from the second leaf sheath, and the maturity is defined as the date when
the dry weight of maize grains first reaches the maximum, more than 80 %
of the outer bracts of the plants turn yellow, and the filaments become dry
(Li et al., 2021). These definitions in AMS
are challenging to measure from remote sensing, and they are slightly
different in terms of their definitions.
Comparison of long-term mean phenological indicators derived
from Landsat (satellites) and AMS (in situ). The error bars of the x- and y-axes represent uncertainty (i.e., one standard deviation) of multi-year
phenological indicators and the mean phenological indicators within a
certain extent (5 km) of the AMS, respectively. (a)–(b) and (c)–(d) represent
results from spring and summer maize, respectively.
The annual dynamics of derived phenological indicators (i.e., v3 and maturity) also
agree well with the AMS observations (Fig. 8). The comparison of annual
results is similar to that of the long-term mean phenology. In general, the
annual dynamics of phenological indicators in summer maize are better than
that of spring maize (especially at maturity phases), and this finding is
consistent with previous studies
(Huang et al., 2019a). The
correlations of phenological indicators (i.e., v3 and maturity) of summer maize
derived from Landsat and AMS are 0.34 and 0.59, respectively and for spring
maize, the correlations of v3 and maturity indicators from the two datasets
are 0.51 and 0.16. The difference between these two datasets is mainly
attributed to (1) lack of accurate locations of the crop in the AMS data,
(2) the crop planting patterns may be altered over the years (Fig. 9) and
(3) different definitions.
Comparison of annual dynamics of derived phenological
indicators from Landsat data and AMS observations from 2001 to 2010,
including v3 and maturity of spring (a–b) and summer (c–d) maize.
Using the phenology mapping approach in this study, we observed a good
agreement between Landsat derived and PhenoCam derived phenological
indicators (Fig. 10). We chose the United States (US) because no PhenoCam
data are available in China. The same approach adopted in China for crop
phenological indicator mapping was used in the US with agriculture sites
where PhenoCam data are accessible. Thus, the feasibility of our approach
can be evaluated. The phenology dataset provided by Richardson et al. (2018) is extracted from continuous observations of vegetation growth
collected by digital cameras. PhenoCam sites in Fig. 10 are mainly
distributed in agriculture ecosystems, with records spanning from 2015 to
2018. Definitions of phenological indicators from Landsat and PhenoCam are
consistent, i.e., definitions of transition_10 and transition_50 date when VI series data crossed
10 % and 50 % of the green chromatic coordinate index
(Richardson et al., 2018). The
correlations of v3 and maturity dates from Landsat and PhenoCam are 0.74 and
0.63, respectively, with the root mean square error (RMSE) of 7.61 (v3) and
7.11 (maturity) days. Observations from these two datasets are located
around the 1:1 line, suggesting the adopted mapping approach of phenology
from satellite data can well match the in situ observations. Possible
reasons behind explaining their difference can be attributed to (1)
different vegetation indices used (i.e., EVI and GCC) and (2) the scale
effect caused by the data sources.
Selected PhenoCam sites in agriculture ecosystems (a). The annual
v3 (b) and maturity (c) dates were compared between the Landsat, and
PhenoCam derived results from 2015 to 2018. The base map is provided by ESRI
(https://www.arcgis.com/apps/mapviewer/index.html, last access: 13 August 2021).
