scholarly journals Quality Assessment of PROBA-V Surface Albedo V1 for the Continuity of the Copernicus Climate Change Service

2020 ◽  
Vol 12 (16) ◽  
pp. 2596
Author(s):  
Jorge Sánchez-Zapero ◽  
Fernando Camacho ◽  
Enrique Martínez-Sánchez ◽  
Roselyne Lacaze ◽  
Dominique Carrer ◽  
...  
Keyword(s):  
Climate Change ◽  
Surface Albedo ◽  
Near Infrared ◽  
Accuracy Assessment ◽  
Performance Criteria ◽  
Climate Data ◽  
Residual Noise ◽  
Space Agency ◽  
Median Error ◽  
Spatio Temporal

The Copernicus Climate Change Service (C3S) includes estimates of Essential Climate Variables (ECVs) as a series of Climate Data Records (CDRs) derived from satellite data. The C3S Surface Albedo (SA) v1.0 CDR is composed of observations from National Oceanic and Atmospheric Administration (NOAA) Very High Resolution Radiometers (AVHRR) (1981–2005), and VEGETATION sensors onboard Satellites for the Observation of the Earth (SPOT/VGT) (1998–2014) and Project for Onboard Autonomy satellite (PROBA-V) (2014–2020), and will continue with Sentinel-3 (from 2020 onwards). The goal of this study is to assess the uncertainties associated with the C3S PROBA-V SA v1.0 product, with a focus on the transition from SPOT/VGT to PROBA-V. The methodology followed the good practices recommended by the Land Product Validation sub-group (LPV) of the Working Group on Calibration and Validation (WGCV) of the Committee on Earth Observing Satellites (CEOS) for the validation of satellite-derived global albedo products. Several performance criteria were evaluated, including an intercomparison with National Aeronautics and Space Agency (NASA) MCD43A3 C6 products. C3S PROBA-V SA v1.0 and MCD43A3 C6 showed similar completeness but had higher fractions of missing data than C3S SPOT/VGT SA v1.0. C3S PROBA-V SA v1.0 showed similar precision (~1%) to MCD43A3 C6, improving the results of SPOT/VGT SA v1.0 (2–3%), but C3S PROBA-V SA v1.0 provided residual noise in the near-infrared (NIR). Good spatio-temporal continuity between C3S PROBA-V and SPOT/VGT SA v1.0 products was found with a mean bias between ±2%. The comparison with MCD43A3 C6 showed positive mean biases (5%, 8%, and 12% for visible, NIR and total shortwave, respectively). The accuracy assessment with ground measurements showed a median error of 18.4% with systematic overestimation (positive bias of 11.5%). The percentage of PROBA-V retrievals complying with the C3S target requirements was 28.6%.

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

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

<p>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). </p><p>The European Commission’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.  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.</p><p>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.</p><p>The development of the ESA CCI products has been supported by ESA’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.</p>

scholarly journals An ecological ‘footprint’ of climate change

2005 ◽  
Vol 272 (1571) ◽  
pp. 1427-1432 ◽  
Author(s):  
Gian-Reto Walther ◽  
Silje Berger ◽  
Martin T Sykes
Keyword(s):  
Climate Change ◽  
Species Distribution ◽  
Global Climate ◽  
Ground Truth ◽  
Warming Trend ◽  
Climate Data ◽  
Northern Margin ◽  
Winter Temperatures ◽  
Bioclimatic Model ◽  
Spatio Temporal

Recently, there has been increasing evidence of species' range shifts due to changes in climate. Whereas most of these shifts relate ground truth biogeographic data to a general warming trend in regional or global climate data, we here present a reanalysis of both biogeographic and bioclimatic data of equal spatio-temporal resolution, covering a time span of more than 50 years. Our results reveal a coherent and synchronous shift in both species' distribution and climate. They show not only a shift in the northern margin of a species, which is in concert with gradually increasing winter temperatures in the area, they also confirm the simulated species' distribution changes expected from a bioclimatic model under the recent, relatively moderate climate change.

scholarly journals Spatio-Temporal Analysis of Historical and Future Climate Data in the Texas High Plains

