Understanding long-term (1982–2013) patterns and trends in winter wheat spring green-up date over the North China Plain

International Journal of Applied Earth Observation and Geoinformation 57 (2017) 235–244

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Research paper

Understanding long-term (1982–2013) patterns and trends in winter wheat spring green-up date over the North China Plain Sisi Wang a,b , Xingguo Mo a,∗ , Zhengjia Liu a,c,∗ , Muhammad Hasan Ali Baig d , Wenfeng Chi e a Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China b University of Chinese Academy of Sciences, Beijing, 100049, China c Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100101, China d Institute of Meteorology and Geophysics, Pakistan Meteorological Department, Karachi, Pakistan e Desert Science and Engineering College, Inner Mongolia Agricultural University, Hohhot, 010018, China

a r t i c l e

i n f o

Article history: Received 10 September 2016 Received in revised form 14 January 2017 Accepted 16 January 2017 Available online 21 January 2017 Keywords: Spring green-up date Winter wheat North China Plain GIMMS3g NDVI Soil moisture

a b s t r a c t Monitoring the spring green-up date (GUD) has grown in importance for crop management and food security. However, most satellite-based GUD models are associated with a high degree of uncertainty when applied to croplands. In this study, we introduced an improved GUD algorithm to extract GUD data for 32 years (1982–2013) for the winter wheat croplands on the North China Plain (NCP), using the third-generation normalized difference vegetation index from Global Inventory Modeling and Mapping Studies (GIMMS3g NDVI). The spatial and temporal variations in GUD with the effects of the pre-season climate and soil moisture conditions on GUD were comprehensively investigated. Our results showed that a higher correlation coefficient (r = 0.44, p < 0.01) and lower root mean square error (22 days) and bias (16 days) were observed in GUD from the improved algorithm relative to GUD from the MCD12Q2 phenology product. In spatial terms, GUD increased from the southwest (less than day of year (DOY) 60) to the northeast (more than DOY 90) of the NCP, which corresponded to spatial reductions in temperature and precipitation. GUD advanced in most (78%) of the winter wheat area on the NCP, with significant advances in 37.8% of the area (p < 0.05). GUD occurred later at high altitudes and in coastal areas than in inland areas. At the interannual scale, the average GUD advanced from DOY 76.9 in the 1980s (average 1982–1989) to DOY 73.2 in the 1990s (average 1991–1999), and to DOY 70.3 after 2000 (average 2000–2013), indicating an average advance of 1.8 days/decade (r = 0.35, p < 0.05). Although GUD is mainly controlled by the preseason temperature, our findings underline that the effect of the pre-season soil moisture on GUD should also be considered. The improved GUD algorithm and satellite-based long-term GUD data are helpful for improving the representation of GUD in terrestrial ecosystem models and enhancing crop management efficiency. © 2017 Elsevier B.V. All rights reserved.

1. Introduction Vegetation phenology, which refers to the periodic timing of plant life cycle events, is a sensitive indictor of the dynamic response of terrestrial ecosystems to climate warming (Ault et al., 2015; Chmielewski et al., 2004; Penuelas et al., 2009; Zhang and Tao, 2013). The studies of vegetation phenology are currently of

∗ Corresponding authors at: Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China E-mail addresses: [email protected] (X. Mo), [email protected] (Z. Liu). http://dx.doi.org/10.1016/j.jag.2017.01.008 0303-2434/© 2017 Elsevier B.V. All rights reserved.

great interest to global ecological and environmental research communities (Broich et al., 2015; Fu et al., 2015; Melaas et al., 2013; Shen et al., 2015; Wang et al., 2015; White et al., 2014). For example, several studies have demonstrated that spring warming and an earlier spring green-up date (GUD) will have a marked impact, by increasing northern terrestrial ecosystems as carbon sinks (Jeong et al., 2012; Schwartz et al., 2013). It has also been reported that in forests, advances in GUD and delayed autumn dormancy extend the length of the growing season, which potentially increases gross ecosystem photosynthesis (Richardson et al., 2010). Hence, clarifying the spatial and temporal dynamics of vegetation phenology will increase our understanding of the biological feedback mechanisms involved in how plants respond to climate change.

