A long-term case study of a large sub-Alpine lake

Availability of remotely sensed multi-spectral images since the 1980’s, which cover three decades of voluminous data could help researchers to study the changing dynamics of bio-physical characteristics of land and water. In this study, we introduce a new methodology to develop homogenised Lake Surface Water Temperature (LSWT) from multiple polar orbiting satellites. Precisely, we developed homogenised 1 km daily LSWT maps covering the last 30 years (1986 to 2015) combining data from 13 satellites. We used a split-window technique to derive LSWT from brightness temperatures and a modified diurnal temperature cycle model to homogenise data which were acquired between 8:00 to 17:00 UTC. Gaps in the temporal LSWT data due to the presence of clouds were filled by applying Harmonic ANalysis of Time Series (HANTS). The satellite derived LSWT maps were validated based on long-term monthly in-situ bulk temperature measurements in Lake Garda, the largest lake in Italy. We found the satellite derived homogenised LSWT being significantly correlated to in-situ data. The new LSWT time series showed a significant annual rate of increase of 0.020 °C yr−1 (*P < 0.05), and of 0.036 °C yr−1 (***P < 0.001) during summer

Trends in surface temperature from new long–term homogenized thermal data by applying remote sensing techniques and its validation using in-situ data of five southern European lakes

Recent studies, based on a combination of long-term in-situ and satellite derived temperature data indicate that lakes are rapidly warming at the global scale. Since Lake Surface Water Temperature (LSWT) is highly responsive to long-term modifications in the thermal structure of lakes, it is a good indicator of changes in lake characteristics. There have not been done many studies at a regional scale to understand the lakes’ response to climate change, mainly due to lack of high spatio-temporal data. Therefore, further studies are needed to understand variation in trends, impacts and consequences at a regional scale. It is essential to have highly frequent spatially explicit data to understand the spatiotemporal thermal variations of LSWT. Continuous in-situ water temperature data measured at high temporal resolution from permanently installed stations are becoming increasingly available through GLEON (Global Lake Ecological Observatory Network: http://gleon.org/) or NetLake (Networking Lake Observatories in Europe). But these data are often heterogeneous with different sources and time line, point based, and not available for many lakes around the globe. To establish permanent weather stations for all the large lakes in the world is also not economically viable. As an alternative to direct measurements, remote sensing is considered as a promising approach to reconstruct complete time series of LSWT where direct measurements are missing. Temperature of land/water surfaces is one of the direct and accurate measurements using satellite data acquired in the thermal infra-red spectral region. Furthermore, the availability of daily satellite data since the 1980s at a moderate resolution of 1 km from multiple polar orbiting satellites is an opportunity not to be missed. But owing to the complexities related to earlier satellite missions, and the need of high level of processing, the potential of the historical satellite data in deriving a homogenised LSWT is still not explored well. There is a gap in the availability of long-term time series of LSWT from the satellite data which could be used in understanding the patterns and drivers of thermal variations in large lakes. This thesis aims to fill this gap by developing reproducible and extendable methods to derive homogenised daily LSWT for thirty years from 1986 to 2015. Hence, the main objectives of this thesis are i) to reconstruct thirty years (1986-2015) of daily satellite thermal data as a homogenised time series of LSWT for five large Italian