The data used in this study are shown in Table 1. MODIS (Moderate-Resolution Imaging Spectroradiometer) is a new generation of “atlas-integrated” optical remote sensing instruments. Its data products help us to understand in depth the global dynamics of land, ocean, and lower atmosphere (Justice et al., 2002). GLDAS (Global Land Data Assimilation System) can generate the best land surface states and flux fields for global implementation at high resolution (Rodell et al., 2004). TerraClimate is a monthly climate dataset for the global land surface that combines the WorldClim dataset with CRU Ts4.0 and JRA55 to create a high spatial resolution dataset covering a wider range of time records (Abatzoglou et al., 2018). The AVHRR evapotranspiration product uses satellite remote sensing data combined with atmospheric reanalysis products and daily ground-based meteorological data to produce a global terrestrial evapotranspiration dataset (Zhang et al., 2010). The SRTM (Shuttle Radar Topography Mission) data were jointly measured by NASA and the National Mapping Agency (NIMA) of the Department of Defense. They used the C-band in the SAR method to collect 80% of the global terrestrial topography (Farr et al., 2007). Some of the temperature and precipitation data are downscaled over China using the Delta Spatial Downscaling scheme based on the global 0.5° climate dataset published by CRU and the global high-resolution climate dataset published by WorldClim (Ding and Peng, 2020).

From 2001 to 2018, the annual average NPP of vegetation in the NAR largely showed a spatial pattern of high in the north, low in the south, high in the west, and low in the east. The Ili Valley, Tianshan Mountains, Altay Mountains, and Qilian Mountains were the high value areas of Nav NPP, with values ranging from 343.09 to 711.29 g C·m⁻²·a⁻¹. The Arv high value areas were relatively small in distribution and were located mainly in the Ili Valley and Qilian Mountains, with Arv NPP values ranging from 300 to 542.02 g C·m⁻²·a⁻¹. The southern part of the NAR was the low value area of Nav NPP, with NPP values mainly in the range of 40 g C·m⁻²·a⁻¹. Arv NPP low value areas were primarily in the desert-oasis transition zone in southern Xinjiang, with NPP values mostly below 42.51 g C·m⁻²·a⁻¹ (Figure 4A).

The areas of significant increase in NPP were widely distributed, with Arv clustered around oases and Nav occurring in mountainous areas (Figure 4B). In contrast, the mountain-oasis interface and the oasis-desert transition zone were the main areas of decrease in Nav and Arv, respectively. Climate change can explain 36.38 and 25.70% of the variations in NPP of natural and natural vegetation in the NAR, respectively. Meanwhile, precipitation was the dominant climate factor affecting variations in vegetation NPP, whereas temperature played a major role only in 4.04% (Nav) and 5.89% (Nav) of the area, respectively (Figure 4C).

To better understand the future trends of vegetation NPP in the NAR, this study conducted an overlay analysis of the vegetation NPP Hurst index and interannual variation trends, which were divided into five classes (Figure 4D). Except for areas with insignificant changes, continuous improvement and improvement to degradation were distributed throughout the study area and had the largest area share. The areas of continuous degradation and degradation to improvement as well as their distributions were roughly equivalent, but the distribution of degradation to improvement was more concentrated in the northern border. In general, the development trend of the Nav improvement area in the NAR is consistent with that of Arv, mainly improvement to degradation. However, the degraded area in the future period is expected to be mainly degradation to improvement in Nav and continuous degradation in Arv (Tables 3 and 4).

During the study period, vegetation WUE and NPP exhibited similar spatial distributions. The mountainous regions of northern Xinjiang were the high value areas of Nav WUE, with multi-year mean WUE values generally above 1.29 g C·mm⁻¹·a⁻². The low value areas were mainly distributed around the Kunlun Mountains, with multi-year mean WUE values mostly below 0.40 g C·mm^-1·a⁻². The oasis center was the high value area of Arv WUE, while the low value area was mainly located at the desert edge, showing a multi-year mean WUE value below 0.35 g C·mm⁻¹·a⁻² (Figure 5A). The long-term trend of Nav WUE change compared to Nav NPP shows some similarities. Areas of insignificant change in Nav NPP were the predominant type and the largest area (59.23%), while areas of increase and decrease in Nav WUE were 39 and 1.77%, respectively. Arv WUE and Arv NPP changed in the same direction with the largest area of growth covering 47.96%, followed by areas of insignificant change and decrease covering 47.90 and 4.14%, respectively. Overall, WUE changes were similar to NPP changes in northern and southern NAR, but there were some regions with different spatial trends. Nav WUE in northeastern NAR and northern Tarim Basin showed a more pronounced upward and downward trend than NPP, while WUE changes in the Qilian Mountains were not as pronounced as NPP changes in the eastern NAR region (Nav NPP). The trends in WUE and NPP suggest that the two indices respond differently to the environmental factors that cause them to change (Figure 5B).

Whether in reference to Nav WUE or Arv WUE, climate change explains less of the change in WUE dynamics than does NPP. Precipitation was the dominant climatic factor affecting the change in vegetation WUE in the NAR (Nav: 17.37%, Arv: 13.91%). Furthermore, the dominant role of precipitation is obvious in the Qilian Mountains. Meanwhile, temperature had less influence on vegetation WUE in the NAR (Nav. 5.89%, Arv: 2.09%), but dominated the change in WUE in southern Xinjiang (Figure 5C). Future trends in WUE are more pronounced than for NPP, but future trends in Nav WUE are consistent with those for Nav NPP. Arv WUE shows the opposite trend, with areas of improved Arv WUE dominated by sustained improvement (41.83%) and degraded areas dominated by sustained degradation (10.66%; Figure 5D).

