日光诱导叶绿素荧光（Solar-induced chlorophyll fluorescence，SIF）是一种有效的植被光合作用探针，能够直接反映植被对极端气候事件地响应过程。利用SIF开展植被生态对全球气候变化背景下极端干旱事件的响应研究，对区域农业生产、生态环境恢复以及水资源的规划和管理等具有重要的理论和现实意义。本文以黄土高原为研究区，以GOSIF数据表征植被光合作用，采用时空趋势分析方法，研究黄土高原2001-2020年SIF时空变化特征，利用黄土高原及周边95个气象站点计算了标准蒸散发指数（SPEI）分析了2001-2020年黄土高原干旱趋势及干旱事件特征，结合植被指数以及植被总初级生产力（GPP）探究了SIF对干旱的响应特征，得到以下结论：

（1）2001-2020年，黄土高原SIF整体呈显著增长趋势，增长趋势为0.00164W⋅m-2⋅μm-1⋅sr-1⋅a-1 （R²=0.920，P<0.001），空间上最大增长趋势率为0.00663W⋅m-2⋅μm-1⋅sr-1⋅a-1 ，增长区域主要位于山西西部区域、陕西北部区域和甘肃南部地带。植被SIF不同月份的增长趋势随着植被物候期变化有所不同，7月份SIF增长速率在所有月份中最大，为0.0442/10a（R²=0.800，P<0.001），Sen空间最大趋势率为0.218/10a，通过了MK显著性检验。不同地貌单元中，河谷平原区植被SIF最高，增长速率最快，为0.00228W⋅m-2⋅μm-1⋅sr-1⋅a-1 （R²=0.921，P<0.001），不同地貌单元SIF增长速率大小排序为：河谷平原区>丘陵沟壑区>晋豫土石山区>高原沟壑区>农业灌溉区>沙地沙漠区。不同植被类型SIF均表现出显著增长趋势，其中以草地SIF变化趋势率最大，为0.00065W⋅m-2⋅μm-1⋅sr-1⋅a-1 （R²=0.78，P<0.001），不同植被SIF增长趋势大小排序为：草地>农田>稀树草原>落叶阔叶林。

（2）2001-2020年，黄土高原SPEI整体呈不显著减缓趋势，仅在内蒙古呼包河套区、宁夏银川平原区、山西省和河南省晋豫河谷区，SPEI表现出不显著变干特征，干旱有增加的风险。不同时间尺度SPEI反映出黄土高原在2001-2010年存在干旱事件，在2003年之后干旱加剧，2005-2007年发生干旱最为严重。小波分析结果显示，2001-2008年SPEI周期变化为4~5a左右，能量主峰出现在2005年，影响时域为2003~2008年，2008年以后进行周期性变化，循环周期为2a。季节干旱表现为春旱和夏旱，月尺度干旱主要发生于1月、3月、5月、9月和12月。黄土高原2001-2020年共发生了16次干旱事件，其中2005-2007年黄土高原发生了连续长时间的干旱事件，期间共发生两次干旱事件，在两场干旱事件中，干旱面积占比均超过50%，属于大面积的区域性干旱。在所有干旱事件中，黄土高原北侧干旱发生时间较长，陕西中部、宁夏干旱时间较短，内蒙古和山西北部干旱发生次数较多。熵权-灰色关联度结果显示，降水对干旱的关联度为0.559，气温对干旱的关联度为0.288，日照时数对干旱的关联度仅为0.008，干旱影响的气象因子主要为气温和降水。

（3）SIF植被相比于传统VIs，SIF指数与干旱的相关性更强，滞后性时间更短，滞后性面积分布更少，即SIF指数具有更好的监测干旱能力。SIF相比于其他VIs，SIF与GPP之间相关性更强，在不同GPP产品间，基于SIF的GPP产品与APAR相关性更强。不同植被类型SIF对干旱的敏感性大小排序为：草地>农田>稀树草原>落叶阔叶林。典型干旱事件期间，SIF标准化距平在干旱初期阶段主要呈负距平，随着干旱程度的加剧，负距平的面积占比增加。典型干旱事件发生初期，草地和农田植被类型SIF下降幅度大于GPP下降幅度，干旱衰退时期，草地、农田、落叶落叶林和稀树草原植被类型SIF上升幅度均大于GPP上升幅度，SIF对干旱响应比GPP更敏感，其中草地对干旱的响应最为敏感，落叶阔叶林植被对干旱的响应较弱，但落叶阔叶林固碳能力较强，干旱也会影响SIF-GPP之间的线性关系，干旱发生会导致植被SIF-GPP关系的减弱，在干旱事件衰退时期相较于干旱未发生时期，SIF-GPP线性关系会加强。