lakes by combining thermal data from multiple satellites, ii) to assess the quality of the satellite derived LSWT using long-term in-situ data collected from the same lakes, iii) to report the seasonal and annual trends in LSWT using robust statistical tests. The first part of the thesis deals with the accurate processing of historical Advanced Along-Track Scanning Radiometer (AVHRR) sensor data to derive time series of LSWT. A new method to resolve the complex geometrical issues with the earlier AVHRR data obtained from National Oceanic and Atmospheric Administration (NOAA) satellites has been developed. The new method can accurately process historical AVHRR data and develop time series of geometrically aligned thermal channels in the spectral range of 10.5-12.5 µm. The validation procedure to check the accuracy of image to image co-registration using 2000 random images (from a total of 22,507 images) reported sub-pixel accuracy with an overall Root Mean Square Error (RMSE) of 755.65 m. The usability of newly derived time series of thermal channels to derive LSWT for lakes were tested and validated. Furthermore, crossplatform and inter-platform validations were performed using corresponding same day observations which reported an overall RMSE of less than 1.5 °C. In the second part of the thesis, a new method was developed to derive homogenised daily LSWT standardized at 12:00 UTC from thermal channels of thirteen different satellites. The new method is implemented for Lake Garda in Northern Italy developing time series of homogenised daily LSWT for last thirty years from 1986 to 2015. The sensors used in this study are the AVHRR from multiple NOAA satellites, Along Track Scanning Radiometer (ATSR) series from European Remote Sensing (ERS) satellites and Moderate Resolution Imaging Spectroradiometer (MODIS) from Aqua and Terra satellites. The LSWT time series are then validated using long-term in-situ data obtained from a deep and a shallow sampling location in the lake. Validation of LSWT from individual satellites against corresponding in-situ data reported an overall RMSE of 0.92 °C. The validation between final homogenised LSWT and the in-situ data reported a coefficient of determination (R2) of 0.98 and a RMSE of 0.79 °C. In the third part of the thesis, homogenised daily LSWT for the last thirty years (1986-2015) were developed for five large lakes in Italy using the newly developed methods. The LSWT time series was validated against the in-situ data collected from the respective lakes. Furthermore, long-term trend analysis to study the seasonal and annual variations in LSWT over thirty years was performed over the newly developed LSWT data. The validation procedure reported an average RMSE and Mean Absolute Error (MAE) of 1.2 °C and 0.98 °C, respectively, over all the lakes. The trend analysis reported an overall regional summer warming rate of 0.03 °C yr-1 and an annual warming rate of 0.017 °C yr-1. During summer, all studied sub-Alpine lakes showed high coherence in LSWT to each other. The summer mean LSWT of Lake Garda, located in the sub-Alpine region also exhibit high temporal coherence with that of central Italian Lake Trasimeno. Annually, mean LSWT of all subAlpine lakes were found to be highly coherent to each other, while mean LSWT of Lake Trasimeno resulted less coherent to the other lakes. Overall, the thesis aims at contributing to the accurate processing of the various historical satellite data and the development of a new method which allows to merge them into a unified, longest possible time series of LSWT. The newly developed methods used open source geospatial software tools, which ensure the reproducibility and also extensibility to any other geographic location given the availability of satellite data. Although this study is using LSWT as the primary physical variable, the developed methods can be used to derive any other time series of land and water based regional products from satellite dat

