Data assimilation: From photon counts to Earth System forecasts

Data assimilation – the set of techniques whereby information from observing systems and models is combined optimally – is rapidly becoming prominent in endeavours to exploit Earth Observation for Earth sciences, including climate prediction. This paper explains the broad principles of data assimilation, outlining different approaches (optimal interpolation, three-dimensional and four-dimensional variational methods, the Kalman Filter), together with the approximations that are often necessary to make them practicable. After pointing out a variety of benefits of data assimilation, the paper then outlines some practical applications of the exploitation of Earth Observation by data assimilation in the areas of operational oceanography, chemical weather forecasting and carbon cycle modelling. Finally, some challenges for the future are noted.

An earth-system prediction initiative for the twenty-first century

The necessity and benefits for establishing the international Earth-system Prediction Initiative (EPI) are discussed by scientists associated with the World Meteorological Organization (WMO) World Weather Research Programme (WWRP), World Climate Research Programme (WCRP), International Geosphere–Biosphere Programme (IGBP), Global Climate Observing System (GCOS), and natural-hazards and socioeconomic communities. The proposed initiative will provide research and services to accelerate advances in weather, climate, and Earth system prediction and the use of this information by global societies. It will build upon the WMO, the Group on Earth Observations (GEO), the Global Earth Observation System of Systems (GEOSS) and the International Council for Science (ICSU) to coordinate the effort across the weather, climate, Earth system, natural-hazards, and socioeconomic disciplines. It will require (i) advanced high-performance computing facilities, supporting a worldwide network of research and operational modeling centers, and early warning systems; (ii) science, technology, and education projects to enhance knowledge, awareness, and utilization of weather, climate, environmental, and socioeconomic information; (iii) investments in maintaining existing and developing new observational capabilities; and (iv) infrastructure to transition achievements into operational products and services.

Big Data Analytics for Earth Sciences: the EarthServer approach

Big Data Analytics is an emerging field since massive storage and computing capabilities have been made available by advanced e-infrastructures. Earth and Environmental sciences are likely to benefit from Big Data Analytics techniques supporting the processing of the large number of Earth Observation datasets currently acquired and generated through observations and simulations. However, Earth Science data and applications present specificities in terms of relevance of the geospatial information, wide heterogeneity of data models and formats, and complexity of processing. Therefore, Big Earth Data Analytics requires specifically tailored techniques and tools. The EarthServer Big Earth Data Analytics engine offers a solution for coverage-type datasets, built around a high performance array database technology, and the adoption and enhancement of standards for service interaction (OGC WCS and WCPS). The EarthServer solution, led by the collection of requirements from scientific communities and international initiatives, provides a holistic approach that ranges from query languages and scalability up to mobile access and visualization. The result is demonstrated and validated through the development of lighthouse applications in the Marine, Geology, Atmospheric, Planetary and Cryospheric science domains.

Big Data Analytics for Earth Sciences: the EarthServer approach

Big Data Analytics is an emerging field since massive storage and computing capabilities have been made available by advanced e-infrastructures. Earth and Environmental sciences are likely to benefit from Big Data Analytics techniques supporting the processing of the large number of Earth Observation datasets currently acquired and generated through observations and simulations. However, Earth Science data and applications present specificities in terms of relevance of the geospatial information, wide heterogeneity of data models and formats, and complexity of processing. Therefore, Big Earth Data Analytics requires specifically tailored techniques and tools. The EarthServer Big Earth Data Analytics engine offers a solution for coverage-type datasets, built around a high performance array database technology, and the adoption and enhancement of standards for service interaction (OGC WCS and WCPS). The EarthServer solution, led by the collection of requirements from scientific communities and international initiatives, provides a holistic approach that ranges from query languages and scalability up to mobile access and visualization. The result is demonstrated and validated through the development of lighthouse applications in the Marine, Geology, Atmospheric, Planetary and Cryospheric science domains.

Earth Surface Properties

Resources created at the University of Southampton for the module Remote Sensing for Earth Observation