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GUD has an ecologically important effect in cropland ecosystems (Chmielewski et al., 2004; Semenov, 2009). It directly or indirectly affects carbon, water, and energy cycling. Traditionally, observers record field GUD information by monitoring the length of the new leaves reaching 1–2 cm after winter dormancy (Zhou et al., 2013). Although these field-observed GUD records generally have higher accuracy, they cannot cover entire region or globe. In contrast, satellite remote sensing provides consistent spatial and temporal data that have been widely used to identify grasslands and forests GUD at the regional or global scale (Fu et al., 2015; Piao et al., 2015). Compared with studies of GUD in forests and grasslands (Hou et al., 2014; Richardson et al., 2010; Shen et al., 2014; Wu et al., 2016), studies of GUD in croplands (e.g., winter wheat) have been rarely reported, which further restricts our ability to predict future crop GUD changes under conditions of global warming. Two satellite-based methods are commonly used to extract the timing of GUD: the predefined threshold method and the maximum change rate method. The former is usually limited when applied to large-scale eco-climatic regions. Instead, the latter is considered more reliable. For example, some studies have used logistic algorithm or modified logistic algorithm and in combination with the enhanced vegetation index (EVI) to produce global phenology products (Zhang et al., 2003). Recent studies have indicated that the effectiveness of the logistic method is limited, since natural vegetation does not follow a well-defined S-shaped logistic temporal profile (Cao et al., 2015; Wu et al., 2016). The polynomial function has also been extensively used to determine GUD, and have been shown to match the growth of single-peak natural vegetation that does not grow under ideal conditions. However, Cong et al. (2012) demonstrated that the polynomial method, which is based on the maximum values of the normalized difference vegetation index (NDVI) change rate, generally under- or overestimated GUD for croplands. An important reason is that the multiple growth periods of croplands potentially increase the complexity of spring phenology (Cong et al., 2012). Thus, traditional satellite-based models entail a high degree of uncertainty in estimating GUD for agricultural fields. Multi-model analyses have reported that the standard deviation of various models in estimating GUD is generally >60 days (Cong et al., 2012; White et al., 2009), indicating a pressing need to improve the accuracy of GUD estimates. Therefore, developing a new algorithm to determine cropland GUD is particularly important. The North China Plain (NCP) is one of the most important granaries in China. The prevailing double cropping system involves winter wheat and summer maize. Climate change has greatly affected the GUD of winter wheat over recent decades, and also affects crop productivity. Monitoring the changes in GUD on the NCP could improve our understanding of the biological response of winter wheat to climate change, and improve the decisions made on the dates used in crop management. Previous studies have reported the analysis of cropland GUD on the NCP, but several limitations remain. Firstly, the algorithms used to extract GUD generally lack solid validation based on ground measurements (Cong et al., 2012; Zhang et al., 2003). Secondly, earlier studies focused mainly on the patterns in GUD (Lu et al., 2014) rather than on the analysis of the trends in its long-term change. Thirdly, most studies have concentrated on the impacts of climate on GUD, but have not considered the impact of soil moisture. In previous studies, precipitation has usually been regarded as the major factor in water stress in natural ecosystems (e.g., grasslands and forests) (Bradley et al., 2011; Ramos et al., 2015). However, in cropland ecosystems, irrigation also plays an important role in regulating crop growth, in addition to precipitation. For example, to ensure that the crop water requirements on the NCP are met and that the winter wheat green-up occurs, irrigation is commonly used to compensate for insufficient precipitation (Wang et al., 2016).

