Evolution of the Earth Observing System (EOS) Data and Information System (EOSDIS)

One of the strategic goals of the U.S. National Aeronautics and Space Administration (NASA) is to "Develop a balanced overall program of science, exploration, and aeronautics consistent with the redirection of the human spaceflight program to focus on exploration". An important sub-goal of this goal is to "Study Earth from space to advance scientific understanding and meet societal needs." NASA meets this subgoal in partnership with other U.S. agencies and international organizations through its Earth science program. A major component of NASA s Earth science program is the Earth Observing System (EOS). The EOS program was started in 1990 with the primary purpose of modeling global climate change. This program consists of a set of space-borne instruments, science teams, and a data system. The instruments are designed to obtain highly accurate, frequent and global measurements of geophysical properties of land, oceans and atmosphere. The science teams are responsible for designing the instruments as well as scientific algorithms to derive information from the instrument measurements. The data system, called the EOS Data and Information System (EOSDIS), produces data products using those algorithms as well as archives and distributes such products. The first of the EOS instruments were launched in November 1997 on the Japanese satellite called the Tropical Rainfall Measuring Mission (TRMM) and the last, on the U.S. satellite Aura, were launched in July 2004. The instrument science teams have been active since the inception of the program in 1990 and have participation from Brazil, Canada, France, Japan, Netherlands, United Kingdom and U.S. The development of EOSDIS was initiated in 1990, and this data system has been serving the user community since 1994. The purpose of this chapter is to discuss the history and evolution of EOSDIS since its beginnings to the present and indicate how it continues to evolve into the future. this chapter is organized as follows. Sect. 7.2 provides a discussion of EOSDIS, its elements and their functions. Sect. 7.3 provides details regarding the move towards more distributed systems for supporting both the core and community needs to be served by NASA Earth science data systems. Sect. 7.4 discusses the use of standards and interfaces and their importance in EOSDIS. Sect. 7.5 provides details about the EOSDIS Evolution Study. Sect. 7.6 presents the implementation of the EOSDIS Evolution plan. Sect. 7.7 briefly outlines the progress that the implementation has made towards the 2015 Vision, followed by a summary in Sect. 7.8

Land and cryosphere products from Suomi NPP VIIRS: overview and status

[1] The Visible Infrared Imaging Radiometer Suite (VIIRS) instrument was launched in October 2011 as part of the Suomi National Polar-Orbiting Partnership (S-NPP). The VIIRS instrument was designed to improve upon the capabilities of the operational Advanced Very High Resolution Radiometer and provide observation continuity with NASA's Earth Observing System's Moderate Resolution Imaging Spectroradiometer (MODIS). Since the VIIRS first-light images were received in November 2011, NASA- and NOAA-funded scientists have been working to evaluate the instrument performance and generate land and cryosphere products to meet the needs of the NOAA operational users and the NASA science community. NOAA's focus has been on refining a suite of operational products known as Environmental Data Records (EDRs), which were developed according to project specifications under the National Polar-Orbiting Environmental Satellite System. The NASA S-NPP Science Team has focused on evaluating the EDRs for science use, developing and testing additional products to meet science data needs, and providing MODIS data product continuity. This paper presents to-date findings of the NASA Science Team's evaluation of the VIIRS land and cryosphere EDRs, specifically Surface Reflectance, Land Surface Temperature, Surface Albedo, Vegetation Indices, Surface Type, Active Fires, Snow Cover, Ice Surface Temperature, and Sea Ice Characterization. The study concludes that, for MODIS data product continuity and earth system science, an enhanced suite of land and cryosphere products and associated data system capabilities are needed beyond the EDRs currently available from the VIIRS