Satellite thermal remote sensing of the volcanoes of Alaska and Kamchatka during 1994-1996, and the 1994 eruption of Kliuchevskoi volcano

Thesis (M.S.) University of Alaska Fairbanks, 2001The Advanced Very High Resolution Radiometers on the NOAA polar orbiting satellites were used to routinely observe the volcanoes of Alaska and Kamchatka from May 1994 to July 1996, as part of the monitoring effort of the Alaska Volcano Observatory. The largest eruption observed during this period occurred at Kliuchevskoi Volcano between September 8 and October 2, 1994. Radiative temperature measurements made during this eruption were used to develop quantitative methods for analyzing volcanic thermal anomalies. Several parameters, including maximum temperature, anomalous pixels, and total volcanic signal (TVS), were compared to viewing angle and date. A new quantity, TVS7, may most effectively monitor the temporal evolution of the eruption using thermal data. By combining several observations of the thermal state of the volcano, the general nature of the volcanic activity can be described. These observations may indicate an elevation in temperature twelve to 24 hours before an ash-producing event

REMOTE SENSING DATA ANALYSIS FOR ENVIRONMENTAL AND HUMANITARIAN PURPOSES. The automation of information extraction from free satellite data.

This work is aimed at investigating technical possibilities to provide information on environmental parameters that can be used for risk management. The World food Program (WFP) is the United Nations Agency which is involved in risk management for fighting hunger in least-developed and low-income countries, where victims of natural and manmade disasters, refugees, displaced people and the hungry poor suffer from severe food shortages. Risk management includes three different phases (pre-disaster, response and post disaster) to be managed through different activities and actions. Pre disaster activities are meant to develop and deliver risk assessment, establish prevention actions and prepare the operative structures for managing an eventual emergency or disaster. In response and post disaster phase actions planned in the pre-disaster phase are executed focusing on saving lives and secondly, on social economic recovery. In order to optimally manage its operations in the response and post disaster phases, WFP needs to know, in order to estimate the impact an event will have on future food security as soon as possible, the areas affected by the natural disaster, the number of affected people, and the effects that the event can cause to vegetation. For this, providing easy-to-consult thematic maps about the affected areas and population, with adequate spatial resolution, time frequency and regular updating can result determining. Satellite remote sensed data have increasingly been used in the last decades in order to provide updated information about land surface with an acceptable time frequency. Furthermore, satellite images can be managed by automatic procedures in order to extract synthetic information about the ground condition in a very short time and can be easily shared in the web. The work of thesis, focused on the analysis and processing of satellite data, was carried out in cooperation with the association ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action), a center of research which works in cooperation with the WFP in order to provide IT products and tools for the management of food emergencies caused by natural disasters. These products should be able to facilitate the forecasting of the effects of catastrophic events, the estimation of the extension and location of the areas hit by the event, of the affected population and thereby the planning of interventions on the area that could be affected by food insecurity. The requested features of the instruments are: • Regular updating • Spatial resolution suitable for a synoptic analysis • Low cost • Easy consultation Ithaca is developing different activities to provide georeferenced thematic data to WFP users, such a spatial data infrastructure for storing, querying and manipulating large amounts of global geographic information, and for sharing it between a large and differentiated community; a system of early warning for floods, a drought monitoring tool, procedures for rapid mapping in the response phase in a case of natural disaster, web GIS tools to distribute and share georeferenced information, that can be consulted only by means of a web browser. The work of thesis is aimed at providing applications for the automatic production of base georeferenced thematic data, by using free global satellite data, which have characteristics suitable for analysis at a regional scale. In particular the main themes of the applications are water bodies and vegetation phenology. The first application aims at providing procedures for the automatic extraction of water bodies and will lead to the creation and update of an historical archive, which can be analyzed in order to catch the seasonality of water bodies and delineate scenarios of historical flooded areas. The automatic extraction of phenological parameters from satellite data will allow to integrate the existing drought monitoring system with information on vegetation seasonality and to provide further information for the evaluation of food insecurity in the post disaster phase. In the thesis are described the activities carried on for the development of procedures for the automatic processing of free satellite data in order to produce customized layers according to the exigencies in format and distribution of the final users. The main activities, which focused on the development of an automated procedure for the extraction of flooded areas, include the research of an algorithm for the classification of water bodies from satellite data, an important theme in the field of management of the emergencies due to flood events. Two main technologies are generally used: active sensors (radar) and passive sensors (optical data). Advantages for active sensors include the ability to obtain measurements anytime, regardless of the time of day or season, while passive sensors can only be used in the daytime cloud free conditions. Even if with radar technologies is possible to get information on the ground in all weather conditions, it is not possible to use radar data to obtain a continuous archive of flooded areas, because of the lack of a predetermined frequency in the acquisition of the images. For this reason the choice of the dataset went in favor of MODIS (Moderate Resolution Imaging Spectroradiometer), optical data with a daily frequency, a spatial resolution of 250 meters and an historical archive of 10 years. The presence of cloud coverage prevents from the acquisition of the earth surface, and the shadows due to clouds can be wrongly classified as water bodies because of the spectral response very similar to the one of water. After an analysis of the state of the art of the algorithms of automated classification of water bodies in images derived from optical sensors, the author developed an algorithm that allows to classify the data of reflectivity and to temporally composite them in order to obtain flooded areas scenarios for each event. This procedure was tested in the Bangladesh areas, providing encouraging classification accuracies. For the vegetation theme, the main activities performed, here described, include the review of the existing methodologies for phenological studies and the automation of the data flow between inputs and outputs with the use of different global free satellite datasets. In literature, many studies demonstrated the utility of the NDVI (Normalized Difference Vegetation Index) indices for the monitoring of vegetation dynamics, in the study of cultivations, and for the survey of the vegetation water stress. The author developed a procedure for creating layers of phenological parameters which integrates the TIMESAT software, produced by Lars Eklundh and Per Jönsson, for processing NDVI indices derived from different satellite sensors: MODIS (Moderate Resolution Imaging Spectroradiometer), AVHRR (Advanced Very High Resolution Radiometer) AND SPOT (Système Pour l'Observation de la Terre) VEGETATION. The automated procedure starts from data downloading, calls in a batch mode the software and provides customized layers of phenological parameters such as the starting of the season or length of the season and many others