2020 ◽  
Vol 12 (15) ◽  
pp. 6036
Author(s):  
Yong Chen ◽  
Gary W. Marek ◽  
Thomas H. Marek ◽  
Dana O. Porter ◽  
Jerry E. Moorhead ◽  
...  
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
General Circulation Models ◽  
Quality Data ◽  
High Plains ◽  
Climate Change Scenarios ◽  
Climate Data ◽  
Air Temperatures ◽  
Texas High Plains ◽  
Spatio Temporal

Agricultural production in the Texas High Plains (THP) relies heavily on irrigation and is susceptible to drought due to the declining availability of groundwater and climate change. Therefore, it is meaningful to perform an overview of possible climate change scenarios to provide appropriate strategies for climate change adaptation in the THP. In this study, spatio-temporal variations of climate data were mapped in the THP during 2000–2009, 2050–2059, and 2090–2099 periods using 14 research-grade meteorological stations and 19 bias-corrected General Circulation Models (GCMs) under representative concentration pathway (RCP) scenarios RCP 4.5 and 8.5. Results indicated different bias correction methods were needed for different climatic parameters and study purposes. For example, using high-quality data from the meteorological stations, the linear scaling method was selected to alter the projected precipitation while air temperatures were bias corrected using the quantile mapping method. At the end of the 21st century (2090–2099) under the severe CO2 emission scenario (RCP 8.5), the maximum and minimum air temperatures could increase from 3.9 to 10.0 °C and 2.8 to 8.4 °C across the entire THP, respectively, while precipitation could decrease by ~7.5% relative to the historical (2000–2009) observed data. However, large uncertainties were found according to 19 GCM projections.

scholarly journals Operational implementation of the burned area component of the Copernicus Climate Change Service: from MODIS 250 m to OLCI 300 m data

2021 ◽  
Author(s):  
Joshua Lizundia-Loiola ◽  
Magí Franquesa ◽  
Martin Boettcher ◽  
Grit Kirches ◽  
M. Lucrecia Pettinari ◽  
...  
Keyword(s):  
Climate Change ◽  
Near Infrared ◽  
Burned Area ◽  
Landsat 8 ◽  
Full Time ◽  
Temporal Consistency ◽  
Climate Data ◽  
Thermal Anomalies ◽  
Commission Errors ◽  
Omission Errors

Abstract. This paper presents a new global, operational burned area (BA) product at 300 m, called C3SBA10, generated from Sentinel-3 Ocean and Land Colour Instrument (OLCI) near-infrared (NIR) reflectance and Moderate Resolution Imaging Spectroradiometer (MODIS) thermal anomaly data. This product was generated within the Copernicus Climate Change Service (C3S). Since C3S is a European service, it aims to use extensively the European Copernicus satellite missions, named Sentinels. Therefore, one of the components of the service is adapting previous developed algorithms to the Sentinel sensors. In the case of BA datasets, the precursor BA dataset (FireCCI51), which was developed within the European Space Agency's (ESA) Climate Change Initiative (CCI), was based on the 250 m-resolution NIR band of the MODIS sensor, and the effort has been focused on adapting this BA algorithm to the characteristics of the Sentinel-3 OLCI sensor, which provides similar spatial and temporal resolution to MODIS. As the precursor BA algorithm, the OLCI's one combines thermal anomalies and spectral information in a two-phase approach, where first thermal anomalies with a high probability of being burned are selected, reducing commission errors, and then a contextual growing is applied to fully detect the BA patch, reducing omission errors. The new BA product includes the full time-series of S3 OLCI data (2017–present). Following the specifications of the FireCCI project, the final datasets are provided in two different formats: monthly full-resolution continental tiles, and monthly global files with aggregated data at 0.25-degree resolution. To facilitate the use by global vegetation dynamics and atmospheric emission models several auxiliary layers were included, such as land cover and cloud-free observations. The C3SBA10 product detected 3.77 Mkm2, 3.59 Mkm2, and 3.63 Mkm2 of annual BA from 2017 to 2019, respectively. The quality and consistency assessment of C3SBA10 and the precursor FireCCI51 was done for the common period (2017–2019). The global spatial validation was performed using reference data derived from Landsat-8 images, following a stratified random sampling design. The C3SBA10 showed commission errors between 14–22 % and omission errors from 50 to 53 %, similar to those presented by the FireCCI51 product. The temporal reporting accuracy was also validated using 4.7 million active fires. 88 % of the detections were made within 10 days after the fire by both products. The spatial and temporal consistency assessment performed between C3SBA10 and FireCCI51 using four different grid sizes (0.05º, 0.10º, 0.25º, and 0.50º) showed global, annual correlations between 0.93 and 0.99. This high consistency between both products ensures a global BA data provision from 2001 to present. The datasets are freely available through the Copernicus Climate Data Store (CDS) repository (DOI: https://doi.org/10.24381/cds.f333cf85, Lizundia-Loiola et al. (2020a)).