Comparison with MODIS phenology dataset
The derived phenological indicators from Landsat and MODIS have a consistent
temporal trend (Fig. 11). The MODIS phenology product (MCD12Q2) provides multiple
phenological indicators (e.g., mid-green-down). For areas with two vegetation
cycles, we selected the phenological indicators of the second cycle (summer
maize) for comparison. In the green-down segment of each crop cycle, the
MCD12Q2 product provides three phenological indicators, i.e., dormancy,
mid-green-down, and senescence, defined as 90 %, 50 %, and 10 % of the
segment EVI2 amplitude in a specific cycle, respectively. We selected the mid-green-down
indicator in the MODIS phenology product to compare in this study because it
has the same definition as the maturity date in Landsat-derived results. We
aggregated the fine-resolution maize data to the same resolution as MODIS
and only kept those relatively pure pixels (maize pixels accounting for more
than 50 % of them) for comparison. We found the temporal trends of derived
phenological indicators of spring and summer maize from Landsat images are
consistent with those derived from MODIS data in most years (Fig. 11b). Our
approach can easily capture the crop growth phase dynamics (i.e., delay and
advancement). The magnitude difference between maturity date derived from
Landsat observations and mid-green-down derived from MCD12Q2 is within 3
days in most years. Different data sources and fitting methods (i.e., spline
fit was used in MCD12Q2) likely cause discrepancies between the two
phenology datasets. In addition, it is worth noting that there is a high
correlation of the maturity dates derived from Landsat and MODIS (Fig. 11c).
Phenological indicators derived from Landsat observations also have a close
spatial pattern to the MODIS phenology product but more spatial details
(Fig. 12). For example, there is a noticeable advancement of maturity in 2018
and a delay in 2015 (red boxes in Fig. 12), and these variations are successfully captured by the two phenology datasets. Besides, we can note
that Landsat-derived phenological indicators (e.g., maturity) depict the
difference in crop growth stages with more spatial details compared to the
MODIS phenology product.
Comparison of the Landsat derived maturity date and the MODIS derived mid-green-down date from 2011 to 2018. The selected scene is case 1 in Fig. 11. Red
boxes are highlighted regions where these two products have a noticeable
difference.
Analysis with climate data
Summer maize has a higher requirement for hydrothermal conditions
(especially for temperature) than spring maize (Fig. 13). The Northeast China
Plain and Huang-Huai-Hai Plain (Fig. 1b) are the two largest maize-producing
areas in China, about 60 % of spring maize is grown in the Northeast China
Plain (Fig. 13b), and more than 80 % of summer maize is distributed in the Huang-Huai-Hai Plain (Fig. 13c). In addition, we used the monthly mean air
temperature and the total precipitation from May to October
(Peng
et al., 2019) in the study area for analyses from 2011 to 2020. Overall, the
mean total precipitation and mean temperature change range within the spring
maize planting areas are larger than in the summer maize. Meanwhile, summer
maize growing areas are mainly distributed in high-temperature areas (i.e.,
above 20 ∘C). These results suggest that summer maize has a higher
requirement for hydrothermal conditions than spring maize.
The spatial distribution of maize across the study area
(a) and pixel values represent the coverage of maize in 1 km and
(b) and (c) indicate the coverage of spring and summer maize.
Additionally, we also provided the proportion of maize in major agricultural
zones. The mean temperature and mean annual total precipitation during the
growing period of the crop (from May to October) from 2011 to 2020 are
presented in (d) and (e). (f) and (g) show the kernel density
curves of the mean temperature and mean annual total precipitation in the study
area. The abbreviations are as follows: NCP: Northeast China Plain; LP:
Loess Plateau; Nor: northern arid and semiarid region; HHH: Huang-Huai-Hai
Plain; MLYP: middle-lower Yangtze Plain.
We observed a noticeable difference in the temporal trends of the derived
maize phenological indicators before and after 2000 (Fig. 14). The temporal
trends of derived phenological indicators, including v3 and maturity date of
spring and summer maize before and after 2000, are notably different. For
climate variables, the temperature within maize planting areas has a steeper
upward trend (the slope is more than 0.5 ∘C per decade) before 2000
than after 2000. The mean total precipitation shows different trends before
and after 2000. It is worth noting that the precipitation within the spring
maize producing area has a diverse and sharper tendency compared with that
of the summer maize grown area. For phenological indicators, the changes in
spring maize phenology are mainly concentrated in the segments after 2000. The
v3 date is advanced (-0.37 d yr-1), and the maturity date is delayed
(0.38 d yr-1). The v3 and maturity indicators of summer maize have an
advanced tendency before 2000, while the maturity date is delayed after
2000. The annual dynamics of maize phenological indicators may be partly
attributed to the rising temperature and annual variations of total
precipitation. In this research, we did not consider the impact of other
factors (such as photoperiod and genotype of maize) on the variations of
maize phenology and the response of maize phenology and growth season
duration to climate change was also not comprehensively considered.
