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Spatial heterogeneity in vegetation greenness (VG) can influence earth surface process and resource ecology. However, its long-term change and driving forces remain poorly understood. In this study, MODIS enhanced vegetation index (EVI) data was used to examine the change in spatial heterogeneity in VG and its relationships with vegetation greening in mainland China during 2000–2017. Moving window standard deviation and range of growing season mean EVI (GSEVI) were used as proxies for spatial heterogeneity in VG. It was found that moving window standard deviation of GSEVI increased significantly over 33.8% to 53.7% of mainland China during 2000–2017, while it decreased significantly over less than 5% of mainland China. The results of moving window range of GSEVI were similar to moving window standard deviation of GSEVI. These may be explained by: (1) increased standard deviation and range of GSEVI accompanied by increased GSEVI value; and (2) faster greening speed in dense than in sparse vegetated areas. Additionally, the increased spatial heterogeneity in VG means the increased difference in VG between arid and humid regions, and between urban cores (UCs) and rural areas. These may primarily be attributed to slower greening speed in arid than in humid regions, and in UCs than in rural areas. Thus some benefits from vegetation greening may be much less in arid than in humid regions, and in UCs than in rural areas. Overall, this study analyzed an interesting phenomenon that vegetation greening may increase spatial heterogeneity in VG, which has a series of implications for environment and human activities.

Separating anthropogenic contributions from observed vegetation change is helpful for improving our understanding of the effects of human activities on regional ecosystems. In this study, using 1982–2015 GIMMS3g normal difference vegetation index (observed NDVI), monthly climatic variables and land use data, we investigated anthropogenic contributions on vegetation change in the farming-pastoral ecotone of northern China (FPEN). Specifically, given that large-area ecological engineering was practiced since 1999 and large-area land use changes were recorded after 2000, we assumed that human activities only had little impact before 1999. Based on this assumption, we developed a climate-based NDVI model (also termed as TPR-based NDVI model) by using 1982–1999 observed NDVI and assembled monthly precipitation, temperature and solar radiation. Subsequently, the TPR-based NDVI model as well as the residual analysis method were used to separate anthropogenic contributions from observed NDVI in the period of 2000–2015. Results showed that most FPEN performed a greening trend for the period of 1982–2015. Yet a browning trend was also found in central and northern FPEN. The browning trend was largely related to changes of observed NDVI after 2000. Spatial statistics for the best related climatic variable with observed NDVI displayed that temperature, precipitation and solar radiation separately accounted for 42.45%, 31.05% and 26.50% of FPEN, implying their similar importance for vegetation growth in space. Importantly, this study found that anthropogenic contributions dominated trends of observed NDVI over FPEN. Human activities significantly increased NDVI in western and southeastern FPEN (p

Frequent drought events are threatening regional/global food security. However, current models for predicting crop yield loss suffered from drought threat is still limited. In this study, seven drought indices were employed to develop an aggregate drought index (ADI) to quantify the drought effect on crop yield in the North China Plain (NCP). Based on time-series and panel regression models, relationships between climatic yield anomalies of winter wheat (ΔY) and ADI values before sowing of winter wheat and during its growth were investigated. Results showed that ADI assembled the advantages of drought indices and reasonably described the range, severity and duration of drought events. During the study period, trends of annual ADI displayed remarkable spatial heterogeneity in the growth stages. Increasing trends of ADI over 0.005/year were found in most areas of NCP at STAGE 1 (from July to September in last year). At STAGE 4 (from grain-filling to maturation), increasing and decreasing trends were observed in northern and southern NCP, respectively. Based on panel model, ADI in the four stages could predict about 20% variations of ΔY in areas with high irrigation supplies. Meanwhile, more than 40% variations of ΔY may be predicted in eastern NCP with low irrigation supplies. The study highlights the importance of integrating drought information and irrigation for assessment of winter wheat yield variations.