REMOTE SENSING DATA ANALYSIS FOR ENVIRONMENTAL AND HUMANITARIAN PURPOSES. The automation of information extraction from free satellite data

This work is aimed at investigating technical possibilities to provide information on environmental parameters that can be used for risk management. The World food Program (WFP) is the United Nations Agency which is involved in risk management for fighting hunger in least-developed and low-income countries, where victims of natural and manmade disasters, refugees, displaced people and the hungry poor suffer from severe food shortages. Risk management includes three different phases (pre-disaster, response and post disaster) to be managed through different activities and actions. Pre disaster activities are meant to develop and deliver risk assessment, establish prevention actions and prepare the operative structures for managing an eventual emergency or disaster. In response and post disaster phase actions planned in the pre-disaster phase are executed focusing on saving lives and secondly, on social economic recovery. In order to optimally manage its operations in the response and post disaster phases, WFP needs to know, in order to estimate the impact an event will have on future food security as soon as possible, the areas affected by the natural disaster, the number of affected people, and the effects that the event can cause to vegetation. For this, providing easy-to-consult thematic maps about the affected areas and population, with adequate spatial resolution, time frequency and regular updating can result determining. Satellite remote sensed data have increasingly been used in the last decades in order to provide updated information about land surface with an acceptable time frequency. Furthermore, satellite images can be managed by automatic procedures in order to extract synthetic information about the ground condition in a very short time and can be easily shared in the web. The work of thesis, focused on the analysis and processing of satellite data, was carried out in cooperation with the association ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action), a center of research which works in cooperation with the WFP in order to provide IT products and tools for the management of food emergencies caused by natural disasters. These products should be able to facilitate the forecasting of the effects of catastrophic events, the estimation of the extension and location of the areas hit by the event, of the affected population and thereby the planning of interventions on the area that could be affected by food insecurity. The requested features of the instruments are: • Regular updating • Spatial resolution suitable for a synoptic analysis • Low cost • Easy consultation Ithaca is developing different activities to provide georeferenced thematic data to WFP users, such a spatial data infrastructure for storing, querying and manipulating large amounts of global geographic information, and for sharing it between a large and differentiated community; a system of early warning for floods, a drought monitoring tool, procedures for rapid mapping in the response phase in a case of natural disaster, web GIS tools to distribute and share georeferenced information, that can be consulted only by means of a web browser. The work of thesis is aimed at providing applications for the automatic production of base georeferenced thematic data, by using free global satellite data, which have characteristics suitable for analysis at a regional scale. In particular the main themes of the applications are water bodies and vegetation phenology. The first application aims at providing procedures for the automatic extraction of water bodies and will lead to the creation and update of an historical archive, which can be analyzed in order to catch the seasonality of water bodies and delineate scenarios of historical flooded areas. The automatic extraction of phenological parameters from satellite data will allow to integrate the existing drought monitoring system with information on vegetation seasonality and to provide further information for the evaluation of food insecurity in the post disaster phase. In the thesis are described the activities carried on for the development of procedures for the automatic processing of free satellite data in order to produce customized layers according to the exigencies in format and distribution of the final users. The main activities, which focused on the development of an automated procedure for the extraction of flooded areas, include the research of an algorithm for the classification of water bodies from satellite data, an important theme in the field of management of the emergencies due to flood events. Two main technologies are generally used: active sensors (radar) and passive sensors (optical data). Advantages for active sensors include the ability to obtain measurements anytime, regardless of the time of day or season, while passive sensors can only be used in the daytime cloud free conditions. Even if with radar technologies is possible to get information on the ground in all weather conditions, it is not possible to use radar data to obtain a continuous archive of flooded areas, because of the lack of a predetermined frequency in the acquisition of the images. For this reason the choice of the dataset went in favor of MODIS (Moderate Resolution Imaging Spectroradiometer), optical data with a daily frequency, a spatial resolution of 250 meters and an historical archive of 10 years. The presence of cloud coverage prevents from the acquisition of the earth surface, and the shadows due to clouds can be wrongly classified as water bodies because of the spectral response very similar to the one of water. After an analysis of the state of the art of the algorithms of automated classification of water bodies in images derived from optical sensors, the author developed an algorithm that allows to classify the data of reflectivity and to temporally composite them in order to obtain flooded areas scenarios for each event. This procedure was tested in the Bangladesh areas, providing encouraging classification accuracies. For the vegetation theme, the main activities performed, here described, include the review of the existing methodologies for phenological studies and the automation of the data flow between inputs and outputs with the use of different global free satellite datasets. In literature, many studies demonstrated the utility of the NDVI (Normalized Difference Vegetation Index) indices for the monitoring of vegetation dynamics, in the study of cultivations, and for the survey of the vegetation water stress. The author developed a procedure for creating layers of phenological parameters which integrates the TIMESAT software, produced by Lars Eklundh and Per Jönsson, for processing NDVI indices derived from different satellite sensors: MODIS (Moderate Resolution Imaging Spectroradiometer), AVHRR (Advanced Very High Resolution Radiometer) AND SPOT (Système Pour l'Observation de la Terre) VEGETATION. The automated procedure starts from data downloading, calls in a batch mode the software and provides customized layers of phenological parameters such as the starting of the season or length of the season and many other

