IN43B-1176
Investigating Satellite Rainfall Based Flood Modeling in Anticipation of GPM: Understanding the Worth of Spatial Downscaling and Satellite Rainfall Uncertainty
Realistic flood modeling in medium-large river basins requires rainfall data at hydrologically relevant scales ranging from 1--5 km. However, satellite rainfall data has historically been available at spatial resolutions that can be considered somewhat coarse for predicting the dynamic flood phenomenon (~ 25--100km). As a natural response to this limitation that has persisted for over a decade, hydrologists have devised numerous statistical spatial downscaling schemes till now. With the proposed Global Precipitation Measurement (GPM) mission, satellite rainfall data will gradually become more available at smaller scales (~ 10 km) in the next decade, prompting us to re-evaluate the worth of spatial downscaling for flood modeling. In this study, we therefore seek an answer to the question--- Which is the better option for satellite rainfall based flood simulation when rainfall data is available at coarse scale---a) an error propagation based ensemble streamflow scheme at the coarse (native) resolution or a probabilistically downscaled based ensemble streamflow scheme? The study is performed on the 970 km2 basin of the Upper Cumberland River in southeastern Kentucky bordering with Virginia and Tennessee. NASA satellite rainfall data products from the TRMM Multi-satellite Precipitation Analysis (TMPA) are used for the investigation. A statistical downscaling scheme of Perica and Foufoula-Georgiou (1996) and a satellite rainfall error modeling scheme of Hossain and Anagnostou (2006) are used for resolving the posed science question. Findings indicate that spatial downscaling does not unconditionally guarantee more accurate flood simulations as scale becomes smaller. The narrow range of uncertainty in flood simulation due to downscaling often misses the observed peak flow. On the other hand, error propagation at the native scale based on satellite rainfall uncertainty information tends to capture the natural variability during peak flows with much greater confidence.
IN43B-1177
Using Simulated OCO Measurements for Assessing Terrestrial Carbon Pools in the Southern United States
The Orbiting Carbon Observatory (OCO) mission will make the first global, space-based measurements of atmospheric carbon dioxide (CO2) with the precision, resolution, and coverage needed to characterize CO2 sources and sinks on regional scales. This Rapid Prototyping Concept (RPC) experiment is focused towards the evaluation of CO2 column measurements from simulated OCO data and relating these datasets to terrestrial biospheric exchange of carbon from terrestrial surfaces in south east and south central United States. In particular, this investigation intends to leverage multiple NASA sensors, the terrestrial ecosystem model (CASA), and a transport model to undertake a RRPC experiment to address the need to quantify the carbon exchange over different ecosystems. Further, test how well data from OCO observations and CO2 measurement networks, models constrain CO2 fluxes at model-grid resolution. In this ongoing work, the CASA model estimates of carbon products are calibrated with field-based measurements of crop production, forest ecosystem fluxes, and inventory estimates of carbon pool sizes at multiple locations in the southeastern and south central United States. Comparative predictions from an atmospheric transport model (e.g. GISS Model E) and measurements of atmospheric carbon abundances from OCO and at observation sites distributed over the regions of interest would also be conducted.
IN43B-1178
A computational framework for the evaluation of satellite precipitation estimates for hydrological applications
The planned Global Precipitation Measurement (GPM) mission will provide better coverage and more accurate satellite-based rain fall estimates than the current satellite measurements. The capabilities of the GPM-era rainfall products to meet the decision making needs for water resources management applications are being evaluated using land surface and hydrological modeling. A number of precipitation products that are derived from both satellite data and ground observation are being evaluated at spatial and temporal scales that are relevant for water management applications. Routine evaluation techniques and metrics, such as root mean squared error, false alarm ratio and other skill scores, used in the research community have been adopted in a rapid prototyping computational environment. In addition, novel fuzzy-based methodologies are also be implemented to characterize the uncertainties in the rainfall data. The Noah land surface model (LSM), incorporated with the NASA Land Information System (LIS), is used to simulate land surface and hydrological properties that are relevant for the decision making needs of the water resources management applications. Since June 2007, GPM proxy data, based on the NRL-Blended algorithm, was implemented for data collection over the continental United States and surrounding areas (0N-50N, 130W-50W). The collection of the various precipitation data sets has been automated. These precipitation data are then catalogued and distributed via the Unidata THREDDS server. The statistical verification algorithms are being incorporated into an "evaluation toolbox" used to characterize the uncertainties of the various rainfall estimates. The integration of the THREDDS server and the evaluation toolbox will provide a common framework for the evaluation of rainfall estimation techniques and their application using the land surface models in NASA LIS.
