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首页> 外文期刊>International journal of remote sensing >Estimate of extended long-term LAI data set derived from AVHRR and MODIS based on the correlations between LAI and key variables of the climate system from 1982 to 2009
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Estimate of extended long-term LAI data set derived from AVHRR and MODIS based on the correlations between LAI and key variables of the climate system from 1982 to 2009

机译：基于1982-2009年LAI与气候系统关键变量之间的相关性，根据AVHRR和MODIS得出的扩展长期LAI数据集的估计

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摘要

The global long-term leaf area index (LAI) series is a critical variable to validate the terrestrial ecological processes of simulation by Earth system models (ESMs) and ESM input. However, the lack of long-term LAI data restricts studies on the interaction between atmosphere and biosphere. This study focuses on obtaining a robust long-term LAI data set through combining Advanced Very High Resolution Radiometer (AVHRR; available from August 1981 to May 2001) and Moderate Resolution Imaging Spectroradiometer (MODIS; available from January 2000 to December 2009) data sets, and on investigating the relationship between LAI data and the key variables of the climate system. Regional discrepancies in LAI exist between these two data sets. In high northern latitudes, there are significant differences (＞1.7 m~2 m~(-2)) between AVHRR- and MODIS-derived LAI during the overlapping period from January 2000 to May 2001 because of effects of vegetation structure, low saturation threshold of remote-sensing data and cloud contamination, and the effects of aerosols and atmospheric water vapour on the AVHRR sensor. Using the LAI data set derived from MODIS as the benchmark, AVHRR-derived LAI data from the same periods as those of MODIS were first calibrated through a region-based linear regression method. Then the regression relationship was employed to other periods of AVHRR-derived LAI, resulting in a complete long-term LAI data set. The results showed that the data set has a better convergence and continuity than the original data set, with the regional discrepancies in LAI significantly reduced. Further analyses of correlations between LAI and variables of the climate system demonstrate that the modified LAI is more suitable to describe the response of vegetation to variables in the climate system. This is probably attributed to temperature as the main driver affecting vegetation and to the persistent presence of frozen soil in this region. In comparison with results from previous studies, the response to temperature, precipitation, and soil moisture in the long-term modified LAI data is more reasonable than for unmodified LAI.
机译：全球长期叶面积指数（LAI）系列是一个关键变量，可通过地球系统模型（ESM）和ESM输入来验证模拟的陆地生态过程。但是，缺乏长期的LAI数据限制了对大气与生物圈之间相互作用的研究。这项研究的重点是通过结合先进的超高分辨率辐射计（AVHRR； 1981年8月至2001年5月）和中分辨率成像光谱仪（MODIS； 2000年1月至2009年12月）来获得可靠的长期LAI数据集，研究LAI数据与气候系统关键变量之间的关系。这两个数据集之间存在LAI的区域差异。在2000年1月至2001年5月的重叠期间，北部高纬地区的AVHRR和MODIS衍生的LAI之间存在显着差异（＞ 1.7 m〜2 m〜（-2）），这是由于植被结构的影响，饱和度阈值较低遥感数据和云污染，以及气溶胶和大气水蒸气对AVHRR传感器的影响。以源自MODIS的LAI数据集为基准，首先通过基于区域的线性回归方法对来自与MODIS相同周期的AVHRR得出的LAI数据进行了校准。然后将回归关系应用于AVHRR衍生的LAI的其他时期，从而得出完整的长期LAI数据集。结果表明，该数据集具有比原始数据集更好的收敛性和连续性，并且LAI中的区域差异显着减少。对LAI和气候系统变量之间的相关性的进一步分析表明，修改后的LAI更适合描述植被对气候系统变量的响应。这可能归因于温度是影响植被的主要驱动力，以及该地区冻土的持续存在。与以前的研究结果相比，长期修改的LAI数据对温度，降水和土壤水分的响应比未修改的LAI更为合理。

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  • 来源
    《International journal of remote sensing》 |2013年第22期|7761-7778|共18页
  • 作者

    Jing Peng; Li Dan; Wenjie Dong;

  • 作者单位

    Key Laboratory of Regional Climate-Environment for Temperate East Asia (RCE-TEA), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 10029, China;

    Key Laboratory of Regional Climate-Environment for Temperate East Asia (RCE-TEA), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 10029, China;

    Key Laboratory of Regional Climate-Environment for Temperate East Asia (RCE-TEA), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 10029, China,State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Beijing Normal University, Beijing 100875, China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
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