目前物候遥感监测研究主要基于归一化植被指数(NDVI)和增强型植被指数(EVI) (吉珍霞等, 2021; Zhang et al., 2021)。这些基于反射率的植被指数是对植被表征形态的度量(Liu et al., 2018), 往往只能反映植被的绿度信息, 仅表示植被的“潜在光合作用”, 又受积雪、土壤背景、云等影响, 探测精度受限(刘啸添等, 2018; 李艳等, 2019)。而亚热带区域多为常绿植被, 其冠层的绿度随季节变化较弱, 依据植被指数对亚热带植被物候进行反演与植被实际光合作用开始、结束时间有较大偏差(周蕾等, 2020)。因此, 当前实现对亚热带植被物候的遥感监测仍然存在较大的挑战。
洞庭湖流域位于我国亚热带季风气候区, 其生物多样性高, 生态功能显著, 良好的生态环境对自然保护、生态文明建设和社会经济发展有着重要作用。本研究以洞庭湖流域植被春季光合物候(春季光合作用开始时间)与秋季光合物候(秋季光合作用结束时间)为研究对象, 利用2000-2018年SIF遥感数据提取洞庭湖流域的植被光合物候参数, 分析洞庭湖流域植被的春季光合物候、秋季光合物候和生长季长度的时空变化特征, 并结合降水、最高气温、最低气温和辐射强度数据, 探究研究区植被光合物候对气候变化的响应规律及其潜在机制, 以期为洞庭湖流域植被光合物候的气候响应机制研究及区域生态系统碳收支评估提供科学参考。
1 材料和方法
1.1 研究区概况
洞庭湖流域是长江中下游第二大亚流域, 对防洪灌溉、调节气候和全球生物多样性保护等有极其重要的作用。作为典型的亚热带季风区, 洞庭湖流域林区较多, 是我国南方主要林区。植被多为常绿阔叶林、针阔叶混交林、草地、灌丛等。因此, 对洞庭湖流域植被的保护和研究是长江综合保护不可缺少的一部分, 对全球生态系统和碳循环具有重要意义。根据研究区的特性, 选取洞庭湖流域5种植被类型, 分别是常绿针叶林、常绿阔叶林、落叶阔叶林、灌丛和草地(图1)。
图1
1.2 数据来源与预处理
1.2.1 GOSIF数据
1.2.2 CSIF数据
在计算过程中, 用最大值合成法将CSIF数据的时间分辨率从4 d变为8 d, 以匹配GOSIF数据的时间分辨率。
1.2.3 气象数据
气象数据采用CMFD (China Meteorological Forcing Dataset)数据, 来源于国家青藏高原科学数据中心(
1.2.4 植被数据
本研究采用由欧盟联合研究中心利用2000年的空间分辨率为1 km的SPOT-4 VGT S10数据和其他地理数据组合制作的GLC2000数据。该数据采用了联合国粮农组织制定的土地覆盖分类体系(LCCS), 共22类, 中国区域部分由中国科学院遥感应用研究所承担(徐文婷等, 2005)。为进一步去除耕地, 保留自然植被, 土地利用数据采用2018年土地利用类型1 km分辨率栅格数据, 来源于中国科学院资源环境科学数据中心(
1.3 研究方法
1.3.1 物候的提取
式中, SIFratio是植被物候对应的提取阈值, SIFt是给定时间t的SIF值, SIFmax和SIFmin是SIF的最大值和最小值。在本研究中, 将阈值设置为0.5 (Wu et al., 2018)。
式中,
由于基于两种物候估算方法估算的光合物候的时空格局相似, 为了消除不同物候估算方法的影响, 本研究先对两种方法反演的不同数据源光合物候进行均值处理。为减小单个数据源带来的不确定性,采用GOSIF数据和CSIF数据提取光合物候的平均值进行分析。
1.3.2 趋势分析
植被生长季长度定义为植被秋季光合物候值减去春季光合物候值。然后采用简单线性回归计算研究区春季光合物候(即植被春季光合作用开始时间(SOP))、秋季光合物候(即植被秋季光合作用结束时间(EOP))和植被生长季长度(LOP)及各植被类型的光合物候参数的变化趋势。光合物候参数(y)与时间(x)之间的关系可用如下线性模型表示:
式中, k为变化趋势的斜率, b为未知常数, ε为误差项。求解未知参数k, 公式如下:
式中, n是样本数, i为第i个样本, y为第i个样本所对应的y值。当k > 0时, 表明光合物候参数随时间的变化趋势是推迟或延长; 当k < 0时, 表明光合物候参数随时间的变化趋势为提前或缩短; 当k = 0时, 表明光合物候参数无明显变化。
1.3.3 偏相关分析
在探究植被光合物候对降水量、最高温度、最低温度和辐射的响应规律时采用偏相关分析法。偏相关分析能够在剔除其他变量影响情况下分析两个变量之间的相关程度, 其公式表示为:
式中, z为需剔除的其他变量m, Rxy,z为控制变量z后x与y的相关系数, Rxy、Rxz、Ryz分别代表变量x和y、x和z、y和z的相关性系数。
根据本研究目的及以往的研究(Liu et al., 2016b), 用于探究植被光合物候对气候变化响应规律的气候因子的时间尺度为季前5个月(以1个月为时间尺度向前递进), 季前开始的时间采用研究区各栅格多年的均值。对于不同植被类型, 也相应地计算了其物候和气象因子的相关性。在整个分析中, 将所有数据的空间分辨率调整为0.1°, 以匹配分辨率最粗的数据。此外, 也排除了植被覆盖度低的区域及耕地, 保留了涵盖研究所选取的5种植被类型的区域。
