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

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 form 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 pre-season 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.     

祁连山区植被物候遥感监测与变化趋势

基于1982-2006年GIMMS NDVI时间序列数据,利用Double Logistic拟合方法提取了祁连山区植被的生长季始期、生长季末期和生长季长度参数,分析了植被物候期的时间变化趋势及空间分异特征。结果表明:祁连山植被从东南向西北逐渐变绿,而从西北到东南逐渐变黄,植被生长季呈现出东南地区比西北地区长、河谷地区比高山地区长的特征。25年内植被年生长季始期呈提前趋势,提前幅度为0.044 d·a^-1,年代趋势为延迟-提前-延迟;年生长季末期也呈提前趋势,提前幅度为0.059 d·a^-1,年代趋势为延迟-提前;生长季长度略有缩短,缩短幅度为0.015 d·a^-1,年代趋势为缩短-延长-缩短。25年内祁连山区植被生长季始期、末期提前不明显的区域主要为高山地区,分别占51.46%、42.77%;生长季始期、末期推迟不明显区域主要为河谷地区,分别占44.41%、52.91%;植被生长季高山地区延长不明显,河谷地区缩短不明显,总体上植被物候没有出现明显变化。     

Analyzing vegetation dynamic trend on the Mongolian Plateau based on the Hurst exponent and influencing factors from 1982–2013

This study analyzed the spatial and temporal variations in the Normalized Difference Vegetation Index (NDVI) on the Mongolian Plateau from 1982–2013 using Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g data and explored the effects of climate factors and human activities on vegetation. The results indicate that NDVI has slight upward trend in the Mongolian Plateau over the last 32 years. The area in which NDVI increased was much larger than that in which it decreased. Increased NDVI was primarily distributed in the southern part of the plateau, especially in the agro-pastoral ecotone of Inner Mongolia. Improvement in the vegetative cover is predicted for a larger area compared to that in which degradation is predicted based on Hurst exponent analysis. The NDVI-indicated vegetation growth in the Mongolian Plateau is a combined result of climate variations and human activities. Specifically, the precipitation has been the dominant factor and the recent human effort in protecting the ecological environments has left readily detectable imprints in the NDVI data series.     

中国第二、三级阶梯地形过渡带山前植被物候时空变化探析

基于1982—2015年GIMMS NDVI 3g V1.0数据、3小时温度、逐日降水和日太阳辐射数据集、数字高程模型、中国植被区划数据及实测物候验证数据,利用季节性植被物候提取法、Theil-Sen median趋势分析法和偏最小二乘回归分析等方法,研究中国第二、三级阶梯地形过渡带植被物候的时空变化规律,探讨植被物候对海拔、经纬度和气候变化的响应。结果表明：① 34 a间过渡带山前植被物候时空变化显著。时间上,植被物候呈返青期（Start Of Season, SOS）提前（-0.3187 d/a, p<0.01）、枯黄期（End Of Season, EOS）推迟（0.1171 d/a, p>0.1）和生长季长度（Length Of Growing Season, LOS）延长（0.4358 d/a, p<0.01）趋势;空间上,按SOS像元的86.24%提前、EOS像元的69.66%推迟和LOS像元的84.42%延长分布。② 34 a间过渡带山前植被物候地带性特征明显。垂直地带性方面,在中低纬度地区的物候始末期受以400 m等高线为界的海拔梯度影响,由平原到山地产生SOS平均提前8d,EOS提前25~36 d的分段式变化;水平地带性方面,低纬度和中高纬度地区的植被物候以35°N（秦岭-淮河一线,中国南北方的分界线）、43.5°N（暖温带落叶阔叶林区与温带草原区的分界）为转折点,由南向北SOS以-0.78 d/°、4.89 d/°和-1.56 d/°分段变化,EOS以-3.96 d/°、-1.85 d/°和0.89 d/°分段变化。③ 34 a间过渡带植被物候受气象因素驱动。对于植被返青期,气温对中纬度地区SOS的影响最大,降水的贡献随着纬度的降低而增大,太阳辐射在中纬度地区的贡献力大于低纬度地区;对于植被枯黄期,中纬度地区对EOS的多因素贡献力为太阳辐射>气温>降水（太阳辐射对草原区无贡献力）,低纬度地区贡献力排序与之相反;本研究对宏观地理带中不同植被区划的物候变化认知有学术意义,也为地理因素与气候因素共同影响的植被物候变化提供了新的认识。