Validation is mandatory to quantify the reliability of satellite biophysical products that are now routinely generated by a range of sensors. This paper presents the VALERI project dedicated to the validation of the products derived from medium resolution satellite sensors (www. avignon.inra.fr/valeri/). It describes the sites used, and the methodology developed to get the high spatial resolution map of the biophysical variables considered, i.e. LAI , fAPAR and fCover that can be estimated from ground level gap fraction measurements. Sites were selected to represent , with the other validation projects, the large variation of biomes and conditions observed over the Earth’s surface. Each site is about 3 × 3 km² in size and should be flat and relatively homogeneous at the medium resolution scale. For each site, the methodology used to generate the high spatial resolution biophysical variable maps is described. It is mainly based on concurent use of local gound measurements and a high spatial resolution satellite image, generally SPOT-HRV. Local ground measurements should be representative of an elementary sampling unit (ESU) that has approximately the same size as a SPOT-HRV pixel. The ground measurements mainly consist of gap fraction measurements achieved with LAI-2000 or hemispherical photographs. The ESUs are selected over the whole 3 × 3 km² site in order to sample the range of vegetation types observed. A transfer function is subsequently established over the ESUs to relate the ground measurements of the biophysical variables considered to the correspodonding high spatial resolution satellite image data. Finally, co-kriging is applied to generate the high spatial resolution map of the biophysical variables over the 3 × 3 km² area. The methodology presented in this paper can serve as a basis for validating medium resolution satellite products. These methodological aspects are discussed and conclusions drawn on the limitations and prospects of beforementioned validation activity.

Information about the uncertainty associated with Earth science observational data is fundamental to use, re-use, and overall evaluation of the data being used to produce science and support decision making. The associated uncertainty information leads to a quantifiable level of confidence in both the data and the science informing decisions produced using the data. The current breadth and cross-domain depth of understanding and application of uncertainty information, however, are still evolving as the practices associated with quantifying and characterizing uncertainty across various types of Earth observation data are diverse. Since its re-establishment in 2015, the Information Quality Cluster (IQC) of the Earth Science Information Partners (ESIP) has convened numerous sessions within the auspices of ESIP and the American Geophysical Union (AGU) to help collect expert-level information focusing on key aspects of uncertainty of Earth science data and addressed key concerns such as: 1) how uncertainty is quantified (UQ) and characterized (UC), 2) understanding the strengths and limitations of common techniques used in producing and evaluating uncertainty information, 3) implications using uncertainty information as a quality indicator 4) impacts of uncertainty on data fusion/assimilation, 5) various methods for documenting and conveying the uncertainty information to data users, and 6) understanding why certain user communities care about uncertainty and others do not. A key recommendation and action item from the ESIP Summer Meeting 2017 was for the IQC to develop a white paper to establish a clearer understanding of the concept of uncertainty and its communication to data users. The information gathered for this white paper has been provided by Earth science data and informatics experts spanning diverse disciplines and observation systems in the cross-domain Earth sciences. The intention of this white paper is to provide a diversely sampled exposition of both prolific and unique policies and practices, applicable in an international context of diverse policies and working groups, made toward quantifying, characterizing, communicating and making use of uncertainty information throughout the diverse, cross-disciplinary Earth science data landscape.