2 结果和分析
2.1 洞庭湖流域植被光合物候的时空格局
图2
图2
2000-2018年洞庭湖流域植被光合物候的空间格局及频率分布。
Fig. 2
Spatial pattern and frequency distribution of vegetation photosynthetic phenology and growing season length in Dongting Lake basin from 2000 to 2018. EOP, the end date of photosynthesis; LOP, the length of photosynthesis; SOP, the start date of photosynthesis.
从整体来看, 2000-2018年, 洞庭湖流域SOP呈显著提前的趋势, 平均提前0.75 d·a-1; EOP呈延后的趋势(平均延迟0.17 d·a-1), 但延迟不显著; 植被生长季长度呈显著延长的趋势, 平均延长0.90 d·a-1 (图3)。从面积上看, 研究区SOP呈提前趋势的区域占整个研究区的97.2%, 其中有大约72.0%的区域呈显著提前趋势(图3A)。EOP呈延迟趋势的区域占总面积的67.4%, 呈显著延迟趋势的占26.2% (图3B)。植被生长季长度呈延长趋势的区域占整个研究区的89.0%, 呈延长趋势的区域中有62.0%的区域呈现出显著延长的趋势(图3C)。此外, 对于不同类型的植被, 其SOP和EOP的变化趋势相似(图4)。5种类型植被的SOP均呈提前的趋势, 除落叶阔叶林外, 其余4种类型植被的SOP均显著提前(>0.67 d·a-1)。而所有类型植被的EOP均呈延后的趋势, 但延迟均不显著。
图3
图3
2000-2018年洞庭湖流域植被光合物候和生长季长度变化趋势空间格局和年际变化。P代表系数为正的比例, 表示光合物候呈延后(延长)趋势; N代表系数为负的比例, 表示光合物候呈提前(缩短)趋势; 括号内为p < 0.05的统计比例。slope, 斜率。
Fig. 3
Spatial distribution patterns of the linear trend and annual variation of vegetation photosynthetic phenology and growing season length in Dongting Lake basin from 2000 to 2018. EOP, the end date of photosynthesis; LOP, the length of photosynthesis; SOP, the start date of photosynthesis. P indicated the percentage of positive coefficients, indicating that the photosynthetic phenology tends to delay (prolong); N indicated the percentage of negative coefficients, indicating that the photosynthetic phenology tends to advance (shorten); percentage of significant correlations in parentheses (p < 0.05) are provided.
图4
图4
2000-2018年洞庭湖流域各类型植被光合物候变化趋势。正值代表物候指标延后, 负值代表物候指标提前。*, p < 0.05。
Fig. 4
Linear trends of vegetation photosynthetic phenology across the biomes in Dongting Lake basin from 2000 to 2018. EOP, the end date of photosynthesis; SOP, the start date of photosynthesis. DBF, deciduous broadleaf forest; EBF, evergreen broadleaf forest; ENF, evergreen needleleaf forest. Positive values represented the delay of phenological indicators, and negative values represented the advance of phenological indicators. *, p < 0.05.
