The ESA Climate Change Initiative: Satellite Data Records for Essential Climate Variables

Hollmann, Rainer; Merchant, Christopher J.; Saunders, R.; Downy, Catherine; Buchwitz, Michael; Cazenave, Anny; Chuvieco, Emilio; Defourny, Pierre; Leeuw, G. de; Forsberg, R.; Popp, Thomas; Paul, Frank; Sandven, Stein; Sathyendranath, Shubha; Roozendaël, Michel Van; Wagner, Wolfgang

doi:10.1175/bams-d-11-00254.1

Cited by 405 publications

(265 citation statements)

References 19 publications

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“…For a spatially explicit depiction of present land cover we used the ESA Land Cover consortium maps (ESA, 2016;Hollman et al, 2013). The consortium produced a time series of three global 300 m spatial resolution land cover data sets predominantly based on MERIS satellite data, representative for the CE, 2003CE and 2008-2012 To produce the satellite reference map of HYDE 3.2, the most recent epoch has been used, representing 2010 CE.…”

Section: Cropland and Grazing Land Statisticsmentioning

confidence: 99%

Anthropogenic land use estimates for the Holocene – HYDE 3.2

Goldewijk

Beusen

Doelman

et al. 2017

Earth Syst. Sci. Data

705

624

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Abstract. This paper presents an update and extension of HYDE, the History Database of the Global Environment (HYDE version 3.2). HYDE is an internally consistent combination of historical population estimates and allocation algorithms with time-dependent weighting maps for land use. Categories include cropland, with new distinctions for irrigated and rain-fed crops (other than rice) and irrigated and rain-fed rice. Grazing lands are also provided, divided into more intensively used pasture and less intensively used rangeland, and further specified with respect to conversion of natural vegetation to facilitate global change modellers. Population is represented by maps of total, urban, rural population, population density and built-up area. The period covered is 10 000 before Common Era (BCE) to 2015 Common Era (CE). All data can be downloaded from https://doi.org/10.17026/dans-25g-gez3.We estimate that global population increased from 4.4 million people (we also estimate a lower range < 0.01 and an upper range of 8.9 million) in 10 000 BCE to 7.257 billion in 2015 CE, resulting in a global population density increase from 0.03 persons (or capita, in short cap) km −2 (range 0-0.07) to almost 56 cap km −2 respectively. The urban built-up area evolved from almost zero to roughly 58 Mha in 2015 CE, still only less than 0.5 % of the total land surface of the globe.Cropland occupied approximately less than 1 % of the global land area (13 037 Mha, excluding Antarctica) for a long time period until 1 CE, quite similar to the grazing land area. In the following centuries the share of global cropland slowly grew to 2.2 % in 1700 CE (ca. 293 Mha, uncertainty range 220-367 Mha), 4.4 % in 1850 CE (578 Mha, range 522-637 Mha) and 12.2 % in 2015 CE (ca. 1591 Mha, range 1572-1604 Mha). Cropland can be further divided into rain-fed and irrigated land, and these categories can be further separated into rice and non-rice. Rain-fed croplands were much more common, with 2.2 % in 1700 CE (289 Mha, range 217-361 Mha), 4.2 % (549 Mha, range 496-606 Mha) in 1850 CE and 10.1 % (1316 Mha, range 1298-1325 Mha) in 2015 CE, while irrigated croplands used less than 0.05 % (4.3 Mha, range 3.1-5.5 Mha), 0.2 % (28 Mha, range 25-31 Mha) and 2.1 % (277 Mha, range 273-278 Mha) in 1700, 1850 and 2015 CE, respectively. We estimate the irrigated rice area (paddy) to be 0.1 % (13 Mha, range 9-16 Mha) in 1700 CE, 0.2 % (28 Mha, range 26-31 Mha) in 1850 CE and 0.9 % (118 Mha, range 117-120 Mha) in 2015 CE.The estimates for land used for grazing are much more uncertain. We estimate that the share of grazing land grew from 5.1 % in 1700 CE (667 Mha, range 507-820 Mha) to 9.6 % in 1850 CE (1192 Mha, range 1068-1304 Mha) and 24.9 % in 2015 CE (3241 Mha, range 3211-3270 Mha). To aid the modelling community we have divided land used for grazing into more intensively used pasture, less intensively used converted rangeland and less or unmanaged natural unconverted rangeland. Pasture occupied 1.1 % in 1700 CE (145 Mha, range 79-175 Mha), 1.9 % in 1850 CE (253 Mha...

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Section: Cropland and Grazing Land Statisticsmentioning

confidence: 99%

Anthropogenic land use estimates for the Holocene – HYDE 3.2

Goldewijk

Beusen

Doelman

et al. 2017

Earth Syst. Sci. Data

705

624

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“…The algorithm is a core algorithm within ESA's CCI (Hollmann et al, 2013;Buchwitz et al, 2013b;Dils et al, 2014) aiming at delivering high-quality satellite retrievals. Here we use the most recent product version (02.00.08) of SCIA-MACHY BESD, which is part of the Climate Research Data Package (CRDP#2) of the CCI project.…”

Section: Sciamachy Besd Algorithmmentioning

confidence: 99%

“…This data product has been used in several key European projects, e.g. ESA's Climate Change Initiative (CCI, www.esa-ghg-cci.org and Buchwitz et al, 2013b;Hollmann et al, 2013) and the EU's Monitoring of Atmospheric Composition and Climate (MACC, Hollingsworth et al, 2008) project.…”

