21 Jan 2022
21 Jan 2022
Status: a revised version of this preprint is currently under review for the journal BG.

Monitoring Vegetation Condition using Microwave Remote Sensing: The Standardized Vegetation Optical Depth Index SVODI

Leander Moesinger1, Ruxandra-Maria Zotta1, Robin van der Schalie2, Tracy Scanlon1, Richard de Jeu2, and Wouter Dorigo1 Leander Moesinger et al.
  • 1Technische Universität Wien, Department of Geodesy and Geoinformation, Vienna, Austria
  • 2VanderSat, Wilhelminastraat 43A, 2011 VK Haarlem, The Netherlands

Abstract. Vegetation conditions can be monitored on a global scale using remote sensing observations in various wavelength domains. In the microwave domain, data from various spaceborne microwave missions are available from the late 1970s on- wards. From these observations, vegetation optical depth (VOD) can be estimated, which is an indicator of the total canopy wa- ter content and hence of above-ground biomass and its moisture state. Observations of VOD anomalies would thus complement indicators based on visible and near-infrared observations, which are primarily an indicator of an ecosystem’s photosynthetic activity.

Reliable long-term vegetation state monitoring needs to account for the varying number of available observations over time caused by changes in the satellite constellation. To overcome this, we introduce the Standardized Vegetation Optical Depth Index (SVODI), which is created by combining VOD estimates from multiple passive microwave sensors and frequencies. Different frequencies are sensitive to different parts of the vegetation canopy. Thus, by combining them into a single index makes this index sensitive to deviations in any of the vegetation parts represented. SSM/I, TMI, AMSR-E, WindSat and AMSR2-derived C-, X- and Ku-band VOD are merged in a probabilistic manner resulting in a vegetation condition index spanning from 1987 to the present.

SVODI shows similar temporal patterns as the well-established optical vegetation health index (VHI) derived from optical and thermal data. In regions where water availability is the main control of vegetation growth, SVODI also shows similar temporal patterns as the meteorological drought index scPDSI and soil moisture anomalies from ERA-land. Temporal SVODI patterns relate to the climate oscillation indices SOI and DMI in the relevant regions. It is further shown that anomalies occur in VHI and soil moisture anomalies before they occur in SVODI.

The results demonstrate both the potential of VOD to monitor the vegetation condition supplementing existing optical indices. It comes with the advantages and disadvantages inherent to passive microwave remote sensing, such as being less susceptible to cloud coverage and solar illumination but at the cost of a lower spatial resolution. The index generation is not specific to VOD and could therefore find applications in other fields.

SVODI is open-access and available at xy [once the paper is through review] .

Leander Moesinger et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on bg-2021-360', Anonymous Referee #1, 08 Feb 2022
    • AC1: 'Reply on RC1', Leander Moesinger, 11 Apr 2022
  • RC2: 'Comment on bg-2021-360', Anonymous Referee #2, 14 Mar 2022
    • AC3: 'Reply on RC2', Leander Moesinger, 11 Apr 2022

Leander Moesinger et al.

Leander Moesinger et al.


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Short summary
The Standardized Vegetation Optical Depth index (SVODI) can be used to monitor the vegetation condition, such as whether the vegetation is unusually dry or wet. SVODI has global coverage and spans the past three decades and is derived from multiple space-borne passive microwave sensors of that period. SVODI is based on a new probabilistic merging method that allows the merging of normally distributed data even if the data is not gap-free.