An Intelligent Archive Testbed Incorporating Data Mining

Many significant advances have occurred during the last two decades in remote sensing instrumentation, computation, storage, and communication technology. A series of Earth observing satellites have been launched by U.S. and international agencies and have been operating and collecting global data on a regular basis. These advances have created a data rich environment for scientific research and applications. NASA s Earth Observing System (EOS) Data and Information System (EOSDIS) has been operational since August 1994 with support for pre-EOS data. Currently, EOSDIS supports all the EOS missions including Terra (1999), Aqua (2002), ICESat (2002) and Aura (2004). EOSDIS has been effectively capturing, processing and archiving several terabytes of standard data products each day. It has also been distributing these data products at a rate of several terabytes per day to a diverse and globally distributed user community (Ramapriyan et al. 2009). There are other NASA-sponsored data system activities including measurement-based systems such as the Ocean Data Processing System and the Precipitation Processing system, and several projects under the Research, Education and Applications Solutions Network (REASoN), Making Earth Science Data Records for Use in Research Environments (MEaSUREs), and the Advancing Collaborative Connections for Earth-Sun System Science (ACCESS) programs. Together, these activities provide a rich set of resources constituting a value chain for users to obtain data at various levels ranging from raw radiances to interdisciplinary model outputs. The result has been a significant leap in our understanding of the Earth systems that all humans depend on for their enjoyment, livelihood, and survival. The trend in the community today is towards many distributed sets of providers of data and services. Despite this, visions for the future include users being able to locate, fuse and utilize data with location transparency and high degree of interoperability, and being able to convert data to information and usable knowledge in an efficient, convenient manner, aided significantly by automation (Ramapriyan et al. 2004; NASA 2005). We can look upon the distributed provider environment with capabilities to convert data to information and to knowledge as an Intelligent Archive in the Context of a Knowledge Building system (IA-KBS). Some of the key capabilities of an IA-KBS are: Virtual Product Generation, Significant Event Detection, Automated Data Quality Assessment, Large-Scale Data Mining, Dynamic Feedback Loop, and Data Discovery and Efficient Requesting (Ramapriyan et al. 2004)

Lossless compression of image data products on th e FIFE CD-ROM series

How do you store enough of the key data sets, from a total of 120 gigabytes of data collected for a scientific experiment, on a collection of CD-ROM's, small enough to distribute to a broad scientific community? In such an application where information loss in unacceptable, lossless compression algorithms are the only choice. Although lossy compression algorithms can provide an order of magnitude improvement in compression ratios over lossless algorithms the information that is lost is often part of the key scientific precision of the data. Therefore, lossless compression algorithms are and will continue to be extremely important in minimizing archiving storage requirements and distribution of large earth and space (ESS) data sets while preserving the essential scientific precision of the data

Intercomparison of MODIS Albedo Retrievals and In Situ Measurements Across the Global FLUXNET Network

Surface albedo is a key parameter in the Earth's energy balance since it affects the amount of solar radiation directly absorbed at the planet surface. Its variability in time and space can be globally retrieved through the use of remote sensing products. To evaluate and improve the quality of satellite retrievals, careful intercomparisons with in situ measurements of surface albedo are crucial. For this purpose we compared MODIS albedo retrievals with surface measurements taken at 53 FLUXNET sites that met strict conditions of land cover homogeneity. A good agreement between mean yearly values of satellite retrievals and in situ measurements was found (R(exp 2)= 0.82). The mismatch is correlated to the spatial heterogeneity of surface albedo, stressing the relevance of land cover homogeneity when comparing point to pixel data. When the seasonal patterns of MODIS albedo is considered for different plant functional types, the match with surface observation is extremely good at all forest sites. On the contrary, in non-forest sites satellite retrievals underestimate in situ measurements across the seasonal cycle. The mismatch observed at grasslands and croplands sites is likely due to the extreme fragmentation of these landscapes, as confirmed by geostatistical attributes derived from high resolution scenes

Legal Challenges and Market Rewards to the Use and Acceptance of Remote Sensing and Digital Information as Evidence

