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Seasonal Responses of Net Primary Productivity of Vegetation to Phenological Dynamics in the Loess Plateau, China

Hongzhu HAN Jianjun BAI Gao MA Jianwu YAN Xiaohui WANG Zhijie TA Pengtao WANG

HAN Hongzhu, BAI Jianjun, MA Gao, YAN Jianwu, WANG Xiaohui, TA Zhijie, WANG Pengtao, 2022. Seasonal Responses of Net Primary Productivity of Vegetation to Phenological Dynamics in the Loess Plateau, China. Chinese Geographical Science, 32(2): 340−357 doi:  10.1007/s11769-022-1270-8
Citation: HAN Hongzhu, BAI Jianjun, MA Gao, YAN Jianwu, WANG Xiaohui, TA Zhijie, WANG Pengtao, 2022. Seasonal Responses of Net Primary Productivity of Vegetation to Phenological Dynamics in the Loess Plateau, China. Chinese Geographical Science, 32(2): 340−357 doi:  10.1007/s11769-022-1270-8

doi: 10.1007/s11769-022-1270-8

Seasonal Responses of Net Primary Productivity of Vegetation to Phenological Dynamics in the Loess Plateau, China

Funds: Under the auspices of MOE (Ministry of Education in China) Project of Humanities and Social Sciences (No. 20YJC840027); Natural Science Basic Research Program of Shaanxi, China (No. 2021JQ-771, No. 2021JQ-768);Soft Science Project of Xi’an Science and Technology Bureau, Shaanxi Province (No. 2021-0013)
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  • Figure  1.  Location and elevation of Loess Plateau, China

    Figure  2.  Annual mean phenological metrics and its change trend in the Loess Plateau, China from 1982 to 2015; a−c represent the multiyear averages of the start of the growing season (SOS), the end of the growing season (EOS) and the length of the growing season (LOS); d−e represent the change trends of the SOS, EOS and LOS

    Figure  3.  Annual and seasonal mean net primary productivity (NPP) and its change trend in the Loess Plateau from 1982 to 2015; a−d represent the multiyear average of the annual scale of NPP, spring, summer and autumn; e−h represent the change trend of the annual scale of NPP, spring, summer and autumn

    Figure  4.  Correlation analysis of phenology to seasonal net primary productivity (NPP) in the Loess Plateau from 1982 to 2015; a and b represent the correlation coefficients between the start of the growing season (SOS) and the spring NPP and between the SOS and the summer NPP, respectively; c and d represent the correlation coefficients between the end of the growing season (EOS) and the summer NPP and between the EOS and the autumn NPP, respectively; and e represents the correlation coefficient between the length of the growing season (LOS) and the annual NPP

    Figure  5.  Changes in phenology and net primary productivity (NPP) with altitude in the Loess Plateau from 1982 to 2015; a−d denote the changes in the start of the growing season (SOS), the end of the growing season (EOS), the length of the growing season (LOS) and net primary productivity (NPP) with altitude

    Figure  6.  Correlation analysis of phenology and net primary productivity (NPP) with seasonal temperature in the Loess Plateau from 1982 to 2015; a−c represent the correlation coefficients between NPP and spring, summer and autumn temperatures, respectively; (d) represents the correlation coefficients between the start of the growing season (SOS) and spring temperature; and e−f represent the correlation coefficients between the end of the growing season (EOS) and summer and autumn temperatures, respectively

    Figure  7.  Correlation analysis of phenology and net primary productivity (NPP) with seasonal precipitation in the Loess Plateau from 1982 to 2015; a−c represent the correlation coefficients between NPP and spring, summer and autumn precipitation; d represents the correlation coefficients between the start of the growing season (SOS) and spring precipitation; and e−f represent the correlation coefficients between the end of the growing season (EOS) and summer and autumn precipitation

    Figure  8.  Changes in the trends of the start of the vegetation growing season (SOS) (a), the end of the growing season (EOS) (b), the length of the growing season (LOS) (c), net primary productivity (NPP) (d), precipitation and temperature with temperature (e) and precipitation gradients (f) in the Loess Plateau from 1982 to 2015, respectively

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出版历程
  • 收稿日期:  2021-03-18
  • 录用日期:  2021-06-26
  • 刊出日期:  2022-03-05

Seasonal Responses of Net Primary Productivity of Vegetation to Phenological Dynamics in the Loess Plateau, China

doi: 10.1007/s11769-022-1270-8
    基金项目:  Under the auspices of MOE (Ministry of Education in China) Project of Humanities and Social Sciences (No. 20YJC840027); Natural Science Basic Research Program of Shaanxi, China (No. 2021JQ-771, No. 2021JQ-768);Soft Science Project of Xi’an Science and Technology Bureau, Shaanxi Province (No. 2021-0013)
    通讯作者: BAI Jianjun. E-mail: bjj@snnu.edu.cn