IN43B-1179
A data fusion toolbox to optimize precipitation estimates for land surface modeling applications
The precipitation estimates from the planned Global Precipitation Measurement (GPM) mission will complement a host of existing rainfall products. We are investigating and evaluating intelligent techniques to merge various precipitation sources and optimize them for land surface and hydrological modeling applications. The decision making agencies, such as NOAA, USBR and USGS, are faced with the problem of inadequate rainfall estimates in the western regions of the United States which does not have a adequate network of in-situ measurements. Hence, satellite-based rainfall estimates offer the promise of improving the precipitation estimates in data- sparse regions with difficult water management problems. A suite of GPM proxy data is being produced using different combinations of existing satellites, currently in orbit. A number of techniques are being incorporated into a data fusion toolbox, including a dynamic four dimensional objective analysis techniques (such as EnKF) and intelligent methods (ANN, Bayesian merging) to optimally merge various precipitation estimates. Further spatial downscaling and temporal disaggregation techniques are also implemented to derive precipitation forcings for land surface modeling and to evaluate the optimized and downscaled products by running land surface model experiments. The suite of land surface models (LSM) in the Land Information System (LIS) will be used in sensitivity analyses. The NRL-Blend is being run in 10 parallel modes, each simulating a different GPM-Era satellite constellation, to generate an ensemble of precipitation data sets that as input to the merging process.
IN43B-1180
A Rapid Prototyping Capability Experiment to Evaluate CrIS / ATMS Observations for Urban Modeling Applications
The goal of this project is to evaluate the potential for data from the Advanced Technology Microwave Sounder (ATMS) and the Crosstrack Infrared Sounder (CrIS) to impact forecasting of a significant mesoscale weather event over a major urban center along the coast of the Gulf of Mexico. The ATMS and the CrIS will be deployed as part of a suite of atmospheric sensors aboard the National Polar-orbiting Operational Environmental Satellite System (NPOESS) and the preceding NPOESS Preparatory Project (NPP) satellite, scheduled to be launched in 2009. An Observing System Simulation Experiment (OSSE) methodology is adopted to characterize the uncertainties associated with instrument measurement and retrieval processes. The methodology will be based on the procedures adopted by NASA Global Modeling and Assimilation Office (GMAO) and NOAA Environmental Modeling Center (EMC). Within the OSSE framework, a nature run (NR) is a proxy for real atmospheric and land surface conditions; it is based on a "free run" of a global-scale forecast model. For an OSSE, it is very important that different data-assimilating models be used to generate the NR and subsequent sensitivity tests. Otherwise, a "fraternal twin" problem may result, in which a low error bias between models does not realistically portray the error bias expected with assimilation of the candidate sensor data. A regional-scale NR (RSNR) will be produced using the MM5, and will serve as "truth" for our modeling experiments. The RSNR will be a nest simulation within one of the larger scale NRs produced from the ECMWF model. To simulate observations from the candidate sensors, error and bias characteristics may be adopted from the NPOESS Aircraft Sounder Testbed (NAST), in which prototypes of the ATMS and the CrIS were tested aboard aircraft. The WRF model is in conjunction with the MM5 to perform the sensitivity experiments involving the assimilation of existing sensor data and synthesized data representing candidate sensors. We expect that data transmitted from the ATMS and the CrIS will greatly supplement the existing networks of surface observations and upper-air observations in the analysis of atmospheric temperature, humidity, and pressure; enhance the quality of data assimilated into operational forecast models; and subsequently help to improve the simulation of weather on regional and local scales.
IN43B-1181
Optimal Spherical Triangulation for Global Multiresolution Analysis and Synthesis
Discrete spherical harmonic computations with Chebychev quadratures and least squares using equiangular grids in latitude and longitude are very advantageous and well known. However, for some global multiresolution applications, different strategies are required as equiangular grids are not appropriate for various reasons. Optimal triangulation approaches based on the octahedron and the icosahedron are presented and discussed along with the implied discrete spherical harmonic computations for multiresolution analysis and synthesis. Examples of applications using simulated and common geopotential models will also be included with comments about other methodologies.