Insights to urban dynamics through landscape spatial pattern analysis

Urbanisation is a dynamic complex phenomenon involving large scale changes in the land uses at local levels. Analyses of changes in land uses in urban environments provide a historical perspective of land use and give an opportunity to assess the spatial patterns, correlation, trends, rate and impacts of the change, which would help in better regional planning and good governance of the region. Main objective of this research is to quantify the urban dynamics using temporal remote sensing data with the help of well-established landscape metrics. Bangalore being one of the rapidly urbanising landscapes in India has been chosen for this investigation. Complex process of urban sprawl was modelled using spatio temporal analysis. Land use analyses show 584% growth in built-up area during the last four decades with the decline of vegetation by 66% and water bodies by 74%. Analyses of the temporal data reveals an increase in urban built up area of 342.83% (during 1973-1992), 129.56% (during 1992-1999), 106.7% (1999-2002), 114.51% (2002-2006) and 126.19% from 2006 to 2010. The Study area was divided into four zones and each zone is further divided into 17 concentric circles of 1 km incrementing radius to understand the patterns and extent of the urbanisation at local levels. The urban density gradient illustrates radial pattern of urbanisation for the period 1973-2010. Bangalore grew radially from 1973 to 2010 indicating that the urbanisation is intensifying from the central core and has reached the periphery of the Greater Bangalore. Shannon's entropy, alpha and beta population densities were computed to understand the level of urbanisation at local levels. Shannon's entropy values of recent time confirms dispersed haphazard urban growth in the city, particularly in the outskirts of the city. This also illustrates the extent of influence of drivers of urbanisation in various directions. Landscape metrics provided in depth knowledge about the sprawl. Principal component analysis helped in prioritizing the metrics for detailed analyses. The results clearly indicates that whole landscape is aggregating to a large patch in 2010 as compared to earlier years which was dominated by several small patches. The large scale conversion of small patches to large single patch can be seen from 2006 to 2010. In the year 2010 patches are maximally aggregated indicating that the city is becoming more compact and more urbanised in recent years. Bangalore was the most sought after destination for its climatic condition and the availability of various facilities (land availability, economy, political factors) compared to other cities. The growth into a single urban patch can be attributed to rapid urbanisation coupled with the industrialisation. Monitoring of growth through landscape metrics helps to maintain and manage the natural resources. (C) 2012 Elsevier B.V. All rights reserved.

An assessment of terrestrial water storage, rainfall and river discharge over Northern India from satellite data

Terrestrial water storage (TWS) plays a key role in the global water cycle and is highly influenced by climate variability and human activities. In this study, monthly TWS, rainfall and Ganga-Brahmaputra river discharge (GBRD) are analysed over India for the period of 2003-12 using remote sensing satellite data. The spatial pattern of mean TWS shows a decrease over a large and populous region of Northern India comprising the foothills of the Himalayas, the Indo-Gangetic Plains and North East India. Over this region, the mean monthly TWS exhibits a pronounced seasonal cycle and a large interannual variability, highly correlated with rainfall and GBRD variations (r > 0.8) with a lag time of 2 months and 1 month respectively. The time series of monthly TWS shows a consistent and statistically significant decrease of about 1 cm year(-1) over Northern India, which is not associated with changes in rainfall and GBRD. This recent change in TWS suggests a possible impact of rapid industrialization, urbanization and increase in population on land water resources. Our analysis highlights the potential of the Earth-observation satellite data for hydrological applications.

Optical Remote Sensing of Coastal Habitats, Moss Landing, California, 9-11 January, 2006: workshop proceedings

The Alliance for Coastal Technologies (ACT) Workshop on Optical Remote Sensing of Coastal Habitats was convened January 9-11, 2006 at Moss Landing Marine Laboratories in Moss Landing, California, sponsored by the ACT West Coast regional partnership comprised of the Moss Landing Marine Laboratories (MLML) and the Monterey Bay Aquarium Research Institute (MBARI). The "Optical Remote Sensing of Coastal Habitats" (ORS) Workshop completes ACT'S Remote Sensing Technology series by building upon the success of ACT'S West Coast Regional Partner Workshop "Acoustic Remote Sensing Technologies for Coastal Imaging and Resource Assessment" (ACT 04-07). Drs. Paul Bissett of the Florida Environmental Research Institute (FERI) and Scott McClean of Satlantic, Inc. were the ORS workshop co-chairs. Invited participants were selected to provide a uniform representation of the academic researchers, private sector product developers, and existing and potential data product users from the resource management community to enable development of broad consensus opinions on the role of ORS technologies in coastal resource assessment and management. The workshop was organized to examine the current state of multi- and hyper-spectral imaging technologies with the intent to assess the current limits on their routine application for habitat classification and resource monitoring of coastal watersheds, nearshore shallow water environments, and adjacent optically deep waters. Breakout discussions focused on the capabilities, advantages ,and limitations of the different technologies (e.g., spectral & spatial resolution), as well as practical issues related to instrument and platform availability, reliability, hardware, software, and technical skill levels required to exploit the data products generated by these instruments. Specifically, the participants were charged to address the following: (1) Identify the types of ORS data products currently used for coastal resource assessment and how they can assist coastal managers in fulfilling their regulatory and management responsibilities; (2) Identify barriers and challenges to the application of ORS technologies in management and research activities; (3) Recommend a series of community actions to overcome identified barriers and challenges. Plenary presentations by Drs. Curtiss 0. Davis (Oregon State University) and Stephan Lataille (ITRES Research, Ltd.) provided background summaries on the varieties of ORS technologies available, deployment platform options, and tradeoffs for application of ORS data products with specific applications to the assessment of coastal zone water quality and habitat characterization. Dr. Jim Aiken (CASIX) described how multiscale ground-truth measurements were essential for developing robust assessment of modeled biogeochemical interpretations derived from optically based earth observation data sets. While continuing improvements in sensor spectral resolution, signal to noise and dynamic range coupled with sensor-integrated GPS, improved processing algorithms for georectification, and atmospheric correction have made ORS data products invaluable synoptic tools for oceanographic research, their adoption as management tools has lagged. Seth Blitch (Apalachicola National Estuarine Research Reserve) described the obvious needs for, yet substantial challenges hindering the adoption of advanced spectroscopic imaging data products to supplement the current dominance of digital ortho-quad imagery by the resource management community, especially when they impinge on regulatory issues. (pdf contains 32 pages)