Rural development degree (RDD) evaluation is a very valuable guidance for rural sustainable development. Earlier studies of RDD evaluatons more focusd on establishing index system, analysing spatial–temporal evolving patterns and functional differentiations. Weighting assignment (WA) method selection is a vital step for rural development degree (RDD) evaluation, but impacts of different WA methods on indicator weight determination and RDD evaluation were not well clarified. This study therefore employed three dominant WA methods, covering equal weight method, entropy method and mean square error method, along with a developed RDD evaluation index system to compare the differences of indicator weights and uncertainties of RDD evaluation. The results indicated that the spatial patterns of three WA-based RDD maps had great differences although using the same evaluation indicators. The RDD types with the largest proportion generated from different WA methods were also spatially various. Spatial distributions of RDD generated by various WA methods regions performed largely differences in central, northeastern and southwestern China. Our analyses found that the differences from industrial prosperity dimension and ecology livability dimension owing to utilizing different WA methods were largely responsible for the RDD spatial distributions in this study. This study gave some potential suggestions for WA method selection in RDD evaluation according to the data characteristics, WA method principles and application requirements. Among them, entropy method was suitable for indicator data with great dispersion degree and mean square error method would be better describe indicator differences with large indicator number. Besides, this study also underlined that WA-derived uncertainties should be paid more attentions in rural development and rural revitalization evaluation.

Advances in the timing of spring green-up date are a typical response of vegetation global change. Long-term observations of plant phenology have been used to track vegetation response to climate change. Normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) have been the most commonly used indicators in reconstructing spring green-up dates from remote sensing over the past several decades. The Moderate Resolution Imaging Spectroradiometer (MODIS) based phenology time series from NDVI and EVI with an enhanced TIMESAT algorithm are the two operational phenology products developed recently with sun-sensor geometry corrected reflectance. However, a comprehensive intercomparison and evaluation of these two spring green-up datasets using intensive ground observations have not been conducted, limiting their applications in regional interpretation of land surface phenology. Therefore, we used 455 ground observations from USA National Phenology Network (NPN) and 106 observations from 21 AmeriFlux sites to assess and validate the spring green-up dates for United States for 2000–2013. Our results indicate that the spring green-up dates from NDVI and EVI showed a good agreement in eastern United States, while substantial differences (70 days) were found in western and southern areas. Ground observations generally had a significant correlation (p < 0.01) with the two MODIS spring green-up dates, especially for deciduous broadleaf forests with root mean square error (RMSE) of 12 days and 16 days when compared with USA-NPN and AmeriFlux observations, respectively. Spring green-up dates from EVI overall showed a better relationship and a lower RMSE in reference to both USA-NPN and AmeriFlux observations than NDVI-based spring green-up dates. Our results highlight the importance of a rigorous validation of remote sensing products to better understand their limitations in operational applications.

Climate conditions are the major driving factors of vegetation phenology. However, there is limited effort to monitor dynamics of vegetation phenology and its responses to climate change and urbanization. Using NDVI data from 1982 to 2015, this study investigated the spatiotemporal change of spring green-up date (GUD) across the Yellow River Basin (YRB) and estimated the possible effects of different climatic factors on it. Additionally, the urban-rural differences in GUD and its linkage to spring land surface temperature (LST) for YRB’s major cities over 2001–2015 were investigated. The results showed that the GUD significantly advanced at a rate of 0.42 days yr−1 and delayed spatially from southeast to northwest. The interannual variations in regionally averaged GUD were driven mainly by preseason min-temperature (Tmin). Spatially, the effect of preseason Tmin was strongest in the central and western region. The confounding effects of preseason maximum temperature (Tmax) and precipitation jointly affected the GUD, while insolation had a weak impact on GUD. Moreover, the sensitivity of GUD to preseason Tmin and precipitation weakened with an increasing mean annual preseason precipitation gradient, but the sensitivity to preseason Tmax was gradually enhanced. Furthermore, the difference in the GUD between urban and rural areas presented a significant logarithmic relationship with the distance away from the urban center, and it was strongly related to the regional LST. Our findings confirmed the importance of Tmin and urbanization in regulating changes in GUD and further suggested that LST should be considered to develop an improved model of GUD under future climate change and urban development.