English Abstract

HAN Hongzhu, BAI Jianjun, MA Gao, YAN Jianwu, WANG Xiaohui, TA Zhijie, WANG Pengtao, 2022. Seasonal Responses of Net Primary Productivity of Vegetation to Phenological Dynamics in the Loess Plateau, China. Chinese Geographical Science, 32(2): 340−357 doi:  10.1007/s11769-022-1270-8
Citation: HAN Hongzhu, BAI Jianjun, MA Gao, YAN Jianwu, WANG Xiaohui, TA Zhijie, WANG Pengtao, 2022. Seasonal Responses of Net Primary Productivity of Vegetation to Phenological Dynamics in the Loess Plateau, China. Chinese Geographical Science, 32(2): 340−357 doi:  10.1007/s11769-022-1270-8
    • Since the 20th century, according to the Fifth Assessment Report of the International Panel on Climate Change (IPCC), the global average surface temperature has increased by 0.85°C (0.65−1.06°C) (IPCC, 2013). In the past 20 yr, temperatures in the northern region have risen sharply in spring and autumn, reaching increases of 1.1°C and 0.8°C, respectively (Piao et al., 2008). Climate change has significantly affected the occurrence time of global phenology (Schwartz et al., 2006). With an increase in global temperature, the timing of phenological events for species has changed significantly compared with that 35 yr ago. The phenology progress among species is 4.0 d/decade, which is much higher than the 2.7 d/decade estimated before 1981 (Kharouba et al., 2018). The phenomena in early spring and late autumn have gradually increased (Caparros-Santiago et al., 2021). Compared with 50 yr ago, the leaves of deciduous plants in the Mediterranean ecosystem unfold 16 d earlier on average, and the unfolding of deciduous leaves is delayed by 13 d on average (Peñuelas and Filella, 2001). During the period 1955−2002, the Northern Hemisphere had a confirmed advance of early spring by approximately 1.2 d/decade (Schwartz et al., 2006). The conclusions presented in 64 papers on autumn phenology in the Northern Hemisphere published from 1931 to 2010 indicated that the senescence of plant leaves was delayed in some areas (Gill et al., 2015). In the temperate regions of China, a distinct advance of the start of the season (SOS) and delay of the end of the season (EOS) has been observed, and the time of vegetation turning green in spring occurs 0.79 d earlier than it did in previous years (Wang et al., 2019). Vegetation phenology determines the duration of photosynthesis, which affects carbon exchange in the ecosystem. A phenological index was created to describe the seasonal vegetation photosynthetic activity of different vegetation types and to estimate the annual changes in carbon assimilation (Gu et al., 2003). Richardson used data from 21 FLUXNET sites to study the relationship between forest phenology and productivity in temperate and northern regions (Richardson et al., 2010). Four forest ecosystems were simulated, and warmer growth conditions increased carbon sequestration and storage (Dymond et al., 2016). In general, warming leads to a longer growing season, which means that plants will have more time for carbon sequestration, which may increase productivity (Piao et al., 2018). The warming climate and extension of the growing season have a positive impact on the carbon uptake of alpine wetland ecosystems (Kang et al., 2016). The growing season of plants in the Northern Hemisphere was extended by 1 d, resulting in an increase in annual gross primary productivity (GPP) of 5.8 g/(m2∙yr) C and an increase in annual net primary productivity (NPP) of 2.8 g/(m2∙yr) C (Piao et al., 2007). The relationship between net carbon absorption and the length of the growing season (LOS) in northern deciduous broad-leaved forests is approximately 5.6 g/(m2∙yr) C (Baldocchi, 2008). However, a warming climate will also increase respiration and seasonal water scarcity (especially in summer), which will reduce productivity (Richardson et al., 2010). Under the condition of 3.4°C above normal ambient temperature, warming increased the leaf respiration intensity of 10 North American tree species by an average of 5% (Reich et al., 2016). A significant decline in summer productivity is attributed to an earlier spring in the forests of northern North America (Buermann et al., 2013). Therefore, the relationship between the LOS and productivity is uncertain. NPP refers to the total amount of dry organic matter in vegetation and is an important indicator of the carbon cycle in the ecosystem (Keenan and Williams, 2018). Accurately describing and quantifying the phenological response of NPP will help us understand the carbon balance between increased productivity and increased respiration caused by a prolonged growing season, and increasing attention has been given to this research topic (Chen et al., 2019). Simulating the climate change effect on the monthly net productivity of 18 million hectares of temperate forests in the northeastern United States to 2100 revealed that phenology will affect future NPP (Duveneck and Thompson, 2017). Wang et al. (2017) analyzed the temporal and spatial changes in the phenology and NPP of vegetation in Tibetan Plateau. The seasonal trends of phenology, NPP and carbon allocation of nine lowland forest surface data points in the Amazon basin were also investigated (Girardin et al., 2016).

      Dryland ecosystems account for more than 40% of the Earth’s land surface (Smith et al., 2019), providing ecosystem services to more than two billion people; these ecosystems have an important role in the trend and inter-annual variation in the global carbon cycle (Poulter et al., 2014; Bestelmeyer et al., 2015). The trend and inter-annual variation in the global carbon cycle are mainly controlled by arid ecosystems, and their carbon balance is closely related to cycle-driven changes in precipitation and temperature (Ahlstrom et al., 2015). Here, vegetation is not only affected by temperature but is also restricted by water to a great extent (Huxman et al., 2004). Due to the drought caused by climate warming, a decline in productivity is common (Beck and Goetz, 2011; Buermann et al., 2013). Water stress caused by drought was the main reason for the decrease in plant productivity in the forests of northern Canada (Ma et al., 2012). Massive drought reduced global NPP by 0.55 thousand megagrams in 2000−2009 (Zhao and Running, 2010). Among the five most severe droughts in China from 1982 to 2015, the NPP was reduced by approximately 30% in most areas (Lai et al., 2018). Monitoring the spatiotemporal dynamics of dryland ecosystem structure (such as leaf area index) and functions (such as productivity and evapotranspiration (ET)) is a high-priority research topic (Smith et al., 2019).