scholarly journals Development and Evaluation of a New “Snow Water Index (SWI)” for Accurate Snow Cover Delineation

2019 ◽  
Vol 11 (23) ◽  
pp. 2774 ◽  
Author(s):  
Abhilasha Dixit ◽  
Ajanta Goswami ◽  
Sanjay Jain
Keyword(s):  
Snow Cover ◽  
Near Infrared ◽  
Accuracy Assessment ◽  
Spectral Characteristics ◽  
Index Theory ◽  
Kappa Statistics ◽  
Water Index ◽  
Snow Water ◽  
Spatio Temporal ◽  
The Individual

The current study started by examining the three most established snow indices, namely the NDSI (normalized difference snow index), S3, and NDSII-1 (normalized difference snow and ice index), based on their capabilities to differentiate snow pixels from cloud, debris, vegetation, and water pixels. Furthermore, considering the limitations of these indices, a new spectral index called the snow water index (SWI) is proposed. SWI uses spectral characteristics of the visible, SWIR (shortwave infrared), and NIR (near infrared) bands to achieve significant contrast between snow/ice pixels and other pixels including water bodies. A three-step accuracy assessment technique established the dominance of SWI over NDSI, S3, and NDSII-1. In the first step, image thresholding using standard value (>0), individual index theory (fixed threshold), histogram, and GCPs (ground control points) derived threshold were used to assess the performance of the selected indices. In the second step, comparisons of the spectral separation of features in the individual band were made from the field spectral observations collected using a spectroradiometer. In the third step, GCPs collected using field surveys were used to derive the user’s accuracy, producer’s accuracy, overall accuracy, and kappa coefficient for each index. The SWI threshold varied between 0.21 to 0.25 in all of the selected observations from both ablation and accumulation time. Spectral separability plots justify the SWI’s capability of extraction and removal of the most critical water pixels in integration with other impure classes from snow/ice pixels. GCP enabled accuracy assessment resulted in a maximum overall accuracy (0.93) and kappa statistics (0.947) value for the SWI. Thus, the results of the accuracy assessment justified the supremacy of the SWI over other indices. The study revealed that SWI demonstrates a considerably higher correlation with actual snow/ice cover and is prominent for spatio-temporal snow cover studies globally.

Development and Evaluation of a New “Snow Water Index (SWI)” for Accurate Snow Cover Delineation

2020 ◽  
Author(s):  
Abhilasha Dixit ◽  
Ajanta Goswami
Keyword(s):  
Snow Cover ◽  
Near Infrared ◽  
Accuracy Assessment ◽  
Spectral Characteristics ◽  
Index Theory ◽  
Kappa Statistics ◽  
Water Index ◽  
Snow Water ◽  
Spatio Temporal ◽  
The Individual

