Wolfe, Robert: NASA GSFC (Project Lead)
Project Funding:
2011 - 2014
NRA: 2010 NASA: NPP Science Team for Climate Data Records
Funded by NASA
Abstract:
Geometric calibration and validation is essential in producing Earth System Data Records (ESDRs) and Climate Data Records (CDRs) over heterogeneous terrestrial surfaces using the Visible Infrared Imager Radiometer Suite (VIIRS) sensor. In particular, accurate operational geolocation (Earth-location) is necessary for generating temporally composited products needed to support accurate retrieval of biophysical and geophysical parameters on multi-day temporal scales, and to enable inter-comparisons of multi-day composites. This proposed work will address geometric calibration and validation of the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP) VIIRS in support of higher level ESDRs and CDRs.
Because of the heterogeneity of terrestrial surfaces, accurate geolocation is essential to the production of ESDRs and CDRs from VIIRS moderate resolution sensor data. The Earth Observing System Moderate Resolution Imaging Spectrometer (MODIS) science team has developed remote sensing algorithms for deriving global time-series data products of various geophysical parameters used by the Earth science community. MODIS has demonstrated that it is possible to operationally produce geolocation data to the sub-pixel accuracies needed to support accurate retrieval of terrestrial biophysical and geophysical parameter ESDRs and CDRs. As NASA moves from missions to measurements, accurate geolocation is also needed to enable the retrieval of a new generation of ESDRs and CDRs from the NPP VIIRS. One of the key underlying capabilities is the ability to accurately inter-compare data from VIIRS to the MODIS data record at the same Earth locations.
The MODIS sub-pixel accuracies are achieved in forward processing and further improved through multiple reprocessing activities. Recent improvements in the MODIS geolocation algorithm to enable the use of finer resolution 500 m global digital elevation model data will result in improved accuracy in rough terrain. This is expected to improve the quality of the Land products that are needed in or challenged by rough terrain (e.g. snow cover, albedo and land surface type). We are proposing to provide the new algorithm for VIIRS implementation and testing. Based on the expected improvements in the Land EDR quality we will then provide the algorithm along with documentation of the benefits to the EDRs for consideration for inclusion in operational system.
This proposal's Principle Investigator Robert Wolfe and Co-Investigators Masahiro Nishihama and Guoqing Lin have developed a detailed understanding of the VIIRS instrument geometric characteristics and ancillary data requirements, and have evaluated the accuracy of the operational VIIRS geolocation algorithm. All three are also part of the NPP Instrument Calibration and Support Element. Beginning in 2004, we have participated in the NPP VIIRS ambient and Thermal Vacuum tests with the goal of assuring that the operational NPP and future NPOESS VIIRS geolocation has the accuracy required to produce ESDRs and CDRs. As members of the government independent instrument characterization and assessment team, we have played a key role in developing a better understanding of the ambient and the TVAC test sources and conditions, which are key to interpreting the measurements and results.
Currently, an automatic filtering technique is being developed that will enable nearly autonomous long-term trending of MODIS geolocation data. This proposal will continue the work in understanding the VIIRS sensor and its pre-flight characterization and in evaluation of automatic long-term trending techniques. The proposal work will extend this activity into the NPP VIIRS post-launch era, particularly in the areas of post-launch sensor geometric characterization, evaluation of the on-orbit geolocation algorithm performance, and evaluation of automated long-term trending of the VIIRS geolocation data.
More details may be found in the following project profile(s):