Bakgrund I den nutida forskningen är det essentiellt att företag tar hänsyn till medarbetarnas motivation så att de gynnas av det arbetssätt som tillämpas. En arbetsmetod som blivit allt vanligare är konceptet Lean som ursprungligen kommer från den japanska bilindustrin. Lean har idag utvecklats till ett allmängiltigt koncept som tillämpas i flertalet branscher världen över. Trots att konceptet innebär flertalet positiva aspekter har det fått utstå stark kritik när det kommer till de mänskliga aspekterna och forskare har ställt sig frågan om Lean är "Mean". Kritiken härleds främst till medarbetares arbetsmiljö i form av stress och brist på variation, självbestämmande, hälsa och välmående. Få empiriska studier har däremot genomförts som undersöker konsekvenserna som Lean får på medarbetares upplevda motivation. Syfte Vårt syfte är att undersöka och öka förståelsen för medarbetares upplevelser av motivationen i företag som tillämpar Lean. Vidare har studien för avsikt att utreda om det föreligger en paradox mellan Lean och vad som motiverar medarbetare på en arbetsplats. Metod Studien har utgått från en kvalitativ metod via intervjuer. För att göra en djupare undersökning och analysera hur vårt fenomen, motivation, upplevs i en kontext med Lean tillämpade vi Små-N-studier. Vi har även haft en iterativ forskningsansats som förenat den deduktiva och induktiva ansatsen där studien pendlat mellan teorier och empiriska observationer fram tills det slutgiltiga resultatet. Slutsatser Utefter medarbetarnas upplevelser har vi identifierat att det inte föreligger någon paradox mellan Lean och motivation eftersom övervägande antal medarbetare upplevde att de är motiverade även om företaget tillämpar Lean. Dock har studien kunnat urskilja både stödjande och motverkande faktorer när det kommer till medarbetarnas upplevda arbetsförhållanden som i sin tur inverkar på motivationen. De motverkande faktorerna menar vi främst beror på att arbetsförhållandena i somliga fall innehåller höga prestationskrav, målstyrning samt standardiseringar. Vidare upplevs motivationen överlag som mer positiv när företagen använder en mjukare form av Lean där samtliga medlemmars intressen beaktas.Background In modern research, it is essential that companies consider employees’ motivation so that they benefit from the applied practices. A working method that has become increasingly common is the concept Lean, which has its origin in the Japanese automotive industry. Today, Lean has evolved into a universal concept that is applied in many industries worldwide. Although the concept involves numerous positive aspects it has endured strong criticism when it comes to the human aspects and researchers have raised the question if Lean is "Mean". Criticism is derived primarily to employees’ working conditions in terms of stress and lack, variation, autonomy, health and wellbeing. However, few empirical studies have been carried out that examines the impact that Lean has on employees’ experienced motivation. Aim The aim is to increase the understanding of employees’ experienced motivation in companies that practice Lean. Further on the study has the intention to investigate if there is a paradox between Lean and what motivates employees on work. Methodology The study has been conducted through a qualitative method by interviews and to be able to do a deeper examination and analyze how our phenomenon, motivation, is experienced in a Lean context we applied small-N-studies. Our strategy has been iterative, combining both a deductive and inductive approach, where the study has varied between theories and empirical observations until the final result. Conclusions We have identified that there is no paradox between Lean and motivation since the majority of employees’ experienced that they are motivated even though the company practice Lean. Nevertheless the study shows that there are both supportive and counteractive factors when it comes to the employees’ experienced working conditions. The counteractive factors consists foremost of high performance standards, goal steering and standardizations, and have in some cases a negative influence on the working conditions. Furthermore the experienced motivation is more positive overall when the companies use a softer form of Lean where all the members’ interests are taken into account

Influence of Wind on Suspended Matter in the Water of the Albufera of Valencia (Spain)

Wind significantly influences suspended matter in lakes, especially in shallow lagoons. To know how wind affects the water in Albufera of Valencia, a shallow coastal lagoon, the measured variables of turbidity and transparency have been correlated with the estimates by processing Sentinel-2 satellite images with the Sen2Cor processor. Data from four years of study of winds show that most of them are light to gentle easterly breezes and moderate to fresh westerly breezes. The obtained results show significant correlations between the measured variables and those obtained from the satellite images for total suspended matter and water transparency, as well as with the average daily wind speed. There is no significant correlation between wind and chlorophyll a. Moderate to fresh breezes resuspend the fine sediment reaching concentration values from 100 to 300 mg/L according to satellite data. However, it is necessary to obtain field data for the values of moderate and fresh winds, as for now, there are no experimental data to verify the validity of the satellite estimates

Using MODIS Satellite Images to Confirm Distributed Snowmelt Model Results in a Small Arctic Watershed

