Multi-year global land cover mapping at 300 m and characterization for climate modelling: achievements of the Land Cover component of the ESA Climate Change Initiative

Bontemps, Sophie; Boettcher, Martin; Brockmann, Carsten; Kirches, Grit; Lamarche, Céline; Radoux, Julien; Santoro, Maurizio; Vanbogaert, Eric; Wegmüller, U.; Herold, Martin; Achard, Frédéric; Ramoino, Fabrizio; Arino, Olivier; Defourny, Pierre

doi:10.5194/isprsarchives-xl-7-w3-323-2015

Cited by 65 publications

(46 citation statements)

References 4 publications

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“…600 m 3 ·s −1 is reached in March (Figure 2). Land cover in the surroundings of the Kafue Flats consists mainly of cropland, however, also areas with tree cover are found at higher elevations according to the European Space Agency's (ESA) Climate Change Initiative (CCI) Land-Cover dataset (Figure 1c) [37]. Information about vegetation within the wetland can be derived from optical imagery acquired at the end of the dry season which is largely cloud-free and whose reflectance values are not affected by flooding [38].…”

Section: Study Areamentioning

confidence: 99%

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Mapping Wetlands in Zambia Using Seasonal Backscatter Signatures Derived from ENVISAT ASAR Time Series

Schlaffer

Chini

Dettmering³

et al. 2016

Remote Sensing

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Wetlands are considered a challenging environment for mapping approaches based on Synthetic Aperture Radar (SAR) data due to their often complex internal structures and the diverse backscattering mechanisms caused by vegetation, soil moisture and flood dynamics contributing to the resulting imagery. In this study, a time series of >100 SAR images acquired by ENVISAT during a time period of ca. two years over the Kafue River basin in Zambia was compared to water heights derived from radar altimetry and surface soil moisture from a reanalysis dataset. The backscatter time series were analyzed using a harmonic model to characterize the seasonality in C-band backscatter caused by the interaction of flood and soil moisture dynamics. As a result, characteristic seasonal signatures could be derived for permanent water bodies, seasonal open water, persistently flooded vegetation and seasonally flooded vegetation. Furthermore, the analysis showed that the influence of local incidence angle could be accounted for by a linear shift in backscatter averaged over time, even in wetland areas where the dominant scattering mechanism can change depending on the season. The retrieved harmonic model parameters were then used in an unsupervised classification to detect wetland backscattering classes at the regional scale. A total area of 7800 km 2 corresponding to 7.6% of the study area was classified as either one of the wetland backscattering classes. The results demonstrate the value of seasonality parameters extracted from C-band SAR time series for wetland mapping.

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Section: Study Areamentioning

confidence: 99%

“…According to the reference land-cover dataset [37], the two non-wetland AOIs D and E are covered by trees and rainfed cropland, respectively. Both show similar seasonal patterns with a steep increase in σ 0 at the onset of the rainy season and a slow gradual decline reaching a minimum around October.…”

Section: Analysis Of Time Series From Aoismentioning

confidence: 99%

Mapping Wetlands in Zambia Using Seasonal Backscatter Signatures Derived from ENVISAT ASAR Time Series

Schlaffer

Chini

Dettmering³

et al. 2016

Remote Sensing

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“…A global map of open permanent water bodies at ∼300 m resolution was released in October 2014 by the Land Cover Climate Change Initiative (LC CCI) project (Defourny & Bontemps, ; Bontemps et al ., ). This is of widespread interest, since water bodies play an important role in climate and global water cycles.…”

Section: Introductionmentioning

confidence: 99%

Datasets related to in‐land water for limnology and remote sensing applications: distance‐to‐land, distance‐to‐water, water‐body identifier and lake‐centre co‐ordinates