2.2 植被光合物候与气候因子的偏相关关系
基于偏相关分析的结果显示, 洞庭湖流域植被SOP与季前降水量呈正相关关系的像元较多, 占总研究区域的81.3%, 其中4.4%的区域呈显著正相关关系。与季前降水量相比, 季前最高气温、最低气温和辐射强度与SOP呈负相关关系的像元较多, 分别占研究区的72.3%、79.3%和59.4%, 其中显著负相关比例为12.3%、11.8%、5.7% (图5A)。对于EOP, 其与季前降水量大部分为正相关, 占总研究区域的69.3%, 其中显著正相关的占10.2%。与季前最低气温、季前辐射强度也呈大面积正相关关系, 分别占总研究区的63.0%和65.0%, 其中呈显著正相关关系的像元均超过8.0%。与季前降水量、最低气温和辐射强度相比, EOP与季前最高气温呈负相关关系的区域较多, 占研究区的58.1% (图5B)。
图5
图5
植被春季、秋季光合物候与季前气候因子偏相关性系数的空间格局及频率分布。P代表正相关比例, 表明气候因子的增加导致物候的延后; N代表负相关比例, 表明气候因子的增加导致物候的提前; 括号内为p < 0.05的统计比例。
Fig. 5
Spatial pattern and frequency distribution of partial correlation coefficient between the spring and autumn photosynthetic phenology of vegetation and preseason climatic factors. Pre, precipitation; Srad, radiation intensity; Tmax, maximum air temperature; Tmin, minimum air temperature. P means the proportion of positive correlation coefficients, indicating that the increase of climate factors led to the delay of phenology; N means the proportion of negative correlation coefficients, indicating that the increase of climate factors led to the advance of phenology; percentage of significant correlations in parentheses (p < 0.05) are provided. EOP, the end date of photosynthesis; LOP, the length of photosynthesis; SOP, the start date of photosynthesis.
此外, 不同植被类型的SOP与EOP对气候变化的响应存在明显差异(图6)。对于植被SOP, 除落叶阔叶林外, 其余植被类型的SOP均与季前降水量呈正相关关系, 与季前辐射强度呈负相关关系。所有植被类型的SOP均与季前最高气温、最低气温呈负相关关系。与季前降水量、最高气温和辐射强度相比, 常绿针叶林、常绿阔叶林、灌丛和草地的SOP与季前最低气温的相关性更强(偏相关系数(RP) < -0.5) (图6A)。对于EOP, 5种植被类型的EOP均与季前降水量、季前最低气温呈正相关关系。除落叶阔叶林外, 其余4种植被类型的EOP与季前辐射强度呈正相关关系, 且相比于其他3种气象因子, 常绿针叶林、灌丛和草地的EOP对季前辐射强度的响应更强(RP > 0.42)。除常绿阔叶林, 其余4种植被类型EOP与季前最高气温呈负相关关系(图6B)。
图6
图6
不同类型植被光合物候与季前气候因子的偏相关关系。*, p < 0.05; **, p < 0.01。
Fig. 6
Partial correlation coefficient between the photosynthetic phenology of different vegetation and preseason climatic factors. EOP, the end date of photosynthesis; SOP, the start date of photosynthesis. DBF, deciduous broadleaf forest; EBF, evergreen broadleaf forest; ENF, evergreen needleleaf forest. Pre, precipitation; Srad, radiation intensity; Tmax, maximum air temperature; Tmin, minimum air temperature. *, p < 0.05; **, p < 0.01.