Information about the uncertainty associated with Earth science observational data is fundamental to use, re-use, and overall evaluation of the data being used to produce science and support decision making. The associated uncertainty information leads to a quantifiable level of confidence in both the data and the science informing decisions produced using the data. The current breadth and cross-domain depth of understanding and application of uncertainty information, however, are still evolving as the practices associated with quantifying and characterizing uncertainty across various types of Earth observation data are diverse. Since its re-establishment in 2015, the Information Quality Cluster (IQC) of the Earth Science Information Partners (ESIP) has convened numerous sessions within the auspices of ESIP and the American Geophysical Union (AGU) to help collect expert-level information focusing on key aspects of uncertainty of Earth science data and addressed key concerns such as: 1) how uncertainty is quantified (UQ) and characterized (UC), 2) understanding the strengths and limitations of common techniques used in producing and evaluating uncertainty information, 3) implications using uncertainty information as a quality indicator 4) impacts of uncertainty on data fusion/assimilation, 5) various methods for documenting and conveying the uncertainty information to data users, and 6) understanding why certain user communities care about uncertainty and others do not. A key recommendation and action item from the ESIP Summer Meeting 2017 was for the IQC to develop a white paper to establish a clearer understanding of the concept of uncertainty and its communication to data users. The information gathered for this white paper has been provided by Earth science data and informatics experts spanning diverse disciplines and observation systems in the cross-domain Earth sciences. The intention of this white paper is to provide a diversely sampled exposition of both prolific and unique policies and practices, applicable in an international context of diverse policies and working groups, made toward quantifying, characterizing, communicating and making use of uncertainty information throughout the diverse, cross-disciplinary Earth science data landscape.

Scientific data stewardship is an important part of long-term preservation and the use/reuse of digital research data. It is critical for ensuring trustworthiness of data, products, and services, which is important for decision-making. Recent U.S. federal government directives and scientific organization guidelines have levied specific requirements, increasing the need for a more formal approach to ensuring that stewardship activities support compliance verification and reporting. However, many science data centers lack an integrated, systematic, and holistic framework to support such efforts. The current business- and process-oriented stewardship frameworks are too costly and lengthy for most data centers to implement. They often do not explicitly address the federal stewardship requirements and/or the uniqueness of geospatial data. This work proposes a data-centric conceptual enterprise framework for managing stewardship activities, based on the philosophy behind the Plan-Do-Check-Act (PDCA) cycle, a proven industrial concept. This framework, which includes the application of maturity assessment models, allows for quantitative evaluation of how organizations manage their stewardship activities and supports informed decision-making for continual improvement towards full compliance with federal, agency, and user requirements.

The key objective of the NOAA Climate Data Record (CDR) program is the sustained production of high-quality, multidecadal time series data describing the global atmosphere, oceans, and land surface that can be used for informed decision-making. The challenges of a long-term program of sustaining CDRs, as contrasted with short-term efforts of traditional 3-yr research programs, are substantial. The sustained production of CDRs requires collaboration between experts in the climate community, data management, and software development and maintenance. It is also informed by scientific application and associated user feedback on the accessibility and usability of the produced CDRs. The CDR program has developed a metric for assessing the maturity of CDRs with respect to data management, software, and user application and applied it to over 30 CDRs. The main lesson learned over the past 7 years is that a rigorous team approach to data management, employing subject matter experts at every step, is critical to open and transparent production. This approach also makes it much easier to support the needs of users who want near-real-time production of CDRs for monitoring and users who want to use CDRs for tailored, derived information, such as a drought index.

Ensuring and improving quality and usability is an important part of scientific stewardship of digital environmental data products, but the roles of the responsible parties — those who manage quality and usability — have been evolving over time and have not always been clearly defined. Recognizing that in the Open Data and Big Data era, effective long-term scientific stewardship of data products requires an integrated and coordinated team effort of experts in multiple knowledge domains — data management, science, and technology — we introduce the following stewardship roles for each of these domains: data steward, scientific steward, and technology steward. This article defines their roles and high-level responsibilities as well as the responsibilities of other major product stakeholders, including data originators and distributors. Defining roles and formalizing responsibilities will facilitate the process of curating and communicating quality information to users. Clearly defined roles will allow effective cross-disciplinary communication and better resource allocation for data stewardship, supporting organizations in meeting the challenges of stewarding digital environmental data products in the Open Data and Big Data era.

