Satellite Land surface temperature (LST) is defined as the radiative skin temperature of the land. It has been widely used in many aspects of the geosciences, e.g., studies of net radiation budget at the Earth surface, monitoring state of crops and vegetation. It is an important indicator of both the greenhouse effect and the physics of land-surface processes at local through global scales. Thus, LST has been listed as an Essential Climate Variable (ECV) in the Global Climate Observation System (GCOS).
LST is one of the baseline products for the GOES-R series satellites measured from the Advanced Baseline Imager (ABI). The algorithm derivation was developed at NOAA/NESDIS center for SaTellite Applications and Research (STAR), based on a traditional split-window technique. It is primarily estimated from the top-of-atmosphere (TOA) brightness temperature (BT) at one ABI thermal infrared channel and corrected by the BT difference to the near-by thermal infrared channel. Quality of the LST estimation may vary depending on cloud fraction, water vapor, view zenith angle, etc. Such quality information, recorded as quality flags and metadata, is provided with the LST product for user reference, product monitoring and evaluation analysis.
Comprehensive evaluation of the GOES-R LST product has been conducted using radiative transfer simulation datasets and proxy ABI data, before the launch of the first GOES-R satellite (i.e. GOES-16). Since then we have performed its evaluation using over one year of the on-orbit ABI SDR and LST dataset, towards its beta and provisional validated maturity levels. Quality flags and metadata of the LST product are tested and verified with local independent computation. LST retrievals were compared to in-situ LST data derived from the SURFRAD station measurements. This presentation shows our evaluation results, as well as the ABI LST derivation details, which are helpful in users’ product applications
In development of retrieval algorithm for satellite land surface temperature (LST) measurements, it is crucial yet difficult
to estimate precision and accuracy of the algorithm using ground measurements. In this effort, we built up a theoretical
model for estimating the random error of the satellite measurement. The method requires a series of surface temperature
measurements from three independent data sources. In our case, they were collected from the GOES-8, GOES-10
Imagers and the SURFace RADiation (SURFRAD) budget network stations. SURFRAD data at five sites in the year
2001 were processed along with the corresponding GOES-8 and -10 Imager data. A manual cloud filtering procedure
was applied to ensure a high quality cloud-free data set. An LST retrieval algorithm developed for the GOES-R mission
was applied to the GOES-8 and -10 data, while the SURFRAD data provided the third independent LST estimation.
Standard deviation errors of the three measurements were calculated from the theoretical model, and biases of the
measurements were estimated with some assumptions. The method was particularly developed for evaluating GOES-R
LST algorithm. It may have wider applications in remote sensing development and applications.
Evaluation of satellite land surface temperature (LST) is one of the most difficult tasks in LST retrieval algorithm
development, because of spatial and temporal variability of land surface temperature and surface emissivity
variations. A large number of high quality "match-up" satellite and ground LST data is needed for the evaluation
process. In developing a LST algorithm for the GOES-R Advanced Baseline Imager, we produced a set of
"match-up" dataset from SURFace RADiation (SURFRAD) budget network ground measurements and GOES-8
and -10 satellite measurements. The dataset covers one-year GOES Imager data over six SURFRAD sites in the
United States. A stringent cloud filtering procedure was applied to minimize cloud contamination in the match-up
dataset. Each of the SURFRAD sites contains enough match-up data pairs for ensuring significance of statistical
analyses of the LST algorithm. The evaluation was performed by directly and indirectly comparing the
SURFRAD and satellite LSTs of each site. The direct comparison was illustrated using scatter plots and histogram
plots of the ground and the satellite LSTs, while the indirect comparison was performed using a matrix analysis
model developed by Flynn (2006)[1]. We demonstrated that LST measurements from the SURFRAD instrument
can be used in our evaluation of the GOES-R LST algorithm development and the precision of the GOES-R LST
algorithm can be fairly well estimated.
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.
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.
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