Carrea

Embury

Merchant

2015

Geoscience Data Journal

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Datasets containing information to locate and identify water bodies have been generated from data locating static‐water‐bodies with resolution of about 300 m (1/360∘) recently released by the Land Cover Climate Change Initiative (LC CCI) of the European Space Agency. The LC CCI water‐bodies dataset has been obtained from multi‐temporal metrics based on time series of the backscattered intensity recorded by ASAR on Envisat between 2005 and 2010. The new derived datasets provide coherently: distance to land, distance to water, water‐body identifiers and lake‐centre locations. The water‐body identifier dataset locates the water bodies assigning the identifiers of the Global Lakes and Wetlands Database (GLWD), and lake centres are defined for in‐land waters for which GLWD IDs were determined. The new datasets therefore link recent lake/reservoir/wetlands extent to the GLWD, together with a set of coordinates which locates unambiguously the water bodies in the database. Information on distance‐to‐land for each water cell and the distance‐to‐water for each land cell has many potential applications in remote sensing, where the applicability of geophysical retrieval algorithms may be affected by the presence of water or land within a satellite field of view (image pixel). During the generation and validation of the datasets some limitations of the GLWD database and of the LC CCI water‐bodies mask have been found. Some examples of the inaccuracies/limitations are presented and discussed. Temporal change in water‐body extent is common. Future versions of the LC CCI dataset are planned to represent temporal variation, and this will permit these derived datasets to be updated.

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“…By default, we use the yearly land-cover maps created for the CMIP6 release of the model. These maps have been created through aggregation of the latest ESA-CCI land-cover map for 2010 (v2.0.7b) at 300 m resolution from 38 ESA land-cover classes into 15 ORCHIDEE PFTs at 0.5 • resolution [51,52]. For producing historical PFT maps (1860-2010) the resulting map has been further merged with the LUH2 dataset (Land-Use Harmonization, http://luh.umd.edu/).…”

Section: Land Covermentioning

confidence: 99%

Confronting Soil Moisture Dynamics from the ORCHIDEE Land Surface Model With the ESA-CCI Product: Perspectives for Data Assimilation

et al. 2018

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Soil moisture plays a key role in water, carbon and energy exchanges between the land surface and the atmosphere. Therefore, a better representation of this variable in the Land-Surface Models (LSMs) used in climate modelling could significantly reduce the uncertainties associated with future climate predictions. In this study, the ESA-CCI soil moisture (SM) combined product (v4.2) has been confronted to the simulated top-first layers/cms of the ORCHIDEE LSM (the continental part of the IPSL Earth System Model) for the years 2008-2016, to evaluate its potential to improve the model using data assimilation techniques. The ESA-CCI data are first rescaled to match the climatology of the model and the signal representative depth is selected. Results are found to be relatively consistent over the first 20 cm of the model. Strong correlations found between the model and the ESA-CCI product show that ORCHIDEE can adequately reproduce the observed SM dynamics. As well as considering two different atmospheric forcings to drive the model, we consider two different model parameterizations related to the soil resistance to evaporation. The correlation metric is shown to be more sensitive to the choice of meteorological forcing than to the choice of model parameterization. Therefore, the metric is not optimal in highlighting structural deficiencies in the model. In contrast, the temporal autocorrelation metric is shown to be more sensitive to this model parameterization, making the metric a potential candidate for future data assimilation experiments.

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Multi-year global land cover mapping at 300 m and characterization for climate modelling: achievements of the Land Cover component of the ESA Climate Change Initiative

Cited by 65 publications

References 4 publications

Mapping Wetlands in Zambia Using Seasonal Backscatter Signatures Derived from ENVISAT ASAR Time Series

Mapping Wetlands in Zambia Using Seasonal Backscatter Signatures Derived from ENVISAT ASAR Time Series

Datasets related to in‐land water for limnology and remote sensing applications: distance‐to‐land, distance‐to‐water, water‐body identifier and lake‐centre co‐ordinates

Confronting Soil Moisture Dynamics from the ORCHIDEE Land Surface Model With the ESA-CCI Product: Perspectives for Data Assimilation

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