植被物候是陆地生态系统响应气候变化的敏感指标。因此，准确监测全球及区域尺度的植被物候动态对理解气候—生物圈的相互作用具有深远意义。本研究以毛乌素沙地为研究区，利用GIMMS NDVI3g、MOD13Q1数据，通过对比STNLFFM、ESTARFM、FSDAF三种时空数据融合算法，选择STNLFFM融合算法将两种不同传感器数据进行融合，形成长时间较高分辨率的NDVI时间序列数据，进一步对比S-G、D-L、AG三种拟合重构方法，最终选择AG拟合法提取生长季始期（Start of Growing Season，SOS）、生长季末期（End of Growing Season，EOS）及生长季长度（Length of Growing season，LOS）三个关键物候参数，分析研究区植被物候时空变化特征，厘清关键物候参数对气候变化的时空响应，揭示植被物候变化的驱动因素。研究结论如下： （1）三种时空数据融合结果显示，在空间异质性较强的区域，ESTARFM、FSDAF算法融合精度整体优于STNLFFM算法；在同质性较强的区域，STNLFFM算法融合精度相对高于其他两个算法。另外，STNLFFM对不同季节、不同年份的时空融合结果差异不大且受时空差异影响更小。总体上来说基于非局部滤波融合算法（STNLFFM）的融合精度整体最高，ESTARFM、FSDAF算法次之。因此选取STNLFFM方法对GIMMS NDVI与MODIS NDVI数据进行融合。 （2）对比三种物候拟合重构方法发现，S-G滤波法、AG拟合法、D-L拟合法都对噪声进行了不同程度的去除，拟合后的NDVI时序曲线较初始的NDVI时序曲线质量均有提高。其中AG拟合方法和D-L拟合方法对时序曲线的光滑处理较好，S-G滤波法更接近原始曲线，但对噪声的去除效果不显著。AG拟合方法在全年以及生长季内与原始曲线的相关性最好，更适于毛乌素沙地植被NDVI时序曲线拟合重构。 （3）对毛乌素沙地植被物候时空变化研究表明，植被SOS主要集中在一年中的第130-150天，EOS主要集中在第300-320天，GSL主要集中在150-160天。植被SOS在空间上具有明显的海拔差异，海拔较低的地区植被最先进入生长季，海拔较高的地区植被进入生长季较晚。植被EOS在空间上具有明显的南北差异，研究区北部的植被生长季结束较早，一般在9月下旬至10月初，研究区南部植被生长季结束一般在10月下旬至11月初。植被GSL受植被SOS与植被EOS的共同影响，在海拔较低的地区的神木市、榆阳区植被生长季最长，在海拔较高的地区的伊金霍洛旗、鄂托克旗、灵武县、定边县、靖边县等地植被生长季最短。毛乌素沙地植被物候也表现出明显的时间分段特征，植被SOS在1982-1999年间表现为推迟趋势，1999-2008年间表现为提前趋势，2008-2019年间提前趋势有所减缓。植被EOS在1982-1995年间表现为提前趋势，1995-2004年间提前趋势加快，2004-2019年间表现为推迟趋势。毛乌素沙地植被物候变化具有明显的海拔依赖性，随海拔升高植被SOS推迟，GSL缩短，在海拔较低的地区植被SOS提前趋势和植被GSL延长趋势最为显著，植被EOS变化不显著。 （4）毛乌素沙地植被物候对气候变化的响应分析结果显示，植被物候与气温降水的相关性显著。不同月份的降水量对植被SOS的影响不同，植被SOS与春季季前温度、春季季前降水表现出显著的相关性。植被EOS与秋季季前温度、秋季季前降水以及9月份降水表现出显著的相关性。其中植被EOS与9月份降水量相关性最强，并具有一定的滞后性。海拔较低的地区对气温响应敏感，气温的升高有利于植物避免霜冻。在海拔较高的区域的植被物候期对气温和降水变化响应都显著。降水不足时，气温的单方面升高加剧蒸发而致水分流失，导致植被SOS推迟和植被EOS提前，降水显著增加时，这些地区水热组合条件得到改善，从而使得植被SOS提前，植被EOS推迟。

