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Todd J Hawbake - One of the best experts on this subject based on the ideXlab platform.

  • validation of the usgs landsat burned area essential Climate Variable baecv across the conterminous united states
    Remote Sensing of Environment, 2017
    Co-Authors: Melanie K Vanderhoof, Nicole M Fairau, Yenju G Eal, Todd J Hawbake
    Abstract:

    Abstract The Landsat Burned Area Essential Climate Variable (BAECV), developed by the U.S. Geological Survey (USGS), capitalizes on the long temporal availability of Landsat imagery to identify burned areas across the conterminous United States (CONUS) (1984–2015). Adequate validation of such products is critical for their proper usage and interpretation. Validation of coarse-resolution products often relies on independent data derived from moderate-resolution sensors (e.g., Landsat). Validation of Landsat products, in turn, is challenging because there is no corresponding source of high-resolution, multispectral imagery that has been systematically collected in space and time over the entire temporal extent of the Landsat archive. Because of this, comparison between high-resolution images and Landsat science products can help increase user's confidence in the Landsat science products, but may not, alone, be adequate. In this paper, we demonstrate an approach to systematically validate the Landsat-derived BAECV product. Burned area extent was mapped for Landsat image pairs using a manually trained semi-automated algorithm that was manually edited across 28 path/rows and five different years (1988, 1993, 1998, 2003, 2008). Three datasets were independently developed by three analysts and the datasets were integrated on a pixel by pixel basis in which at least one to all three analysts were required to agree a pixel was burned. We found that errors within our Landsat reference dataset could be minimized by using the rendition of the dataset in which pixels were mapped as burned if at least two of the three analysts agreed. BAECV errors of omission and commission for the detection of burned pixels averaged 42% and 33%, respectively for CONUS across all five validation years. Errors of omission and commission were lowest across the western CONUS, for example in the shrub and scrublands of the Arid West (31% and 24%, respectively), and highest in the grasslands and agricultural lands of the Great Plains in central CONUS (62% and 57%, respectively). The BAECV product detected most (> 65%) fire events > 10 ha across the western CONUS (Arid and Mountain West ecoregions). Our approach and results demonstrate that a thorough validation of Landsat science products can be completed with independent Landsat-derived reference data, but could be strengthened by the use of complementary sources of high-resolution data.

  • evaluation of the u s geological survey landsat burned area essential Climate Variable across the conterminous u s using commercial high resolution imagery
    Remote Sensing, 2017
    Co-Authors: Melanie K Vanderhoof, Yenju G Eal, Nicole M Unne, Todd J Hawbake
    Abstract:

    The U.S. Geological Survey has produced the Landsat Burned Area Essential Climate Variable (BAECV) product for the conterminous United States (CONUS), which provides wall-to-wall annual maps of burned area at 30 m resolution (1984–2015). Validation is a critical component in the generation of such remotely sensed products. Previous efforts to validate the BAECV relied on a reference dataset derived from Landsat, which was effective in evaluating the product across its timespan but did not allow for consideration of inaccuracies imposed by the Landsat sensor itself. In this effort, the BAECV was validated using 286 high-resolution images, collected from GeoEye-1, QuickBird-2, Worldview-2 and RapidEye satellites. A disproportionate sampling strategy was utilized to ensure enough burned area pixels were collected. Errors of omission and commission for burned area averaged 22 ± 4% and 48 ± 3%, respectively, across CONUS. Errors were lowest across the western U.S. The elevated error of commission relative to omission was largely driven by patterns in the Great Plains which saw low errors of omission (13 ± 13%) but high errors of commission (70 ± 5%) and potentially a region-growing function included in the BAECV algorithm. While the BAECV reliably detected agricultural fires in the Great Plains, it frequently mapped tilled areas or areas with low vegetation as burned. Landscape metrics were calculated for individual fire events to assess the influence of image resolution (2 m, 30 m and 500 m) on mapping fire heterogeneity. As the spatial detail of imagery increased, fire events were mapped in a patchier manner with greater patch and edge densities, and shape complexity, which can influence estimates of total greenhouse gas emissions and rates of vegetation recovery. The increasing number of satellites collecting high-resolution imagery and rapid improvements in the frequency with which imagery is being collected means greater opportunities to utilize these sources of imagery for Landsat product validation.