Monitoring and simulation of water, heat,and CO2 fluxes in terrestrial ecosystems based on the APEIS-FLUX system

The Integrated Environmental Monitoring (IEM) project, part of the Asia-Pacific Environmental Innovation Strategy (APEIS) project, developed an integrated environmental monitoring system that can be used to detect, monitor, and assess environmental disasters, degradation, and their impacts in the Asia-Pacific region. The system primarily employs data from the moderate resolution imaging spectrometer (MODIS) sensor on the Earth Observation System- (EOS-) Terra/Aqua satellite,as well as those from ground observations at five sites in different ecological systems in China. From the preliminary data analysis on both annual and daily variations of water, heat and CO2 fluxes, we can confirm that this system basically has been working well. The results show that both latent flux and CO2 flux are much greater in the crop field than those in the grassland and the saline desert, whereas the sensible heat flux shows the opposite trend. Different data products from MODIS have very different correspondence, e.g. MODIS-derived land surface temperature has a close correlation with measured ones, but LAI and NPP are quite different from ground measurements, which suggests that the algorithms used to process MODIS data need to be revised by using the local dataset. We are now using the APEIS-FLUX data to develop an integrated model, which can simulate the regional water,heat, and carbon fluxes. Finally, we are expected to use this model to develop more precise high-order MODIS products in Asia-Pacific region.

Traceable Radiometry Underpinning Terrestrial- and Helio-Studies (truths)

The Traceable Radiometry Underpinning Terrestrial- and Helio- Studies (TRUTHS) mission offers a novel approach to the provision of key scientific data with unprecedented radiometric accuracy for Earth Observation (EO) and solar studies, which will also establish well-calibrated reference targets/standards to support other EO missions. This paper presents the TRUTHS mission and its objectives. TRUTHS will be the first satellite mission to calibrate its EO instrumentation directly to SI in orbit, overcoming the usual uncertainties associated with drifts of sensor gain and spectral shape by using an electrical rather than an optical standard as the basis of its calibration. The range of instruments flown as part of the payload will also provide accurate input data to improve atmospheric radiative transfer codes by anchoring boundary conditions, through simultaneous measurements of aerosols, particulates and radiances at various heights. Therefore, TRUTHS will significantly improve the performance and accuracy of EO missions with broad global or operational aims, as well as more dedicated missions. The provision of reference standards will also improve synergy between missions by reducing errors due to different calibration biases and offer cost reductions for future missions by reducing the demands for on-board calibration systems. Such improvements are important for the future success of strategies such as Global Monitoring for Environment and Security (GMES) and the implementation and monitoring of international treaties such as the Kyoto Protocol. TRUTHS will achieve these aims by measuring the geophysical variables of solar and lunar irradiance, together with both polarised and unpolarised spectral radiance of the Moon, Earth and its atmosphere.

Particulate optical scattering coefficients along an Atlantic Meridional Transect

The particulate optical backscattering coefficient (bbp) is a fundamental optical property that allows monitoring of marine suspended particles both in situ and from space. Backscattering measurements in the open ocean are still scarce, however, especially in oligotrophic regions. Consequently, uncertainties remain in bbp parameterizations as well as in satellite estimates of bbp. In an effort to reduce these uncertainties, we present and analyze a dataset collected in surface waters during the 19th Atlantic Meridional Transect. Results show that the relationship between particulate beam-attenuation coefficient (cp) and chlorophyll-a concentration was consistent with published bio-optical models. In contrast, the particulate backscattering per unit of chlorophyll-a and per unit of cp were higher than in previous studies employing the same sampling methodology. These anomalies could be due to a bias smaller than the current uncertainties in bbp. If that was the case, then the AMT19 dataset would confirm that bbp:cp is remarkably constant over the surface open ocean. A second-order decoupling between bbp and cp was, however, evident in the spectral slopes of these coefficients, as well as during diel cycles. Overall, these results emphasize the current difficulties in obtaining accurate bbp measurements in the oligotrophic ocean and suggest that, to first order, bbp and cp are coupled in the surface open ocean, but they are also affected by other geographical and temporal variations.

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

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.

Sun glint estimation in marine satellite images: a comparison of results from calculation and radiative transfer modeling

The intensity and location of Sun glint in two Medium Resolution Imaging Spectrometer (MERIS) images was modeled using a radiative transfer model that includes elevation features as well as the slope of the sea surface. The results are compared to estimates made using glint flagging and correction approaches used within standard atmospheric correction processing code. The model estimate gives a glint pattern with a similar width but lower peak level than any current method, or than that estimated by a radiative transfer model with surfaces that include slope but not height. The MERIS third reprocessing recently adopted a new slope statistics model for Sun glint correction; the results show that this model is an outlier with respect to both the elevation model and other slope statistics models and we recommend that its adoption should be reviewed.