Reconciling Flagging Strategies for Multi-Sensor Satellite Soil Moisture Climate Data Records

Reliable soil moisture retrievals from passive microwave satellite sensors are limited during certain conditions, e.g., snow coverage, radio-frequency interference, and dense vegetation. In these cases, the retrievals can be masked using flagging algorithms. Currently available single- and multi-sensor soil moisture products utilize different flagging approaches. However, a clear overview and comparison of these approaches and their impact on soil moisture data are still lacking. For long-term climate records such as the soil moisture products of the European Space Agency (ESA) Climate Change Initiative (CCI), the effect of any flagging inconsistency resulting from combining multiple sensor datasets is not yet understood. Therefore, the first objective of this study is to review the data flagging system that is used within multi-sensor ESA CCI soil moisture products as well as the flagging systems of two other soil moisture datasets from sensors that are also used for the ESA CCI soil moisture products: The level 3 Soil Moisture and Ocean Salinity (SMOS) and the Soil Moisture Active/Passive (SMAP). The SMOS and SMAP soil moisture flagging systems differ substantially in number and type of conditions considered, critical flags, and data source dependencies. The impact on the data availability of the different flagging systems were compared for the SMOS and SMAP soil moisture datasets. Major differences in data availability were observed globally, especially for northern high latitudes, mountainous regions, and equatorial latitudes (up to 37%, 33%, and 32% respectively) with large seasonal variability. These results highlight the importance of a consistent and well-performing approach that is applicable to all individual products used in long-term soil moisture data records. Consequently, the second objective of the present study is to design a consistent and model-independent flagging strategy to improve soil moisture climate records such as the ESA CCI products. As snow cover, ice, and frozen conditions were demonstrated to have the biggest impact on data availability, a uniform satellite driven flagging strategy was designed for these conditions and evaluated against two ground observation networks. The new flagging strategy demonstrated to be a robust flagging alternative when compared to the individual flagging strategies adopted by the SMOS and SMAP soil moisture datasets with a similar performance, but with the applicability to the entire ESA CCI time record without the use of modelled approximations.

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The development and relevance of a consistent flagging strategy for multi-sensor satellite soil moisture climate records

10.5194/egusphere-egu21-10740 ◽

2021 ◽

Author(s):

Mendy van der Vliet ◽

Richard de Jeu ◽

Nemesio Rodriguez-Fernandez ◽

Tracy Scanlon ◽

Andreas Colliander ◽

...

Keyword(s):

Soil Moisture ◽

Radio Frequency ◽

Heavy Precipitation ◽

Data Availability ◽

Radio Frequency Interference ◽

Mountainous Regions ◽

Dense Vegetation ◽

Climate Records ◽

The Impact

The quality of soil moisture retrievals from passive microwave satellite sensors is limited during certain conditions, e.g. snow coverage, radio-frequency interference and dense vegetation. Therefore, masking the retrievals in these conditions by data flagging algorithms is vital for the production of reliable satellite-based products. However, these products utilise different flagging methods. A clear overview and comparison of these methods and their impact on the data are lacking. For long-term soil moisture records such as the ESA CCI soil moisture products, the impact of any flagging inconsistency from combining multiple sensor datasets was not assessed.Recently, Van der Vliet et al. (2020) provided a review of the data flagging system that is used within multi-sensor ESA CCI soil moisture products as well as the flagging systems of two other soil moisture datasets from sensors that are also used for the ESA CCI soil moisture products: The level 3 Soil Moisture and Ocean Salinity (SMOS) and the Soil Moisture Active/Passive (SMAP). Substantial differences were detected between the SMOS and SMAP soil moisture flagging systems in terms of the number and type of conditions considered, critical flags, and data source dependencies. The impact on the data availability of the different flagging systems was shown to differ globally and especially for northern high latitudes, mountainous regions, and equatorial latitudes (up to 37%, 33%, and 32% respectively) with large seasonal variability. These results highlighted the relevance of a consistent and well-performing flagging approach that is applicable to all individual products used in long-term soil moisture data records.Consequently, Van der Vliet et al. (2020) designed a consistent and model-independent flagging strategy to improve soil moisture climate records. For the snow cover, ice, and frozen conditions, which were found to have the highest impact on data availability, a uniform satellite driven flagging strategy was designed and evaluated against two ground observation networks. Compared to the individual flagging approaches adopted by the SMOS and SMAP soil moisture datasets, the new flagging approach was demonstrated to be a robust flagging alternative, with a similar performance, but with the applicability to the full ESA CCI historical record without the use of modelled approximations.&#160;A part of the designed flagging decision tree demonstrated to form a good base for the filtering of bare grounds and heavy precipitation events as well. A future extension of the flagging strategy is expected to mask these conditions, as well as other conditions such as radio frequency interference and dense vegetation.

