Sensitivity of remote sensing-based vegetation proxies to climate and sea surface temperature variabilities in Australia and parts of Southeast Asia

Abstract

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.