<p>The current study started by examining the three most established snow indices, namely the NDSI (normalized difference snow index), S3, and NDSII-1 (normalized difference snow and ice index), based on their capabilities to differentiate snow pixels from cloud, debris, vegetation, and water pixels. Furthermore, considering the limitations of these indices, a new spectral index called the snow water index (SWI) is proposed. SWI uses spectral characteristics of the visible, SWIR (shortwave infrared), and NIR (near infrared) bands to achieve significant contrast between snow/ice pixels and other pixels including water bodies. A three-step accuracy assessment technique established the dominance of SWI over NDSI, S3, and NDSII-1. In the first step, image thresholding using standard value (>0), individual index theory (fixed threshold), histogram, and GCPs (ground control points) derived threshold were used to assess the performance of the selected indices. In the second step, comparisons of the spectral separation of features in the individual band were made from the field spectral observations collected using a spectroradiometer. In the third step, GCPs collected using field surveys were used to derive the user’s accuracy, producer’s accuracy, overall accuracy, and kappa coefficient for each index. The SWI threshold varied between 0.21 to 0.25 in all of the selected observations from both ablation and accumulation time. Spectral separability plots justify the SWI’s capability of extraction and removal of the most critical water pixels in integration with other impure classes from snow/ice pixels. GCP enabled accuracy assessment resulted in a maximum overall accuracy (0.93) and kappa statistics (0.947) value for the SWI. Thus, the results of the accuracy assessment justified the supremacy of the SWI over other indices. The study revealed that SWI demonstrates a considerably higher correlation with actual snow/ice cover and is prominent for spatio-temporal snow cover studies globally.</p>

scholarly journals Response of Carbon Dynamics to Climate Change Varied among Different Vegetation Types in Central Asia

2018 ◽  
Vol 10 (9) ◽  
pp. 3288 ◽  
Author(s):  
Qifei Han ◽  
Geping Luo ◽  
Chaofan Li ◽  
Shoubo Li
Keyword(s):  
Climate Change ◽  
Central Asia ◽  
Net Primary Productivity ◽  
Climate Adaptation ◽  
Carbon Dynamics ◽  
The Other ◽  
Vegetation Types ◽  
Climate Data ◽  
A Value ◽  
Spatio Temporal

The effect of climate change on the spatio-temporal patterns of the terrestrial carbon dynamics in Central Asia have not been adequately quantified despite its potential importance to the global carbon cycle. Therefore, the modified BioGeochemical Cycles (Biome-BGC) model was applied in this study to evaluate the impacts of climatic change on net primary productivity (NPP) and net ecosystem productivity. Four vegetation types were studied during the period 1979 to 2011: cropland, grassland, forest, and shrubland. The results indicated that: (1) The climate data showed that Central Asia experienced a rise in annual mean temperature and a decline in precipitation from 1979 to 2011; (2) the mean NPP for Central Asia in 1979–2011 was 281.79 gC m−2 yr−1, and the cropland had the highest NPP compared with the other vegetation types, with a value of 646.25 gC m−2 yr−1; (3) grassland presented as a carbon source (−0.21 gC m−2 yr−1), whereas the other three types were carbon sinks; (4) the four vegetation types showed similar responses to climate variation during the past 30 years, and grassland is the most sensitive ecosystem in Central Asia. This study explored the possible implications for climate adaptation and mitigation.

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

2013 ◽  
Vol 94 (10) ◽  
pp. 1541-1552 ◽  
Author(s):  
R. Hollmann ◽  
C. J. Merchant ◽  
R. Saunders ◽  
C. Downy ◽  
M. Buchwitz ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Modeling ◽  
European Space Agency ◽  
Climate Science ◽  
Climate Variables ◽  
Climate Data ◽  
Change Initiative ◽  
System Perspective ◽  
Space Agency ◽  
Over 40 Years

Observations of Earth from space have been made for over 40 years and have contributed to advances in many aspects of climate science. However, attempts to exploit this wealth of data are often hampered by a lack of homogeneity and continuity and by insufficient understanding of the products and their uncertainties. There is, therefore, a need to reassess and reprocess satellite datasets to maximize their usefulness for climate science. The European Space Agency has responded to this need by establishing the Climate Change Initiative (CCI). The CCI will create new climate data records for (currently) 13 essential climate variables (ECVs) and make these open and easily accessible to all. Each ECV project works closely with users to produce time series from the available satellite observations relevant to users' needs. A climate modeling users' group provides a climate system perspective and a forum to bring the data and modeling communities together. This paper presents the CCI program. It outlines its benefit and presents approaches and challenges for each ECV project, covering clouds, aerosols, ozone, greenhouse gases, sea surface temperature, ocean color, sea level, sea ice, land cover, fire, glaciers, soil moisture, and ice sheets. It also discusses how the CCI approach may contribute to defining and shaping future developments in Earth observation for climate science.