Fourthly, previous studies have been limited to specific sites (Guo et al., 2016; Tao et al., 2012; Xiao et al., 2013) or shorter periods (Lu et al., 2014). For example, Guo et al. (2016) investigated the sitescale performances of satellite-derived phenological timing against 112 agro-meteorological stations across China from 1993 to 2008. Studies of Xiao et al. (2013) and Tao et al. (2012) concentrated upon the change of site-scale observed phenology over the NCP and China, respectively. Lu et al. (2014) showed spatial patterns of winter wheat two phenological metrics, but their study only included the period of 2003–2007. In this study, we employed the third-generation Global Inventory Modeling and Mapping Studies (GIMMS3g) NDVI to assess the GUD of winter wheat on the NCP over the longest available NDVI time series (from 1982 to 2013). The overall objectives of the study were: (1) to develop a new cropland GUD extraction algorithm, and evaluate it performance using ground phenology records; (2) to investigate the spatiotemporal pattern of winter wheat GUD and its trends over the past three decades; and (3) to clarify the environmental drivers of the winter wheat GUD. 2. Materials and methods 2.1. Study area The NCP (32◦ 00 −40◦ 24 N, and 112◦ 48 −122◦ 45 E) is located in the eastern part of China (Fig. 1). It has a typical temperate monsoonal climate, with an annual mean air temperature of 8–15 ◦ C and annual precipitation of 500–1000 mm. Winter wheat is one of the major food crops in this region. The yield of winter wheat accounts for ∼60% of China’s total wheat yield (Lu et al., 2014). 2.2. Remote sensing and spatial meteorological data The 1/12 ◦ spatial resolution GIMMS3 g NDVI with a temporal resolution of 15-day was obtained from the NASA Earth Exchange (NEX). The data were derived from Advanced Very High Resolution Radiometer (AVHRR) sensors on board several NOAA satellites. Corrections were made to account for the different sensors and physical conditions, including the effects of calibration loss and latitudinal variations in the solar zenith angles due to orbital drift and volcanic eruptions (Pinzon and Tucker, 2014). These data have previously been widely used to monitor global environmental changes (Garonna et al., 2016; Wang et al., 2014a; Zhu et al., 2013). Noise caused by cloud contamination or poor atmospheric conditions is however inevitable in remote sensing data. Some studies have reported that the modified Savitzky-Golay (mSG) filter has stronger ability in reconstructing a high-quality NDVI (Chen et al., 2004; Liu et al., 2017). To allow more accurate assessment of the GUD of winter wheat, we thus used the mSG filter with smoothing window size of 2m + 1 (m = 4) and quartic polynomial to smooth all GIMMS3g NDVI data (Fig. 2). Throughout the NCP, winter wheat generally ripens in May or early June, and is harvested before the middle of July. Therefore, only GIMMS3g NDVI data from January to July for the period 1982–2013 were used in this study. To assess the accuracy of predicted GUD on the basis of the new algorithm developed in this study, MCD12Q2 land cover dynamics (also termed as “MODIS phenology”) for the period 2001–2008 were obtained from the Land Processes Distributed Active Archive Center (LPDAAC). MCD12Q2 product provided phenological transition dates at annual time steps with 500 m spatial resolution (Table 1). The band, onset-greenness-increase in the first mode, was interpreted as GUD of the MCD12Q2, because it was compared with the predicted winter wheat GUD. For each agro-meteorological station, we extracted ∼1/12◦ MCD12Q2 GUD data (17 × 17 cropland pixels) around the center of the station, and determined GUD for

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Fig. 1. Location of the North China Plain, its topography (DEM), and the spatial distribution of 92 meteorological stations, including the nine agro-meteorological stations used in the analysis of the winter wheat spring green-up date (GUD).

Fig. 2. Flow chart for GUD extraction. Table 1 Summary of data used in this study. Data name GIMMS3g NDVI MCD12Q2 GUD ECV soil moisture Land use map SRTM DEM Observed GUD data Meteorological data

Spatial resolution ◦

1/12 500m 1/4◦ 1 km 1 km in-situ in-situ

Temporal resolution

Period

Interpolation method

15-day annual daily annual

1982–2013 2001–2008 1981–2013 2010 2004 1980–2008 1981–2013

– Mean of pixels with good quality Bilinear method Majority method Bilinear method – Thin-plate smoothing spline method

