ABSTRACT The development of remote sensing (RS) technology has enabled the dynamics of various vegetation biophysical parameters to be monitored, such as the water content of vegetation, fraction of green vegetation, and fluorescence relating to photosynthesis. This study aims to estimate and compare the influence of climate and sea surface temperature (SST) variabilities on vegetation dynamics in Australia and parts of Southeast Asia by conducting lagged Pearson’s correlation coefficient (r), multilinear regression, and teleconnection analyses using the Empirical Orthogonal Teleconnection (EOT). The monthly vegetation anomalies from January 2013 to September 2018 (69 months) from several RS-based proxies such as, Solar Induced Fluorescence (SIF) from the Global Ozone Monitoring Experiment (GOME)-2B, Moderate Resolution Imaging Spectroradiomater (MODIS) based-Normalized Differenced Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI), and X-, C- and Ku-band microwave-based Vegetation Optical Depth Climate Archive (VODCA), were linked with precipitation and rainfall anomalies in Global Land Data Assimilation System (GLDAS) data and Optimum Interpolation Sea Surface Temperature (OISST) anomalies from National Oceanic and Atmospheric Administration (NOAA). The results showed the correlation strengths between vegetation dynamics and precipitation and rainfall were −0.23 (X- and Ku-band VOD) to 0.35 (SIF) and −0.41 (NDVI) to 0.39 (SIF), respectively. The climate variabilities can explain 22% to 37% ( of 19% to 35%) of the variance in vegetation dynamics in the study area. In addition, the two modes generated from EOT analysis formed spatial patterns relating to El Nino Southern Oscillation (ENSO) events that can explain 18% (SIF) to 62% (Ku-band VOD) of the variance in vegetation dynamics. These results highlight the influence of climate variabilities and ENSO on various vegetation biophysical properties.

中文翻译：

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基于遥感的植被代理对澳大利亚和东南亚部分地区气候和海面温度变化的敏感性

摘要 遥感 (RS) 技术的发展使得能够监测各种植被生物物理参数的动态，例如植被的含水量、绿色植被的比例以及与光合作用相关的荧光。本研究旨在通过使用经验正交遥相关进行滞后 Pearson 相关系数 (r)、多元线性回归和遥相关分析，估计和比较气候和海面温度 (SST) 变化对澳大利亚和东南亚部分地区植被动态的影响。 (EOT)。从 2013 年 1 月到 2018 年 9 月（69 个月）的月植被异常来自几个基于 RS 的代理，例如来自全球臭氧监测实验（GOME）-2B 的太阳诱导荧光（SIF），基于中分辨率成像光谱仪 (MODIS) 的标准化差分植被指数 (NDVI) 和增强型植被指数 (EVI) 以及基于 X、C 和 Ku 波段微波的植被光学深度气候档案 (VODCA) 与全球陆地数据同化系统 (GLDAS) 数据中的降水和降雨异常以及国家海洋和大气管理局 (NOAA) 的最佳插值海面温度 (OISST) 异常。结果表明，植被动态与降水和降雨之间的相关强度分别为-0.23（X-和Ku-波段VOD）至0.35（SIF）和-0.41（NDVI）至0.39（SIF）。气候变化可以解释研究区植被动态变化的 22% 到 37%（19% 到 35%）。此外，EOT 分析产生的两种模式形成了与厄尔尼诺南方涛动 (ENSO) 事件相关的空间模式，可以解释植被动态变化的 18% (SIF) 至 62% (Ku 波段 VOD)。这些结果突出了气候变化和 ENSO 对各种植被生物物理特性的影响。