3 讨论
3.1 洞庭湖流域植被光合物候的变化
本研究的结果表明洞庭湖流域植被的春季光合物候以0.75 d·a-1的速度显著提前, 而植被秋季光合物候呈不显著的延后趋势。多个以往的研究均揭示了植被春季物候呈现提前的趋势, 但在不同地区、时期提前的速率有所不同。比如, 欧洲4种典型木本植物的春季物候以0.33-0.75 d·a-1的速率提前(1980-2014年) (林少植等, 2021); 中国温带草原和荒漠区植被春季物候提前速率为0.14 d·a-1 (1982- 2015年) (李耀斌等, 2019)。在时间尺度上, 以往的研究还发现1982-1999年, 北美地区植被春季物候提前8 d, 中国温带地区提前14 d等(Zhou et al., 2001; Piao et al., 2006)。然而自2000年以来, 植被春季物候的提前趋势有所减缓。2000-2008年, 北半球春季物候平均提前0.2 d (Jeong et al., 2011)。这表明, 北半球春季物候整体的变化趋势是相似的, 但与其他区域相比, 中国亚热带区域植被春季光合物候具有更快的提前趋势。就不同植被类型春季光合物候而言, 落叶阔叶林春季光合物候提前的趋势并不显著, 这可能与气候因子的作用差异有关。在落叶阔叶林区域, 降水量呈现出减少的趋势, 这可能会导致落叶阔叶林春季光合物候的延迟。与降水的影响不同, 该区域辐射强度下降的趋势则会导致春季光合物候的提前(表1)。因此, 气候因子影响的相互作用可能导致落叶阔叶林春季光合物候的不显著提前趋势。此外, 在研究区植被春季光合物候的年际变化上, 2010年春季光合物候发生了较大的波动, 我们推测可能与2009年冬季的厄尔尼诺事件有关。有研究表明前期冬季的厄尔尼诺事件会使流域春季降水量增加(刘仲藜等, 2021), 而在相对潮湿的洞庭湖流域, 降水量的增加会延迟春季光合物候, 从而会导致植被春季光合物候日期较晚。
表1 落叶阔叶林区域气候因子的年际变化
Table 1
| 气候因子 Climate factor | 变化速率 Rate of change | p |
|---|---|---|
| 降水 Precipitation | -1.321 2 (mm·a-1) | 0.756 1 |
| 最高气温 Maximum air temperature | -0.080 8 (℃·a-1) | 0.603 5 |
| 最低气温 Minimum air temperature | -0.052 3 (℃·a-1) | 0.595 7 |
| 辐射强度 Radiation intensity | -1.211 8 (W·m-2·a-1) | 0.477 0 |
3.2 洞庭湖流域植被光合物候对气候变化的响应
以往的研究表明, 气温在调节植被物候方面具有关键作用(胡植等, 2021)。本研究表明在洞庭湖流域, 季前最低气温的升高会导致植被春季光合物候的显著提前。一方面, 最低气温的升高会增加夜间叶片碳水化合物的利用, 从而刺激植被的光合作用(Beier et al., 2004)。另一方面, 季前最低气温的升高会降低植被在春季受到的霜冻危害, 使得植被春季光合物候提前。而季前最低气温与植被秋季光合物候呈正相关关系。这可能是因为季前最低气温的升高能够延缓秋季叶片着色, 且减少在降水量较多地区由于夜间低温而造成的冷害(Yang et al., 2017; 萨日盖等, 2020)。与季前最低气温相比, 最高气温与植被春季光合物候、秋季光合物候均呈负偏相关关系。白天气温对植被的碳固定和能量捕获具有重要作用, 因此白天气温升高刺激植被春季光合物候开始(Piao et al., 2015)。季前最高气温升高导致植被秋季光合物候提前可以用以下两个原因解释。一是洞庭湖流域位于亚热带, 夏秋季气温相对较高, 白天气温的升高会导致进行光合作用的酶活性降低, 从而抑制植被光合作用使得植被秋季光合物候提前(Rossi et al., 2017)。另一方面, 白天气温升高使得蒸散发较高, 土壤水分利用效率降低, 从而导致秋季植被光合作用提前结束(Estiarte & Peñuelas, 2015)。
研究结果表明季前辐射的增加会导致植被春季光合物候的提前和秋季光合物候的延迟。由于气候变暖, 植被低温积累减少, 可能会推迟植被春季光合物候(李晓婷等, 2019), 而季前辐射的增加可能抵消部分冬季寒冷的不足(付永硕等, 2020), 从而导致春季光合物候提前。从夏季到秋季, 洞庭湖流域辐射量逐渐减少, 夜间长度增加, 当辐射量低于生长临界值时会导致植被叶片衰老(Wareing, 1956), 而季前辐射量的增加会维持植被的生长状态, 从而使得植被秋季光合物候延迟。本研究还显示季前降水量对春季光合物候的影响程度较大: 一方面, 可能与研究区春季的干旱化趋势有关(曹博等, 2018)。由于研究区春季呈现干旱化的趋势, 水分对植被生长的重要性加强, 这也与以往的研究一致, 即水分对植物的生长具有重要作用(Liang et al., 2019)。同时这也表明, 水分可以通过影响植被春季光合物候在潮湿地区发挥关键作用(Li et al., 2021)。另一方面, 降水量还通过影响其他气候因子(辐射、最高气温、最低气温等)进而影响植被春季光合物候。降水量的减少与云量的减少有关, 增强了太阳辐射, 也伴随着更晴朗的白天, 增加了白天地表温度, 减少了夜间向下的长波辐射, 从而影响植被春季光合物候(Wang et al., 2022)。此外, 在相对潮湿的洞庭湖区域, 降水量的增加会导致春季光合物候的延迟, 这可能为研究区中部植被生长季较短提供了解释。在研究区中部, 植被生长季较短可能与该区域植被春季光合物候日期较晚有关。该区域较为靠近河渠, 河渠带来充足的水分, 导致春季光合物候延迟, 从而导致植被生长季长度较短。就秋季光合物候而言, 季前降水量的增加会导致植被秋季光合物候延迟, 这是因为季前降水量的增加会加强植被对养分的吸收, 促进植被光合作用的效率, 从而延后植被生长季结束期(Estiarte & Peñuelas, 2015)。
综上, 采用SIF提取洞庭湖流域的植被光合物候参数, 结果表明洞庭湖流域植被春季光合物候的提前对生长季延长起主导作用。生长季的显著延长表明洞庭湖流域的碳汇效应呈现显著增加的趋势, 而春季光合物候的提前是当前碳汇增加的主导因素, 这为区域碳收支的评估提供了参考。随着全球气候的持续变暖, 未来洞庭湖流域植被春季光合物候仍可能会呈现提前的趋势, 进而导致区域碳汇效应的持续加强。
降水量、最高温度、最低温度和辐射强度对洞庭湖流域植被春季光合物候、秋季光合物候的影响不同, 这种春、秋季光合物候对气候响应机制的差异对亚热带植被物候模型的建立具有重要意义。研究区春季光合物候对气候变化的响应更为敏感, 尤其是对气温(最高气温、最低气温)的变化。而植物秋季光合物候对温度的响应并不敏感, 其中最高气温和最低气温对研究区大部分区域秋季光合物候的相反效应可能是重要原因。此外, 最新的研究也表明辐射强度对物候的温度响应敏感性会产生限制作用, 尤其是秋季光合物候的气候敏感性(Zhang et al., 2020)。这些都表明对于陆地生态系统碳收支模型的物候参数化, 不能简单依赖其与气候因子的经验关系, 应综合考虑春季光合物候、秋季光合物候对全球变化的响应差异, 以提高对亚热带植被生态系统碳收支的预测能力。