IN43B-1182
Enabling GPU Acceleration of HIRDLS Scientific Data Processing
The HIRDLS team involves scientists, software developers and students from Atmospheric Science, Computer Science and various engineering disciplines, and from numerous agencies, universities, and centers. The focus of research is determining vertical profiles of the Earth's atmospheric chemical composition from infrared samplings. The processing techniques used to calculate the scientific data rely heavily on mathematical algorithms. While the data processing involves large sets of data, there is much repetition of smaller well defined kernels. Currently all processing has been done on large, expensive, CPU clusters. Programming these machines effectively to take advantage of obvious parallelism in the data streams is difficult and time consuming. In this paper we will show the suitability for scientific processing in a GPGPU environment. Given the nature of the data streams as well as the types of processing, it will be shown that there can be a very high potential benefit in using GPUs. We will show several examples of how GPGPU will directly impact the current production processing, as well as some other off-line housekeeping procedures. Also, we will demonstrate the usefulness of GPGPU programs to accelerate in the field calculations performed by scientists in various programming environments. Lastly, we will present a timeline for enabling GPGPU in the HIRDLS processing environment and the expected impact on future processing of science data.
IN43B-1183
Extending Atmospheric Composition Processing to the Community
The Ozone Monitoring Instrument Science Investigator-led Processing System (OMI SIPS) has been the central data processing system for OMI since its launch on NASA's Aura spacecraft in July, 2004. As part of NASA's evolution from mission based processing to measurement based processing, we are evolving the system into a community oriented Atmospheric Composition Processing System (ACPS). This involves changing focus from the mission (OMI) to the measurement (total column ozone), and a widening of our focus from the mission science teams to the overall scientific community. The current system dispatches and executes software developed by scientists on a computer cluster; archiving the results and distributing the data to numerous parties. Although this works well for the production environment, access to centralized systems has been naturally limited. Ideally, scientists should be able to easily get the data, run their software, make changes and repeat the process until they are happy with the solution to the problems they are trying to solve. In addition it should be simple to migrate research improvements from the community back into the formal production system. Through NASA's "Advancing Collaborative Connections for Earth-Sun System Science," we have extended publicly accessible interfaces into the production system. The system provides an open API via a set of SOAP/XML and REST based web services, enabling scientists, researchers and operators to interact directly with the data and services offered by the central system. The system includes metadata, archive, and planner subsystems. The metadata server stores metadata about the data products and provides the ability for processing software to evaluate production rules to determine the appropriate input data files for a given data processing job. The archive server stores the data files themselves and makes then available for clients to retrieve the files as needed. The planner plans out the set of jobs to be run in the production system and tracks the work being planned and executed. The RunApp client software executes the software. RunApp may also reside on a remote scientist's workstation and can communicate with the metadata and archive servers to evaluate production rules and retrieve needed data from the central system. It can execute the production versions of certain algorithms as well as locally developed or enhanced research versions. Since the algorithms are identically integrated, they can be very easily migrated into the production environment for large scale bulk processing when appropriate. This system provides the scientific community with flexible data processing capabilities that are scalable for development, research and production environments. This will accelerate the use and validation of the data for science and applications.
IN43B-1184
Soil Moisture Estimation Using Hyperspectral SWIR Imagery
The U.S. Geological Survey (USGS) is engaged with the U.S. Department of Agriculture's (USDA) Agricultural Research Service (ARS) and the University of Georgia's National Environmentally Sound Production Agriculture Laboratory (NESPAL) both in Tifton, Georgia, USA, to develop transformations for medium and high resolution remotely sensed images to generate moisture indicators for soil. The Institute for Technology Development (ITD) is located at the Stennis Space Center in southern Mississippi and has developed hyperspectral sensor systems that, when mounted in aircraft, collect electromagnetic reflectance data of the terrain. The sensor suite consists of sensors for three different sections of the electromagnetic spectrum; the Ultra-Violet (UV), Visible/Near InfraRed (VNIR) and Short Wave InfraRed (SWIR). The USDA/ ARS' Southeast Watershed Research Laboratory has probes that measure and record soil moisture. Data taken from the ITD SWIR sensor and the USDA/ARS soil moisture meters were analyzed to study the informatics relationships between SWIR data and measured soil moisture. The geographic locations of 29 soil moisture meters provided by the USDA/ARS are in the vicinity of Tifton, Georgia. Using USGS Digital Ortho Quads (DOQ), flightlines were drawn over the 29 soil moisture meters. The SWIR sensor was installed into an aircraft. The coordinates for the flightlines were also loaded into the navigational system of the aircraft. This airborne platform was used to collect the data over these flightlines. In order to prepare the data set for analysis, standard preprocessing was performed. These standard processes included sensor calibration, spectral subsetting, and atmospheric calibration. All 60 bands of the SWIR data were collected for each line in the image data, 15 bands of which were stripped from the data set leaving 45 bands of information in the wavelength range of 906 to 1705 nanometers. All the image files were calibrated using the regression equations generated by using radiometer data collected over calibration tarps. Regions of Interest (ROI) were drawn over the image data set corresponding with the location of the soil moisture meters. Scripts written in ENVI's Interactive Data Language (IDL) were developed to extract the spectra from each of the processed hyperspectral image data over each soil moisture meter from its corresponding ROI. The informatics relationship between soil moisture and SWIR spectra was identified by using the resulting data set.