This paper presents a stewardship maturity assessment model in the form of a matrix for digital environmental datasets. Nine key components are identified based on requirements imposed on digital environmental data and information that are cared for and disseminated by U.S. Federal agencies by U.S. law, i.e., Information Quality Act of 2001, agencies’ guidance, expert bodies’ recommendations, and users. These components include: preservability, accessibility, usability, production sustainability, data quality assurance, data quality control/monitoring, data quality assessment, transparency/traceability, and data integrity. A five-level progressive maturity scale is then defined for each component associated with measurable practices applied to individual datasets, representing Ad Hoc, Minimal, Intermediate, Advanced, and Optimal stages. The rationale for each key component and its maturity levels is described. This maturity model, leveraging community best practices and standards, provides a unified framework for assessing scientific data stewardship. It can be used to create a stewardship maturity scoreboard of dataset(s) and a roadmap for scientific data stewardship improvement or to provide data quality and usability information to users, stakeholders, and decision makers.

Thermal infrared satellite observations of the Earth's surface are widely used to retrieve Land Surface Temperature (LST) and monitor LST changes around the world. Since January 2012, the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-Orbiting Partnership (S-NPP) has provided daily observations of LST with a spatial resolution of 750m at nadir. Comparison of the standard VIIRS LST product with the equivalent daily standard product from the Moderate Resolution Imaging Spectroradiometer (MODIS) collection-5 and with ground-based measurements over vegetated and inland water surfaces showed good agreement. Analysis indicated the accuracy and precision of the VIIRS product over these cover types was 0.2K and 2.0K respectively provided the analyses included appropriate compensation for any spatial heterogeneity in LST within the validation site. However, comparisons between in situ LST and the VIIRS and MODIS LST over arid and semi-arid regions indicate both satellite products significantly underestimate the LST, and the VIIRS algorithm can have large errors in the retrieved LST over areas of high atmospheric water vapor. Errors of up to 4K were observed over semi-arid and arid areas due to incorrect characterization of emissivity, and differences of up to 15K were observed over areas with high atmospheric water content between the VIIRS LST and matching MODIS LST.

Thermal infrared satellite observations of the Earth's surface are key components in estimating the surface skin temperature over global land areas. However, depending on sun illumination and viewing directional configurations, satellites measure different surface radiometric temperatures, particularly over sparsely vegetated regions where the radiometric contributions from soil and vegetation vary with the sun and viewing geometry. Over an oak tree woodland located near the town of Evora, Portugal, we compare different satellite-based land surface temperature (LST) products from the Moderate Resolution Imaging Spectroradiometer on board the Terra and Aqua polar-orbiting satellites and from the Spinning Enhanced Visible and Infrared Imager on board the geostationary Meteosat satellite with ground-based LST. The observed differences between LSTs derived from polar and geostationary satellites are up to 12 K due to directional effects. In this letter, we develop a methodology based on a radiative transfer model and dedicated field radiometric measurements to interpret and validate directional remote sensing measurements. The methodology is used to estimate the quantitative uncertainty in LST products derived from polar-orbiting satellites over a sparse vegetation canopy.

Thermal infrared satellite observations of the Earth's surface are key components in estimating the surface skin temperature over global land areas. This work presents validation methodologies to estimate the quantitative uncertainty in Land Surface Temperature (LST) product derived from the Visible Infrared Imager Radiometer Suite (VIIRS) onboard Suomi National Polar-orbiting Partnership (NPP) using ground-based measurements currently made operationally at many field and weather stations around the world. Over heterogeneous surfaces in terms of surface types or biophysical properties (e.g., vegetation density, emissivity), the validation protocol accounts for land surface spatial variability around the ground station. Over sparse vegetation canopies, the methodology accounts for viewing directional effects and sun configuration when validating VIIRS LST products.

