The Geostationary Operational Environmental Satellite (GOES) program is developing a new generation sensor, the
Advanced Baseline Imager (ABI), to be carried on the GOES-R satellite to be lunched in approximately in 2014.
Compared to the current GOES imager, ABI will have significant advantages for measuring land surface temperature as
well as to providing qualitative and quantitative data for a wide range of applications. Specifically, spatial resolution of
the ABI sensor is 2 km, and the infrared window noise equivalent temperature is 0.1 K, which are very close to the polarorbiting
satellite sensors such as AVHRR. Most importantly, ABI observes the full disk every five minutes, which not
only provides more cloud-free measurements but also makes daily temperature variation analysis possible. In this study
we developed split window algorithms for the LST measurement from the ABI sensor. We generated the ABI sensor
data using MODTRAN radiative transfer model and NOAA88 atmospheric profiles and ran regression analyses for the
LST algorithm development. The algorithms are developed by optimizing existing split window LST algorithms and
adding a path length correction term to minimize the retrieval errors due to difference atmospheric path absorption from
nadir view to the edge-of-scan. The algorithm coefficients are stratified for dry and moist atmospheric conditions, as well
as for the daytime and nighttime. The algorithm sensitivity to land surface emissivity uncertainty is analyzed to ensure
the algorithm performance.
NASA, NOAA, and USGS collections of Earth science data are large, federated, and have active user
communities and collections. Our experience raises five categories of issues for long-term archival:
*Organization of the data in the collections is not well-described by text-based categorization
*Metadata organization for these data is not well-described by Dublin Core and needs attention to data
access and data use patterns
*Long-term archival requires risk management approaches to dealing with the unique threats to
knowledge preservation specific to digital information
*Long-term archival requires careful attention to archival cost management
*Professional data stewards for these collections may require special training.
This paper suggests three mechanisms for improving the quality of long-term archival:
*Using a maturity model to assess the readiness of data for accession, for preservation, and for future
*Developing a risk management strategy for systematically dealing with threats of data loss
*Developing a life-cycle cost model for continuously evolving the collections and the data centers that
Long term data records require the effective integration of new sensor technologies and improved algorithms to better characterize global and climate change impacts on ecosystems, while preserving the fundamental attributes of the existing data record. In this study, we investigated key determinants in the spectral translation and extension of MODIS Vegetation Index products across current sensor systems and to the NPOESS (VIIRS) era. We used simulated sensor-specific data sets derived from hyperspectral data using field spectroroadiometers and Hyperion sensors to investigate inter-sensor translation and continuity issues of the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). We also investigated the use of data fusion of satellite VI time series with in-situ flux tower time series measurements of photosynthesis, and the use of data fusion with tower-based continuous measures of broadband/hemispherical VI's as possible reference data sets for the inter-calibration of satellite VI time series from different sensor systems. Preliminary comparisons are presented with actual satellite VI measurements from SPOT-VEGETATION, Terra- and Aqua-MODIS, and AVHRR sensors. We found that with a consistent atmosphere correction scheme and a generalized compositing procedure, translation of multi-sensor datasets can be achieved with certain limitations.
The emissivity variation of the land surface is the most difficult effect to correct for when retrieving land surface
temperature (LST) from satellite measurements. This is not only because of the emissivity inter-pixel variability, but also
because each individual pixel is a combination of different surface types with different emissivies. For different
illumination-observation geometries, this heterogeneity leads to different ensemble (scene) emissivities. The modified
geometric project (MGP) model has been demonstrated to be able to simulate such effect when the surface structural
characteristics are available. In this study, we built a lookup table to correct the surface emissivity variation effect in
LST retrievals. The lookup table is calculated using the MGP model and the MODTRAN radiative transfer model. The
MGP model, assumes that the land surface visible to the satellite sensor is a composite of homogeneous vegetation and
soil background surface types. The homogeneous or "pure" surface types and their emissivity values are adopted from
Snyder's surface type classification. Our simulation procedure was designed to calculate the emissivity directional
variation for multiple scenarios with different surface types, solar-view angles, tree cover fractions, and leaf area index.
Analysis of the MODTRAN simulation results indicates that an error of over 1.4 K can be observed in the retrieved LST
if surface emissivity directional variability is not accounted for. Several MODIS granule data were selected to evaluate
the correction method. The results are compared with the current MODIS LST products.
Kernel-driven linear bidirectional reflectance models are gaining increasing attention for their potential use in operational processing of global remote sensing data. Nevertheless, the ability of these models to simulate actual reflectance anisotropy has not been completely explored with remote sensing data. To assess the suitability of linear models for the MODIS atmospheric correction system, we inverted a series of models with AVHRR and MODIS airborne simulator (MAS) data. For comparison, we also fit 2-stream turbid medium models to the respective data sets. Although the more complex models produced more accurate fits, the linear models were acceptably accurate and considerably faster. We conclude that linear models perform with sufficient speed and accuracy for atmospheric correction algorithms.
Conference Committee Involvement (6)
Remote Sensing and Modeling of Ecosystems for Sustainability VIII
23 August 2011 | San Diego, California, United States
Remote Sensing and Modeling of Ecosystems for Sustainability VII
3 August 2010 | San Diego, California, United States
Remote Sensing and Modeling of Ecosystems for Sustainability VI
5 August 2009 | San Diego, California, United States
Remote Sensing and Modeling of Ecosystems for Sustainability V
13 August 2008 | San Diego, California, United States
Remote Sensing and Modeling of Ecosystems for Sustainability IV
28 August 2007 | San Diego, California, United States
Remote Sensing and Modeling of Ecosystems for Sustainability III
14 August 2006 | San Diego, California, United States