annual monthly

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the station as the average value of all good-quality pixels (referring to quality control file). Daily gridded soil moisture data (1/4 ◦ ) for the period 1981–2013 were derived from the Essential Climate Variables (ECV) soil moisture (Dorigo et al., 2012; Dorigo et al., 2015; Liu et al., 2011; Wang et al., 2016), which was derived from the combination of two active (AMI and ASCAT) and four passive (SMMR, SSM/I, TMI, and AMSR-E) microwave radiometers. Wang et al. (2016) used in-situ soil moisture measurements to validate the ECV soil moisture data for the NCP, and suggested that these data were potentially suitable for trend analysis in this area. The 1 km land-use map for 2010 was obtained from the Resources and Environment Data Center, Chinese Academy of Sciences (Liu et al., 2014). Satellite-derived soil moisture and land use map were resampled at a spatial resolution of 1/12◦ , in accordance with GIMMS3g NDVI. Meteorological data for the 1981–2013 periods were obtained from the China Meteorological Data Sharing Service. These included monthly mean air temperature and total precipitation (including rainfall and snowfall) data for 92 meteorological stations in and around the NCP. The thin-plate smoothing spline method was used for spatial interpolation, in combination with a digital elevation model (DEM) (Hutchinson et al., 2009; Liu et al., 2012).

2.3. Field-observed GUD records for winter wheat Field-observed GUD data for winter wheat, from representative agro-meteorological stations, were obtained from the Chinese Meteorological Administration (CMA). According to the CMA phenology observation guide, GUD for winter wheat is defined as the date when the leaves begin to turn green after winter dormancy, and the length of the new leaves reaches 1–2 cm (Zhou et al., 2013). However, in practice, these qualitative descriptions are difficult to apply in determining GUD, because different observers and observation frequencies (generally, once a day) can markedly affect the consistency of the field observations (Guo et al., 2016). To obtain high-quality records, we applied quality control criteria to the raw GUD records: (1) GUD records beyond the day of the year (DOY) 30 to DOY 140 range were excluded, because the GUD for winter wheat on the NCP is unlikely to occur before early January or after the middle of May; and (2) only GUD records covering the period 2001–2008 were used, because the products of 005 version MCD12Q2 have been available since 2001, and the field-observed GUD records that we could obtain covered the period from 1980 to 2008. We finally selected nine agro-meteorological stations on the NCP, which provided 72 GUD records for winter wheat. These selected records covered the winter wheat production area from north to south of the NCP (Fig. 1).

2.4. Improved GUD extraction algorithm Previous studies have used a polynomial function to fit NDVI time series and to determine the maximum change rate with which to derive GUD (Cong et al., 2012; Piao et al., 2006; Yang et al., 2015). In contrast to this method, we used an improved GUD extraction algorithm based on a cumulative NDVI (cumNDVI), and solved the transition point from the fitted cumNDVI curve (Fig. 3). Importantly, the use of cumNDVI effectively overcomes the confounding effects of environmental factors (Hou et al., 2014). The specific operational steps involved in the improved algorithm are described below. For each year, we first used the GIMMS3 g NDVI time series to compute the cumNDVI for each 15-day interval. The polynomial function was then used to fit the relationship between the 15-day

Fig. 3. Schematic diagram showing how the satellite-derived green-up date (GUD) is computed using the maximum values in change rate of fitted curve (data from Zhumadian station: 33.00◦ N, 114.02◦ E). The triangles represent the 15-day cumulative NDVI (cumNDVI), which is the averaged cumNDVI for the period 1982–2013. The dashed curve is the fitted cumNDVI. The solid curve shows the change rate of the fitted cumNDVI. The cyan-colored column shows the standard deviation range for the satellite-derived GUD, the vertical dashed line is the average satellite-derived GUD, and the solid vertical line is the average field-observed GUD.

cumNDVI and the normalized Julian day from January to July, as follows: y = p0 + p1 X + p2 X 2 + · · · + pn X n

(1)

where y is the cumNDVI, X is the normalized value for the corresponding Julian day (e.g., when DOY is the first day of the first 15-day interval, X = 1/365), p0, p1, . . . pn are the fitted coefficients, and n is set as 6 (Piao et al., 2006). Finally, the transition point for the fitted cumNDVI and the corresponding DOY was inferred from the maximum point of change rate. The equation-solving function is similar to the curvature formula. K=



y 1 + y 2

(2)

3

GUD = f (max (K)) y

(3)

y

where and are the first and second derivatives of function y, respectively; of wihch y = p1 + 2p2 X + · · · + npn Xn−1 and y = 2p2 + · · · + n (n − 1) pn X n−2 (n = 6); and GUD is a function of the maximum value of K. 2.5. Statistical analysis To evaluate the performance of the improved algorithm in GUD extraction, the correlation coefficient (r), the p value, the root mean square error (RMSE), and the difference between the simulated and observed data (bias) were computed. Using these criteria, we also investigated the difference between the improved GUD and the MCD12Q2 GUD. The least square method was used to solve the trend (or slope) of winter wheat GUD at the spatial scale (Piao et al., 2011; Wang et al., 2014b).



