Retrieval of snow properties from the Sentinel-3 Ocean and Land Colour Instrument

The Sentinel Application Platform (SNAP) architecture facilitates Earth Observation data processing. In this work, we present results from a new Snow Processor for SNAP. We also describe physical principles behind the developed snow property retrieval technique based on the analysis of Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3A/B measurements over clean and polluted snow fields. Using OLCI spectral reflectance measurements in the range 400–1020 nm, we derived important snow properties such as spectral and broadband albedo, snow specific surface area, snow extent and grain size on a spatial grid of 300 m. The algorithm also incorporated cloud screening and atmospheric correction procedures over snow surfaces. We present validation results using ground measurements from Antarctica, the Greenland ice sheet and the French Alps. We find the spectral albedo retrieved with accuracy of better than 3% on average, making our retrievals sufficient for a variety of applications. Broadband albedo is retrieved with the average accuracy of about 5% over snow. Therefore, the uncertainties of satellite retrievals are close to experimental errors of ground measurements. The retrieved surface grain size shows good agreement with ground observations. Snow specific surface area observations are also consistent with our OLCI retrievals. We present snow albedo and grain size mapping over the inland ice sheet of Greenland for areas including dry snow, melted/melting snow and impurity rich bare ice. The algorithm can be applied to OLCI Sentinel-3 measurements providing an opportunity for creation of long-term snow property records essential for climate monitoring and data assimilation studies—especially in the Arctic region, where we face rapid environmental changes including reduction of snow/ice extent and, therefore, planetary albedo.publishedVersio

Diversity II water quality parameters for 300 lakes worldwide from ENVISAT (2002-2012)

The use of ground sampled water quality information for global studies is limited due to practical and financial constraints. Remote sensing is a valuable means to overcome such limitations and to provide synoptic views of ambient water quality at appropriate spatio-temporal scales. In past years several large data processing efforts were initiated to provide corresponding data sources. The Diversity II water quality dataset consists of several monthly, yearly and 9-year averaged water quality parameters for 340 lakes worldwide and is based on data from the full ENVISAT MERIS operation period (2002–2012). Existing retrieval methods and datasets were selected after an extensive algorithm intercomparison exercise. Chlorophyll-a, total suspended matter, turbidity, coloured dissolved organic matter, lake surface water temperature, cyanobacteria and floating vegetation maps, as well as several auxiliary data layers, provide a generically specified database that can be used for assessing a variety of locally relevant ecosystem properties and environmental problems. For validation and accuracy assessment, we provide matchup comparisons for 24 lakes and a group of reservoirs representing a wide range of bio-optical conditions. Matchup comparisons for chlorophyll-a concentrations indicate mean absolute errors and bias in the order of median concentrations for individual lakes, while total suspended matter and turbidity retrieval achieve significantly better performance metrics across several lake-specific datasets. We demonstrate the use of the products by illustrating and discussing remotely sensed evidence of lake-specific processes and prominent regime shifts documented in the literature

Empty polyetheretherketone (PEEK) cages in anterior cervical diskectomy and fusion (ACDF) show slow radiographic fusion that reduces clinical improvement: results from the prospective multicenter “PIERCE-PEEK” study

Abstract Background Anterior cervical diskectomy and fusion (ACDF) is a well-established surgical treatment for radiculopathy and myelopathy. Previous studies showed that empty PEEK cages have lower radiographic fusion rates, but the clinical relevance remains unclear. This paper’s aim is to provide high-quality evidence on the outcomes of ACDF with empty PEEK cages and on the relevance of radiographic fusion for clinical outcomes. Methods This large prospective multicenter clinical trial performed single-level ACDF with empty PEEK cages on patients with cervical radiculopathy or myelopathy. The main clinical outcomes were VAS (0–10) for pain and NDI (0–100) for functioning. Radiographic fusion was evaluated by two investigators for three different aspects. Results The median (range) improvement of the VAS pain score was: 3 (1–6) at 6 months, 3 (2–8) at 12 months, and 4 (2–8) at 18 months. The median (range) improvement of the NDI score was: 12 (2–34) at 6 months, 18 (4–46) at 12 months, and 22 (2–44) at 18 months. Complete radiographic fusion was reached by 126 patients (43%) at 6 months, 214 patients (73%) at 12 months, and 241 patients (83%) at 18 months. Radiographic fusion was a highly significant (p < 0.001) predictor of the improvement of VAS and NDI scores. Conclusion This study provides strong evidence that ACDF is effective treatment, but the overall rate of radiographic fusion with empty PEEK cages is slow and insufficient. Lack of complete radiographic fusion leads to less improvement of pain and disability. We recommend against using empty uncoated pure PEEK cages in ACDF. Trial registration ISRCTN42774128 . Retrospectively registered 14 April 2009

Evaluation of the tropical water vapor of CMIP6 GCMs with ESA CCI+ “Water Vapor” climate data records: Insights from large-scale atmospheric circulation