Clouds and the Earth's Radiant Energy System (CERES) Algorithm Theoretical Basis Document

The theoretical bases for the Release 1 algorithms that will be used to process satellite data for investigation of the Clouds and Earth's Radiant Energy System (CERES) are described. The architecture for software implementation of the methodologies is outlined. Volume 3 details the advanced CERES methods for performing scene identification and inverting each CERES scanner radiance to a top-of-the-atmosphere (TOA) flux. CERES determines cloud fraction, height, phase, effective particle size, layering, and thickness from high-resolution, multispectral imager data. CERES derives cloud properties for each pixel of the Tropical Rainfall Measuring Mission (TRMM) visible and infrared scanner and the Earth Observing System (EOS) moderate-resolution imaging spectroradiometer. Cloud properties for each imager pixel are convolved with the CERES footprint point spread function to produce average cloud properties for each CERES scanner radiance. The mean cloud properties are used to determine an angular distribution model (ADM) to convert each CERES radiance to a TOA flux. The TOA fluxes are used in simple parameterization to derive surface radiative fluxes. This state-of-the-art cloud-radiation product will be used to substantially improve our understanding of the complex relationship between clouds and the radiation budget of the Earth-atmosphere system

Vegetation monitoring through retrieval of NDVI and LST time series from historical databases.