IN43B-1185
Interoperable Geoprocessing for Rapid Prototyping of Landuse/Landcover, Topographical and Meteorological Datasets for Hydrological Simulation
Geoprocessing of landuse/landcover, topographical, and meteorological datasets is ubiquitous in the initial set- up of environmental models' applications. Geoprocessing provides parameterized geographic/meteorological information to models, organized per geographic sub-region or in the form of time-series. Environmental models use the summarized information for simulation of past, present or future events of interest. Traditionally, geoprocessing is performed following protocols and methodologies built-in into the environmental models. Each geoprocessing routine is tailored by the model's developers and is not re-usable or transferable to other models. Furthermore, metadata documenting the geoprocessing steps are usually not detailed. This paper proposes the use of the Geospatial Object Library for Environmental Modeling (GEOLEM) as an alternative or complementary tool for calculating land use, topographical, and meteorological parameters and time-series. GEOLEM has the capability of providing re-usable and transferable geoprocessed information to environmental models. Although this research focuses on the calculation of the geographical parameters and time-series needed by the Hydrological Simulation Program Fortran (HSPF), potential uses of GEOLEM in other environmental modeling frameworks are also addressed. The project area is located in the Saint Louis Bay watershed in the Mississippi Gulf coast. Interferometric Synthetic Aperture Radar (IFSAR) Digital Surface Model (5-m horizontal, 0.01-m vertical resolution), NASA's Shuttle Radar Topography Mission (SRTM) DTED Level 2 (30- m horizontal, 0.01-m vertical), National Elevation Data (NED) (30-m horizontal, 1-m vertical), and USGS DEM (300- m horizontal, 1-m vertical) topographical datasets were used in this research. Additionally, three landuse/landcover datasets were included: Geographic Retrieval and Analysis System (GIRAS), National Land Cover Dataset (NLCD), and NASA's Moderate Resolution Imaging Spectroradiometer (MODIS MOD12Q1). Meteorological time-series produced by NASA's Land Information System (NASA-LIS) were geoprocessed for updating/filling existing meteorological stationīs data. NASA's simulated VIIRS imagery were also included in the study for landuse/lancover characterization and parameterization.
IN43B-1186
The NASA Earth Science Knowledge Base (ESKB)
Through the Mississippi Research Consortium, NASA has sponsored a Solutions Network project to develop and deliver knowledge base tools and technologies for compiling NASA Earth Science research results. The primary objectives of the knowledge base tools are to enable 1) compiling the results of NASA Earth Science research, 2) associating the research and results with NASA observing systems, sensors, data products, models, and decision support tools, and 3) stratifying the research and results by relevance to applications of national significance and science focus areas. This integrated set of tools and technologies has been named the Earth Science Knowledge Base (ESKB). The ESKB is designed as a user-friendly database application with client-server database functionalities for populating information about NASA research and results. The ESKB provides enhanced exploration capabilities for users to readily identify NASA projects of interest and search for desired research results. The ESKB design has incorporated database tables that contain information about NASA partners and projects, NASA observing systems, sensors, and data products, as well as models and decision support tools. The ESKB will provide an extensible basis for NASA and NASA-funded researchers to provide vital information about the results of past and existing NASA research. This information will deliver enhanced understanding about critical earth science questions as well as insight as to potential research directions and NASA assets that may be brought to bear on the research questions of the present and future.
