|Njoku, Eni -|
|Dunbar, Scott -|
|Chan, Steven -|
Submitted to: International Geoscience and Remote Sensing Symposium Proceedings
Publication Type: Abstract Only
Publication Acceptance Date: July 15, 2010
Publication Date: August 1, 2010
Citation: Crow, W.T., Njoku, E., Dunbar, S., Chan, S. 2010. The SMAP science data system algorithm and application simulation testbed [abstract]. International Geoscience and Remote Sensing Symposium Proceedings. 2010 CDROM. Technical Abstract: Slated for launch in 2015, the NASA Soil Moisture Active/Passive mission represents a significant advance in our ability to globally observe time and space variations in surface soil moisture fields. The SMAP mission concept is based on the integrated use of L-band active radar and passive radiometry measurements to optimize both the accuracy and resolution of remotely-sensed surface soil moisture estimates. In order to facilitate the pre-launch development of science algorithms, the SMAP mission has established a SMAP Science Data System (SDS) Testbed. This presentation will discuss the SDS Testbed and describe its potential use as the basis for SMAP application development studies. The SDS Testbed consists of three major architectural components, each with distinct functions: (1) The Science Processing Prototype (SPP) code forms the core of the Testbed, incorporating all essential functionality required to process data from lower-level sensor data (Level 1) to higher-level geophysical products (Level 3). The design of the SPP is driven by the detailed contents of the input and output data products and the algorithm functions, including ancillary tables and geophysical model data. The algorithm processors will implement the baseline and optional algorithms under consideration for SMAP. The SPP will be used to design, test and optimize the processing sequence, and to select among competing algorithms. (2) The Science Simulation (SciSim) is a model of the SMAP measurement system. The SciSim combines detailed knowledge of the mission instrument and spacecraft design to realistically sample an input geophysical field. Using forward radiative transfer and scattering models, SciSim generates high-fidelity simulated radiometer and radar sensor data from simulated “truth”, for algorithm experiments. (3) Observational Data from SMAP-specific field campaigns and other satellite missions will be the other major source of input data for the SPP. Simulation is useful for testing the overall self-consistency of the processing model, but real data are indispensible for pre-launch algorithm tuning and validation. Observational data from appropriate satellite, airborne, and ground-based sensors that can mimic the SMAP sensor observational configuration (active and passive L-band at 40° incidence) will be used to test the algorithms prior to launch.