slope =

n

Xi Yi −



n

Xi2

−

  Xi

Yi

 2

(4)

Xi

where Xi is the ith year, Yi is the GUD of the ith year, and n is the number of years. Spatially, if the slope value of a pixel is a positive, it shows that the trend of long-term series time GUD is advanced, and vice versa. Student’s t test was used to test the significance of the trends in the changes in GUD. A partial correlation analysis was used to investigate the impact of pre-season climatic factors and remote sensing soil moisture on the winter wheat GUD. Among them, pre-season precipitation, pre-season temperature and preseason soil moisture referred to cumulative precipitation, averaged

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Fig. 4. Validation of the predicted green-up date (GUD, red symbols represent MCD12Q2 GUD and blue symbols represent GUD derived from this study) in relation to the field-observed GUD. The various symbols represent the nine agrometeorological stations. The mark ** represents p < 0.01. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

temperature and cumulative soil moisture during the periods (0–5 months) before the mean GUD. For example, “0 months” refers to the month in which GUD occurred (March), and “5 months” refers to the period from the previous October through to March. 3. Results 3.1. Performance of the improved GUD extraction algorithm We developed and applied an improved cropland GUD extraction algorithm to infer GUD for winter wheat on the NCP. The salient features of the improved algorithm are the use of cumNDVI, a sixthdegree polynomial function, and the GUD transition point, based on the maximum value of curvature. Firstly, the use of the cumNDVI avoids the potential impact of the NDVI time series. As described in a previous study (Cao et al., 2015), climatic or environmental

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factors can markedly affect the ideal crop growth trajectories, but cumNDVI is generally directly proportional to the change in DOY (Hou et al., 2014; Wu et al., 2016). Secondly, a sixth-degree polynomial function was used for fitting the cumNDVI time series instead of the usual logistic model, because the sixth-degree polynomial function provides a better fit for crop growth trajectories under natural conditions (Cong et al., 2013; Piao et al., 2006; Yang et al., 2015). Thirdly, the GUD transition point was extracted based on the maximum value of the fitted cumNDVI curve, which is regarded as a better phenological extraction method, and has been widely used in recent studies (Wu et al., 2016). The validation results (Fig. 4) showed a significant correlation (r = 0.44, p < 0.01) between the satellite-derived GUD based on the improved algorithm and the observed GUD based on 72 ground observations from nine agro-meteorological stations. The corresponding RMSE and bias were 22 and 16 days, respectively. Although the satellite-derived GUD slightly overestimated the observed GUD, the results showed a better correlation than that reported in a previous study (Cong et al., 2012) or the MCD12Q2 GUD. We found that the MCD12Q2 GUD correlated only weakly with the observed GUD (r = 0.14, p > 0.05), and was associated with a high RMSE (90 days) and bias (71 days). These findings suggest that the improved algorithm is appropriate for the analysis of the spatial and temporal patterns of variation in the GUD for winter wheat on the NCP.

3.2. Spatial patterns of satellite-derived GUD The multi-year average GUD and standard deviation ranged from DOY 40 to DOY 123 and from 2.7 to 20.7 days, respectively. The regions with GUD before DOY 90 accounted for approximately 86% of the winter wheat area of the NCP. The GUD for winter wheat mainly ranged from DOY 50 to DOY 85. Less than 9% of winter wheat fields had a GUD later than DOY 100, and these fields were located in the northeast and mountain areas. As shown in Fig. 5, the multiyear average GUD increased from the southwest to the northeast on the NCP, but the standard deviation decreased. In the southwestern region, GUD was generally before DOY 60 (standard deviation, approximately 9 days), whereas in the central region, it ranged from DOY 60 to DOY 80 (standard deviation <7 days). For 89.4% of the winter wheat fields, the standard deviation was less than 11 days.

Fig. 5. Spatial pattern of multiyear averaged green-up date (GUD) during the period 1982–2013 (a), and associated standard deviations (b).