      The Loess Plateau located in Northwest China, has low vegetation coverage and is a typically and ecologically vulnerable region (Li et al., 2010; Liu et al., 2017). This plateau has semihumid and semiarid climatic conditions; evaporation is generally higher than the actual precipitation; and vegetation is very sensitive to climate change (Liu et al., 2010; Zhang et al., 2017; Gou et al., 2018). In recent decades, global warming has dominated plant phenological changes in this region (Feng et al., 2016). Generally, temperature is considered the controlling factor of seasonal variation in vegetation growth in the Loess Plateau (Kong et al., 2018), and higher temperatures tend to promote the growth of vegetation in areas with less water. Climate change simultaneously affects the carbon sequestration capacity of the Loess Plateau ecosystem (Jiang et al., 2019). A distinct advance in the SOS has occurred in the Yellow River Basin in China; this change has a significant impact on the carbon cycle (Wang et al., 2019). The temporal and spatial changes in the phenology and productivity of semiarid ecosystems in North China have shown that drought reduces vegetation productivity (Kang et al., 2018). Warming may extend the length of the season, thereby increasing crop yields, which is significant to sparsely vegetated areas of the Loess Plateau (Liu et al., 2019b). The impact of climate change on global vegetation productivity is not consistent, especially in the arid areas of the Northern Hemisphere, and the phenology drivers for seasonal variation in net primary productivity has not been widely explored (Duveneck and Thompson, 2017; Smith et al., 2019). Therefore, a comprehensive and systematic understanding of the changes in vegetation phenology and NPP in the Loess Plateau and of the relationship between phenology and NPP is necessary. This information helps us understand and predict the global carbon cycle under climate change.

      In this paper, we analyzed the temporal and spatial changes in the long-term phenology and NPP in the Loess Plateau from 1982 to 2015 and explored the response of NPP to phenological changes during different seasons. The primary research questions are as follows: 1) what are the temporal and spatial changes in vegetation phenology and NPP? 2) what is the relationship between phenology and NPP? 3) how does climate change affect the phenological response of NPP? Exploring the characteristic pattern of vegetation growth under climate change from seasonal variation will help us better understand the role of vegetation phenology in complex dryland ecosystems and its effect on global carbon cycle changes.

    • The Loess Plateau (Fig. 1) is located in Northwest China (33°N−41°N, 100°E−114°E); it is the largest loess geomorphology in the world, with a total area of approximately 640 000 km2 (He et al., 2009). The average elevation in the Loess Plateau ranges from approximately 800 to 3000 m (Xin et al., 2007). The Loess Plateau belongs to the semihumid and semiarid continental monsoon climate region. Precipitation decreased in a stepwise pattern from southeast to northwest, with an average annual precipitation of approximately 300−600 mm. The climate in the Loess Plateau is rainy in summer and autumn and dry in winter and spring. The Loess Plateau has a large population, sparse vegetation, and serious soil erosion; thus, the prospects of the ecological environment are not optimistic.

      Figure 1.  Location and elevation of Loess Plateau, China

    • Global Inventory Modeling and Mapping Studies (GIMMS: https://ecocast.arc.nasa.gov/) normalized difference vegetation index (NDVI)3g v1 is the third-generation NDVI dataset based on the Advanced Very High-Resolution-Radiometer (AVHRR). The spatial resolution and temporal resolution of this dataset are 8 km and 15-d, respectively. In this study, GIMMS NDVI3g v1 was the source of the basic data for the production of remote sensing vegetation phenology and NPP. A 34-yr period between 1982 and 2015 was selected for this study as the GIMMS does not yet provide up-to-date data.

      The temperature and precipitation data employed in the study are obtained from the China Meteorological Data (http://data.cma.cn/) sharing network. The original data comprise daily data. The monthly average temperature and monthly precipitation hours were obtained by performing daily cumulative and average calculations. The meteorological interpolation software ANUS-PLIN4.3 was utilized to interpolate the meteorological data to obtain the raster data set with the same spatial and temporal resolution as that of the NDVI.

      The land cover classification data and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) V2 dataset were downloaded from the Geospatial Data Cloud (http://www.gscloud.cn) of the Computer Network Information Center, Chinese Academy of Sciences. Land cover classification data were applied in the NPP estimation, and a digital elevation model (DEM) was obtained by ASTER GDEM V2, which is employed as auxiliary data for spatial interpolation of meteorological data by ANUSPLIN.