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ESA CCI and C3S Soil Moisture: latest product updates and climate assessments

10.5194/egusphere-egu2020-19198 ◽

2020 ◽

Author(s):

Wouter Dorigo ◽

Wolfgang Preimesberger ◽

Adam Pasik ◽

Alexander Gruber ◽

Leander Moesinger ◽

...

Keyword(s):

Climate Change ◽

Soil Moisture ◽

European Space Agency ◽

Climate Data ◽

Change Initiative ◽

Space Agency ◽

Data Store ◽

Climate Data Record ◽

Level 2

As part of the European Space Agency (ESA) Climate Change Initiative (CCI) a more than 40 year long climate data record (CDR) is produced by systematically combining Level-2 datasets from separate missions. Combining multiple level 2 datasets into a single consistent long-term product combines the advantages of individual missions and allows deriving a harmonised long-term record with optimal spatial and temporal coverage. The current version of ESA CCI Soil Moisture includes a PASSIVE (radiometer-based) dataset covering the period 1978 to 2019, an ACTIVE (scatterometer-based) product covering the period 1991-2019 and a COMBINED product (1978-2019).&#160;The European Commission&#8217;s Copernicus Climate Changes Service (C3S) uses the ESA CCI soil moisture algorithm to produce similar climate data records from near-real-time Level-2 data streams.&#160; These products are continuously extended within 10 days after data acquisition and instantaneously made available through the C3S Climate Data Store. In addition to a daily product, monthly aggregates as well as a dekadal (10-days) products are produced.In this presentation we give an overview of the latest developments of the ESA CCI and C3S Soil Moisture datasets, which include the integration of SMAP and various algorithmic updates, and use the datasets to assess the hydrological conditions of 2019 with respect to a 30-year historical baseline.The development of the ESA CCI products has been supported by ESA&#8217;s Climate Change Initiative for Soil Moisture (Contract No. 4000104814/11/I-NB and 4000112226/14/I-NB). The Copernicus Climate Change Service (C3S) soil moisture product is funded by the Copernicus Climate Change Service implemented by ECMWF through C3S 312b Lot 7 Soil Moisture service.

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A methodology to quantify and monitor the impact of environmental restoration using climate data records from satellite observations

10.5194/ems2021-9 ◽

2021 ◽

Author(s):

Mendy van der Vliet ◽

Richard de Jeu ◽

Jaap Schellekens ◽

Robin van der Schalie

Keyword(s):

Climate Change ◽

Surface Temperature ◽

European Space Agency ◽

Quality Data ◽

Climate Mitigation ◽

Environmental Restoration ◽

Climate Data ◽

Great Opportunity ◽

The Impact

Environmental restoration has the potential to constrain human-induced land degradation, loss of biodiversity and climate change. Although the practise is increasingly integrated into natural resource and climate mitigation strategies, scientific studies underline that the effectiveness and impact of these restoration projects are currently difficult to monitor and assess. In order to measure the global community&#8217;s progress towards the Sustainable Development Goals (SDGs), restoration interventions need to be assessed in a systematic and objective manner. However, the long-term and high-quality data records that are required for this are often lacking in both time and space. Satellite data products that can detect changes in land use, surface temperature and hydrological conditions over time in a consistent manner, can fill this gap.Over the last few decades, the scientific community has made great efforts to merge different satellites into multi-decadal historical datasets of climate variables. Examples of such long-term climate data records (CDRs) are the soil moisture (from 1978 onwards), land surface temperature (since 1995) and land cover (since 2008) datasets of the European Space Agency Climate Change Initiative (ESA CCI). These consistent datasets, combined with near real-time observations, offer a great opportunity to quantify and monitor the impact of restoration interventions on degraded landscapes. In order to monitor restoration projects affecting areas smaller than the native resolutions of these datasets (up to approximately 25 km), downscaling techniques can be used to increase the spatial level of detail (approximately in the 0.1-1 km range). The resulting monitoring service could help managers of restoration programs and green investment funds to steer decisions and communicate on effectiveness towards their donors.&#160;The satellite datasets were investigated in space and time in relation to the effects of the restoration projects. For each restoration project area, several surface conditions were monitored and compared to those in an unaffected control area to detect and attribute the effects of the restoration program. The present work focuses on several case studies in which the relevance of satellite-based CDRs for the end users&#8217; operational practises related to impact monitoring is assessed in the context of the SDGs 12 (Responsible production and consumption), 13 (Life on land) and 15 (Climate action).