The demand for climate information, with long observational records spanning decades to centuries and the information's broad application for decision making across many socioeconomic sectors, requires that geophysicists adopt more rigorous processes for the sustained production of climate data records (CDRs). Such processes, methods, and standards are more typically found in the systems engineering community and have not generally been adopted in the climate science community. We propose the use of a maturity matrix for climate data records that characterizes the process of moving from a basic research product (e.g., raw data and initial product) to a sustained and routinely generated product (e.g., a quality‐controlled homogenized data set). This model of increasing product and process maturity is similar to NASA's technical readiness levels for flight hardware and instrumentation and the software industry's capability maturity model. Over time, engineers who have worked on many projects developed a set of best practices that identified the processes required to optimize cost, schedule, and risk. In the NASA maturity model, they identified steps in technology readiness, denoted as the technology readiness level (TRL). TRL 1 occurs when basic research has taken the first steps toward application. TRL 9 is when a technology has been fully proven to work consistently for the intended purpose and is operational.

NOAA will soon use the new Visible Infrared Imager Radiometer Suite (VIIRS) on the Joint Polar Satellite System (JPSS) as its primary polar-orbiting satellite imager. Employing a near real-time processing system, NOAA will generate a series of Environmental Data Records (EDRs) from VIIRS data. For example, the VIIRS Land Surface Temperature (LST) EDR will estimate the surface skin temperature over all global land areas and provide key information for monitoring Earth surface energy and water fluxes. Because both VIIRS and its processing algorithms are new, NOAA is conducting a rigorous calibration and validation program to understand and improve product quality. This paper presents a new validation methodology to estimate the quantitative uncertainty in the LST EDR, and contribute to improving the retrieval algorithm. It employs a physically-based approach to scaling up point LST measurements currently made operationally at many field and weather stations around the world. The scaling method consists of the merging information collected at different spatial resolutions within a land surface model to fully characterize large area (km × km scale) satellite products. The approach can be used to explore scaling issues over terrestrial surfaces spanning a large range of climate regimes and land cover types, including forests and mixed vegetated areas. The methodology was tested successfully with NASA/MODIS data, indicating an absolute error for MODIS LST products of 2.0 K at a mixed agricultural site (Bondville, IL) when accounting for scaling, and higher than 3 K without scaling. The VIIRS LST EDR requires a 1.5 K measurement accuracy and 2.5 K measurement precision. Ultimately, this validation approach should lead to an accurate and continuously-assessed VIIRS LST product suitable to support weather forecast, hydrological applications, or climate studies. It is readily adaptable to other moderate resolution satellite systems.

The spring and summer (March through August) of 2011–2012 set many new climatological records across the contiguous United States, including the hottest month in the instrumental record: July 2012. Various measures of temperature extremes and drought severity serve to put this period into historical perspective (1895 to present) and to assess to what extent the recent anomalies are consistent with observed trends. During spring and summer, anomalously high temperatures can combine with unusually dry conditions to amplify temperature and drought feedbacks. Observational data from 2011 and 2012 are strongly suggestive of such an amplification and reveal a number of significant trends for various measures of high temperatures in the United States.