4 结论
本研究以亚热带洞庭湖流域植被为研究对象, 采用SIF遥感数据估算植被的光合物候, 分析其时空变化特征及其对气象因子的响应规律。结果表明研究区植被春季光合物候呈显著提前趋势(提前速率为0.75 d·a-1), 秋季光合物候呈不显著延迟趋势, 生长季长度呈显著延长趋势(延长速率为0.90 d·a-1)。季前最低气温和最高气温是研究区春季光合物候显著提前的主要影响因素(负相关比例分别为79.3%、72.3%), 而季前降水量、最低气温、辐射强度均促进秋季光合物候的延迟(正相关比例分别为69.3%、63.0%、65.0%)。对于不同类型的植被, 其春、秋季光合物候对气候变化的响应具有物种差异。本研究表明洞庭湖流域植被春季光合物候对气候变化的响应更为敏感, 区域的碳汇能力主要受植被春季光合物候提前的影响。这为亚热带植被光合物候的气候响应机制研究提供了新的视角, 也为亚热带植被物候模型的构建和碳收支评估提供有益参考。
致谢
感谢北京大学城市与环境学院张尧博士提供的CSIF数据, 新罕布什尔大学地球、海洋和空间研究所地球系统研究中心肖劲峰博士提供的GOSIF数据(
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近年来,全球变暖对植物春季物候期产生了显著影响。很多研究报道了中国地区木本植物春季物候期变化的时空格局,但在同处于北半球温带地区的欧洲则相关研究较少。为了增进物候变化及其对气候变化响应规律的区域对比,本研究利用欧洲地区展叶始期(1980—2014年)数据和相应的气象数据,研究欧洲七叶树、垂枝桦、欧洲山毛榉和夏栎4种典型木本植物展叶始期的时空变化格局,并识别影响物候变化的主要气候因子。结果表明: 1980—2014年,研究区4种植物的展叶始期以3.3～7.5 d·10 a<sup>-1</sup>的趋势显著提前。展叶始期自南向北以每纬度2.03～3.19 d的速率推迟,自西向东以每经度0.19～0.80 d的速率推迟(除欧洲山毛榉外),海拔自低到高以2.25～3.44 d·100 m<sup>-1</sup>的速率推迟。展叶始期的提前主要与春季温度的增高和冬春季降水量的增加有关,而秋冬季温度的升高对展叶始期有一定的推迟效应。
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Extension of the growing season increases vegetation exposure to frost
DOI:10.1038/s41467-017-02690-y [本文引用: 1]
Phenological characteristics of temperate coniferous and broad-leaved mixed forests based on multiple remote sensing vegetation indices, chlorophyll fluorescence and CO2 flux data
基于多种遥感植被指数、叶绿素荧光与CO2通量数据的温带针阔混交林物候特征对比分析
Relationship between droughts/floods throughout a year over the Dongting Lake Basin and atmospheric circulation and sea surface temperature over key sea areas
DOI:10.13284/j.cnki.rddl.003378
[本文引用: 1]
Droughts are the most common natural disasters with the most significant impact on human society. They are caused by water deficiency over extended periods. The Dongting Lake Basin is alternately controlled by winter, southwest, and southeast monsoons throughout a year, with meteorological droughts occurring each season. Additionally, the controlling factors for these droughts are distinct. The precipitation amount directly illustrates droughts and floods, affected by atmospheric circulation and water vapor conditions. Anomalies in atmospheric circulation are closely related to the evolution of Sea Surface Temperature (SST), which changes over long durations and is of significant importance for drought and flood forecasting over the basin. At present, studies that determine the linkage between SST and droughts/floods over the Dongting Lake Basin primarily show the statistical relationship between them; however, atmospheric circulation is the direct influencing factor of floods/droughts over the basin. Therefore, determining the relationship between meteorological elements and SST is conducive to revealing the mechanism of their statistical relationship. There are few studies on this research field in the Dongting Lake Basin. To determine the mechanism of the linkage between sea surface temperature