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 onwards. From these observations, vegetation optical depth (VOD) can be estimated, which is an indicator of the total canopy water 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, 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 VODs are merged in a probabilistic manner resulting in a vegetation condition index spanning from 1987 to the present.
SVODI shows similar temporal patterns to the well-established optical vegetation health index (VHI) derived from optical and thermal data. In regions where water availability is the main control on vegetation growth, SVODI also shows similar temporal patterns to the meteorological drought index scPDSI (self-calibrating Palmer drought severity index) and soil moisture anomalies from ERA5-Land. Temporal SVODI patterns relate to the climate oscillation indices SOI (Southern Oscillation index) and DMI (dipole mode index) 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 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.
The SVODI products
Monitoring vegetation conditions by remote sensing is important for a variety of purposes, such as agricultural yield prediction
Numerous variables and metrics have been developed to monitor vegetation conditions from spaceborne observations. 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
Often, radiometric information is translated into biogeophysical or chemical variables such as the leaf area index (LAI) or the fraction of absorbed photosynthetic radiation
All these features have in common that they show some aspect of the vegetation at a given time and location.
To assess whether the state of the vegetation is unusual at a given time and location, it is usually compared against the expected value at that time of year, derived from long-term observations. There are multiple ways to do this. The most straightforward way is to calculate anomalies by subtracting the multiyear seasonal average from the observation. This allows one to directly see whether an observation is higher or lower than usual. The drawback of such raw anomalies is that their magnitude depends on the average conditions at a given location. Therefore, the anomalies between different locations cannot be compared against each other and it requires expert knowledge to know whether an anomaly of a certain magnitude is a very strong outlier or a minor deviation
This can be solved by expressing the vegetation condition as an index. While anomalies show deviations as absolute differences to some mean, indices show the likelihood of observing a deviation of a certain magnitude. Indices are easier to interpret as they follow a well-defined distribution which allows one to discern quickly whether a value is relatively high or low. Some well-known example indices are the vegetation condition index (VCI, computed from NDVI;
Over the past 4 decades, various platforms carrying multi-frequency microwave radiometers have been orbiting the Earth. From these observations it is possible to derive the vegetation optical depth (VOD), which describes the attenuation of microwave radiation by vegetation
Long-term VOD datasets, such as VODCA
To solve this issue, the standardized VOD index (SVODI) is proposed, which uses a probabilistic merging method to generate a long-term dataset for global vegetation condition monitoring based on VOD. After a technical evaluation, its relationship to other vegetation-related indices is explored. This assures that SVODI behaves reasonably in the case of an event affecting the vegetation and gives insight into how it differs from the currently used indices for vegetation condition monitoring.
VOD estimates from various microwave radiometers and frequencies have been obtained with LPRM v6.0
For this study, the exact same data as for VODCA
SSM/I (Special Sensor Microwave/Imager) F08, F11 and F13 on board DMSP satellites are used for a total time span of 1987 to 2015. VOD retrieved from the Ku-band with a resolution of
TMI, the TRMM Microwave Imager on board TRMM, is used from 1997 to 2015. The VOD retrieved from the X- and Ku-band are used. Flying in a non-near-polar orbit, it only covered the area 35
AMSR-E, the Advanced Microwave Scanning Radiometer for EOS (the Earth Observing System) on board Aqua, is used from 2002 to 2011. The VODs retrieved from the C-, X- and Ku-band are used, which have a spatial footprint of
WindSat on board Coriolis observes the C-, X- and Ku-band with a spatial resolution of
AMSR2, the Advanced Microwave Scanning Radiometer 2 on board GCOM-W1, is used from June 2012 onwards. It is the follow-up to AMSR-E and as such is very similar but with slightly higher spatial resolution of
Multiple auxiliary datasets are used to evaluate SVODI. Most of these datasets do not follow a standard normal distribution, either by design or because the preprocessing (regridding and temporal resampling) changed their distribution. To facilitate the comparison of all datasets, they are standardized using a basic
The vegetation health index (VHI)
The TCI is defined similarly but based on land surface temperature (LST),
VHI, VCI and TCI derived from AVHRR NDVI
An alternative to AVHRR data might be the newer MODIS data. However, the long-term availability of AVHRR allows for comparisons over the whole duration of SVODI, while MODIS is only available after 2000. Additionally, neither MODIS's higher spectral resolution (not relevant for VCI calculation) nor its higher spatial resolution (the AVHRR resolution is already much higher than our 0.25
The self-calibrating Palmer drought severity index (scPDSI)
ERA5-Land is a reanalysis of the global atmosphere, land surface and ocean waves since 1950
The Southern Oscillation index (SOI)
Both are available at NOAA, at
SVODI is computed from C-, X- and Ku-band VOD from
multiple sensors. It is assumed that all sensors and bands are equally fit as an indicator of the vegetation condition but show different aspects of it. Ku-band is mostly sensitive to surface canopy leaves, while longer wavelengths are also affected by the woody part
Exemplary correlation coefficients between C-, X- and Ku-band VOD anomalies of AMSR-E, derived with LPRM (Sect.
