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] .
Highlights
25 Monitoring vegetation conditions by remote sensing is important for a variety of purposes, such as agricultural yield prediction (Petersen, 2018; Crocetti et al, 2020), forestry (Pause et al, 2016), fire ecology (Szpakowski and Jensen, 2019), and to track long-term ecosystem changes (Vogelmann et al, 2012)
We introduce the Standardized Vegetation Optical Depth Index (SVODI), which is created by combining vegetation optical depth (VOD) estimates from multiple passive microwave sensors and frequencies
An index is created for each sensor and band and the multiple indices are merged into SVODI (c)
Summary
25 Monitoring vegetation conditions by remote sensing is important for a variety of purposes, such as agricultural yield prediction (Petersen, 2018; Crocetti et al, 2020), forestry (Pause et al, 2016), fire ecology (Szpakowski and Jensen, 2019), and to track long-term ecosystem changes (Vogelmann et al, 2012). Some use the spectral or radiometric information directly to create a feature related to the vegetation. This includes features such as the Normalized Difference Vegetation Index (NDVI), which is widely used as a measure of live green vegetation (Huang et al, 2021; Tucker et al, 2005), or the cross-polarization ratio (CR) which is related to polarization changes of active microwaves caused by vegetation structure and moisture content (Vreugdenhil et al, 2020). An ecosystem’s condition can be observed through its response to stress, e.g., by measuring changes in land surface temperature (Kogan, 1990) or evaporation (Martens et al, 2017). All these features have in common that they show some aspect of the vegetation at a given time and location
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