Temporal trends of phenological indicators (i.e., v3, maturity)
and climate variables (i.e., mean temperature and mean total precipitation)
during the growing period (from May to October), from 1985 to 2020. Two
segments (i.e., 1985–2000 and 2001–2020) were
independently fitted due to their distinct difference in temporal trends. We
provide the temporal trend of variables across the study area and the
interannual variations within different major agricultural zones.
Detailed band information in each formation.
Band nameYearContentRangeBand1Start yearMaize phenology1–365Band2Start year + 1……Band N-1End yearBand NMaize type1 – Spring maize,2 – Summer maize
Note: The range of phenology was set between 1 and 366 for leap years.
Data availability
This dataset provides the annual dynamics of maize phenological indicators
with a fine spatial resolution (30 m) and a long temporal span (1985–2020) in
China. The extracted phenology indicators include v3 (the date when the
third leaf is fully expanded) and maturity (when the dry weight of maize
grains first reaches the maximum). The format of this dataset is GeoTiff,
with a spatial reference of WGS84. Each file in this dataset is named based
on phenological indicators, start year, end year, and province. We divided the
maize phenology into three parts: 1985–2000, 2001–2010, and 2011–2020 (Table 1). We included 17 provinces in our study, i.e., Beijing, Tianjin, Hebei,
Henan, Shanxi, Shaanxi, Shandong, Hubei, Anhui, Jiangsu, Inner Mongolia,
Ningxia, Gansu, Xinjiang, Heilongjiang, Jilin, and Liaoning. The derived
annual maize phenology data in China from 1985 to 2020 are available at
10.6084/m9.figshare.16437054 (Niu et al.,
2021).
Conclusions
In this study, we generated the first annual maize phenology product with a
fine spatial resolution (30 m) and a long temporal span (1985–2020) in China,
using all available Landsat images on the GEE platform. First, we extracted
long-term mean phenological indicators (including v3 and maturity) from
multi-year Landsat observations using the harmonic model. Second, we
identified the annual dynamics of phenological indicators by measuring the
difference of dates when the EVI in specific years equals the fitted value.
The maize phenology product derived from Landsat data agrees with the
commonly used phenology dataset. Our derived maize phenology datasets
consistently meet the in situ observations from the AMS and the PhenoCam
phenology network. In addition, the phenology dataset in this study has
similar temporal trends and can provide more spatial details than the MODIS
phenology product. Furthermore, we observed a noticeable difference in the
temporal trend of maize phenology before and after 2000, which is likely
attributable to increasing temperature and annual variations of
precipitation.
The extracted maize phenology dataset has great implications for crop field
management and studies of the response of maize phenology to the changing
environment. There are noticeable differences in crop growth due to diverse
local climates, soil properties, and anthropogenic activities (such as
sowing dates). The derived phenology product with a fine spatial resolution
can delineate the difference and provide corresponding information to
improve the field management and yield estimation
(Zeng et al., 2020; Bolton and
Friedl, 2013). In addition, this phenology product can also be used to
investigate the response of crop phenology to global warming
(Badeck et al., 2004; Niu et al., 2021).
However, this study does not consider land cover changes (e.g., urban
expansion and planting system change), which needs to be further
investigated. For example, the maize distribution was regarded as consistent in our study over the past decades.
The supplement related to this article is available online at: https://doi.org/10.5194/essd-14-2851-2022-supplement.
Author contributions
QN, XL, and JH designed the research, performed the
analysis, and wrote the paper; HH, XH, WS, and WY revised the manuscript.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We would like to thank all the co-workers who participated in this research. And we would also acknowledge the editor and reviewers for their valuable comments.
Financial support
This research has been supported by the National Natural Science Foundation of China (grant no. 41971383).
Review statement
This paper was edited by Alexander Gruber and reviewed by two anonymous referees.
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