L-band VOD is notably absent from the list of used frequencies. It has very different temporal characteristics than the other bands due to it being mostly sensitive to slow structural changes in the vegetation
Previous multivariate indices
But there is an issue with this approach:
Bi-variate example of a probabilistic multivariate index as in
The negative bias of multivariate indices computed as above makes the index hard to interpret as the resulting index is no longer normally distributed. Also, in the case that the individual indices have data gaps, the expected mean depends on the available input indices, leading to a higher expected value for periods where fewer sensors are available than for periods with more sensors available. This issue is solved by scaling
After some basic preprocessing (temporal resampling of swath data to daily values and masking invalid values, the same as in
Long-term VOD changes are related to biomass changes
Overview of VOD datasets used in this study with their temporal coverage, local ascending equatorial crossing times (AECT), whether the ascending (A) or descending (D) overpass is used, and frequencies (GHz) used for each product. The C- and X-band retrievals are based on
Then, all VOD values of the respective band, VOD
The scaled VOD values are standardized in the following way: for a day of the year (DOY
Temporal subset of an example time series at different processing stages in Western Australia (24.9
Fraction of pixels that are extreme of SVODI
The indices of the individual sensors and bands,
Since
SVODI describes the vegetation condition with regard to abnormal vegetation water content. There is no absolute reference to compare SVODI to, and therefore it is evaluated by comparing it to other well-established vegetation indicators. This is mostly done by basic correlation analysis but also by studying temporal shifts and the evolution of extreme values over time. The latter is explained in more detail below.
Quantile–quantile (QQ) plots for different numbers of input observations. For example,
Is it of interest whether events can be seen first in the microwave or optical domain and how large their temporal difference is for a variety of applications, such as drought prediction. For this purpose the temporal shifts between SVODI and VHI and soil moisture anomalies are determined by finding the temporal lag at which a dataset pair correlates most strongly. This is done by grid search, calculating the correlation coefficient for every shift within a
Spatial correlation coefficient of SVODI vs. VHI over time
The plots showing the frequency of extreme values over time are inspired by the plots in
For visualization, the data are first standardized to
Figure
Prior to the SVODI calculation, all datasets are detrended. On a global scale it is therefore expected that the percentage of extreme SVODI values, both positive and negative, is more or less constant over time. Indeed, there seems to be no drastic systematic increase or decrease in the percentage of pixels with
If, compared to a simple VODCA standardization, the number of input sensors were to have no effect on the SVODI distribution, then this should be standard normally distributed, irrespective of the number of input sensors. Figure
Generally, SVODI is normally distributed regardless of the number of input sensors used for its computation. Only for extremely low values is a small difference observed. Very low values that are also the result of many sensors have a slight positive bias, while for very high values this discrepancy does not occur. The cause for this problem is not fully understood, but it is assumed to be related to numerical instability of very low values as
Figure
Correlation coefficient without temporal shift between SVODI and VCI
Correlation
Of interest is whether the quality of SVODI changes over time. There exist no ground-based validation data for VOD; therefore a direct validation with some reference data is not possible. Instead, the spatial correlation to VHI over time is used as an indicator of whether SVODI is performing differently during different periods. For each time step, the correlation between the global SVODI and VHI images is calculated. If the signal-to-noise ratio of any of the two datasets were to change, so would the correlation between them.