From the spatial dynamics of NPP-WUE, the Nav degradation phase of the NAR was dominated by late degradation, while the restoration phase was dominated by early restoration. It is worth noting that the area of early restoration was much larger than that of late degradation (Figure 6). Grasslands, shrubs, and forests showed the same trend, with late degradation and early restoration as their main contributors. Meanwhile, the percentage of early degradation and late restoration was small. Unlike other vegetation types, the restoration phase was evident in wetlands, especially the late restoration phase. Compared to Nav, Arv showed a similar trend of predominant restoration, but with a larger area of late degradation (Figure 7).

In terms of spatial distribution, the Nav restoration area was widely distributed, mainly in the central, southern, and northeastern parts of the NAR, but there was relatively concentrated distribution in the mountainous areas. Degraded areas, on the other hand, were sporadically distributed and occurred mostly in the mountain-oasis interface. The degradation phenomenon was most prominent in the Ili Valley. Meanwhile, the oasis located in the northern and southern portions of the Tianshan Mountains was the main restoration area of Arv, with degraded areas distributed in the oasis-desert transition zone in a dotted pattern. Interestingly, the oasis areas with high anthropogenic disturbance were not only the main areas of vegetation restoration, but also hot spots for serious vegetation degradation. The degradation may be related to human negligence in management and/or the cultivation area exceeding the local resource carrying capacity.

The factors affecting plant WUE are somewhat complex, with the primary influences comprising the photosynthesis of leaves, transpiration, and plant stomatal conductance. These factors are also related to the direct effects of species, life type, and intrinsic plant mechanisms, as well as the indirect effects of factors such as the external plant environment (climate, soil, etc.).

Plant transpiration (Plt) and photosynthesis (Pho) form the basis for changes in WUE. Stomata (Sto) usually act as a special channel that controls the exchange of water between air and plant body. Sto also regulate the rate of plant water consumption and carbon assimilation, which in turn has an effect on plant photosynthesis and transpiration, causing additional changes in WUE. When drought occurs, stomatal closure preferentially decreases photosynthesis, which reduces transpiration and contributes to higher plant WUE. Leaf water potential, root system, leaf nutrients (nitrogen content, etc.), specific leaf area, plant genes, and chromosome ploidy influence vegetation WUE as well (Cernusak et al., 2011; Fang et al., 2017).

WUE also varies among habitats and species. The current results of plant WUE response to climatic environment exhibit unevenness (Li et al., 2017; Du et al., 2021). As the research progresses and more plant species are studied, the main climatic factors derived vary; in some cases, the results of the same climatic environment even show opposite conclusions. Therefore, when considering the influence of climatic environment on plant WUE, the compound effect of multiple environments should be considered (Farquhar et al., 1982; Wang et al., 2010, 2019).

Factors affecting vegetation biomass and productivity can simply be divided into two categories: biotic and abiotic factors. Biotic factors include species composition and species density, while abiotic factors are those such as light, temperature, water, CO₂, and soil (Kamali et al., 2020; Koju et al., 2020). For different ecosystems, biomass and productivity will vary due to differences in their plant species, species density, etc. Site conditions include elevation, slope, slope orientation, slope position, soil thickness and soil type, which usually act synergistically with meteorological factors to influence the growth and development of vegetation and thus biomass and productivity. Anthropogenic factors are also a non-negligible aspect and have two sides to vegetation growth (Cao et al., 2020; Yang et al., 2021). Overall, while these studies have improved our understanding of vegetation NPP changes, they also reveal that these changes are the result of a combination of multiple factors (Figure 8).

The NAR is an ecologically fragile yet important construction area for soil and water conservation in China. Given its noted vulnerability, the NAR has historically received extensive research attention. From 2001 to 2018, the region was dominated by the restoration of mountains (natural vegetation) and oases (artificial vegetation), which is consistent with the results from other studies that focused on the Tianshan Mountains (Zheng et al., 2011), Altai Mountains, and oasis areas (He et al., 2021). Additionally, the vegetation restoration observed in the Qilian Mountains (Zuo et al., 2022) is in good agreement. This is also consistent with the increasing trend of WUE in the NAR (Yang et al., 2022). In contrast, vegetation degradation was found in the transition zone around the oasis (Wei et al., 2018).

Precipitation plays a key role in regulating changes in natural NPP and WUE. Accordingly, the different responses of vegetation to NPP and WUE are related to the pattern of gradually decreasing precipitation in the mountain range-oasis-desert system. Air temperature appears to be a major factor only in the western Qilian and Kunlun Mountains and the eastern part of the northern border. The increase in air temperature and precipitation well explains the restoration of vegetation in the eastern portion of Northern Xinjiang (Shen et al., 2013). Moreover, the warming trend in the super-arid region and the high intensity of anthropogenic disturbance explain the degradation trend at the edge of the oasis (Meng et al., 2020).

The restoration of artificial vegetation can be attributed to active vegetation restoration activities and advances in sustainable agricultural techniques, with arable land expansion being the main manifestation. Although there is an increasing trend in oasis cover, anthropogenic-induced water redistribution has also caused local degradation of oases in the northern region. Despite bringing positive ecological benefits for a short period, artificial vegetation saturates and fuels the expansion of artificial oases, causing the degradation of natural oases (Zhang et al., 2020). Therefore, to improve oasis stability, relevant management authorities should further control oasis size and agricultural area.

Based on the evaluation framework of NPP and WUE, this study analyzed the restoration and degradation trends of natural vegetation and artificial vegetation in the arid area of northwest China, and deepened the understanding of its change mechanism. However, there is no entry analysis of their influencing factors. In future studies, breakthroughs should be made in identifying the anthropogenic and natural factors affecting vegetation recovery and degradation and their respective weights, and in-depth studies and researches should be conducted on the attribution factors affecting vegetation recovery and degradation.