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Global soil moisture data derived through machine learning trained with in-situ measurements

Scientific Data ◽

10.1038/s41597-021-00964-1 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Sungmin O. ◽

Rene Orth

Keyword(s):

Machine Learning ◽

Soil Moisture ◽

Large Scale ◽

Short Term Memory ◽

Temporal Dynamics ◽

Soil Moisture Data ◽

Wide Range ◽

Global Soil

AbstractWhile soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learning trained with in-situ measurements, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0–10 cm, 10–30 cm, and 30–50 cm) at 0.25° spatial and daily temporal resolution over the period 2000–2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.

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Corruption, Taxation and the Impact on the Shadow Economy

Economies ◽

10.3390/economies9010018 ◽

2021 ◽

Vol 9 (1) ◽

pp. 18 ◽

Cited By ~ 1

Author(s):

Daniel Němec ◽

Eva Kotlánová ◽

Igor Kotlán ◽

Zuzana Machová

Keyword(s):

Tax Evasion ◽

Capital Accumulation ◽

Shadow Economy ◽

Labour Force ◽

Destructive Effect ◽

Eu Member States ◽

Initial Assumption ◽

The Individual ◽

The Impact

While assessing the economic impacts of corruption, the corruption-related transmission channels which influence taxation as such have to be duly considered. Taking the example of the Czech Republic, this article aims to evaluate the impacts corruption has on the size of the shadow economy as well as on the individual sources of long-term economic growth, making use of a transmission channel through which corruption affects the tax burden components. Using the method of an extended DSGE model, it confirms the initial assumption that an increase in perceived corruption supports the shadow economy’s growth, but at the same time, it demonstrates that corruption and especially its perception has a significantly different effect on two key areas—the capital accumulation and the labour force size. It further identifies another sector of the economy representing taxes which are prone to tax evasion while asserting that corruption has a much more destructive effect on this sector of the economy, offering generalized implications for other post-communist EU member states in a similar situation.

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The Impact of Climate Change on the Water Balance of Oil Sands Reclamation Covers and Natural Soil Profiles

Journal of Hydrometeorology ◽

10.1175/jhm-d-17-0230.1 ◽

2018 ◽

Vol 19 (11) ◽

pp. 1731-1752 ◽

Cited By ~ 6

Author(s):

Md. Shahabul Alam ◽

S. Lee Barbour ◽

Amin Elshorbagy ◽

Mingbin Huang

Keyword(s):

Oil Sands ◽

Weather Generator ◽

First Century ◽

Soil Profiles ◽

Percentage Increase ◽

Climate Data ◽

Historical Climate ◽

Twenty First Century ◽

The Impact

Abstract The design of reclamation soil covers at oil sands mines in northern Alberta, Canada, has been conventionally based on the calibration of soil–vegetation–atmosphere transfer (SVAT) models against field monitoring observations collected over several years, followed by simulations of long-term performance using historical climate data. This paper evaluates the long-term water balances for reclamation covers on two oil sands landforms and three natural coarse-textured forest soil profiles using both historical climate data and future climate projections. Twenty-first century daily precipitation and temperature data from CanESM2 were downscaled based on three representative concentration pathways (RCPs) employing a stochastic weather generator [Long Ashton Research Station Weather Generator (LARS-WG)]. Relative humidity, wind speed, and net radiation were downscaled using the delta change method. Downscaled precipitation and estimated potential evapotranspiration were used as inputs to simulate soil water dynamics using physically based models. Probability distributions of growing season (April–October) actual evapotranspiration (AET) and net percolation (NP) for the baseline and future periods show that AET and NP at all sites are expected to increase throughout the twenty-first century regardless of RCP, time period, and soil profile. Greater increases in AET and NP are projected toward the end of the twenty-first century. The increases in future NP at the two reclamation covers are larger (as a percentage increase) than at most of the natural sites. Increases in NP will result in greater water yield to surface water and may accelerate the rate at which chemical constituents contained within mine waste are released to downstream receptors, suggesting these potential changes need to be considered in mine closure designs.