    • In this article, the classic light-use efficiency (LUE) Carnegie-Ames-Stanford Approach (CASA) model (Potter et al., 1993) was applied to assess the NPP in the Loess Plateau. The CASA model converts absorbed photosynthetically active radiation (APAR) (MJ/m2) by vegetation into NPP based on the LUE (ε) (g/MJ) of vegetation. Therefore, the fundamental structure of the CASA model is the product of the APAR and LUE (ε) (Zhu et al., 2007) as follows:

      $$ NPP\left(x,t\right)=APAR\left(x,t\right)\times \varepsilon \left(x,t\right) $$ (1)

      where x is a computing unit (e.g. pixel), t is time scale (e.g. month), ε is obtained from Zhu et al. (2007); APAR is calculated as:

      $$ APAR\left(x,t\right)=SOL\left(x,t\right)\times FPAR\left(x,t\right)\times 0.5 $$ (2)
      $$ {SOL=SOL}_{0}\times \left(a+b\times \left(\frac{S}{{S}_{0}}\right)\right) $$ (3)

      where SOL0 is the daily astronomical solar radiation (MJ/(m2∙d)); S is the actual number of sunshine hours; S0 is the maximum number of sunshine hours; and a and b are the empirical coefficients: a = 0.167 74, b =0.579 15 (Song et al., 2009). A value of 0.5 represents the proportion of photosynthetically active radiation (PAR) in the total SOL. From Zhu et al. (2007); FPAR (the fraction of photosynthetically active radiation) is calculated as follows:

      $$ FPAR\left(x,t\right)=\alpha {FPAR}_{NDVI}+(1-\alpha ){FPAR}_{RVI} $$ (4)
      $$ \begin{split} {FPAR}_{NDVI\mathrm{o}\mathrm{r}RVI}\left(x,t\right)=\frac{VI\left(x,t\right)-{VI}_{i,\mathrm{m}\mathrm{i}\mathrm{n}}}{{VI}_{i,\mathrm{m}\mathrm{a}\mathrm{x}}-{VI}_{i,\mathrm{m}\mathrm{i}\mathrm{n}}}\times\\ \left({FPAR}_{\mathrm{m}\mathrm{a}\mathrm{x}}-{FPAR}_{\mathrm{m}\mathrm{i}\mathrm{n}}\right)+{FPAR}_{\mathrm{m}\mathrm{i}\mathrm{n}} \end{split}$$ (5)
      $$ RVI\left(x,t\right)=\frac{1+NDVI\left(x,t\right)}{1-NDVI\left(x,t\right)} $$ (6)

      where VI(x, t) is vegetation index (VI: refers to normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) in the article) of x pixel at time t, VIi,max, VIi,min is the maximum and minimum values of VI of the i’th vegetation type, respectively. NDVI(x,t) is NDVI of x pixel at time t, RVI(x, t) is RVI of x pixel at time t. FPARNDVI is calculated by Equation (5), FPARRVI is calculated by Equations (5) and (6), $ \alpha =0.5 $, FPARmax = 0.95 and FPARmin = 0.001.

    • The basis of extracting land surface vegetation phenology based on satellite remote sensing data is that the vegetation index time series curve constructed by the obtained long-term data correctly reflects the vegetation growth cycle. However, the original time series curve of the vegetation index is uneven and abrupt due to poor atmospheric conditions, clouds and other downsides which render the extraction of vegetation phenology more difficult. Therefore, we use the Whittaker smoother method (Eilers, 2003) to convert noisy, temporal input vegetation index data into a smooth time series to facilitate the extraction of vegetation phenology.

      The Whittaker smoother (Eilers, 2003; Atzberger and Eilers, 2011) is based on the principle of penalized least squares, and the aim of smoothing is to balance two conflicting goals: the fidelity to the time series curve and the roughness of the smooth curve. A balanced combination of the two goals is the sum (Q), and the aim of the penalized least squares for a time series curve is to minimize the value of Q (Atzberger and Eilers, 2011). The mathematical formula of Q is expressed as follows (Eilers, 2003; Atzberger and Eilers, 2011; Atkinson et al., 2012):

      $$ Q=S+\lambda R $$ (7)

      where λ is the coefficient defined by the user, which can be generally be determined using cross-validation. Referring to existing studies, we consider λ = 2 (Atkinson et al., 2012); S is the fidelity and R is the roughness, which are calculated as follows:

      $$ S=\sum _{t}{(VI\left(t\right)-{VI}^{*}\left(t\right))}^{2} $$ (8)
      $$ R=\sum _{t}{({VI}^{*}\left(t\right)-{3VI}^{*}\left(t-1\right)+{3VI}^{*}\left(t-2\right)-{VI}^{*}\left(t-3\right))}^{2} $$ (9)

      where VI(t) represents the time t value of a pixel, and VI*(t) represents the time t value of a pixel after smoothing. Equations (8) and (9) show that the final effect of the smoothing curve is controlled by λ. A larger value of λ generates a smoother result, although the curve will exhibit a larger deviation from the original time series curve. Generally, Whittaker smoother parameters are simple, convenient and easy to obtain; the running speed of this method is fast; and the smoothing effect is ideal (Eilers, 2003; Atzberger and Eilers, 2011).