User involvement to prepare a Pan-European climate visualisation platform targeted at decision makers and educational purposes

2021 ◽  
Author(s):  
Andreas Hoy ◽  
Zbigniew Ustrnul ◽  
Agnieszka Wypych ◽  
Evgeny Gordov ◽  
Yulia Gordova ◽  
...  
Keyword(s):  
Climate Change ◽  
Large Range ◽  
User Involvement ◽  
Decision Makers ◽  
National Language ◽  
Climate Information ◽  
Climate Data ◽  
Decision Making Processes ◽  
Spatio Temporal ◽  
Level Of Understanding

<p>This contribution relates to preparatory efforts for creating a transnational, multilinguistic visualisation tool of past, present, forecasted and future (weather and) climate information in Europe, targeted to a large range of users. There is no scarcity of observed or modelled climate data, especially in Europe. However, existing climate information are often hidden, poorly explained or not accessible (in their complexity and structure) for non-scientists in many countries, especially in Eastern Europe and Russia. Needed are hence clear, reliable, and concise facts to easily understand the complex topic of climate change, supported by a high spatial resolution of climate information for local concernment.</p><p>We identified two superordinated target groups: 1) decision makers, demanding easily accessible and digestible, as well as for decision making processes usable climate information and 2) educational institutions and the general public, who will profit from more intuitive ways teaching and informing about climate change. We here present results of a user survey carried out in five countries (in native language) and of key stakeholder interviews within the same countries.</p><p>In the surveys, we approached potential users within different countries to explore what climate data they need, in which complexity, spatio-temporal detail, actuality and visualisation style. We also aimed to explore if similar actors of different countries may have similar or different needs, and how we may prepare an optimal product useful for a large range of users located in different countries. Follow-up key stakeholder interviews helped us deepening the level of understanding of user needs. Such interviews allowed us to go into more details than the questionnaire. It also enabled us to present the users content of existing webpages presenting climate information, to identify what the users like, dislike or miss in existing tools - e.g., in terms of data visualisation, handling, comparison and comprehensiveness. Coordinating country partners served as multipliers to reach users in their national language and to evaluate the results. Those activities disseminated the ideas of our planned platform in those countries and between different users there.</p>

scholarly journals Climate drivers of plague epidemiology in British India, 1898–1949

2020 ◽  
Vol 287 (1928) ◽  
pp. 20200538
Author(s):  
Warren S. D. Tennant ◽  
Mike J. Tildesley ◽  
Simon E. F. Spencer ◽  
Matt J. Keeling
Keyword(s):  
Predictive Models ◽  
Control Measures ◽  
Climate Data ◽  
British India ◽  
Middle Income ◽  
Middle Income Countries ◽  
Complex Interactions ◽  
The World ◽  
Spatio Temporal

Plague, caused by Yersinia pestis infection, continues to threaten low- and middle-income countries throughout the world. The complex interactions between rodents and fleas with their respective environments challenge our understanding of human plague epidemiology. Historical long-term datasets of reported plague cases offer a unique opportunity to elucidate the effects of climate on plague outbreaks in detail. Here, we analyse monthly plague deaths and climate data from 25 provinces in British India from 1898 to 1949 to generate insights into the influence of temperature, rainfall and humidity on the occurrence, severity and timing of plague outbreaks. We find that moderate relative humidity levels of between 60% and 80% were strongly associated with outbreaks. Using wavelet analysis, we determine that the nationwide spread of plague was driven by changes in humidity, where, on average, a one-month delay in the onset of rising humidity translated into a one-month delay in the timing of plague outbreaks. This work can inform modern spatio-temporal predictive models for the disease and aid in the development of early-warning strategies for the deployment of prophylactic treatments and other control measures.

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