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A regionally explicit, global SWI calibration based on ISMN observations

10.5194/egusphere-egu21-496 ◽

2021 ◽

Author(s):

Manolis G. Grillakis

Keyword(s):

Soil Moisture ◽

Soil Water ◽

Large Scale ◽

Root Zone ◽

Soil Depth ◽

European Space Agency ◽

Soil Layer ◽

Added Value ◽

Water Index ◽

Soil Moisture Data

Remote sensing has proven to be an irreplaceable tool for monitoring soil moisture. The European Space Agency (ESA), through the Climate Change Initiative (CCI), has provided one of the most substantial contributions in the soil water monitoring, with almost 4 decades of global satellite derived and homogenized soil moisture data for the uppermost soil layer. Yet, due to the inherent limitations of many of the remote sensors, only a limited soil depth can be monitored. To enable the assessment of the deeper soil layer moisture from surface remotely sensed products, the Soil Water Index (SWI) has been established as a convolutive transformation of the surface soil moisture estimation, under the assumption of uniform hydraulic conductivity and the absence of transpiration. The SWI uses a single calibration parameter, the T-value, to modify its response over time.Here the Soil Water Index (SWI) is calibrated using ESA CCI soil moisture against in situ observations from the International Soil Moisture Network and then use Artificial Neural Networks (ANNs) to find the best physical soil, climate, and vegetation descriptors at a global scale to regionalize the calibration of the T-value. The calibration is then used to assess a root zone related soil moisture for the period 2001 &#8211; 2018.The results are compared against the European Centre for Medium-Range Weather Forecasts, ERA5 Land reanalysis soil moisture dataset, showing a good agreement, mainly over mid-latitudes. The results indicate that there is added value to the results of the machine learning calibration, comparing to the uniform T-value. This work contributes to the exploitation of ESA CCI soil moisture data, while the produced data can support large scale soil moisture related studies.

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QA4SM – An online tool for satellite soil moisture data validation

10.5194/egusphere-egu21-7398 ◽

2021 ◽

Author(s):

Samuel Scherrer ◽

Wolfgang Preimesberger ◽

Monika Tercjak ◽

Zoltan Bakcsa ◽

Alexander Boresch ◽

...

Keyword(s):

Climate Change ◽

Soil Moisture ◽

Land Surface ◽

Global Climate ◽

European Space Agency ◽

Land Surface Model ◽

Surface Model ◽

Space Applications ◽

Online Tool ◽

Soil Moisture Data

To validate satellite soil moisture products and compare their quality with other products, standardized, fully traceable validation methods are required. The QA4SM (Quality Assurance for Soil Moisture; ) free online validation tool provides an easy-to-use implementation of community best practices and requirements set by the Global Climate Observing System and the Committee on Earth Observation Satellites. It sets the basis for a community wide standard for validation studies.QA4SM can be used to preprocess, intercompare, store, and visualise validation results. It uses state-of-the-art open-access soil moisture data records such as the European Space Agency&#8217;s Climate Change Initiative (ESA CCI) and the Copernicus Climate Change Services (C3S) soil moisture datasets, as well as single-sensor products, e.g. H-SAF Metop-A/B ASCAT surface soil moisture, SMOS-IC, and SMAP L3 soil moisture. Non-satellite data include in-situ data from the International Soil Moisture Network (ISMN: ), as well as land surface model or reanalysis products, e.g. ERA5 soil moisture.Users can interactively choose temporal or spatial subsets of the data and apply filters on quality flags. Additionally, validation of anomalies and application of different scaling methods are possible. The tool provides traditional validation metrics for dataset pairs (e.g. correlation, RMSD) as well as triple collocation metrics for dataset triples. All results can be visualised on the webpage, downloaded as figures, or downloaded in NetCDF format for further use. Archiving and publishing features allow users to easily store and share validation results. Published validation results can be cited in reports and publications via DOIs.The new version of the service provides support for high-resolution soil moisture products (from Sentinel-1), additional datasets, and improved usability.We present an overview and examples of the online tool, new features, and give an outlook on future developments.Acknowledgements: This work was supported by the QA4SM & QA4SM-HR projects, funded by the Austrian Space Applications Programme (FFG).

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1156Coronary artery bypass grafting vs. FFR-guided PCI in diabetic patients with multivessel disease

European Heart Journal ◽

10.1093/eurheartj/ehz748.0009 ◽

2019 ◽

Vol 40 (Supplement_1) ◽

Author(s):

G Di Gioia ◽

N Soto Flores ◽

D Franco ◽

I Colaiori ◽

J Sonck ◽

...

