Vegetation phenology is an indicator of vegetation response to natural environmental changes and is of great significance for the study of global climate change and its impact on terrestrial ecosystems.The normalized ...Vegetation phenology is an indicator of vegetation response to natural environmental changes and is of great significance for the study of global climate change and its impact on terrestrial ecosystems.The normalized difference vegetation index(NDVI)and enhanced vegetation index(EVI),extracted from the Moderate Resolution Imaging Spectrometer(MODIS),are widely used to monitor phenology by calculating land surface reflectance.However,the applicability of the vegetation index based on‘greenness'to monitor photosynthetic activity is hindered by poor observation conditions(e.g.,ground shadows,snow,and clouds).Recently,satellite measurements of solar-induced chlorophyll fluorescence(SIF)from OCO-2 sensors have shown great potential for studying vegetation phenology.Here,we tested the feasibility of SIF in extracting phenological metrics in permafrost regions of the northeastern China,exploring the characteristics of SIF in the study of vegetation phenology and the differences between NDVI and EVI.The results show that NDVI has obvious SOS advance and EOS lag,and EVI is closer to SIF.The growing season length based on SIF is often the shortest,while it can represent the true phenology of vegetation because it is closely related to photosynthesis.SIF is more sensitive than the traditional remote sensing indices in monitoring seasonal changes in vegetation phenology and can compensate for the shortcomings of traditional vegetation indices.We also used the time series data of MODIS NDVI and EVI to extract phenological metrics in different permafrost regions.The results show that the length of growing season of vegetation in predominantly continuous permafrost(zone I)is longer than in permafrost with isolated taliks(zone II).Our results have certain significance for understanding the response of ecosystems in cold regions to global climate change.展开更多
植被物候是监测陆地生态系统和全球气候变化的重要生物指标。基于经典遥感植被指数的陆表物候监测在不同植被类型的精确分析方面存在较大挑战,日光诱导叶绿素荧光(SIF)可以直接反映植被实际光合作用的动态变化,能够更精确地刻画出植被...植被物候是监测陆地生态系统和全球气候变化的重要生物指标。基于经典遥感植被指数的陆表物候监测在不同植被类型的精确分析方面存在较大挑战,日光诱导叶绿素荧光(SIF)可以直接反映植被实际光合作用的动态变化,能够更精确地刻画出植被的年际变异。本研究基于2001~2020年GOSIF数据集,通过D-L拟合函数和动态阈值法提取东北地区植被物候参数,结合一元线性回归分析、稳定性和持续性分析,在多时空尺度下分析2001~2020年东北地区植被物候的时空演变特征,并探讨植被物候对气候变化的响应机制。结果表明:(1)植被生长季开始(Start of Season,SOS)、结束(EndofSeason,EOS)、生长季长度(LengthofSeason,LOS)和生长峰值(Position of Peak,POP)整体上分别呈现出提前、推迟、延长和提前趋势;(2)草丛SOS提前、EOS推迟趋势较为显著,针叶林EOS提前趋势显著;SOS提前、EOS推迟导致LOS延长,除针叶林外,所有植被类型LOS均呈现出延长趋势;除草丛和草原外,其余植被类型POP均呈提前趋势;(3)20年来植被SOS、EOS、LOS和POP变化较为稳定,变异系数均小于0.1;(4)大部分区域植被SOS、EOS、LOS和POP的H值介于0.35~0.5之间,说明其变化趋势与过去相反,将呈现微弱延迟、提前、缩短和延长的趋势;(5)整体上气温和降水对植被物候的影响机制相反,即气温升高(降水增加)导致SOS和POP提前(推迟)、EOS推迟(提前)以及LOS延长(缩短);相对湿度与植被物候参数均呈负相关关系。本研究结果有助于理解植被进行光合作用的时空格局变化及对气候变化的响应机制,也为东北地区生态环境的评估和管理提供参考。展开更多
基金Under the auspices of National Key Research and Development Projects(No.2018YFE0207800)National Natural Science Foundation of China(No.41871103)。
文摘Vegetation phenology is an indicator of vegetation response to natural environmental changes and is of great significance for the study of global climate change and its impact on terrestrial ecosystems.The normalized difference vegetation index(NDVI)and enhanced vegetation index(EVI),extracted from the Moderate Resolution Imaging Spectrometer(MODIS),are widely used to monitor phenology by calculating land surface reflectance.However,the applicability of the vegetation index based on‘greenness'to monitor photosynthetic activity is hindered by poor observation conditions(e.g.,ground shadows,snow,and clouds).Recently,satellite measurements of solar-induced chlorophyll fluorescence(SIF)from OCO-2 sensors have shown great potential for studying vegetation phenology.Here,we tested the feasibility of SIF in extracting phenological metrics in permafrost regions of the northeastern China,exploring the characteristics of SIF in the study of vegetation phenology and the differences between NDVI and EVI.The results show that NDVI has obvious SOS advance and EOS lag,and EVI is closer to SIF.The growing season length based on SIF is often the shortest,while it can represent the true phenology of vegetation because it is closely related to photosynthesis.SIF is more sensitive than the traditional remote sensing indices in monitoring seasonal changes in vegetation phenology and can compensate for the shortcomings of traditional vegetation indices.We also used the time series data of MODIS NDVI and EVI to extract phenological metrics in different permafrost regions.The results show that the length of growing season of vegetation in predominantly continuous permafrost(zone I)is longer than in permafrost with isolated taliks(zone II).Our results have certain significance for understanding the response of ecosystems in cold regions to global climate change.
文摘植被物候是监测陆地生态系统和全球气候变化的重要生物指标。基于经典遥感植被指数的陆表物候监测在不同植被类型的精确分析方面存在较大挑战,日光诱导叶绿素荧光(SIF)可以直接反映植被实际光合作用的动态变化,能够更精确地刻画出植被的年际变异。本研究基于2001~2020年GOSIF数据集,通过D-L拟合函数和动态阈值法提取东北地区植被物候参数,结合一元线性回归分析、稳定性和持续性分析,在多时空尺度下分析2001~2020年东北地区植被物候的时空演变特征,并探讨植被物候对气候变化的响应机制。结果表明:(1)植被生长季开始(Start of Season,SOS)、结束(EndofSeason,EOS)、生长季长度(LengthofSeason,LOS)和生长峰值(Position of Peak,POP)整体上分别呈现出提前、推迟、延长和提前趋势;(2)草丛SOS提前、EOS推迟趋势较为显著,针叶林EOS提前趋势显著;SOS提前、EOS推迟导致LOS延长,除针叶林外,所有植被类型LOS均呈现出延长趋势;除草丛和草原外,其余植被类型POP均呈提前趋势;(3)20年来植被SOS、EOS、LOS和POP变化较为稳定,变异系数均小于0.1;(4)大部分区域植被SOS、EOS、LOS和POP的H值介于0.35~0.5之间,说明其变化趋势与过去相反,将呈现微弱延迟、提前、缩短和延长的趋势;(5)整体上气温和降水对植被物候的影响机制相反,即气温升高(降水增加)导致SOS和POP提前(推迟)、EOS推迟(提前)以及LOS延长(缩短);相对湿度与植被物候参数均呈负相关关系。本研究结果有助于理解植被进行光合作用的时空格局变化及对气候变化的响应机制,也为东北地区生态环境的评估和管理提供参考。