  • data release for validation of the usgs landsat burned area essential Climate Variable baecv across the conterminous u s
    2017
    Co-Authors: Melanie K Vanderhoof, Yenju G Eal, Nicole M Unne, Todd J Hawbake
    Abstract:

    The Landsat Burned Area Product Validation dataset was collected to determine the accuracy of The Landsat Burned Area Essential Climate Variable (BAECV) product, developed by the U.S. Geological Survey (USGS). The BAECV maps burned areas across the conterminous United States (CONUS) for the entire Landsat archive (1984 2015). Rigorous validation of such products is critical for their proper usage and interpretation. The sampling design used to derive this validation dataset was adapted from the methods used by European Space Agency s (ESA) Climate Change Initiative (CCI) fire_cci project to generate the first statistically rigorous global reference dataset for a burned area product that meets the CEOS LPVS stage 3 validation requirements. Our validation dataset consists of 28 Landsat path/rows across the CONUS which were selected using a stratified sampling scheme across the major Olson biomes, as summarized by the fire_cci project (Olson et al., 2001; Padilla et al. 2014). Within the CONUS this included temperate forest, Mediterranean forest, temperate grassland and savannah, tropical and subtropical grasslands and savannah, and other which included desert/xeric shrub and flooded grasslands (Padilla et al. 2014). Path/rows selected within each biome were meant to represent high and low burned areas as specified by the Global Fire Emissions Database (GFED) version 3 (Giglio et al, 2009, 2010). We used systematic sampling to select 5 validation years spaced out in 5 year increments (2008, 2003, 1998, 1993 and 1988). The validation dataset was then independently generated by three different analysts. Each analyst mapped new burned areas using Landsat pre-fire and post-fire image pairs. The burned area polygons were generated using the Burned Area Mapping Software (BAMS), which is a semi-automated algorithm developed by the University of Alcala, Madrid, and implemented by the fire_cci project (Bastarrika et al., 2014; Padilla et al., 2014). The outputs were manually edited using visual interpretation. From these outputs, three renditions of the validation datasets were generated in which burned area extent ranged from liberal (or inclusive) (Level 1) to conservative (Level 3). Burned area extent was defined as (1) at least one analyst identified a given pixel as burned (Level 1), (2) at least two of the three analysts were required to agree a given pixel was burned (Level 2), (3) all three analysts were required to agree a pixel was burned (Level 3). Full details of the methods used to derive this validation dataset are provided in Vanderhoof et al. (In review).

Melanie K Vanderhoof - One of the best experts on this subject based on the ideXlab platform.

  • validation of the usgs landsat burned area essential Climate Variable baecv across the conterminous united states
    Remote Sensing of Environment, 2017
    Co-Authors: Melanie K Vanderhoof, Nicole M Fairau, Yenju G Eal, Todd J Hawbake
    Abstract:

    Abstract The Landsat Burned Area Essential Climate Variable (BAECV), developed by the U.S. Geological Survey (USGS), capitalizes on the long temporal availability of Landsat imagery to identify burned areas across the conterminous United States (CONUS) (1984–2015). Adequate validation of such products is critical for their proper usage and interpretation. Validation of coarse-resolution products often relies on independent data derived from moderate-resolution sensors (e.g., Landsat). Validation of Landsat products, in turn, is challenging because there is no corresponding source of high-resolution, multispectral imagery that has been systematically collected in space and time over the entire temporal extent of the Landsat archive. Because of this, comparison between high-resolution images and Landsat science products can help increase user's confidence in the Landsat science products, but may not, alone, be adequate. In this paper, we demonstrate an approach to systematically validate the Landsat-derived BAECV product. Burned area extent was mapped for Landsat image pairs using a manually trained semi-automated algorithm that was manually edited across 28 path/rows and five different years (1988, 1993, 1998, 2003, 2008). Three datasets were independently developed by three analysts and the datasets were integrated on a pixel by pixel basis in which at least one to all three analysts were required to agree a pixel was burned. We found that errors within our Landsat reference dataset could be minimized by using the rendition of the dataset in which pixels were mapped as burned if at least two of the three analysts agreed. BAECV errors of omission and commission for the detection of burned pixels averaged 42% and 33%, respectively for CONUS across all five validation years. Errors of omission and commission were lowest across the western CONUS, for example in the shrub and scrublands of the Arid West (31% and 24%, respectively), and highest in the grasslands and agricultural lands of the Great Plains in central CONUS (62% and 57%, respectively). The BAECV product detected most (> 65%) fire events > 10 ha across the western CONUS (Arid and Mountain West ecoregions). Our approach and results demonstrate that a thorough validation of Landsat science products can be completed with independent Landsat-derived reference data, but could be strengthened by the use of complementary sources of high-resolution data.

