Mösinger, L., Zotta, R.-M., van der Schalie, R., Scanlon, T. M., De Jeu, R., & Dorigo, W. A. (2022, May 24). SVODI - A global long-term vegetation condition index based on microwave remote sensing [Poster Presentation]. ESA Living Planet Symposium 2022, Bonn, Germany.
ESA Living Planet Symposium 2022
Knowing the vegetation condition on a global scale is important for many applications such as agricultural yield prediction, fire hazard or drought monitoring. Of special interest are vegetation indices, which do not require extensive expert knowledge about earth observation data and are thus easy to interpret for policy makers. Current optical indices, such as the Vegetation condition index based on NDVI, are limited by cloud coverage and saturation effects . As an alternative, we propose to use a microwave based index, which is unaffected by cloud coverage and offers deeper penetration into the vegetation, albeit at a lower spatial resolution . In the microwave domain, data from various space-borne microwave missions are available from the late 1970s onward. From these observations, vegetation optical depth (VOD) can be estimated, which is an indicator of the vegetation water content. While long-term VOD changes can be attributed to biomass changes, short term deviations are due to fluctuations in relative plant water content and therefore an indicator of plant water status and stress . A VOD-based water-content related index therefore shows potential to supplement traditional optical indices which based on the greenness of the vegetation.
The Standardized VOD index (SVODI) is calculated using a new probabilistic merging method. VOD derived via the Land-parameter retrieval model (LPRM) from multiple microwave radiometers of the past 30 years is used as input. The index values should only depend on the vegetation conditions and be unaffected by the changing quality and quantity of the microwave observations over this period. However, traditional index generation methods assume that the data are evenly distributed over time and have similar error characteristics over the entire period. We present improvements to these methods which deal with these limitations. In theory the index generation method is not limited to VOD and could be applied to completely different variables.
We show that SVODI exhibits similar temporal patterns as the well established optical vegetation condition index (VCI) in the subtropics. In more heavily forested regions such as the tropics or the boreal forests the correlation is very weak, indicating that SVODI is sensitive to different types of vegetation disturbances than VCI. SVODI therefore allows to extend our understanding of vegetation responses to extreme weather conditions. In regions where water availability is the main limit of vegetation growth, SVODI shows as expected similar patterns as meteorological drought indices. Extreme SVODI values also follow the climate oscillation indices SOI and DMI in the relevant regions.