The spatial correlation varies quite strongly over time (Fig.
There is no apparent seasonal dependency of the correlation (Fig.
Mean correlation coefficient if no temporal shifting is done
Percentage area of SVODI greater/smaller than 1/
SVODI is compared to VCI; TCI; and their composite, VHI, to explore their similarities and differences. By comparing SVODI to all three, it is possible to evaluate how it relates to the impact of stress on either vegetation “greenness” (VCI) or temperature (TCI) or a combination of both (VHI).
VCI and SVODI correlate quite strongly, especially in semi-arid climates (Fig.
TCI and SVODI correlate positively in semi-arid regions (Fig.
Percentage area of SVODI
Since VHI is the average of TCI and VCI, SVODI also correlates more strongly with VCI than with VHI (Fig.
Anomalies generally occur first in TCI followed by VCI and SVODI (Fig.
Correlations between SVODI and soil moisture anomalies at various depths were calculated to determine the connection between the different depths and the vegetation condition. SVODI and upper level soil moisture anomalies (0–7 and 7–28 cm) correlate most strongly with each other in areas where vegetation growth is limited by water availability (Fig. December–January–February, March–April–May, etc.
Correlation coefficients between SVODI and soil moisture anomalies decrease with increasing depth when no lag optimization is performed (Fig.
There is a clear relationship between soil depth and temporal shift (Fig.
The Australian summers of 2010 and 2019 were marked by exceptionally high and low precipitation (Fig.
In the Amazonian rainforest, the extreme values of SVODI, VHI and scPDSI do not agree with each other (Fig.
The effects of droughts in the Amazon forest is a highly discussed topic and very challenging
The correlation between SVODI, VHI and scPDSI and the Southern Oscillation index (SOI) (Fig.
Correlation coefficients between SVODI, VHI and scPDSI (top to bottom) vs. SOI
SVODI and VHI correlations show a similar pattern. The highest correlation coefficients to SOI are found in eastern Australia, where vegetation is heavily influenced by the El Niño–Southern Oscillation (ENSO)
The correlations between SVODI and VHI and DMI are similar and show the greatest magnitude in southern Australia, while in most other regions they are close to zero. Correlations between scPDSI and DMI show a more distinguished spatial pattern, including strong positive correlations in north-western Russia. In this region, vegetation growth is limited by temperature and not precipitation, and for this reason no corresponding patterns can be found in the SVODI or VHI plots.
SVODI is a microwave-based vegetation condition index that shows similar patterns to existing optical indices and follows soil moisture in semi-arid regions. It extends the current range of available remote sensing datasets that allow the observation of anomalous vegetation states and increases our understanding of global vegetation dynamics. SVODI patterns are reasonable compared to patterns of VHI, TCI, VCI and soil moisture, but anomalies occur later, which might be an issue for near-real-time applications.
With the exception of extreme low values, the proposed index generation method works well at combining different indices. The merging method itself is not limited to VOD and can potentially be applied to combine arbitrary normally distributed indices. Therefore, this method might find applications in various disciplines. Further efforts will focus on increased numerical stability of the calculations and updating SVODI with more recent observations.
The SVODI products
WD and LM designed the study. LM performed the analyses and wrote the paper together with WD. All authors contributed to discussions about the methods and results and provided feedback on the paper.
The contact author has declared that none of the authors has any competing interests.
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article is part of the special issue “Microwave remote sensing for improved understanding of vegetation–water interactions (BG/HESS inter-journal SI)”. It is not associated with a conference.
The authors acknowledge TU Wien Bibliothek for financial support through its Open Access Funding Programme.
This research has been supported by the Vienna University of Technology (TU Wien) through its Open Access Funding Programme.
This paper was edited by Jean-Christophe Calvet and reviewed by Nataniel Holtzman and two anonymous referees.