The global long-term microwave Vegetation Optical Depth Climate Archive (VODCA)

Abstract

Abstract. Since the late 1970s, space-borne microwave radiometers have been providing measurements of radiation emitted by the Earth’s surface. From these measurements it is possible to derive vegetation optical depth (VOD), a model-based indicator related to the density, biomass, and water content of vegetation. Because of its high temporal resolution and long availability, VOD can be used to monitor short- to long-term changes in vegetation. However, studying long-term VOD dynamics is generally hampered by the relatively short time span covered by the individual microwave sensors. This can potentially be overcome by merging multiple VOD products into a single climate data record. However, combining multiple sensors into a single product is challenging as systematic differences between input products like biases, different temporal and spatial resolutions, and coverage need to be overcome. Here, we present a new series of long-term VOD products, the VOD Climate Archive (VODCA). VODCA combines VOD retrievals that have been derived from multiple sensors (SSM/I, TMI, AMSR-E, WindSat, and AMSR2) using the Land Parameter Retrieval Model. We produce separate VOD products for microwave observations in different spectral bands, namely the Ku-band (period 1987–2017), X-band (1997–2018), and C-band (2002–2018). In this way, our multi-band VOD products preserve the unique characteristics of each frequency with respect to the structural elements of the canopy. Our merging approach builds on an existing approach that is used to merge satellite products of surface soil moisture: first, the data sets are co-calibrated via cumulative distribution function matching using AMSR-E as the scaling reference. To do so, we apply a new matching technique that scales outliers more robustly than ordinary piecewise linear interpolation. Second, we aggregate the data sets by taking the arithmetic mean between temporally overlapping observations of the scaled data. The characteristics of VODCA are assessed for self-consistency and against other products. Using an autocorrelation analysis, we show that the merging of the multiple data sets successfully reduces the random error compared to the input data sets. Spatio-temporal patterns and anomalies of the merged products show consistency between frequencies and with leaf area index observations from the MODIS instrument as well as with Vegetation Continuous Fields from the AVHRR instruments. Long-term trends in Ku-band VODCA show that since 1987 there has been a decline in VOD in the tropics and in large parts of east-central and north Asia, while a substantial increase is observed in India, large parts of Australia, southern Africa, southeastern China, and central North America. In summary, VODCA shows vast potential for monitoring spatial–temporal ecosystem changes as it is sensitive to vegetation water content and unaffected by cloud cover or high sun zenith angles. As such, it complements existing long-term optical indices of greenness and leaf area. The VODCA products (Moesinger et al., 2019) are open access and available under Attribution 4.0 International at https://doi.org/10.5281/zenodo.2575599.

A physiology‐based Earth observation model indicates stagnation in the global gross primary production during recent decades

“…The time series of the LRF-GPP budget also showed particularly strong trends during the eighties and nineties, and a stagnation 2000-2015 ( Figure 7g), indicating that trends in the LRF-GPP may be mainly driven by temperature rather than precipitation, irradiance or atmospheric CO 2 concentrations. We note that the trend of the global terrestrial carbon sink as estimated by the global carbon project is also larger during the eighties and nineties than during the period after 2000 (Le , and state-of-the-art Vegetation Optical Depths based on the microwave Ku band (VODCA) trends are also substantially more positive for 1987-2016 than for 2002-2017 (Moesinger et al, 2020). The LRF-GPP budgets thereby support the studies claiming that the pause in the growth rate of atmospheric CO 2 is caused by reduced respiration or land-use emission rates, rather than an enhanced CO 2 uptake (Ballantyne et al, 2017;Keenan et al, 2016;Piao et al, 2018).…”

Anatomy of the 2018 agricultural drought in the Netherlands using in situ soil moisture and satellite vegetation indices

“…We found a decline in NIRv and VOD to occur only once surface soil moisture had already reached its lowest level. Satellite-derived observations of the soil's subsurface can certainly serve as early predictors for drought onset (Ford et al, 2015;Otkin et al, 2018); yet drought also leads to decoupling of the soil moisture signal over depth (Carranza et al, 2018), rendering satellite-derived soil moisture or in situ surface soil moisture observations uninformative about root water uptake and drought impact status. This effect, in combination with the sandy texture of the soils in both networks, can also explain why we find values for θ critical that are lower than those from recent estimates based on satellite soil moisture (Denissen et al, 2020).…”

Global Monitoring of the Vegetation Dynamics from the Vegetation Optical Depth (VOD): A Review

“…VODCA is an approach developed to combine multiple VOD datasets, derived from multiple sensors (SSM/I, TMI, AMSR-E, Windsat, and AMSR-2), that have in common to have been processed using LPRM [90,92]. The merging approach is composed of three steps ) [94]:…”