Abstract
Remote sensing of phenology usually works at the regional and global scales, which imposes considerable variations in the solar zenith angle (SZA) across space and time. Variations in SZA alters the shape and profile of the surface reflectance and vegetation index (VI) time series, but this effect on remote-sensing-derived vegetation phenology has not been adequately evaluated. The objective of this study is to understand the behaviour of VIs response to SZA, and to further improve the interpretation of satellite observed vegetation dynamics, across space and time. In this study, the sensitivity of two widely used VIs—the normalised difference vegetation index (NDVI) and the enhanced vegetation index (EVI)—to SZA was investigated at four northern Australian savanna sites, over a latitudinal distance of 9.8° (~1100 km). Complete time series of surface reflectances, as acquired with different SZA configurations, were simulated using Bidirectional Reflectance Distribution Function (BRDF) parameters provided by MODerate Resolution Imaging Spectroradiometer (MODIS). The sun-angle dependency of the four phenological transition dates were assessed. Results showed that while NDVI was very sensitive to SZA, such sensitivity was nearly absent for EVI. A negative correlation was also observed between NDVI sensitivity to SZA and vegetation cover, with sensitivity declining to the same level as EVI when vegetation cover was high. Different sun-angle configurations resulted in considerable variations in the shape and magnitude of the phenological profiles. The sensitivity of VIs to SZA was generally greater during the dry season (with only active trees present) than in the wet season (with both active trees and grasses), thus, the sun-angle effect on VIs was phenophase-dependent. The sun-angle effect on NDVI time series resulted in considerable differences in the phenological metrics across different sun-angle configurations. Across four sites, the sun-angle effect caused 15.5 days, 21.6 days, and 20.5 days differences in the start, peak, and the end of the growing season derived from NDVI time series, with seasonally varying SZA at local solar noon, as compared to those metrics derived from NDVI time series with fixed SZA. In comparison, those differences in the start, peak, and end of the growing season for EVI were significantly smaller, with only 4.8 days, 4.9 days, and 3 days, respectively. Our results suggest the potential importance of considering the seasonal SZA effect on VI time series prior to the retrieval of phenological metrics.
Highlights
Phenology is the study and analysis of the life cycles of flora and fauna and their interactions with climate and other seasonal environmental drivers [1]
Satellite-based studies of vegetation phenology generally rely on time series of vegetation indices, either without correction of changing viewing and illumination geometries (e.g., MOD09 or MOD13), or only with partial correction of the view-angle effect (e.g., MCD43 NBAR)
The sun-angle effect on normalised difference vegetation index (NDVI) and enhanced vegetation index (EVI) was examined through a complete simulation of surface reflectances, with multiple sun-angle configurations
Summary
Phenology is the study and analysis of the life cycles of flora and fauna and their interactions with climate and other seasonal environmental drivers [1]. Vegetation dynamics and the phenological metrics derived from ground or remote sensing observations for describing these dynamics, e.g., leaf flush or onset of growing season, are key indicators of ecosystem responses to climate variability and change [2]. With its synoptic characteristics across large temporal and spatial scales, provide an unparalleled way for the detection and mapping of vegetation phenology across space, thereby complementing the restricted coverage afforded by ground-based plots. Remote sensing examines broader scale phenomena that allow retrievals of whole-system phenological metrics, such as the timing and magnitudes of greening, peak activity, and the drying phases of the growing season [5]. Calibrating time-limited satellite observations with space-limited ground observations is an important issue in remote sensing of phenology studies
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