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Fig. 6. Spatial trends in GUD in the period 1982–2013 (a), and the significance levels (b). NS: not statistically significant.

Fig. 7. Trends of long-term time series of satellite-based GUD at selected sites. The symbol ** is significant at the 0.01 level, and * is significant at the 0.05 level.

Fig. 8. Partial correlations between satellite-derived green-up date (GUD) and pre-season precipitation (P), temperature (T) and remote sensing soil moisture (SM) for the winter wheat field in North China Plain. (a) The dynamic relationships in different pre-season periods. The x-coordinate represents the number of cumulative months before the average GUD. The solid horizontal line is significant at the 0.01 level, and the dashed horizontal line at the 0.05 level. (b) The spatial distributions of major factors controlling the GUD in 1982–2013. NS: not statistically significant.

3.3. Variations in the satellite-derived GUD Fig. 6 showed the trends in the satellite-derived GUD, and the significance level for each pixel. The data demonstrated that the regional average GUD advanced significantly in the past 32 years, from DOY 76.9 in the 1980s (average, 1982–1989) to DOY 73.2 in the 1990s (average, 1991–1999) and DOY 70.3 since 2000 (average, 2000–2013). The average advance was 1.8 days/decade (r = 0.35,

p < 0.05). For the entire winter wheat area, 78% of all pixels showed an advanced trend, and in 37.8%, the advance was either significant (13.4%, p < 0.05) or highly significant (24.4%, p < 0.01). In 47.6% of the winter wheat area, GUD advanced 1-3 days. In some parts of the central-west region of the NCP, the advance was more than 4 days/decade (p < 0.01). In most of the southwest, the advance ranged from 1 to 6 days/decade, but was not statistically significant (p > 0.05). However, in some regions, mainly in the northeast

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4. Discussion 4.1. Improved GUD extraction algorithm

Fig. 9. Relationship between the average satellite-derived green-up date (GUD) across North China Plain (from 1982 to 2013) and the corresponding pre-season temperature in March, which showed the strongest correlation (Fig. 8a).

of the NCP (7.4% of all pixels), GUD was delayed (generally less than 6 days/decade). Meanwhile, trends of long-term time series (1982–2013) of GUD at nine selected sites also were investigated. As showed in Fig. 7, two thirds of selected sites presented advanced trends. Among them, GUD trend in Tangshan (−5.0 day/decade) site was significant at the 0.05 level, and GUD trends in huanghua (−4.4 day/decade) and huimin sites (−3.6 day/decade) were significant at the 0.01 level. However, GUD trends of the rest sites were not statistically significant.

3.4. GUD in relation to climate and soil moisture To investigate the factors causing the changes in GUD, we employed a partial correlation analysis to investigate the relationship between GUD and pre-season climate changes/remotely sensed soil moisture changes during the past 32 years. Fig. 8a showed that the pre-season climate and soil moisture tended to perform negative partial correlations with GUD. However, there was an exception in the relationship between GUD and cumulative precipitation between February and March. In general, the relationship between GUD and pre-season precipitation reached a steady state after cumulative precipitation for 3 months (r = –0.35). The pre-season temperature (0–2 months) correlated very significantly negatively with GUD, and the temperature in March (0 months) showed the highest correlation with GUD. After an increase in the cumulative values for several months, the relationships between GUD and both pre-season temperature and soil moisture gradually declined, and the relationships appeared to be stable after a cumulative period of 5 months (r = –0.1 for pre-season temperature, and r = –0.08 for pre-season soil moisture). In particular, our results showed that pre-season soil moisture had a stronger effect on GUD than pre-season precipitation before cumulative 2 months. To determine in which areas the changes in GUD were mainly controlled by climate or soil moisture, we plotted the spatial distribution of the major controlling factors, based on the maximum coefficient of partial correlation (p < 0.05) between GUD and each of these parameters over various periods. As Fig. 8b showed, pre-season temperature was the major driving force for the interannual variation in GUD over more than 74% of the winter wheat area, whereas pre-season precipitation and pre-season soil moisture controlled the interannual variation in GUD over only 5.7% and 4.8% of the area, respectively. GUD was controlled by precipitation in the northern NCP and by soil moisture in the eastern NCP.