  • evaluation of the u s geological survey landsat burned area essential Climate Variable across the conterminous u s using commercial high resolution imagery
    Remote Sensing, 2017
    Co-Authors: Melanie K Vanderhoof, Yenju G Eal, Nicole M Unne, Todd J Hawbake
    Abstract:

    The U.S. Geological Survey has produced the Landsat Burned Area Essential Climate Variable (BAECV) product for the conterminous United States (CONUS), which provides wall-to-wall annual maps of burned area at 30 m resolution (1984–2015). Validation is a critical component in the generation of such remotely sensed products. Previous efforts to validate the BAECV relied on a reference dataset derived from Landsat, which was effective in evaluating the product across its timespan but did not allow for consideration of inaccuracies imposed by the Landsat sensor itself. In this effort, the BAECV was validated using 286 high-resolution images, collected from GeoEye-1, QuickBird-2, Worldview-2 and RapidEye satellites. A disproportionate sampling strategy was utilized to ensure enough burned area pixels were collected. Errors of omission and commission for burned area averaged 22 ± 4% and 48 ± 3%, respectively, across CONUS. Errors were lowest across the western U.S. The elevated error of commission relative to omission was largely driven by patterns in the Great Plains which saw low errors of omission (13 ± 13%) but high errors of commission (70 ± 5%) and potentially a region-growing function included in the BAECV algorithm. While the BAECV reliably detected agricultural fires in the Great Plains, it frequently mapped tilled areas or areas with low vegetation as burned. Landscape metrics were calculated for individual fire events to assess the influence of image resolution (2 m, 30 m and 500 m) on mapping fire heterogeneity. As the spatial detail of imagery increased, fire events were mapped in a patchier manner with greater patch and edge densities, and shape complexity, which can influence estimates of total greenhouse gas emissions and rates of vegetation recovery. The increasing number of satellites collecting high-resolution imagery and rapid improvements in the frequency with which imagery is being collected means greater opportunities to utilize these sources of imagery for Landsat product validation.

  • data release for validation of the usgs landsat burned area essential Climate Variable baecv across the conterminous u s
    2017
    Co-Authors: Melanie K Vanderhoof, Yenju G Eal, Nicole M Unne, Todd J Hawbake
    Abstract:

    The Landsat Burned Area Product Validation dataset was collected to determine the accuracy of The Landsat Burned Area Essential Climate Variable (BAECV) product, developed by the U.S. Geological Survey (USGS). The BAECV maps burned areas across the conterminous United States (CONUS) for the entire Landsat archive (1984 2015). Rigorous validation of such products is critical for their proper usage and interpretation. The sampling design used to derive this validation dataset was adapted from the methods used by European Space Agency s (ESA) Climate Change Initiative (CCI) fire_cci project to generate the first statistically rigorous global reference dataset for a burned area product that meets the CEOS LPVS stage 3 validation requirements. Our validation dataset consists of 28 Landsat path/rows across the CONUS which were selected using a stratified sampling scheme across the major Olson biomes, as summarized by the fire_cci project (Olson et al., 2001; Padilla et al. 2014). Within the CONUS this included temperate forest, Mediterranean forest, temperate grassland and savannah, tropical and subtropical grasslands and savannah, and other which included desert/xeric shrub and flooded grasslands (Padilla et al. 2014). Path/rows selected within each biome were meant to represent high and low burned areas as specified by the Global Fire Emissions Database (GFED) version 3 (Giglio et al, 2009, 2010). We used systematic sampling to select 5 validation years spaced out in 5 year increments (2008, 2003, 1998, 1993 and 1988). The validation dataset was then independently generated by three different analysts. Each analyst mapped new burned areas using Landsat pre-fire and post-fire image pairs. The burned area polygons were generated using the Burned Area Mapping Software (BAMS), which is a semi-automated algorithm developed by the University of Alcala, Madrid, and implemented by the fire_cci project (Bastarrika et al., 2014; Padilla et al., 2014). The outputs were manually edited using visual interpretation. From these outputs, three renditions of the validation datasets were generated in which burned area extent ranged from liberal (or inclusive) (Level 1) to conservative (Level 3). Burned area extent was defined as (1) at least one analyst identified a given pixel as burned (Level 1), (2) at least two of the three analysts were required to agree a given pixel was burned (Level 2), (3) all three analysts were required to agree a pixel was burned (Level 3). Full details of the methods used to derive this validation dataset are provided in Vanderhoof et al. (In review).

Yenju G Eal - One of the best experts on this subject based on the ideXlab platform.

  • validation of the usgs landsat burned area essential Climate Variable baecv across the conterminous united states
    Remote Sensing of Environment, 2017
    Co-Authors: Melanie K Vanderhoof, Nicole M Fairau, Yenju G Eal, Todd J Hawbake
    Abstract:

    Abstract The Landsat Burned Area Essential Climate Variable (BAECV), developed by the U.S. Geological Survey (USGS), capitalizes on the long temporal availability of Landsat imagery to identify burned areas across the conterminous United States (CONUS) (1984–2015). Adequate validation of such products is critical for their proper usage and interpretation. Validation of coarse-resolution products often relies on independent data derived from moderate-resolution sensors (e.g., Landsat). Validation of Landsat products, in turn, is challenging because there is no corresponding source of high-resolution, multispectral imagery that has been systematically collected in space and time over the entire temporal extent of the Landsat archive. Because of this, comparison between high-resolution images and Landsat science products can help increase user's confidence in the Landsat science products, but may not, alone, be adequate. In this paper, we demonstrate an approach to systematically validate the Landsat-derived BAECV product. Burned area extent was mapped for Landsat image pairs using a manually trained semi-automated algorithm that was manually edited across 28 path/rows and five different years (1988, 1993, 1998, 2003, 2008). Three datasets were independently developed by three analysts and the datasets were integrated on a pixel by pixel basis in which at least one to all three analysts were required to agree a pixel was burned. We found that errors within our Landsat reference dataset could be minimized by using the rendition of the dataset in which pixels were mapped as burned if at least two of the three analysts agreed. BAECV errors of omission and commission for the detection of burned pixels averaged 42% and 33%, respectively for CONUS across all five validation years. Errors of omission and commission were lowest across the western CONUS, for example in the shrub and scrublands of the Arid West (31% and 24%, respectively), and highest in the grasslands and agricultural lands of the Great Plains in central CONUS (62% and 57%, respectively). The BAECV product detected most (> 65%) fire events > 10 ha across the western CONUS (Arid and Mountain West ecoregions). Our approach and results demonstrate that a thorough validation of Landsat science products can be completed with independent Landsat-derived reference data, but could be strengthened by the use of complementary sources of high-resolution data.