Keyword(s):

Myocardial Infarction ◽

Artery Bypass ◽

Diabetic Patients ◽

Bypass Grafting ◽

Fractional Flow ◽

Artery Bypass Grafting ◽

The Individual ◽

The Impact

Abstract Background In diabetic patients with multivessel coronary disease (MVD), coronary artery bypass grafting (CABG) has shown long-term benefits in mortality over percutaneous coronary revascularization (PCI). Nevertheless, the impact of fractional flow reserve (FFR)-guided PCI on clinical outcomes has never been investigated in these patients. Purpose To evaluate the long-term (5-year) clinical outcome of diabetic patients with MVD treated with FFR-guided PCI compared to CABG. Methods From February 2010 to February 2018, all diabetic patients undergoing coronary angiography in one centre (n=4622) were screened for inclusion. The inclusion criterion was presence of at least two-vessels CAD defined as with diameters stenosis ≥50%. In case of intermediate coronary stenosis (%DS 30–70%), FFR was performed at the discretion of the operator. Revascularization was performed when FFR ≤0.80. Exclusion criteria were ST-elevation myocardial infarction, prior CABG, and moderate or severe valvular heart dysfunction. To account for confounders, we compared outcomes by calculating an adjusted Kaplan-Meier estimator using inverse probability of treatment weighting (IPTW). Propensity score variables included age, sex, smoking habit, hypertension, hyperlipidemia, insulin therapy, family history of CAD, chronic obstructive pulmonary disease (COPD), glomerular filtration rate (GFR), prior myocardial infarction, peripheral vascular disease (PVD), admission for NSTEMI, ejection fraction, number of angiographic stenotic vessels. Odds ratios were calculated using generalized linear models (GLM). The primary endpoint was major adverse cardiovascular and cerebrovascular events (MACCE), defined as all-cause death, myocardial infarction and stroke. Secondary endpoints were the individual component of MACCE and any repeated revascularization. Results A total of 538 diabetic patients with MVD were included in the analysis. Among them, 317 (59%) patients underwent CABG and 221 (41%) FFR-guided PCI. Patients treated with FFR-guided PCI had more often COPD as compared to patients in the CABG-group, but patients treated with CABG had lower GFR, more PVD, higher number of angiographic stenotic vessels (2.8±0.4 vs. 2.5±0.5; p<0.01) and higher Syntax score (20±7 vs. 14±6; p<0.01) as compared to the FFR-guided PCI group. Clinical follow-up was obtained in 95% of the patients at a median follow-up of 5 years. The incidence of MACCE was similar in the CABG and in the FFR-guided PCI group [27% vs. 29%; OR (95% CI) 1.05 (0.68–1.63); p=0.74]. No differences were found in the individual components of MACCE. Repeat revascularization was more frequent in the FFR-guided PCI group than in the CABG group [27% vs. 7%; OR (95% CI) 4.3 (2.35–7.9); p<0.01]. Conclusions In diabetic patients with MVD undergoing FFR-guided PCI, no differences in major adverse events were observed at a median follow-up of 5 years compared with CABG.

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The Impact of SMOS Soil Moisture Data Assimilation within the Operational Global Flood Awareness System (GloFAS)

Remote Sensing ◽

10.3390/rs12091490 ◽

2020 ◽

Vol 12 (9) ◽

pp. 1490 ◽

Cited By ~ 1

Author(s):

Calum Baugh ◽

Patricia de Rosnay ◽

Heather Lawrence ◽

Toni Jurlina ◽

Matthias Drusch ◽

...

Keyword(s):

United States ◽

Soil Moisture ◽

Data Assimilation ◽

The United States ◽

Streamflow Prediction ◽

Soil Moisture Data ◽

Skill Scores ◽

High Flows ◽

The Impact ◽

Awareness System

In this study the impacts of Soil Moisture and Ocean Salinity (SMOS) soil moisture data assimilation upon the streamflow prediction of the operational Global Flood Awareness System (GloFAS) were investigated. Two GloFAS experiments were performed, one which used hydro-meteorological forcings produced with the assimilation of the SMOS data, the other using forcings which excluded the assimilation of the SMOS data. Both sets of experiment results were verified against streamflow observations in the United States and Australia. Skill scores were computed for each experiment against the observation datasets, the differences in the skill scores were used to identify where GloFAS skill may be affected by the assimilation of SMOS soil moisture data. In addition, a global assessment was made of the impact upon the 5th and 95th GloFAS flow percentiles to see how SMOS data assimilation affected low and high flows respectively. Results against in-situ observations found that GloFAS skill score was only affected by a small amount. At a global scale, the results showed a large impact on high flows in areas such as the Hudson Bay, central United States, the Sahel and Australia. There was no clear spatial trend to these differences as opposing signs occurred within close proximity to each other. Investigating the differences between the simulations at individual gauging stations showed that they often only occurred during a single flood event; for the remainder of the simulation period the experiments were almost identical. This suggests that SMOS data assimilation may affect the generation of surface runoff during high flow events, but may have less impact on baseflow generation during the remainder of the hydrograph. To further understand this, future work could assess the impact of SMOS data assimilation upon specific hydrological components such as surface and subsurface runoff.

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