The improved algorithm was very effective. Our evaluation showed that it slightly overestimated the satellite-derived GUD, by less than 20 days. This discrepancy can be attributed to differences in how GUD is defined, the spatial resolution of the data, and the data uncertainties and completeness (Guo et al., 2016). It is inevitable that the satellite-derived GUD will be less accurate than that determined from field observations. Furthermore, in using a sixth-degree polynomial function, over-fitting at the start and end of the cumNDVI or NDVI curve typically cannot be avoided. To overcome this problem, the range of the inferred GUD was limited from DOY 30 to DOY 140, because it is unlikely that the winter wheat GUD on the NCP would occur before January or after the middle of May. 4.2. Spatial and temporal distributions of GUD Temperature has been reported to significantly affect the spatial distribution of GUD (Badeck et al., 2004; Cong et al., 2013; Cornelissen et al., 2007; Schwartz et al., 2006; Shen, 2011; Shen et al., 2011). We reached a similar conclusion, because our data indicate that the area controlled by temperature accounted for more than 74% of the total winter wheat area. With increasing latitude, the delay in the satellite-derived GUD was approximately 9.5 days/◦ C (r = −0.86, p < 0.01), supporting the hypothesis that temperature is the primary factor controlling GUD (Figs. 5 and 9). Similar results have been reported for other ecosystems, but their amplitudes were lower than that for cropland ecosystems (Cong et al., 2012). Differences in crop management may be responsible for the large differences observed between croplands and other ecosystems. The spatial distribution of the standard deviation of GUD (Fig. 5b) shows that the earlier estimated GUD in the southwest was associated with greater variation, and was more sensitive to temperature and soil moisture (Fig. 10a and c). Frequent spring drought events and crop irrigation may be responsible for fluctuations in GUD, because precipitation did not significantly affect GUD in this region. Although the relationship between GUD and preseason soil moisture was not as strong as that between GUD and pre-season temperature (Fig. 8b), the fluctuations in GUD suggest that irrigation greatly reduced the effect of temperature on GUD in the southwest and eastern parts of NCP. In contrast, the standard deviation associated with GUD in the northern part of the NCP was relatively low. In addition to temperature, precipitation also significantly affected GUD in this area (Fig. 10b), suggesting that the amount of pre-season precipitation may satisfy the water requirements for winter wheat green-up. These results suggest that in studies of the forces driving phenology, the interactions of different variables in cropland systems should be considered in phenological models. Compared with the spatial patterns, the temporal trends in GUD may be more important in quantifying the changes in the GUD of winter wheat in response to interannual changes in the driving forces and the mechanisms of these variations. Many studies have reported an earlier GUD in various ecosystems (Jeong et al., 2011; Julien and Sobrino, 2009; Vitasse et al., 2011). We found that more than 78% of the winter wheat area also showed earlier GUDs with increasing regional temperature, whereas GUD significantly delayed in only 7.4% of the area. The delay in GUD appears to be related to elevation and location, because lower water and temperature conditions are generally observed in the northeastern areas of NCP relative to others areas of NCP. A similar study reported that autumn phenology was also determined less by temperature in

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Fig. 10. Spatial distribution of controlling factors (p < 0.05) including: (a) pre-season temperature, (b) pre-season precipitation, and (c) pre-season soil moisture.