and droughts/floods over Dongting Lake Basin and improve the understanding of forecast-improved factors for droughts/floods, this study analyzed the interannual evolution of droughts and floods in spring, summer, and autumn within the Dongting Lake Basin from 1960 to 2016 based on monthly precipitation data and NCEP/NCAR reanalysis data, investigated the distribution of global SST in typical drought/flood years, and studied the responses of meteorological factors (including precipitable water, sea level pressure, and wind fields at 850 hPa) to El Ni?o and Southern Oscillation (ENSO) and SST over key sea areas, using downscaling technologies and tendency analysis. Results show that in spring, the basin experienced interannual dry and wet alternations and an insignificant drying trend. In summer, droughts were slightly more severe than floods before 1990, and it was the wettest period from 1990 to the beginning of the 21st century. In autumn, the regional flood index (H) and regional drought index (G) remained almost stable, and typical drought and flood years appeared alternately. In spring, ENSO events in the preceding winter exerted a significant impact on droughts/floods in the basin. In addition, the SST over the southwest maritime continent (S1), the Masklin Islands (S2), and the Aleutian Islands (S3) all showed significant correlations with spring precipitation in the basin. These correlations last from the preceding winter to spring. The correlation between the SST at S3 and ENSO was weak. In summer, there was an insignificant statistical correlation between ENSO in the preceding winter and summer precipitation over the basin. The SST over the eastern Australian sea (S4) and the Bay of Bengal (S5) correlated with the summer precipitation in the basin from the preceding winter to summer; the SST signal over S5 was partially covered by the ENSO signal in the preceding winter. In autumn, the global SST had an approximately inverse phase compared to the typical drought and flood years of the basin. The SST anomaly in typical drought (flood) years of the basin was in the negative (positive) phase of the Indian Ocean Dipole (IOD) and La Ni?a (El Ni?o) pattern from the preceding summer to autumn; the two kinds of SST signals (IOD and ENSO) could independently affect droughts/floods in the basin. El Ni?o events in the preceding winter generated high pressures in the South China Sea and the east of the Philippines region in spring and summer, conducive to the transport of moisture from the South China Sea to the basin, resulting in greater precipitation in the basin. The high SST over the Nino3.4 region in the preceding summer also exerted a similar impact in the following autumn. When the SST at S3 was high in spring, the East Asian trough tended to be strong and westerly. In summer, a higher SST at S4 was likely to coincide with a weak East Asian summer monsoon. The mature phase of IOD in autumn was the dominant factor of droughts/floods over the basin.