      After smoothing the time series data, we extracted the phenological parameters from the smoothed time series curves. Three commonly employed metrics for the assessment of vegetation growth, namely, SOS, EOS and LOS, were extracted. Currently, many methods are applied to extract vegetation phenology based on remote sensing, vegetation index time series. The most commonly employed methods are the threshold method, maximum slope method, maximum ratio method and cumulative frequency method (Zhou et al., 2001; Piao et al., 2006; Kafaki et al., 2009; Yu et al., 2010). Considering the computational efficiency and operability, the maximum ratio method was used to extract the vegetation phenology in the study area. The maximum ratio describes the maximum change rate of the vegetation index; the formula is expressed as follows:

      $$ {NDVI}_{ratio}\left(t\right)=\frac{{NDVI}_{t+1}-{NDVI}_{t}}{{NDVI}_{t}} $$ (10)

      where NDVIratio is the maximum change rate of NDVI time series curve, t is the time sequence number (temporal resolution of 15-d). We determined the t value with the maximum NDVIratio and then applied the corresponding Julian day as the SOS. Likewise, we determined the $ t $ value with the minimum NDVIratio and applied the corresponding Julian day at t + 1 as the EOS. The LOS is equal to the EOS minus the SOS.

    • The SOS showed a gradual postponement from east to west in the Loess Plateau (Fig. 2). The SOS was the earliest in the southeast, and the average SOS was concentrated from 80−110 d, while the vegetation SOS around the Weihe River basin in the south appeared as early as 70 d. The average SOS in the western region was relatively late, with an average value of 130 d or later. Similar to the SOS, the LOS changed from east to west. The LOS in the eastern region was 160−190 d, while that in the western region was shorter, with an average LOS of 120−150 d. No distinct change in the EOS was observed, and most areas were concentrated from 260−290 d. The EOS occurred earlier in some areas of the south, with values less than 240 d.

      Figure 2.  Annual mean phenological metrics and its change trend in the Loess Plateau, China from 1982 to 2015; a−c represent the multiyear averages of the start of the growing season (SOS), the end of the growing season (EOS) and the length of the growing season (LOS); d−e represent the change trends of the SOS, EOS and LOS

      The SOS showed no significant delayed trend (P > 0.05), and the rate was 0.06 d/yr. A distinct, regional, spatial difference in the interannual variation in the SOS in the Loess Plateau. The advanced area of the SOS was concentrated in the southeastern Loess Plateau at lower latitudes and elevations, and the proportion of delayed pixels was 50.32% (significantly advanced was approximately 17.10% (P < 0.05). The average advance rate was 0.46 d/yr. The advanced area of the SOS was concentrated in the northwest at higher latitudes and elevations, and the proportion of delayed pixels was 49.68% (significantly advanced was approximately 12.89%, P < 0.05). The average delay rate was 0.64 d/yr. The EOS showed a significant delayed trend (P < 0.05), and the average delayed rate was 0.1 d/yr. Specifically, 69.24% of the total area had a delayed trend. Approximately 22.11% of the regions with significant delays in the EOS were located in the north-central part, where the precipitation increased significantly in autumn. Few regions in the Loess Plateau experienced a significant advance in the EOS, accounting for only 3.1% of the total area. The LOS showed an insignificant shortening trend (P > 0.05), but the shortening was small, with a value of 0.005 d/yr. Similar to the SOS, the LOS had an extendedtrend in the southeastern region, accounting for 51.88% of the total area, with an average extension of 0.65 d/yr. The trend of shortening was observed in northwest of the Loess Plateau, accounting for 51.88% of the total area.

      Fig. 3 shows the spatial distribution of the multiyear mean NPP in the Loess Plateau from 1982 to 2015. The average annual NPP in the past 30 years was 359.86 g/m2 C. Similar to the spatial variation in phenology, the NPP also showed a decreasing trend from the southeast to northwest. The average annual NPP in the southern region was relatively high, with some areas as high as 600 g/m2 C. The areas with low NPP were concentrated in the western part of the Loess Plateau, and the average annual NPP was lower than 150 g/m2 C in most areas. The average annual NPP in the Loess Plateau showed no significant decreasing trend (P > 0.05), and the decreasing rate was 0.22 g/(m2·yr) C. Furthermore, 57.98% of the regional NPP showed an increasing trend, and the growth rate was 1.80 g/(m2·yr) C. The NPP increased significantly, mainly in the middle of the Loess Plateau, with an area of approximately 28.83%, and the growth rate was 2.82 g/(m2·yr) C. The NPP decreased significantly, mainly in the northern and southwestern parts of the Loess Plateau, and the decreasing rate was higher, with a value of approximately 5.25 g/(m2·yr) C. In terms of the seasonal NPP variation in the Loess Plateau, the NPP mainly showed an increasing trend in spring, accounting for 60.03% of the total area, and the growth rate was 0.85 g/(m2·yr) C. The area showing a significant growth trend was approximately 41.25%, and this area was concentrated in the southeastern region of the Loess Plateau. The area with an increasing trend of NPP in summer accounted for 54.52% of the total area, but the complete trend was decreasing, and the rate was 0.28 g/(m2·yr) C. There was minimal difference between the areas that had significant increases and the areas that had significant decreases, with values of 24.04% and 19.41%, respectively. The areas with a significant increase were concentrated in the middle of the Loess Plateau, and the regional distribution was scattered. In autumn, the NPP showed a decreasing trend, and the rate was 0.11 g/(m2·yr) C. However, there was a greater area with an increasing trend, with a proportion of approximately 52.16%. The significantly increased area accounted for 21.63%, and the area was concentrated in the northwest region of the Loess Plateau.

