Optical vegetation indices for monitoring terrestrial ecosystems globally

Vegetation dynamics can be tracked using remotely sensed vegetation indices, but these metrics can result in conflicting conclusions. This Technical Review details the history, application and potential pitfalls associated with vegetation indices and makes recommendations for their best use. Vegetation indices (VIs), which describe remotely sensed vegetation properties such as photosynthetic activity and canopy structure, are widely used to study vegetation dynamics across scales. However, VI-based results can vary between indices, sensors, quality control measures, compositing algorithms, and atmospheric and sun-target-sensor geometry corrections. These variations make it difficult to draw robust conclusions about ecosystem change and highlight the need for consistent VI application and verification. In this Technical Review, we summarize the history and ecological applications of VIs and the linkages and inconsistencies between them. VIs have been used since the early 1970s and have evolved rapidly with the emergence of new satellite sensors with more spectral channels, new scientific demands and advances in spectroscopy. When choosing VIs, the spectral sensitivity and features of VIs and their suitability for target application should be considered. During data analyses, steps must be taken to minimize the impact of artefacts, VI results should be verified with in situ data when possible and conclusions should be based on multiple sets of indicators. Next-generation VIs with higher signal-to-noise ratios and fewer artefacts will be possible with new satellite missions and integration with emerging vegetation metrics such as solar-induced chlorophyll fluorescence, providing opportunities for studying terrestrial ecosystems globally.

Automated summary

Vegetation indices are widely used to study vegetation dynamics, but different indices and factors can lead to conflicting conclusions. Choosing and verifying the indices, as well as analyzing multiple indicators, are necessary for obtaining reliable results.