  • evaluation of the u s geological survey landsat burned area essential Climate Variable across the conterminous u s using commercial high resolution imagery
    Remote Sensing, 2017
    Co-Authors: Melanie K Vanderhoof, Yenju G Eal, Nicole M Unne, Todd J Hawbake
    Abstract:

    The U.S. Geological Survey has produced the Landsat Burned Area Essential Climate Variable (BAECV) product for the conterminous United States (CONUS), which provides wall-to-wall annual maps of burned area at 30 m resolution (1984–2015). Validation is a critical component in the generation of such remotely sensed products. Previous efforts to validate the BAECV relied on a reference dataset derived from Landsat, which was effective in evaluating the product across its timespan but did not allow for consideration of inaccuracies imposed by the Landsat sensor itself. In this effort, the BAECV was validated using 286 high-resolution images, collected from GeoEye-1, QuickBird-2, Worldview-2 and RapidEye satellites. A disproportionate sampling strategy was utilized to ensure enough burned area pixels were collected. Errors of omission and commission for burned area averaged 22 ± 4% and 48 ± 3%, respectively, across CONUS. Errors were lowest across the western U.S. The elevated error of commission relative to omission was largely driven by patterns in the Great Plains which saw low errors of omission (13 ± 13%) but high errors of commission (70 ± 5%) and potentially a region-growing function included in the BAECV algorithm. While the BAECV reliably detected agricultural fires in the Great Plains, it frequently mapped tilled areas or areas with low vegetation as burned. Landscape metrics were calculated for individual fire events to assess the influence of image resolution (2 m, 30 m and 500 m) on mapping fire heterogeneity. As the spatial detail of imagery increased, fire events were mapped in a patchier manner with greater patch and edge densities, and shape complexity, which can influence estimates of total greenhouse gas emissions and rates of vegetation recovery. The increasing number of satellites collecting high-resolution imagery and rapid improvements in the frequency with which imagery is being collected means greater opportunities to utilize these sources of imagery for Landsat product validation.

  • data release for validation of the usgs landsat burned area essential Climate Variable baecv across the conterminous u s
    2017
    Co-Authors: Melanie K Vanderhoof, Yenju G Eal, Nicole M Unne, Todd J Hawbake
    Abstract:

    The Landsat Burned Area Product Validation dataset was collected to determine the accuracy of The Landsat Burned Area Essential Climate Variable (BAECV) product, developed by the U.S. Geological Survey (USGS). The BAECV maps burned areas across the conterminous United States (CONUS) for the entire Landsat archive (1984 2015). Rigorous validation of such products is critical for their proper usage and interpretation. The sampling design used to derive this validation dataset was adapted from the methods used by European Space Agency s (ESA) Climate Change Initiative (CCI) fire_cci project to generate the first statistically rigorous global reference dataset for a burned area product that meets the CEOS LPVS stage 3 validation requirements. Our validation dataset consists of 28 Landsat path/rows across the CONUS which were selected using a stratified sampling scheme across the major Olson biomes, as summarized by the fire_cci project (Olson et al., 2001; Padilla et al. 2014). Within the CONUS this included temperate forest, Mediterranean forest, temperate grassland and savannah, tropical and subtropical grasslands and savannah, and other which included desert/xeric shrub and flooded grasslands (Padilla et al. 2014). Path/rows selected within each biome were meant to represent high and low burned areas as specified by the Global Fire Emissions Database (GFED) version 3 (Giglio et al, 2009, 2010). We used systematic sampling to select 5 validation years spaced out in 5 year increments (2008, 2003, 1998, 1993 and 1988). The validation dataset was then independently generated by three different analysts. Each analyst mapped new burned areas using Landsat pre-fire and post-fire image pairs. The burned area polygons were generated using the Burned Area Mapping Software (BAMS), which is a semi-automated algorithm developed by the University of Alcala, Madrid, and implemented by the fire_cci project (Bastarrika et al., 2014; Padilla et al., 2014). The outputs were manually edited using visual interpretation. From these outputs, three renditions of the validation datasets were generated in which burned area extent ranged from liberal (or inclusive) (Level 1) to conservative (Level 3). Burned area extent was defined as (1) at least one analyst identified a given pixel as burned (Level 1), (2) at least two of the three analysts were required to agree a given pixel was burned (Level 2), (3) all three analysts were required to agree a pixel was burned (Level 3). Full details of the methods used to derive this validation dataset are provided in Vanderhoof et al. (In review).