International audienceWater vapor is one of the fundamental elements in the atmosphere. Its distribution is strongly associated with large-scale atmospheric circulation. Here the new global water vapor climate data records (CDR) generated within the ESA Water Vapor CCI+ project (WV_cci) is used to perform a comprehensive evaluation of total column water vapor provided by 21 global climate models (CMIP6 framework). The ESA WV_cci CDRs cover the period 2002-2017 with a daily frequency and a regular 0.5° spatial resolution. The focus is on the tropical region (30°S - 30°N). The observational diagnostic relies on the decomposition of the tropical atmosphere into large-scale dynamical regimes using the 500 hPa atmospheric vertical velocity w500 (in hPa/day) as a proxy. The ESA WV_cci and the CMIP6 data are then sorted according to dynamical regimes (intervals of 10 hPa/day) allowing to study the evolution of the regimes in terms of frequency of occurrence and is linked to water vapor variation. While the basic picture of the tropical atmosphere is properly represented by the models (moister in ascending branches, drier in subsiding branches) there are noticeable differences in the patterns that will be discussed. The inter-annual variation of water vapor for both observation and the models will be analyzed, and the trend significance are assessed using Mann-Kendall test. This highlights the interest of water vapor climate data records for model evaluation

An Improved Retrieval of Snow and Ice Properties Using Spaceborne OLCI/S-3 Spectral Reflectance Measurements: Updated Atmospheric Correction and Snow Impurity Load Estimation

We present an update of the Snow and Ice (SICE) property retrieval algorithm based on the spectral measurements of Ocean and Land Color Instrument (OLCI) onboard Sentinel-3 satellites combined with the asymptotic radiative transfer theory valid for weakly absorbing turbid media. The main improvements include the introduction of a new atmospheric correction, retrieval of snow impurity load and properties, retrievals for partially snow-covered ground and also accounting for various thresholds to be used to assess the retrieval quality. The technique can be applied to various optical sensors (satellite and ground-based) operated in the visible and near infrared regions of electromagnetic spectra

Influences of leaf area index and albedo on estimating energy fluxes with HOLAPS framework

The High resOlution Land Atmosphere surface Parameters from Space (HOLAPS) programme provides a modeling system to maximize the use of satellite-based products and ensure internally consistent estimation of surface water and energy fluxes. Leaf area index (LAI) and land surface albedo are two key parameters for estimation of latent and sensible heat fluxes with HOLAPS. Thus, to facilitate the generation of long-term high accuracy latent and sensible heat fluxes, high quality global long-term LAI and land surface albedo datasets are essential. The Quality Assurance for Essential Climate Variables (QA4ECV) project released quality-assured long-term LAI and albedo datasets with traceable and reliable uncertainty information provided in the dataset. Taking MODIS-BNU-LAI and MODIS albedo as reference, different global long-term LAI and albedo datasets including GlobAlbedo, QA4ECV and GLOBMAP were investigated for estimation of latent/sensible heat fluxes with HOLAPS in this study. The results show that all albedo datasets show similar accuracy for estimation of latent and sensible heat fluxes when validated against FLUXNET observations. The QA4ECV LAI leads to worse latent heat flux estimation due to its use of effective LAI rather than green leaf LAI. Sensitivity analysis also shows that the HOLAPS estimated latent heat flux (LE) is more sensitive to uncertainty in LAI than land surface albedo. Overall, the combined use of QA4ECV albedo and GLOBMAP LAI is suggested for estimation of latent/sensible heat fluxes with HOLAPS. The root mean square differences (RMSD) between estimations and FLUXNET measurements are 54 (30) W/m2 for hourly (monthly) latent heat flux, and 80.5 (24.5) W/m2 for sensible heat flux, which are comparable to estimates with MODIS and other reported studies.JRC.D.6-Knowledge for Sustainable Development and Food Securit

Can we use satellite-based FAPAR to detect drought?

Drought in Australia has widespread impacts on agriculture and ecosystems. Satellite-based Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) has great potential to monitor and assess drought impacts on vegetation greenness and health. Various FAPAR products based on satellite observations have been generated and made available to the public. However, differences remain among these datasets due to dierent retrieval methodologies and assumptions. The Quality Assurance for Essential Climate Variables (QA4ECV) project recently developed a quality assurance framework to provide understandable and traceable quality information for Essential ClimateVariables (ECVs). The QA4ECV FAPAR is one of these ECVs. The aim of this study is to investigate the capability of QA4ECV FAPAR for drought monitoring in Australia. Through spatial and temporal comparison and correlation analysis with widely used Moderate Resolution Imaging Spectroradiometer (MODIS), Satellite Pour l’Observation de la Terre (SPOT)/PROBA-V FAPAR generated by Copernicus Global Land Service (CGLS), and the Standardized Precipitation Evapotranspiration Index (SPEI) drought index, as well as the European Space Agency’s Climate Change Initiative (ESA CCI) soil moisture, the study shows that theQA4ECVFAPARcan support agricultural drought monitoring and assessment in Australia. The traceable and reliable uncertainties associated with the QA4ECV FAPAR provide valuable information for applications that use the QA4ECV FAPAR dataset in the future.JRC.D.6-Knowledge for Sustainable Development and Food Securit