洞庭湖流域各季节旱涝及其与大气环流和关键区海温的关系
DOI:10.13284/j.cnki.rddl.003378
[本文引用: 1]
利用逐月降水数据和NCEP/NCAR再分析数据，分析了洞庭湖流域春、夏、秋季57年来旱涝异常的年际变化以及典型旱涝异常年份的全球海温分布形势，并利用降尺度和趋势分析方法探究气象因子对ENSO和关键区海温的响应，以加强对流域旱涝前期影响因素的认识。结果表明：1）流域在春、秋季旱涝变化趋势不明显，在夏季较明显地变湿。2）前期冬、夏季ENSO事件分别对流域春、秋季旱涝产生显著影响，而与夏季呈不显著的统计特征。3）在消除前期ENSO信号后，阿留申群岛附近海域（S3）、澳大利亚东部海域（S4）海温和印度洋偶极子（Indian Ocean Dipole, IOD）现象仍分别为春、夏、秋季与流域旱涝有密切联系的海温因素。4）S3区SST对流域春季旱涝的影响通过西风带环流实现，S4区SST偏高似乎是东亚夏季风强度偏弱的表现，成熟的IOD现象为流域秋季旱涝的主导因子。
Mechanistic evidence for tracking the seasonality of photosynthesis with solar-induced fluorescence
DOI:10.1073/pnas.1900278116
PMID:31138693
[本文引用: 1]
Northern hemisphere evergreen forests assimilate a significant fraction of global atmospheric CO but monitoring large-scale changes in gross primary production (GPP) in these systems is challenging. Recent advances in remote sensing allow the detection of solar-induced chlorophyll fluorescence (SIF) emission from vegetation, which has been empirically linked to GPP at large spatial scales. This is particularly important in evergreen forests, where traditional remote-sensing techniques and terrestrial biosphere models fail to reproduce the seasonality of GPP. Here, we examined the mechanistic relationship between SIF retrieved from a canopy spectrometer system and GPP at a winter-dormant conifer forest, which has little seasonal variation in canopy structure, needle chlorophyll content, and absorbed light. Both SIF and GPP track each other in a consistent, dynamic fashion in response to environmental conditions. SIF and GPP are well correlated ( = 0.62-0.92) with an invariant slope over hourly to weekly timescales. Large seasonal variations in SIF yield capture changes in photoprotective pigments and photosystem II operating efficiency associated with winter acclimation, highlighting its unique ability to precisely track the seasonality of photosynthesis. Our results underscore the potential of new satellite-based SIF products (TROPOMI, OCO-2) as proxies for the timing and magnitude of GPP in evergreen forests at an unprecedented spatiotemporal resolution.
Variations in satellite-derived phenology in Chinaʼs temperate vegetation
DOI:10.1111/gcb.2006.12.issue-4 URL [本文引用: 2]
Leaf onset in the northern hemisphere triggered by daytime temperature
DOI:10.1038/ncomms7911 [本文引用: 1]
Strong controls of daily minimum temperature on the autumn photosynthetic phenology of subtropical vegetation in China
DOI:10.1186/s40663-021-00309-9 [本文引用: 1]
Climate change, phenology, and phenological control of vegetation feedbacks to the climate system
DOI:10.1016/j.agrformet.2012.09.012 URL [本文引用: 1]
Heat- induced leaf senescence associated with chlorophyll metabolism in bentgrass lines differing in heat tolerance
Variation in the vegetation fade stage and its relationships with climate and vegetation productivity in Inner Mongolia, China
内蒙古植被枯黄期变化及其与气候和植被生产力的关系
DOI:10.13287/j.1001-9332.202006.019
[本文引用: 1]
基于2001—2018年MODIS NDVI数据,采用累计归一化植被指数(NDVI)的Logistic曲线曲率极值法,识别内蒙古植被枯黄期及其时空变化特征,并在生态区尺度上分析枯黄期对气候因子和NDVI的响应特征。结果表明: 研究期间,内蒙古植被平均枯黄期主要集中在第260～280天。森林生态区枯黄期为第270～280天,从南向北推迟;草原生态区枯黄期最早,介于第257～273天,从东北向西南逐渐推迟;荒漠生态区枯黄期为第270～283天,东北向西南呈推迟态势。2001—2018年间,3个生态区植被枯黄期均呈不显著推迟趋势。植被生产力从东北向西南逐渐降低,在时间上呈增加趋势的面积大于呈减小趋势的面积。全内蒙古和各生态区植被枯黄期受季前2～3个月降水量的正面影响较大,与季前平均温度、最高温度和最低温度均呈正相关关系。全内蒙古和各生态区,8和9月植被生产力的增加(或减少)将推迟(或提前)植被枯黄期,而6和7月植被生产力的增加(或减少)将提前(或推迟)草原和荒漠生态区植被枯黄期。
Decreasing rainfall frequency contributes to earlier leaf onset in northern ecosystems
DOI:10.1038/s41558-022-01285-w [本文引用: 1]
Photoperiodism in woody plants
DOI:10.1146/arplant.1956.7.issue-1 URL [本文引用: 1]
Contrasting responses of autumn-leaf senescence to daytime and night-time warming