      Figure 3.  Annual and seasonal mean net primary productivity (NPP) and its change trend in the Loess Plateau from 1982 to 2015; a−d represent the multiyear average of the annual scale of NPP, spring, summer and autumn; e−h represent the change trend of the annual scale of NPP, spring, summer and autumn

    • Generally, the SOS occurs in spring (March−May), and the EOS occurs in autumn (September−November). However, the changes in the SOS and EOS may affect the growth conditions of vegetation in summer (June−August). Therefore, this section mainly analyzed the influence of the SOS on NPP in spring and summer, the influence of the EOS on NPP in summer and autumn, and the annual influence of the LOS.

      In spring, there is a negative correlation between the SOS and the spring NPP in most parts of the Loess Plateau, accounting for 89.23% of the total area of the whole Loess Plateau region (Fig. 4). For each day, the SOS advances, and the NPP will increase by 0.28 g/m2 C. The proportion of the regional area with a significant negative correlation (P < 0.05) is approximately 47.01%, which is mainly distributed in the southeastern region of the Loess Plateau. The climatic conditions in these areas are better in spring and can better meet the hydrothermal conditions needed for the advancement of vegetation growth and development. The area with a significant positive correlation is only 0.14%. A negative correlation between the SOS and NPP, accounting for 61.67% of the whole region, was observed during summer. For each day, the SOS advances, and the NPP will increase by 0.97 g/m2 C. However, a positive correlation between the SOS and NPP during summer was observed in the northwest. With the advance of the SOS, the NPP in these areas will decrease. For the EOS, most regions (86.65%) have a positive correlation between NPP and the EOS in autumn. Each day the EOS is delayed, the NPP will increase by 1.27 g/m2 C. A total of 23.56% of the area had a significant positive correlation, and these areas were mainly distributed on the southern Loess Plateau. In summer, a negative correlation between the EOS and NPP in most areas, accounting for 74.07% of the total area, is observed. Each day, the EOS is delayed, the NPP will decrease by 1.01 g/m2 C. The area with a significant negative correlation is mainly distributed in the western and northern Loess Plateau, with an area total of approximately 21.60%. The delay of the EOS may cause an increase in the water demand of vegetation in summer, resulting in adverse effects of water stress on vegetation growth, thus reducing vegetation NPP.

      Figure 4.  Correlation analysis of phenology to seasonal net primary productivity (NPP) in the Loess Plateau from 1982 to 2015; a and b represent the correlation coefficients between the start of the growing season (SOS) and the spring NPP and between the SOS and the summer NPP, respectively; c and d represent the correlation coefficients between the end of the growing season (EOS) and the summer NPP and between the EOS and the autumn NPP, respectively; and e represents the correlation coefficient between the length of the growing season (LOS) and the annual NPP

      Due to the advance of the SOS or the delay of the EOS, it is very likely that the LOS will also be prolonged. For each day of LOS prolongation, the average annual NPP will increase by 1.39 g/m2 C in the whole Loess Plateau. The regional difference in the relationship between NPP and the LOS is distinct, and the correlation is weak. The areas with a negative correlation accounted for 47.28% of the total area of the region and were mainly distributed in the northwestern Loess Plateau, of which 6.77% showed a significant negative correlation. The prolongation of the LOS in these areas will adversely affect the accumulation of vegetation NPP. The areas with a positive correlation accounted for 52.19% and were mainly distributed in the southeastern Loess Plateau, of which 8.00% showed a significant positive correlation. The NPP of these areas increased gradually with the prolongation of the LOS.

    • This section investigated the changes in phenology and NPP with different altitude gradients in the Loess Plateau (Fig. 5). In general, the SOS, EOS, LOS and NPP fluctuated obviously with increasing altitude. Specifically, the SOS and LOS were significantly delayed and significantly shortened, respectively, with the increase in altitude (r = 0.8, P < 0.01; r = 0.95, P < 0.01). The SOS was delayed from the 95th d at low altitude (500 m) to the 122nd d at high altitude (2500 m), and the average delay rate was 0.0135 d/m. The LOS was shortened from the 171st day (500 m) to the 140th day (2500 m), and the average shortening rate was 0.155 d/m. The change trend of the EOS was slower than that of the SOS and LOS, and the average rate of advance was 0.0032 day/m (P > 0.05). The NPP showed no significant increasing trend with altitude, but the change was different in different stages. From 500−2000 m, the NPP showed a significant decreasing trend (P < 0.05). However, at altitudes greater than 2000 m, the NPP showed an obvious increasing trend with altitude (P < 0.05). This result is mainly attributed to the type of vegetation in this range of altitudes, which mainly consists of meadows with strong cold-resistant properties, generating an NPP that is higher than that of grasslands. Although the SOS was delayed due to the temperature reduction, it may increase the availability of vegetation water in summer.

      Figure 5.  Changes in phenology and net primary productivity (NPP) with altitude in the Loess Plateau from 1982 to 2015; a−d denote the changes in the start of the growing season (SOS), the end of the growing season (EOS), the length of the growing season (LOS) and net primary productivity (NPP) with altitude

    • The relationship between the growth season length and productivity is not necessarily linear (Richardson et al., 2013). The results show that as the LOS is prolonged, the NPP in the Loess Plateau will not increase. This result seems to confirm that the increased vegetation productivity offset by respiration (Richardson et al., 2010). Therefore, in the following discussion, we focus on identifying the factors that have a key role in the response of seasonal NPP to phenological changes.

