Quantifying how topography impacts vegetation indices at various spatial and temporal scales

Abstract

Satellite-derived vegetation indices (VIs) have been extensively used in monitoring vegetation dynamics at local, regional, and global scales. While numerous studies have explored various factors influencing VIs, a remarkable knowledge gap persists concerning their applicability in mountain areas with complex topographic variations. Here we bridge this gap by conducting a comprehensive evaluation of the topographic effects on ten widely used VIs. We used three evaluation strategies, including: (i) an analytic radiative transfer model; (ii) a 3D ray-tracing radiative transfer model; and (iii) Moderate Resolution Imaging Spectroradiometer (MODIS) products. The two radiative transfer models provided theoretical evaluation results under specific terrain conditions, aiding in the first exploration of the interactions of both shadow and spatial scale effects on VIs. The MODIS-based evaluation quantified the discrepancies in VIs between MODIS-Terra and MODIS-Aqua over flat and rugged terrains, providing new insights into real satellite data across different temporal scales (i.e., from daily to multiple years). Our evaluation results were consistent across these three strategies, revealing three key findings. (i) The normalized difference vegetation index (NDVI) generally outperformed the other VIs, yet all VIs did not perform well in shadow areas (e.g., with a mean relative error (MRE) of 14.7% for NDVI in non-shadow areas and 26.1% in shadow areas). (ii) The topographic impacts exist at multiple spatiotemporal scales. For example, the MREs of NDVI reached 28.5% and 11.1% at 30 m and 3 km resolutions, respectively. The quarterly and annual VIs deviations between MODIS-Terra and MODIS-Aqua also increased with slope. (iii) We found the topography-induced interannual variations in multiple VIs both in simulated data and MODIS data. VIs trend deviations between MODIS-Terra and MODIS-Aqua over the Tibetan Plateau from 2003 to 2020 increased as the slope steepened (i.e., NDVI and enhanced vegetation index (EVI) trend deviations generally doubled). Overall, the sun-target-sensor geometry changes induced by topography, causing shadows in mountains along with obstructions in sensor observations, compromised the reliability of VIs in these terrains. Our study underscores the considerable impacts of topography, particularly shadow effects, on multiple VIs at various spatiotemporal scales, highlighting the imperative of cautious application of VIs-based trend calculation in mountains.