DOI:10.1038/s41558-018-0346-z [本文引用: 1]
China land cover 2000 using SPOT VGT S10 data
用SPOT-VGT数据制作中国2000年度土地覆盖数据
Asymmetric responses of the end of growing season to daily maximum and minimum temperatures on the Tibetan Plateau
DOI:10.1002/jgrd.v122.24 URL [本文引用: 1]
Dynamics of phenology and its response to climatic variables in a warm-temperate mixed plantation
DOI:10.1016/j.foreco.2020.118785 [本文引用: 1]
Light limitation regulates the response of autumn terrestrial carbon uptake to warming
DOI:10.1038/s41558-020-0806-0 [本文引用: 2]
A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks
DOI:10.5194/bg-15-5779-2018
URL
[本文引用: 2]
. Satellite-retrieved solar-induced chlorophyll fluorescence (SIF) has shown\ngreat potential to monitor the photosynthetic activity of terrestrial\necosystems. However, several issues, including low spatial and temporal\nresolution of the gridded datasets and high uncertainty of the individual\nretrievals, limit the applications of SIF. In addition, inconsistency in\nmeasurement footprints also hinders the direct comparison between gross\nprimary production (GPP) from eddy covariance (EC) flux towers and\nsatellite-retrieved SIF. In this study, by training a neural network (NN)\nwith surface reflectance from the MODerate-resolution Imaging\nSpectroradiometer (MODIS) and SIF from Orbiting Carbon Observatory-2 (OCO-2),\nwe generated two global spatially contiguous SIF (CSIF) datasets at moderate\nspatiotemporal (0.05∘ 4-day) resolutions during the MODIS era, one for\nclear-sky conditions (2000–2017) and the other one in all-sky conditions\n(2000–2016). The clear-sky instantaneous CSIF (CSIFclear-inst)\nshows high accuracy against the clear-sky OCO-2 SIF and little bias across\nbiome types. The all-sky daily average CSIF (CSIFall-daily) dataset\nexhibits strong spatial, seasonal and interannual dynamics that are\nconsistent with daily SIF from OCO-2 and the Global Ozone Monitoring\nExperiment-2 (GOME-2). An increasing trend (0.39 %) of annual average\nCSIFall-daily is also found, confirming the greening of Earth in\nmost regions. Since the difference between satellite-observed SIF and CSIF is\nmostly caused by the environmental down-regulation on SIFyield,\nthe ratio between OCO-2 SIF and CSIFclear-inst can be an effective\nindicator of drought stress that is more sensitive than the normalized\ndifference vegetation index and enhanced vegetation index. By comparing\nCSIFall-daily with GPP estimates from 40 EC flux towers across the\nglobe, we find a large cross-site variation (c.v. = 0.36) of the GPP–SIF\nrelationship with the highest regression slopes for evergreen needleleaf\nforest. However, the cross-biome variation is relatively limited\n(c.v. = 0.15). These two contiguous SIF datasets and the derived GPP–SIF\nrelationship enable a better understanding of the spatial and temporal\nvariations of the GPP across biomes and climate.\n
Consistency between sun-induced chlorophyll fluorescence and gross primary production of vegetation in North America
DOI:10.1016/j.rse.2016.05.015 URL [本文引用: 1]
Retrieval of sun-induced chlorophyll fluorescence and advancements in carbon cycle application
日光诱导叶绿素荧光遥感反演及碳循环应用进展
Land surface phenology tracked by remotely sensed sun-induced chlorophyll fluorescence in subtropical evergreen coniferous forests
日光诱导叶绿素荧光对亚热带常绿针叶林物候的追踪
Vegetation phenology in the Northern Hemisphere based on the solar-induced chlorophyll fluorescence
DOI:10.17521/cjpe.2020.0376 URL [本文引用: 1]
基于日光诱导叶绿素荧光的北半球森林物候研究
Extension of the growing season due to delayed autumn over mid and high latitudes in North America during 1982-2006
DOI:10.1111/j.1466-8238.2011.00675.x URL [本文引用: 1]