IN43B-1187
HydroMet: Real-time Forecasting System for Hydrologic Hazards
Recent devastating floods and severe droughts in North Carolina called attention to the need of a reliable nowcasting and forecasting system for these hydrologic hazards. In response to the demand, HydroMet project was launched by RENCI (Renaissance Computing Institute). On a supercomputer in the institute, we integrated (1) WRF (Weather Research and Forecasting) for the mesoscale numerical weather prediction, (2) RHESSys (Regional Hydro-Ecologic Simulation System) for the distributed modeling of runoff generation and soil moisture, and (3) LDAS (Land Data Assimilation Systems) for upgrading the prediction accuracy of soil moisture and energy. By exploiting the powerful parallel computing architecture, the forecasting system was designed to assimilate and produce massive spatio-temporal data in real-time while recalibrating itself automatically. We applied the system for western and central North Carolina as test sites, and forecasted the propagation of flood waves, and the long-term trends of low channel flow and soil moisture at a fine spatial resolution. As we extend the application of the system over the entire North Carolina, it is expected to provide timely and accurate information about floods and droughts in the area, which is prerequisite for more effective prevention and recovery from the hazards.
IN43B-1188
An Observing Systems Simulation Experiment for potential soil moisture retrievals using Aquarius instruments
A pathfinder Observing Systems Simulation Experiment (OSSE) experiment is being used to evaluate the potential of the radiometer and scatterometer instruments on-board the Aquarius satellite to provide soil moisture estimates for earth science application needs of water resources management and agricultural applications. The uncertainties involved in the soil moisture retrieval process using the Aquarius instrument will be evaluated and characterized, using a computational rapid prototyping environment, in the context of decision support. The current capabilities to monitor the state of the hydrosphere over land, either by in-situ network or space-borne measurement systems, are very limited. Aquarius is a new NASA Earth System Science Pathfinder (ESSP) mission to monitor global sea surface salinity (SSS) at a nominal resolution of about 100 km with nearly 7-day repeat cycle in a sun synchronous orbit of 657 km. The instrument consists of an L-band radiometer/scatterometer operating at 1.143 GHz and 1.26 GHz respectively. Though primarily targeted for sea surface salinity (SSS) measurements, Aquarius has the potential to reasonably address the requirements for global soil moisture measurements, particularly in much of the western part of the United States. Our OSSE has been designed to simulate the Aquarius soil moisture retrieval process using: (a) one of the land surface models available in the NASA Land Information System (LIS) to create the "nature run (NR)" considered the "truth"; (b) a forward microwave emission and backscatter model (MEBM) to simulate the synthetic observations of radiometric brightness temperature and radar backscatter; (c) an orbit and sensor model (OSM) in order to sample the synthetic observations according to the orbital and instrument characteristics; and (d) a set of retrieval methods (RM) to derive simulated soil moisture measurements. The next step in the OSSE process is to make series of comparisons of the synthetically derived soil moisture estimates (using the various retrieval methods) against the nature run in order to characterize the uncertainties due to land surface heterogeneity, instrument error, and parameter estimates. This experiment employs three general land modeling tasks in order to evaluate: (a) the impact of Aquarius soil moisture products on land surface predictions; (b) the impact of Aquarius soil moisture products on land surface predictions when running different LSMs; and (c) the impacts of soil moisture products on land surface predictions when using different sets of surface characteristics (vegetation and soil properties). Implementation of our OSSE methodology and preliminary results from the experiment, especially characterizing the nature run, will be discussed during the presentation.