Large-scale climate patterns influenced temperature and weather patterns around the globe in 2011. In particular, a moderate-to-strong La Nina at the beginning of the year dissipated during boreal spring but reemerged during fall. The phenomenon contributed to historical droughts in East Africa, the southern United States, and northern Mexico, as well the wettest two-year period (2010-11) on record for Australia, particularly remarkable as this follows a decade-long dry period. Precipitation patterns in South America were also influenced by La Nina. Heavy rain in Rio de Janeiro in January triggered the country's worst floods and landslides in Brazil's history.The 2011 combined average temperature across global land and ocean surfaces was the coolest since 2008, but was also among the 15 warmest years on record and above the 1981-2010 average. The global sea surface temperature cooled by 0.1 degrees C from 2010 to 2011, associated with cooling influences of La Nina. Global integrals of upper ocean heat content for 2011 were higher than for all prior years, demonstrating the Earth's dominant role of the oceans in the Earth's energy budget. In the upper atmosphere, tropical stratospheric temperatures were anomalously warm, while polar temperatures were anomalously cold. This led to large springtime stratospheric ozone reductions in polar latitudes in both hemispheres. Ozone concentrations in the Arctic stratosphere during March were the lowest for that period since satellite records began in 1979. An extensive, deep, and persistent ozone hole over the Antarctic in September indicates that the recovery to pre-1980 conditions is proceeding very slowly.Atmospheric carbon dioxide concentrations increased by 2.10 ppm in 2011, and exceeded 390 ppm for the first time since instrumental records began. Other greenhouse gases also continued to rise in concentration and the combined effect now represents a 30% increase in radiative forcing over a 1990 baseline. Most ozone depleting substances continued to fall. The global net ocean carbon dioxide uptake for the 2010 transition period from El Nino to La Nina, the most recent period for which analyzed data are available, was estimated to be 1.30 Pg C yr(-1), almost 12% below the 29-year long-term average.Relative to the long-term trend, global sea level dropped noticeably in mid-2010 and reached a local minimum in 2011. The drop has been linked to the La Nina conditions that prevailed throughout much of 2010-11. Global sea level increased sharply during the second half of 2011.Global tropical cyclone activity during 2011 was well-below average, with a total of 74 storms compared with the 1981-2010 average of 89. Similar to 2010, the North Atlantic was the only basin that experienced above-normal activity. For the first year since the widespread introduction of the Dvorak intensity-estimation method in the 1980s, only three tropical cyclones reached Category 5 intensity level-all in the Northwest Pacific basin.The Arctic continued to warm at about twice the rate compared with lower latitudes. Below-normal summer snowfall, a decreasing trend in surface albedo, and above-average surface and upper air temperatures resulted in a continued pattern of extreme surface melting, and net snow and ice loss on the Greenland ice sheet. Warmer-than-normal temperatures over the Eurasian Arctic in spring resulted in a new record-low June snow cover extent and spring snow cover duration in this region. In the Canadian Arctic, the mass loss from glaciers and ice caps was the greatest since GRACE measurements began in 2002, continuing a negative trend that began in 1987. New record high temperatures occurred at 20 m below the land surface at all permafrost observatories on the North Slope of Alaska, where measurements began in the late 1970s. Arctic sea ice extent in September 2011 was the second-lowest on record, while the extent of old ice (four and five years) reached a new record minimum that was just 19% of normal.On the opposite pole, austral winter and spring temperatures were more than 3 degrees C above normal over much of the Antarctic continent. However, winter temperatures were below normal in the northern Antarctic Peninsula, which continued the downward trend there during the last 15 years. In summer, an all-time record high temperature of -12.3 degrees C was set at the South Pole station on 25 December, exceeding the previous record by more than a full degree. Antarctic sea ice extent anomalies increased steadily through much of the year, from briefly setting a record low in April, to well above average in December. The latter trend reflects the dispersive effects of low pressure on sea ice and the generally cool conditions around the Antarctic perimeter.

Moderate resolution satellite data have become an integral part of land remote sensing, which provide observations to support global and climate change research and applications of societal benefit. Starting with the NOAA Advanced Very High Resolution Radiometer (AVHRR), daily data analysis in the time-domain provided important new insights in vegetation studies at the global scale. Combining the temporal characteristics of the AVHRR and extending the spectral characteristics of the Landsat-5 Thematic Mapper, the NASA Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) instruments launched a new era in land remote sensing. The Terra and Aqua MODIS instruments now provide science quality, moderate resolution data with spatial resolutions from 250 m to 1 km. The next generation U.S. moderate resolution sensor, the Visible/Infrared Imager/Radiometer Suite (VIIRS), is scheduled to fly on the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP), expected to launch in October 2011, and subsequently on successive NPOESS platforms. The VIERS instillment will transition much of the capability of the experimental MODIS instruments into the operational domain, a critical step for the land remote sensing community. This chapter provides the background to the current state of moderate resolution land remote sensing, and a description of the VIIRS instrument and the associated Environmental Data Records for the land surface. The VIIRS sensor and product similarities and differences with MODIS are described. The challenges in meeting the future needs for moderate resolution land remote sensing are discussed.