    • As shown in Fig. 6, the effects of temperature on the NPP and SOS in spring from 1982 to 2015 are spatially consistent. The results showed that SOS is mainly negatively correlated with temperature in spring and the proportion of area with a significant negative correlation is 17.87% in the southeastern part of the Loess Plateau. With the increase in temperature in spring, the SOS is advanced, which is consistent with the results of previous studies (Vitasse et al., 2009; White et al., 2009). In these regions, a significant negative correlation between the SOS and NPP in spring and a significant positive correlation between NPP and temperature are observed. These results show that a warm spring causes plants to green earlier, and the increase in productivity caused by the enhancement of plant photosynthesis is greater than the consumption by respiration, causing an increase in NPP (Richardson et al., 2010). Warming may only slightly increase photosynthesis in autumn (Suni et al., 2003), and the results of this study support this statement. NPP and the EOS have weak correlations with temperature in autumn, and few areas exhibit significant correlations. In addition, the increase in summer temperatures in the study area led to the enhancement of plant respiration and transpiration. There was even a summer drought, which slowed vegetation growth and reduced NPP (Reich et al., 2016). However, the results indicate that NPP increases significantly in summer and has a significant positive correlation with air temperature in the middle of the Loess Plateau. The results reveal that these areas mainly comprise agricultural areas, and human field management activities can ensure that the vegetation in these areas has sufficient water in summer and is less affected by drought. In addition, these areas are located near the Yellow River basin and have a sufficient water source, which can effectively alleviate the drought caused by high temperatures in summer.

      Figure 6.  Correlation analysis of phenology and net primary productivity (NPP) with seasonal temperature in the Loess Plateau from 1982 to 2015; a−c represent the correlation coefficients between NPP and spring, summer and autumn temperatures, respectively; (d) represents the correlation coefficients between the start of the growing season (SOS) and spring temperature; and e−f represent the correlation coefficients between the end of the growing season (EOS) and summer and autumn temperatures, respectively

    • As shown in Fig. 7, NPP is positively correlated with precipitation in most parts of the Loess Plateau in spring, which is consistent with previous results (Kang et al., 2018). A significant negative correlation between the SOS and NPP in spring exists; these areas are mostly distributed in the southeastern Loess Plateau. With the advance of the SOS, the NPP increases in spring. Although the area where the SOS is significantly correlated with precipitation in spring is small and the distribution is scattered, the SOS in most areas has advanced. Therefore, in the Loess Plateau, where rain is as expensive as oil in spring, with an increase in precipitation, the effect of drought will be greatly alleviated,photosynthesis will be enhanced, and NPP accumulation will be positively impacted. In autumn, there was a positive correlation between the EOS and NPP, and the significant positive correlation area was approximately 40.07%. This result shows that the delayed EOS can increase NPP in autumn, which is consistent with previous results (Bao et al., 2019). In these results, the EOS and NPP showed distinct spatial heterogeneity affected by precipitation. In the southeastern Loess Plateau, the EOS and NPP were mainly negatively correlated with autumn precipitation. However, in the northwestern Loess Plateau, the EOS and NPP were mainly positively correlated with precipitation. This result may be attributed to the notion that precipitation is not the dominant factor affecting vegetation growth in the southeastern humid Loess Plateau. In contrast, the increase in precipitation causes an increase in cloud cover and a decrease in solar radiation, which weakens the intensity of photosynthesis; thus, NPP decreases in autumn. In the arid northwestern region, increased precipitation delays the dormancy of plants, giving them more time for photosynthesis and increasing NPP. These results show that the effect of precipitation on plant growth in arid ecosystems is highly uncertain (Shen et al., 2011; Du et al., 2019).

      Figure 7.  Correlation analysis of phenology and net primary productivity (NPP) with seasonal precipitation in the Loess Plateau from 1982 to 2015; a−c represent the correlation coefficients between NPP and spring, summer and autumn precipitation; d represents the correlation coefficients between the start of the growing season (SOS) and spring precipitation; and e−f represent the correlation coefficients between the end of the growing season (EOS) and summer and autumn precipitation

    • In semihumid and semiarid areas, the change in vegetation growth caused by climate change may be the result of either the change in precipitation or increase in temperature (Richardson et al., 2013). Therefore, an exploration of the coupling relationship between the effects of precipitation and temperature on vegetation growth and their interaction on vegetation is important (Zhao et al., 2017). We applied every 20 mm of average annual precipitation and 1°C of average annual temperature as a hydrothermal zone. The Loess Plateau was divided into 500 hydrothermal zones and observed changes in the SOS, EOS, LOS, NPP, temperature and precipitation (Fig. 8). Since the average annual temperature in the Loess Plateau is generally 0−15°C and the average annual precipitation is 200−800 mm, some hydrothermal zones in the figure have null values.