Wolfgang Wagner - One of the best experts on this subject based on the ideXlab platform.

  • selecting algorithms for earth observation of Climate within the european space agency Climate change initiative introduction to a special issue
    Remote Sensing of Environment, 2015
    Co-Authors: Christopher J Merchant, Gerrit De Leeuw, Wolfgang Wagner
    Abstract:

    Abstract This special issue is focused on the assessment of algorithms for the observation of Earth’s Climate from environmental satellites. Climate data records derived by remote sensing are increasingly a key source of insight into the workings of and changes in Earth’s Climate system. Producers of data sets must devote considerable effort and expertise to maximise the true Climate signals in their products and minimise effects of data processing choices and changing sensors. A key choice is the selection of algorithm(s) for classification and/or retrieval of the Climate Variable. Within the European Space Agency Climate Change Initiative, science teams undertook systematic assessment of algorithms for a range of essential Climate Variables. The papers in the special issue report some of these exercises (for ocean colour, aerosol, ozone, greenhouse gases, clouds, soil moisture, sea surface temperature and glaciers). The contributions show that assessment exercises must be designed with care, considering issues such as the relative importance of different aspects of data quality (accuracy, precision, stability, sensitivity, coverage, etc.), the availability and degree of independence of validation data and the limitations of validation in characterising some important aspects of data (such as long-term stability or spatial coherence). As well as requiring a significant investment of expertise and effort, systematic comparisons are found to be highly valuable. They reveal the relative strengths and weaknesses of different algorithmic approaches under different observational contexts, and help ensure that scientific conclusions drawn from Climate data records are not influenced by observational artifacts, but are robust.

  • validation of the ascat soil water index using in situ data from the international soil moisture network
    International Journal of Applied Earth Observation and Geoinformation, 2014
    Co-Authors: Christoph Paulik, Wolfgang Wagner, Wouter Dorigo, R Kidd
    Abstract:

    Abstract Soil moisture is an essential Climate Variable and a key parameter in hydrology, meteorology and agriculture. Surface Soil Moisture (SSM) can be estimated from measurements taken by ASCAT onboard Metop-A and have been successfully validated by several studies. Profile soil moisture, while equally important, cannot be directly measured by remote sensing but must be modeled. The Soil Water Index (SWI) product developed for near real time applications within the framework of the GMES project geoland2 aims to provide such a modeled profile estimate using satellite data as input. It is produced from ASCAT SSM estimates using a two-layer water balance model which describes the relationship between surface and profile soil moisture as a function of time. It provides daily global data about moisture conditions for eight characteristic time lengths representing different depths. The objective of this work was to assess the overall quality of the SWI data. Furthermore we tested the assumptions of the used water balance model and checked if ancillary information about topography, water fraction and noise information are useful for identifying observations of questionable quality. SWI data from January 1st 2007 until the end of 2011 was compared to in situ soil moisture data from 664 stations belonging to 23 observation networks which are available through the International Soil Moisture Network (ISMN). These stations delivered 2081 time series at different depths which were compared to the SWI values. The average of the significant Pearson correlation coefficients was 0.54 while being greater than 0.5 for 64.4% of all time series. It was found that the characteristic time length showing the highest correlation increases with in situ observation depth, thus confirming the SWI model assumptions. Relationships of the correlation coefficients with topographic complexity, water fraction, in situ observation depth, and soil moisture noise were found.

  • fusion of active and passive microwave observations to create an essential Climate Variable data record on soil moisture
    ISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences, 2012
    Co-Authors: Wolfgang Wagner, Wouter Dorigo, D Fernandez, Jacques Benveniste, Eva Haas, Martin Ertl
    Abstract:

    Soil moisture was recently included in the list of Essential Climate Variables (ECVs) that are deemed essential for IPCC (Intergovernmental Panel on Climate Change) and UNFCCC (United Nations Framework Convention on Climate Change) needs and considered feasible for global observation. ECVs data records should be as long, complete and consistent as possible, and in the case of soil moisture this means that the data record shall be based on multiple data sources, including but not limited to active (scatterometer) and passive (radiometer) microwave observations acquired preferably in the low-frequency microwave range. Among the list of sensors that can be used for this task are the C-band scatterometers on board of the ERS and METOP satellites and the multi-frequency radiometers SMMR, SSM/I, TMI, AMSR-E, and Windsat. Together, these sensors already cover a time period of more than 30 years and the question is how can observations acquired by these sensors be merged to create one consistent data record? This paper discusses on a high-level possible approaches for fusing the individual satellite data. It is argued that the best possible approach for the fusion of the different satellite data sets is to merge Level 2 soil moisture data derived from the individual satellite data records. This approach has already been demonstrated within the WACMOS project (http://wacmos.itc.nl/) funded by European Space Agency (ESA) and will be further improved within the Climate Change Initiative (CCI) programme of ESA (http://www.esa-cci.org/).

Marechal Gwendal - One of the best experts on this subject based on the ideXlab platform.

  • The Sea State CCI dataset v1: towards a sea state Climate data record based on satellite observations
    'Copernicus GmbH', 2020
    Co-Authors: Dodet Guillaume, Piolle Jean-françois, Quilfen Yves, Abdalla Saleh, Accensi Mickaël, Ardhuin Fabrice, Ash Ellis, Bidlot Jean-raymond, Gommenginger Christine, Marechal Gwendal
    Abstract:

    Sea state data are of major importance for Climate studies, marine engineering, safety at sea and coastal management. However, long-term sea state datasets are sparse and not always consistent, and sea state data users still mostly rely on numerical wave models for research and engineering applications. Facing the urgent need for a sea state Climate data record, the Global Climate Observing System has listed “Sea State” as an Essential Climate Variable (ECV), fostering the launch in 2018 of the Sea State Climate Change Initiative (CCI). The CCI is a programme of the European Space Agency, whose objective is to realise the full potential of global Earth observation archives established by ESA and its member states in order to contribute to the ECV database. This paper presents the implementation of the first release of the Sea State CCI dataset, the implementation and benefits of a high-level denoising method, its validation against in situ measurements and numerical model outputs, and the future developments considered within the Sea State CCI project

  • The Sea State CCI dataset v1 : towards a Sea State Climate Data Record based on satellite observations
    'Copernicus GmbH', 2020
    Co-Authors: Dodet Guillaume, Piolle Jean-françois, Quilfen Yves, Accensi Mickaël, Ardhuin Fabrice, Ash Ellis, Bidlot Jean-raymond, Gommenginger Christine, Abdallah Saleh, Marechal Gwendal
    Abstract:

    Sea state data are of major importance for Climate studies, marine engineering, safety at sea, and coastal management. However, long-term sea state datasets are sparse and not always consistent, and sea state data users still mostly rely on numerical wave models for research and engineering applications. Facing the urgent need for a sea state Climate Data Record, the Global Climate Observing System has listed Sea State as an Essential Climate Variable (ECV), fostering the launch in 2018 of the Sea State Climate Change Initiative (CCI). The CCI is a program of the European Space Agency, whose objective is to realize the full potential of global Earth Observation archives established by ESA and its member states in order to contribute to the ECV database. This paper presents the implementation of the first release of the Sea State CCI dataset, the implementation and benefits of a high-level denoising method, its validation against in-situ measurements and numerical model outputs, and the future developments considered within the Sea State CCI project. The Sea State CCI dataset v1 is freely available on the ESA CCI website (http://cci.esa.int/data) at ftp://anon-ftp.ceda.ac.uk/neodc/esacci/sea_state/data/v1.1_release/. Three products are available: a multi-mission along-track L2P product (https://doi.org/10.5285/f91cd3ee7b6243d5b7d41b9beaf397e1, Piollé et al., 2020a), a daily merged multi mission along-track L3 product (https://doi.org/10.5285/3ef6a5a66e9947d39b356251909dc12b, Piollé et al., 2020b) and a multi-mission monthly gridded L4 product (https://doi.org/10.5285/47140d618dcc40309e1edbca7e773478, Piollé et al., 2020c)