Environmental analysts face the problem of obtaining distributed measurements to evaluate the performance of models with increasingly small spatiotemporal resolution. While U.S. government agencies readily provide both measurement products and data tools for the study of global change occurring over entire seasons and across continental areas, analysts need access to the low-level data that provides the basis for global products. Finally, analysts need to consider sensor errors inherent in low-level products that are accounted for in global, composite products. Hydrologists using tools for managing low-level snow swath measurements, in particular, must consider how measurements are affected by sensor errors like snow-cloud confusion and sensor errors due to low ground illumination at night. This thesis aims to explore the use of remotely sensed snow maps to confirm a time series of model maps. Specifically, snow covered area (SCA) measurements remotely sensed by the National Aeronautics and Space Administration (NASA) are used to confirm SCA predictions modeled by the United States Agriculture Department (USDA). The measurements come from the two Moderate Resolution Imaging Spectroradiometer (MODIS) sensors aboard near-polar, sun-synchronous satellites named Aqua and Terra. The USDA calls the model TOPMODEL-Based Land-Atmosphere Transfer Scheme (TOPLATS). The Upper Kuparuk River Watershed (UKRW) on the North Slope of Alaska acts as the case study location. To meet the map-comparison goal, the Kappa statistic, Kappa statistic variants, and probability density functions expressing measurement uncertainty in discrete scenes all evaluate the ability of MODIS measurements to confirm the accuracy of TOPLATS model maps. Data management objectives to make measured data accessible and comparable to the model output comprise a supporting goal. Results show that individual composite statistics, like the proportion of agreement between two maps, can easily obscure spatiotemporally distributed confirmation information without additional statistics and side-by-side images of measurement maps and model maps. These tools show some promise for using MODIS to confirm model predictions of snowmelt that occur across less than 150 km2 and less than a few days, however, clouds and malfunctioning sensors limit such use

Disaggregation of SMOS soil moisture over West Africa using the Temperature and Vegetation Dryness Index based on SEVIRI land surface parameters

The overarching objective of this study was to produce a disaggregated SMOS Soil Moisture (SM) product using land surface parameters from a geostationary satellite in a region covering a diverse range of ecosystem types. SEVIRI data at 15 minute temporal resolution were used to derive the Temperature and Vegetation Dryness Index (TVDI) that served as SM proxy within the disaggregation process. West Africa (3 N, 26 W; 28 N, 26 E) was selected as a case study as it presents both an important North-South climate gradient and a diverse range of ecosystem types. The main challenge was to set up a methodology applicable over a large area that overcomes the constraints of SMOS (low spatial resolution) and TVDI (requires similar atmospheric forcing and triangular shape formed when plotting morning rise temperature versus fraction of vegetation cover) in order to produce a 0.05 degree resolution disaggregated SMOS SM product at sub-continental scale. Consistent cloud cover appeared as one of the main constraints for deriving TVDI, especially during the rainy season and in the southern parts of the region and a large adjustment window (105x105 SEVIRI pixels) was therefore deemed necessary. Both the original and the disaggregated SMOS SM products described well the seasonal dynamics observed at six locations of in situ observations. However, there was an overestimation in both products for sites in the humid southern regions; most likely caused by the presence of forest. Both TVDI and the associated disaggregated SM product was found to be highly sensitive to algorithm input parameters; especially of conditions of high fraction of vegetation cover. Additionally, seasonal dynamics in TVDI did not follow the seasonal patters of SM. Still, its spatial heterogeneity was found to be a good proxy for disaggregating SMOS SM data; main river networks and spatial patterns of SM extremes (i.e. droughts and floods) not seen in the original SMOS SM product were revealed in the disaggregated SM product for a test case of July-September 2012. The disaggregation methodology thereby successfully increased the spatial resolution of SMOS SM, with potential application for local drought/flood monitoring of importance for the livelihood of the population of West Africa

Space and Earth Science Data Compression Workshop

The workshop explored opportunities for data compression to enhance the collection and analysis of space and Earth science data. The focus was on scientists' data requirements, as well as constraints imposed by the data collection, transmission, distribution, and archival systems. The workshop consisted of several invited papers; two described information systems for space and Earth science data, four depicted analysis scenarios for extracting information of scientific interest from data collected by Earth orbiting and deep space platforms, and a final one was a general tutorial on image data compression