Search

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 are open access and available under Attribution 4.0 International at https://doi.org/10.5281/zenodo.2575599. [ABSTRACT FROM AUTHOR]

Since the late 1970s, spaceborne microwave sensors 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 vegetation density and its relative water content. 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. But, combining multiple sensors into a single product is challenging as systematic differences between input products, e.g. biases, different temporal and spatial resolutions and coverage, need to be overcome. Here, we present a new series of long-term VOD products, which combine multiple VOD data sets derived from several sensors (SSM/I, TMI, AMSR-E, Windsat, and AMSR-2) using the Land Parameter Retrieval Model. We produce separate VOD products for microwave observations in different spectral bands, namely 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 approach to merge the single-sensor VOD products is similar to the one of the ESA CCI Soil Moisture products (Liu et al., 2012; Dorigo et al., 2017): First, the data sets are co-calibrated via cumulative distribution function matching using AMSR-E as scaling reference. We apply a new matching technique that scales outliers more robustly than ordinary piece-wise linear interpolation. Second, we aggregate the data sets by taking the arithmetic mean between temporally overlapping observations of the scaled data, generating a VOD Climate Archive (VODCA). The characteristics of VODCA are assessed for self-consistency and against other products: spatio-temporal patterns and anomalies of the merged products show consistency between frequencies and both with observations of Leaf Area Index derived from the MODIS instrument as well as Vegetation Continuous Fields from AVHRR instruments. Trend analysis shows that since 1987 there has been a decline in VOD in the tropics and in large parts parts of east-central and north Asia along with a strong increase in India, large parts of Australia, south Africa, southeastern China and central north America. 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. In summary, VODCA shows vast potential for monitoring spatio-temporal ecosystem behaviour complementary to existing long-term vegetation products from optical remote sensing. 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. [ABSTRACT FROM AUTHOR]

The blending of soil moisture (SM) time series derived from different active and passiveremote sensing instruments with varying sensor characteristics (frequency, polarization,radiometric accuracy) in the global ESA CCI SM climate data record (CDR), canpotentially lead to artificial, structural breaks at transition times between sensorproducts, which may negatively affect long term studies on climate changes orrisk assessments, which make use of 40 years of SM observations provided in thedataset.We detect relative inhomogeneities in mean and variance between sensor periods usingnon-parametric statistical tests with homogeneous reference SM from MERRA2 land surfacemodel simulations and adjust breaks with respect to corrections from models derivedfrom observations within homogeneous sub-periods of ESA CCI and referenceSM.We compare different methods for adjusting inhomogeneities (relative adjustment based onmatching linear regression models, higher order moments adjustment based on predictionsfrom (non-linear) regression models and adjustment by matching quantile categoriesof empirical distributions) and rate them in terms of their usability on ESA CCISM.We use in-situ SM observations from the international soil moisture network (ISMN)and surface model SM for validation to investigate the impact of break correctionmethods on satellite SM and define a modular, extendable framework to test andadjust multiple breaks in a single observation series, taking into account seasonality,correlations between ESA CCI and reference SM, varying spatial and temporalcoverage of satellite SM as well as different lengths of homogeneous sub-periods. Weexamine the impact of adjustment on long term climate dynamics in ESA CCI SM bydetecting monotonic, significant trends using a non-parametric Mann-Kendall test andcompare our results to trends in other soil moisture data sets and recommend theuse of future versions of ESA CCI SM, for which the number of breaks will bereduced. [ABSTRACT FROM AUTHOR]

Quality assurance of satellite-derived soil moisture datasets is key for ensuring users are provided with datasets of sufficient quality to their applications. Currently, such activities are undertaken on an ad-hoc basis or using a protocol designed for a specific product. These results are useful, but differ in terms of output validation data, plots and reports and therefore are difficult to inter-compare.The aim of the Quality Assurance for Soil Moisture (QA4SM) (www.qa4sm.eodc.eu) project (supported by the Austrian Space Application Programme) is to bring together methodologies and protocols used for the validation and quality control of soil moisture data products and develop this into a coherent methodology allowing the provision of standardised, and above all traceable validation results to the user community. QA4SM is driven by validation best practice guidelines resulting from a collaborative effort of the entire satellite soil moisture validation community, including the Committee on Earth Observation Satellites, the Global Climate Observing System, and the validation teams of the individual satellite missions.An easy-to-use functional, web-based test application has been made available to the user community. The application currently allows users to compare medium coarse resolution satellite datasets to in-situ validation data (made available through the International Soil Moisture Network (ISMN) (http://ismn.geo.tuwien.ac.at)) and global land surface models. Later updates of the system will allow the upload and comparison of user's own datasets as well as the validation of high resolution (1 km) products. The web-based graphical front-end allows users to easily select specific criteria for the comparison process including control of the masking applied to each dataset, the metrics calculated and so on. The validation results are provided as a netCDF file which includes traceability data in the global attributes as well as plots of each metric.Various satellite-derived soil moisture products are available for validation within the QA4SM system including SMAP L3 and the SMOS IC soil moisture products as well as the European Space Agency (ESA) Climate Change Initiative (CCI) and EU Copernicus Climate Changes Services (C3S) soil moisture products. These latter two products are produced by systematically combining Level-2 datasets from separate missions. The combination of individual Level 2 datasets into a single product gives us the opportunity to profit from the advantages of individual missions, and to obtain homogenised CDRs with improved spatial and temporal coverage.The accuracy of the ESA CCI and C3S products are routinely assessed (each time a new version is generated) through comparison to in-situ (ISMN) and global models. The quality assessment also includes consideration of the stability of the product and a completeness / consistency check. The aim of these assessments is to ensure that the highest quality product is provided to the data users. With the availability of the QA4SM system, this can now be achieved in a robust and traceable manner.The goal of this presentation is to present the QA4SM system, show validation results for both the ESA CCI and C3S soil moisture products using the system, and collect feedback from the wider satellite soil moisture validation/calibration community. [ABSTRACT FROM AUTHOR]