colder regions (Yang et al., 2015). Besides, soil moisture also partly explained the delay in GUD in the eastern NCP (Fig. 10c). 4.3. Impact of water stress on GUD Rather than only considering the impact of precipitation on phenology (Cong et al., 2013; Yu et al., 2003), we used remote sensing to directly measure soil moisture. In addition to the role of winter snow (approximately 10% of annual precipitation) in crop greenup, irrigation in early spring also plays an important role in the soil moisture conditions (Hashemian et al., 2015; Seghieri et al., 2009). The correlation analysis also showed that precipitation had nonsignificant impacts on soil moisture at each corresponding period with r ranging from 0.08 to 0.34 (p > 0.05). Our results confirmed the importance of pre-season soil moisture in regulating the winter wheat GUD. Interestingly, the winter wheat area where GUD was controlled by soil moisture (approximately 18% of the total) was mainly concentrated in the southern NCP (Fig. 10c). This can be attributed to the higher temperature and greater evaporation in this area (Mo et al., 2009), where precipitation does not satisfy the winter wheat green-up requirements. Spring irrigation greatly increases the soil moisture content, and offsets the shortage of precipitation at that time (Qiu et al., 2016). Soil moisture significantly affected the variations in GUD in the southern NCP (p < 0.05). In contrast, the lower temperatures and less evaporation in the northern NCP did not limit the strong relationship between precipitation and GUD (∼27% of the area), suggesting that pre-season precipitation has a significant controlling effect on the winter wheat GUD. 4.4. Satellite-based GUD in relation to crop management Winter wheat varieties, sowing dates, irrigation, and fertilization can all alter the vegetation indices and affect GUD. For example, based on 16 specific sites across the Loess Plateau, He et al. (2015) reported that the sowing date and crop variety affected the various phenological phases of winter wheat and the crop yield. A similar result was reported in the corn belt in the USA (Sacks and Kucharik, 2011). Unlike field observation experiments and ecological modeling, remote sensing data have rarely been used to monitor whether winter wheat varieties, sowing dates, or fertilization rates have changed at the spatial scale. However, the advantage of remote sensing over traditional field measurements is that key phenological phases can be monitored rapidly on broad scales to inform crop management decisions. Previous studies have reported that GUD affects the spring tillers of winter wheat and new root growth, which play important roles in plant growth and dry matter accumulation (He et al., 2015; Yue et al., 2012). However, improper crop management can detrimentally affect the distribution of the aboveand belowground biomass after GUD. In addition, the growth of winter wheat changes from the vegetative growth of the roots, stems, and leaves to both the reproductive and vegetative growth

of these plant components and tillers (Siddique and Whan, 1993). Furthermore, winter wheat requires more water and fertilizer to support the rapid growth that occurs in this period. Our findings, including the long-term trend and spatial pattern in GUD, and the regional differences in the driving forces determining GUD, will help crop managers to provide timely fertilization and irrigation, according to the crop requirements in different regions. Overall, the use of satellite-based GUDs can be expected to improve the efficiency of crop management. 5. Conclusions Based on the GIMMS3 g NDVI and an improved GUD algorithm, we inferred that the GUD for winter wheat on the NCP has advanced significantly during the period 1982–2013, primarily because of accelerating climate warming. The relationship between the GUD for winter wheat and pre-season soil moisture suggests that irrigation, as well as temperature and precipitation, strongly affects GUD, especially in areas of higher temperatures and evaporation. These findings not only highlight the importance of water stress for the GUD of winter wheat, but also suggest that regional differences, including the impact of water stress on the winter wheat GUD and the spatial long-term changes in this parameter, should be taken into consideration when assessing various crop management practices. Acknowledgements We are grateful for all data sets used in this study. Constructive comments and suggestions from two anonymous referees and editors are also appreciated. This study was funded by the Natural Science Foundation of China (Grant No. 41471026, 41601582 and 41471227), and China Postdoctoral Science Foundation (Grant No. 2016M590149). Besides, we particularly thank Dr. Dengpan Xiao, who provided the processed GUD data for winter wheat on the North China Plain. References Ault, T.R., Schwartz, M.D., Zurita-Milla, R., Weltzin, J.F., Betancourt, J.L., 2015. Trends and natural variability of spring onset in the coterminous United States as evaluated by a new gridded dataset of spring indices. J. Climate 28 (21), 8363–8378. Badeck, F.W., Bondeau, A.K., Doktor, D., Lucht, W., Schaber, J., 2004. Responses of spring phenology to climate change. New Phytol. 162 (2), 295–309. Bradley, A.V., Gerard, F.F., Barbier, N., Weedon, G.P., Anderson, L.O., Huntingford, C., Aragão, L.E.O.C., Zelazowski, P., Arai, E., 2011. Relationships between phenology, radiation and precipitation in the Amazon region. Global Change Biol. 17 (6), 2245–2260. Broich, M., Huete, A., Paget, M., Ma, X., Tulbure, M., Coupe, N.R., Evans, B., Beringer, J., Devadas, R., Davies, K., 2015. A spatially explicit land surface phenology data product for science, monitoring and natural resources management applications. Environl. Modell. Softw. 64, 191–204.

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