Vegetation phenology is a sensitive index for terrestrial ecosystem of respond to climate change. Therefore, accurate monitoring of vegetation phenology on global and regional scales is of far-reaching significance for understanding the interaction between climate and biosphere. However, various NDVI data sets used to monitor vegetation phenology have shortcomings in time and space resolution. It will be very beneficial to study vegetation phenology to fuse different sensor data and combine the advantages of each data set. Therefore, this study takes Maowusu sandy land as the research area, uses GIMMS NDVI3g and MOD13Q1 data, compares STNLFFM, ESTARFM and FSDAF three time-space data fusion algorithms, and selects STNLFFM fusion algorithm to fuse two different sensor data to form NDVI time series data with high resolution for a long time. Further comparing the three fitting and reconstruction methods of S-G, D-L and AG, finally choosing AG fitting method to denoise NDVI data, extracting three phenological parameters: Start of Growing Season (SOS), End of Growing Season (EOS) and Length of Growing season (LOS), and analyzing the temporal and spatial variation characteristics of vegetation phenology. The research conclusions are as follows:

(1)The results of three fusion methods show that the fusion accuracy of ESTARFM and FSDAF is better than the STNLFFM in the heterogeneity region.The fusion accuracy of STNLFFM is relatively higher than the other two methods in the homogeneity region. In addition, STNLFFM's spatio-temporal fusion results in different seasons and years have little difference and are less affected by spatio-temporal differences. Generally speaking, the fusion accuracy based on non-local filtering fusion algorithm (STNLFFM) is the highest, followed by ESTARFM and FSDAF. Therefore, STNLFFM method is selected to fuse GIMMS NDVI and MODIS NDVI data.

(2)Compared with the initial NDVI time series curve, the quality of NDVI time series curve fitted by three phenological fitting reconstruction methods, namely S-G filtering method, AG fitting method and D-L fitting method, is improved. All the noise has been removed in different degrees, among which AG fitting method and D-L fitting method are better for smoothing the time series curve, and S-G filtering method is closer to the original curve, but the noise removal effect is not significant. AG fitting method has the best correlation with the original curve in the whole year and growing season, and is more suitable for fitting and reconstructing the vegetation NDVI time series curve in Mu Us sandy land.

(3)SOS of vegetation in Mu Us Sandy Land is mainly concentrated in the 130th-15th day of a year, EOS is mainly concentrated in the 300th-30th day and GSL is mainly concentrated in the 150-16th day. There are obvious altitude differences in vegetation phenology space. The vegetation in low altitude areas of Mu Us Sandy Land enters the growing season first, while the vegetation in high altitude areas enters the growing season later. EOS has obvious difference between north and south in space. The growing season of vegetation in the northern part of the study area ended earlier, generally from late September to early October, and the growing season of vegetation in the southern part of the study area ended from late October to early November. GSL is influenced by vegetation SOS and vegetation EOS, with the longest growing season in Shenmu and Yulin at low altitude in Maowusu Sandy Land, and the shortest growing season in Ejinhoro Banner, Otog Banner, Lingwu, Dingbian and Jingbian at high altitude. The reason may be that there are many kinds of vegetation on the edge of sandy land or in low-lying areas. The hillside vegetation at high altitude has single species and weak growth ability, and its growth and development are sensitive to hydrothermal conditions and climate change. The phenological change trend of vegetation in Mu Us Sandy Land also has obvious segmentation characteristics. From 1982 to 1999, vegetation SOS showed a delayed trend, from 1999 to 2008, it showed an advanced trend, and from 2008 to 2019, the advanced trend slowed down. EOS showed an advance trend in 1982-1995, accelerated in 1995-2004 and delayed in 2004-2019. The change trend of vegetation phenology in Mu Us Sandy Land is obviously dependent on altitude. With the increase of altitude, the beginning of the growing season of vegetation is delayed and the length of growing season is shortened. In low altitude areas, the SOS trend of vegetation is the most significant, the GSL trend of vegetation is the most significant, and the EOS change of vegetation is not significant.

(4) The response analysis of vegetation phenology to climate change in Mu Us Sandy Land shows that there is a significant correlation between vegetation phenology and temperature and precipitation. Different monthly precipitation has different effects on SOS, and SOS is significantly correlated with spring pre-season temperature and spring pre-season precipitation. EOS is significantly correlated with autumn pre-season temperature, pre-season precipitation and September precipitation. EOS has the strongest correlation with precipitation in September, and has a certain lag. The lower altitude area is sensitive to the temperature, and the rising of the temperature is beneficial for plants to avoid frost. Vegetation phenology in higher altitude areas responded significantly to changes in temperature and precipitation. When the precipitation is insufficient, the unilateral increase of temperature intensifies evaporation, resulting in water loss, which leads to the delay of SOS and the advance of EOS. When the precipitation increases significantly, the combined conditions of water and heat in these areas are improved, which leads to the delay of SOS and EOS.

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