The PhD dissertation presented here falls into the Earth Observation field, specifically vegetation monitoring. This work consists in the extensive exploitation of historical databases of satellite images for vegetation monitoring through two parameters, which are the land surface temperature (LST) and a vegetation index (NDVI). Up to now, vegetation monitoring has been limited to the use of vegetation indices, so the addition of the land surface temperature parameter represents the main innovative character of this PhD study. This dissertation is divided into 5 chapters. The first chapter begins by introducing the theoretical aspects of NDVI and LST parameters, addressing the means for retrieving them from remotely sensed observations, as well as their main limitations. Then, an introduction to vegetal physiology is developed, which allows for understanding how NDVI and LST parameters are linked to plants. A bibliographical study is then presented, which stresses out the gaps in the exploitation of historical databases. The second describes the data used in this PhD. The instrument providing most of these data is embarked on the NOAA (National Oceanic and Atmospheric Administration) satellite series. This instrument is the AVHRR (Advanced Very High Resolution Radiometer). The AVHRR databases used in this work are the PAL (Pathfinder AVHRR Land) and GIMMS (Global Inventory Modeling and Mapping Studies) databases. Additional data used punctually are also described briefly. The third chapter describes the operations applied to the data to prepare their temporal analysis. These operations start with the calculations of vegetation index and land surface temperature parameters. The AVHRR data used in this work are contaminated by the orbital drift of NOAA satellites, so an important part of this doctorate consisted in developing a technique for correcting this effect. We chose to develop our own technique, which we validated by direct comparison with data retrieved by geostationary satellites. In the fourth chapter, the different methods used for data temporal analysis are presented. Those methods consist of trend detection, harmonic analysis, and fitting the temporal series to annual NDVI evolution curves. Then, a phenological analysis is presented, which allows for retrieval of trends in spring and autumn dates for most of the globe. These trends are validated by comparison with previous studies. The trend analysis for spring dates is then extended to the 1948-2006 period using air temperature data. The long-term observation of different NDVI indicators also allows for the detection of land vegetation changes, even in our case of coarse spatial resolution. Finally, two methods for NDVI temporal analysis are compared. In the fifth chapter, a quick presentation of simultaneous study of NDVI and LST is developed through a revision of previous results, followed by the observations carried out from the orbital drift corrected data. These observations allowed for the determination of indicators of NDVI and LST, thus enabling for the characterization of the vegetation at global scale. A harmonic analysis of NDVI and LST at European scale is also presented. The application of the developed indicators for simultaneous monitoring of NDVI and LST shows promising results. As a conclusion, the main results described above are summarized, and plans for a close future are presented. This PhD has also demonstrated that such work could be carried out in a small structure with limited resources. __________________________________________________________________________________________________ RESUMEN El trabajo de tesis doctoral aquí presentado consiste en el uso extensivo de bases de datos históricas de imágenes de satélite para el seguimiento de la vegetación terrestre, a través de dos parámetros; la temperatura de la superficie terrestre (LST por sus siglas en inglés) y el índice de vegetación NDVI. El primer capítulo de la memoria introduce las nociones de NDVI y LST desde una perspectiva teórica, así como sus principales limitaciones y sus vínculos con la fisiología vegetal. Un estudio bibliográfico permite poner el acento sobre las lagunas en el uso de las bases de datos históricas. El segundo capítulo describe los datos utilizados en este trabajo, proporcionados en su mayoría por el instrumento AVHRR (Advanced Very High Resolution Radiometer) a bordo de la serie de satélites de la NOAA (National Oceanic and Atmospheric Administration) a través de las bases de datos PAL (Pathfinder AVHRR Land) y GIMMS (Global Inventory Modeling and Mapping Studies). También se presentan datos adicionales que se usaron puntualmente. El tercer capítulo describe el proceso para obtener las series temporales de NDVI y LST, las cuales están contaminadas por la deriva orbital de los satélites NOAA. Hemos propuesto una técnica propia para su corrección, validada por comparación directa con datos obtenidos por satélites geoestacionarios. En el cuarto capítulo se introducen diferentes métodos utilizados para el análisis temporal de los datos. Se obtuvieron tendencias acerca de parámetros vinculados a la evolución anual de NDVI para la mayor parte del globo, validadas por comparación con estudios previos. En el quinto capítulo se presenta un análisis conjunto del NDVI y de la LST, seguido por la elaboración de indicadores de la evolución anual de estos dos parámetros. A continuación se presenta un análisis armónico del NDVI y de la LST para Europa. El uso de los indicadores desarrollados para el seguimiento simultáneo del NDVI y de la LST revela resultados prometedores. Por último se presentan las conclusiones más relevantes del trabajo realizado, así como planes de trabajo para un futuro próximo

Space and Earth Sciences, Computer Systems, and Scientific Data Analysis Support, Volume 1

This Final Progress Report covers the specific technical activities of Hughes STX Corporation for the last contract triannual period of 1 June through 30 Sep. 1993, in support of assigned task activities at Goddard Space Flight Center (GSFC). It also provides a brief summary of work throughout the contract period of performance on each active task. Technical activity is presented in Volume 1, while financial and level-of-effort data is presented in Volume 2. Technical support was provided to all Division and Laboratories of Goddard's Space Sciences and Earth Sciences Directorates. Types of support include: scientific programming, systems programming, computer management, mission planning, scientific investigation, data analysis, data processing, data base creation and maintenance, instrumentation development, and management services. Mission and instruments supported include: ROSAT, Astro-D, BBXRT, XTE, AXAF, GRO, COBE, WIND, UIT, SMM, STIS, HEIDI, DE, URAP, CRRES, Voyagers, ISEE, San Marco, LAGEOS, TOPEX/Poseidon, Pioneer-Venus, Galileo, Cassini, Nimbus-7/TOMS, Meteor-3/TOMS, FIFE, BOREAS, TRMM, AVHRR, and Landsat. Accomplishments include: development of computing programs for mission science and data analysis, supercomputer applications support, computer network support, computational upgrades for data archival and analysis centers, end-to-end management for mission data flow, scientific modeling and results in the fields of space and Earth physics, planning and design of GSFC VO DAAC and VO IMS, fabrication, assembly, and testing of mission instrumentation, and design of mission operations center