IN43B-1189
Information Technology for Harvesting NASA Earth Science Research Results
The NASA Applied Sciences Program has funded the Mississippi Research Consortium (MRC) to develop information technology that will facilitate searches for potential applications of NASA assets to various needs in the earth sciences community. In particular, it will help generate ideas for new ways to use NASA missions, research, and/or models in conjunction with operational decision-making processes (or decision support systems) to achieve a particular benefit to society. In this paper, we describe the development of information technology that will facilitate that ability. The resulting system is called the Earth Science Knowledge Base (ESKB). The ESKB contains and index relevant NASA research result publications in a database that is compatible with the evolving NASA "Mission to Models" (M2M) database and shares relevant table space with it. In particular, fields from this system identifying relevant NASA missions, models, and data products are used to cross-index the data collected on published results of research projects. Fields characterizing the research results based on the six earth science focus areas and the twelve applications of national priority are included. In the course of developing the ESKB, novel uses of existing online databases and search tools have been developed. In addition, data mining tools have been developed for facilitating the proper characterization of research results. In addition to research results, the ESKB includes data that characterizes the current network of NASA earth science partners. This includes information on organizations and agencies funded by or partnered with NASA to conduct earth science research, technology, and applications projects. The relationships between NASA programs and project sponsors are also captured in this knowledge base. The ESKB is implemented as a client-server system that will allow access and updates from a distributed network of users. It has a user-friendly interface designed to facilitate the identification of potential solutions which bring benefits to society though the use of NASA missions, data, models, and research results. The system is currently undergoing beta testing but is expected to be more widely available at the time of this paper's presentation, when we will present a status update and describe how to access it.
IN43B-1190
A Comparison of the Hypertemporal Crop Mapping Abilities of NDVI Products Derived from both MODIS Surface Reflectance and Calibrated Geolocated Radiance Data
This project examined the usefulness of Moderate Resolution Imaging Spectroradiometer (MODIS)-derived hypertemporal, curve-fitted vegetation indices for agricultural mapping. The analysis focused on accurately mapping corn and soybean crops cultivated during the 2004 / 2005 growing season within Argentina's Córdoba and Santa Fe provincesone of the world's most productive agricultural regions. Crop fields were selected according to field size in order to allow for the spatial resolution constraints of the MODIS sensor. Hypertemporal data sets were created by temporally filtering multiple dates of customized MODIS NDVI products. Hypertemporal data sets provide the ability to track phenology growth curves of different crop types and therefore provide discriminating temporal signatures as input for classification algorithms. Several supervised classification algorithms were used to investigate the ability of MODIS data to identify crop types. Conclusions were drawn from post classification confusion matrices derived from representative fields. Results will provide computational methods for furthering the informatics and operational capability of the USDA Production Estimates and Crop Assessment Division.
IN43B-1191
Computation Methods for NASA Data-streams for Agricultural Efficiency Applications
Temporal Map Algebra (TMA) is a novel technique for analyzing time-series of satellite imageries using simple algebraic operators that treats time-series imageries as a three-dimensional dataset, where two dimensions encode planimetric position on earth surface and the third dimension encodes time. Spatio-temporal analytical processing methods such as TMA that utilize moderate spatial resolution satellite imagery having high temporal resolution to create multi-temporal composites are data intensive as well as computationally intensive. TMA analysis for multi-temporal composites provides dramatically enhanced usefulness that will yield previously unavailable capabilities to user communities, if deployment is coupled with significant High Performance Computing (HPC) capabilities; and interfaces are designed to deliver the full potential for these new technological developments. In this research, cross-platform data fusion and adaptive filtering using TMA was employed to create highly useful daily datasets and cloud-free high-temporal resolution vegetation index (VI) composites with enhanced information content for vegetation and bio-productivity monitoring, surveillance, and modeling. Fusion of Normalized Difference Vegetation Index (NDVI) data created from Aqua and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) surface-reflectance data (MOD09) enables the creation of daily composites which are of immense value to a broad spectrum of global and national applications. Additionally these products are highly desired by many natural resources agencies like USDA/FAS/PECAD. Utilizing data streams collected by similar sensors on different platforms that transit the same areas at slightly different times of the day offers the opportunity to develop fused data products that have enhanced cloud-free and reduced noise characteristics. Establishing a Fusion Quality Confidence Code (FQCC) provides a metadata product that quantifies the method of fusion for a given pixel and enables a relative quality and confidence factor to be established for a given daily pixel value. When coupled with metadata that quantify the source sensor, day and time of acquisition, and the fusion method of each pixel to create the daily product; a wealth of information is available to assist in deriving new data and information products. These newly developed abilities to create highly useful daily data sets imply that temporal composites for a geographic area of interest may be created for user-defined temporal intervals that emphasize a user designated day of interest. At GeoResources Institute, Mississippi State University, solutions have been developed to create custom composites and cross-platform satellite data fusion using TMA which are useful for National Aeronautics and Space Administration (NASA) Rapid Prototyping Capability (RPC) and Integrated System Solutions (ISS) experiments for agricultural applications.