      Figure 8.  Changes in the trends of the start of the vegetation growing season (SOS) (a), the end of the growing season (EOS) (b), the length of the growing season (LOS) (c), net primary productivity (NPP) (d), precipitation and temperature with temperature (e) and precipitation gradients (f) in the Loess Plateau from 1982 to 2015, respectively

      As shown in Fig. 8, climatic factors had different roles in the response of NPP to phenological changes in different temperature and precipitation zones of the Loess Plateau from 1982 to 2015, with distinct spatial changes. In the hydrothermal zone, where the average annual precipitation is less than 400 mm and the average annual temperature is 0−10°C, the LOS is shortened, which decreases the NPP. With an increase in the average temperature in this zone, the advancing trend of the SOS was intensified, and in some hydrothermal zones where the annual average temperature exceeded 8°C, the advance of the SOS substantially exceeded the average. However, in the hydrothermal zone with an average annual precipitation of 200−400 mm, although the LOS increased gradually with increasing temperature, the NPP decreased. In these hydrothermal zones, although the average annual precipitation has increased significantly, the average annual temperature is also increasing. However, this change may be attributed to the improvement in hydrothermal conditions, in which the growth of plant cover is completed ahead of schedule in this area, and autotrophic respiration is enhanced, reducing the accumulation of NPP. In the hydrothermal zones, where the average annual precipitation is 400−600 mm and the average annual temperature is 8−12°C, most zones of the SOS are advanced, the change in the EOS is not clear, the LOS is prolonged, and the NPP is significantly increased. This result is consistent with other findings (Duveneck and Thompson, 2017). The growing season is longer, the increase in autotrophic respiration in summer and autumn will be offset by the higher productivity produced by the earlier SOS, and a larger annual NPP will be produced. Two different changes in the hydrothermal zone were observed where the average annual precipitation ranged from 400−600 mm and the annual temperature ranged from 4−8°C. When the average annual precipitation was 400−500 mm, the SOS was advanced, the EOS was delayed, the LOS was prolonged, and the NPP increased. On the other hand, the SOS was advanced, the EOS was delayed, and the LOS was prolonged. However, the NPP decreased in the area with an average annual precipitation of 500−600 mm. In the hydrothermal zones, where the average annual precipitation ranged from 400−600 mm and the average annual temperature is below 4°C, the NPP begins to decrease. With a gradual decrease in temperature, the SOS is delayed, the EOS is advanced and the LOS is shortened. In the hydrothermal zone, where the annual precipitation is more than 600 mm and the average annual temperature ranges from 5−12°C, the prolongation of the LOS and the increase in the NPP are most significant. In the hydrothermal zone, where the average annual temperature exceeds 12°C, the advance of the SOS and the delay of the EOS significantly prolong the LOS, but the NPP decreases significantly. This result may be attributed to the finding that high temperature generates water stress in these zones, which affects the normal physiological process of plants and produces a large amount of material consumption. These characteristics indicate the respon-se of vegetation NPP to phenology in different hydrothermal zones of the Loess Plateau and reflect the profound impact of climate change on plant growth.

    • This paper used remote sensing data to estimate vegetation phenology and NPP, which expands the field of vision of traditional ground observations. However, due to the extremely complex changes in the ecosystem, some uncertainties exist in the results. The first uncertainty is the uncertainty of the NPP estimation methods, although the CASA model has been maturely applied to estimate NPP in many regions of the world (Girardin et al., 2016; Li et al., 2018; Liu et al., 2019a). However, due to the characteristics of the model and the differences in the resolution and quality of the data involved, there are differences between the estimated results, representing one of the difficulties in current regional NPP modeling research (Xie et al., 2014). Due to the lack of sufficient station data for NPP and phenological surface observations in the Loess Plateau, it is difficult to fully evaluate the accuracy of the results. The phenological phase observed by the landscape scale method based on remote sensing may be different from that observed at the species level (White et al., 2009; Jeong et al., 2013). Second, this paper do not consider the effects of other factors, such as low temperature in winter, drought, photoperiod, and nutrients, on NPP and phenology (Chen et al., 2017). In the process of determining plant growth and development, the interaction of these factors is still not very clear, and not determine the relative contribution (Piao et al., 2019). Last, root phenology was not considered in this paper, and root yield accounted for 33% of terrestrial NPP (Abramoff and Finzi, 2015). This omission may have a great impact on the results, and we need to further understand the carbon cycle of the global ecosystem from both the aboveground dimensions and underground dimensions in the future.

    • Maximum ratio phenological extraction method and CASA model were utilized based on the long-term series data of GIMMS NDVI, the dynamic spatial changes in vegetation phenology and NPP in the Loess Plateau were investigated, and the response characteristics of NPP to phenology under the influence of global climate change over many years were analyzed. This research showed that global warming from 1982−2015 led to an advance in the SOS in spring, resulting in a significant increase in NPP in the southeastern Loess Plateau. In the northwestern region, the increase in precipitation delayed the EOS and increased the NPP in autumn. In different hydrothermal zones, the response characteristics of NPP to phenology were more complex. The increase in temperature caused a decrease in NPP in the northwestern region with less precipitation due to the longer growing season. However, the NPP increased in the southeastern region in study area, where the weather was moist. This article complements the seasonal phenological theory of vegetation productivity changes in different climates. Exploring the characteristic pattern of vegetation growth under climate change from seasonal variation will help us better understand the role of vegetation phenology in complex dryland ecosystems and its effect on global carbon cycle changes. The results will help to predict and assess the feedback of the global vegetation carbon cycle to climate change.

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