Section: Introductionmentioning

confidence: 99%

Consistent satellite XCO<sub>2</sub> retrievals from SCIAMACHY and GOSAT using the BESD algorithm

et al. 2015

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Abstract. Consistent and accurate long-term data sets of global atmospheric concentrations of carbon dioxide (CO 2 ) are required for carbon cycle and climate-related research. However, global data sets based on satellite observations may suffer from inconsistencies originating from the use of products derived from different satellites as needed to cover a long enough time period. One reason for inconsistencies can be the use of different retrieval algorithms. We address this potential issue by applying the same algorithm, the Bremen Optimal Estimation DOAS (BESD) algorithm, to different satellite instruments, SCIAMACHY on-board ENVISAT (March 2002-April 2012 and TANSO-FTS onboard GOSAT (launched in January 2009), to retrieve XCO 2 , the column-averaged dry-air mole fraction of CO 2 . BESD has been initially developed for SCIAMACHY XCO 2 retrievals. Here, we present the first detailed assessment of the new GOSAT BESD XCO 2 product. GOSAT BESD XCO 2 is a product generated and delivered to the MACC project for assimilation into ECMWF's Integrated Forecasting System. We describe the modifications of the BESD algorithm needed in order to retrieve XCO 2 from GOSAT and present detailed comparisons with ground-based observations of XCO 2 from the Total Carbon Column Observing Network (TC-CON). We discuss detailed comparison results between all three XCO 2 data sets (SCIAMACHY, GOSAT and TC-CON). The comparison results demonstrate the good consistency between SCIAMACHY and GOSAT XCO 2 . For example, we found a mean difference for daily averages of −0.60±1.56 ppm (mean difference ± standard deviation) for GOSAT-SCIAMACHY (linear correlation coefficient r = 0.82), −0.34±1.37 ppm (r = 0.86) for GOSAT-TCCON and 0.10±1.79 ppm (r = 0.75) for SCIAMACHY-TCCON. The remaining differences between GOSAT and SCIAMACHY are likely due to non-perfect collocation (± 2 h, 10• around TCCON sites), i.e. the observed air masses are not exactly identical but likely also due to a still non-perfect BESD retrieval algorithm, which will be continuously improved in the future. Our overarching goal is to generate a satellitederived XCO 2 data set appropriate for climate and carbon cycle research covering the longest possible time period. We therefore also plan to extend the existing SCIAMACHY and Published by Copernicus Publications on behalf of the European Geosciences Union. J. Heymann et al.: SCIAMACHY and GOSAT BESD XCO 2 retrievalsGOSAT data set discussed here by also using data from other missions (e.g. OCO-2, GOSAT-2, CarbonSat) in the future.

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“…Land cover is an essential climate variable [1] as it strongly influences current and future climate, particularly with rapid changes in the landscape due to human activities [2]. Baseline information on land use and land cover (LULC) is also a key input to many different types of models the areas to be mapped.…”

Section: Introductionmentioning

confidence: 99%

Generating Up-to-Date and Detailed Land Use and Land Cover Maps Using OpenStreetMap and GlobeLand30

Fonte

Minghini

Patriarca

et al. 2017

IJGI

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Abstract:With the opening up of the Landsat archive, global high resolution land cover maps have begun to appear. However, they often have only a small number of high level land cover classes and they are static products, corresponding to a particular period of time, e.g., the GlobeLand30 (GL30) map for 2010. The OpenStreetMap (OSM), in contrast, consists of a very detailed, dynamically updated, spatial database of mapped features from around the world, but it suffers from incomplete coverage, and layers of overlapping features that are tagged in a variety of ways. However, it clearly has potential for land use and land cover (LULC) mapping. Thus the aim of this paper is to demonstrate how the OSM can be converted into a LULC map and how this OSM-derived LULC map can then be used to first update the GL30 with more recent information and secondly, enhance the information content of the classes. The technique is demonstrated on two study areas where there is availability of OSM data but in locations where authoritative data are lacking, i.e., Kathmandu, Nepal and Dar es Salaam, Tanzania. The GL30 and its updated and enhanced versions are independently validated using a stratified random sample so that the three maps can be compared. The results show that the updated version of GL30 improves in terms of overall accuracy since certain classes were not captured well in the original GL30 (e.g., water in Kathmandu and water/wetlands in Dar es Salaam). In contrast, the enhanced GL30, which contains more detailed urban classes, results in a drop in the overall accuracy, possibly due to the increased number of classes, but the advantages include the appearance of more detailed features, such as the road network, that becomes clearly visible.

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The ESA Climate Change Initiative: Satellite Data Records for Essential Climate Variables

Cited by 405 publications

References 19 publications

Anthropogenic land use estimates for the Holocene – HYDE 3.2

Anthropogenic land use estimates for the Holocene – HYDE 3.2

Consistent satellite XCO<sub>2</sub> retrievals from SCIAMACHY and GOSAT using the BESD algorithm

Generating Up-to-Date and Detailed Land Use and Land Cover Maps Using OpenStreetMap and GlobeLand30

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The ESA Climate Change Initiative: Satellite Data Records for Essential Climate Variables

Cited by 405 publications

References 19 publications

Anthropogenic land use estimates for the Holocene – HYDE 3.2

Anthropogenic land use estimates for the Holocene – HYDE 3.2

Consistent satellite XCO&lt;sub&gt;2&lt;/sub&gt; retrievals from SCIAMACHY and GOSAT using the BESD algorithm

Generating Up-to-Date and Detailed Land Use and Land Cover Maps Using OpenStreetMap and GlobeLand30

Contact Info

Product

Resources

About

Consistent satellite XCO<sub>2</sub> retrievals from SCIAMACHY and GOSAT using the BESD algorithm