Solar-induced chlorophyll fluorescence (SIF) is an effective probe for vegetation photosynthesis, which can directly reflect the response of vegetation to extreme climate events. Using SIF to study the response of vegetation ecology to extreme drought events under the background of global climate change has important theoretical and practical significance for regional agricultural production, ecological environment restoration and water resources planning and management. In this paper, the Loess Plateau was used as the research area, and the GOSIF data was used to characterize the photosynthesis of vegetation. The spatial and temporal trend analysis method was used to study the spatial and temporal variation characteristics of SIF in the Loess Plateau from 2001 to 2020. The standard evapotranspiration index (SPEI) was calculated by 95 meteorological stations in the Loess Plateau and its surrounding areas. The drought trend and drought event characteristics of the Loess Plateau from 2001 to 2020 were analyzed. The response characteristics of SIF to drought were explored by combining vegetation index and total primary productivity (GPP). The following conclusions were obtained :The main conclusions are as follows :

(1) From 2001 to 2020, the SIF of the Loess Plateau showed a significant growth trend as a whole. The growth trend was 0.00164W⋅m-2⋅μm-1⋅sr-1⋅a-1 （R²=0.920，P<0.001）, and the maximum spatial growth trend rate was 0.00663W⋅m-2⋅μm-1⋅sr-1⋅a-1 . The growth area was mainly located in the western region of Shanxi, the northern region of Shaanxi and the southern region of Gansu. The growth trend of vegetation SIF in different months varies with the change of vegetation phenology. The growth rate of SIF in July is the largest in all months, which is 0.0442/10a（R²=0.800，P<0.001）, The maximum trend rate of Sen space was 0.218 / 10a, which passed the MK significance test. Among different geomorphic units, the vegetation SIF in the valley plain area is the highest and the growth rate is the fastest, which is 0.00228W⋅m-2⋅μm-1⋅sr-1⋅a-1 （R²=0.921，P<0.001）. The order of SIF growth rate in different geomorphic units is as follows :P hilly and gully area > Shanxi and Henan soil and stone mountain area > plateau gully area > agricultural irrigation area > sandy desert area. The SIF of different vegetation types showed a significant growth trend, and the change rate of grassland SIF was the largest, which was 0.00065W⋅m-2⋅μm-1⋅sr-1⋅a-1 （R²=0.78，P<0.001）. The order of SIF growth trend of different vegetation types was grassland > farmland > savanna > deciduous broad-leaved forest.

(2) From 2001 to 2020, the SPEI of the Loess Plateau showed an insignificant slowing trend. Only in the Hubaohetao area of Inner Mongolia, Yinchuan Plain area of Ningxia, Shanxi Province and Jinyu River Valley area of Henan Province, SPEI showed no significant drying characteristics, and there was an increased risk of drought. SPEI at different time scales reflects that there were drought events in the Loess Plateau from 2001 to 2010.After 2003, the drought intensified, and the drought was the most serious from 2005 to 2007. The results of wavelet analysis show that the period of SPEI from 2001 to 2008 is about 4 ~ 5a, the main peak of energy appears in 2005, the influence time domain is from 2003 to 2008, and the period of SPEI is 2a after 2008. The seasonal drought is characterized by spring drought and summer drought, and the monthly drought mainly occurs in January, March, May, September and December. A total of 16 drought events occurred in the Loess Plateau from 2001 to 2020.Among them, there were two drought events in the Loess Plateau from 2005 to 2007.In both drought events, the drought area accounted for more than 50 %, which was a large-scale regional drought. Among all the drought events, the drought in the north of the Loess Plateau occurred for a long time, the drought in central Shaanxi and Ningxia was short, and the drought in Inner Mongolia and northern Shanxi occurred more frequently. The results of entropy weight-grey correlation degree show that the correlation degree of precipitation to drought is 0.559, the correlation degree of temperature to drought is 0.288, and the correlation degree of sunshine hours to drought is only 0.008. The meteorological factors affecting drought are mainly temperature and precipitation.

(3) Compared with traditional VIs, SIF vegetation has stronger correlation with drought, shorter lag time and less lag area distribution, that is, SIF index has better ability to monitor drought. Compared with other VIs, SIF has a stronger correlation with GPP.Among different GPP products, GPP products based on SIF have a stronger correlation with APAR. The sensitivity of SIF to drought in different vegetation types was ranked as follows : grassland > farmland > savanna > deciduous broad-leaved forest. During the typical drought event, the SIF standardized anomaly was mainly negative in the early stage of drought, and the area of negative anomaly increased with the increase of drought degree. In the early stage of typical drought events, the decrease of SIF in grassland and farmland vegetation types was greater than the decrease of GPP. During the drought recession period, the increase of SIF in grassland, farmland, deciduous deciduous deciduous forest and savanna vegetation types was greater than the increase of GPP. SIF was more sensitive to drought than GPP, and grassland was the most sensitive to drought. The response of deciduous broad-leaved forest vegetation to drought was weak, but the carbon sequestration capacity of deciduous broad-leaved forest was strong. Drought will also affect the linear relationship between SIF-GPP, and drought will lead to the weakening of vegetation SIF-GPP relationship. The SIF-GPP linear relationship will be strengthened during the recession period of drought events compared to the period when drought did not occur.

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