At-Surface Reflectance and Albedo from Satellite for Operational Calculation of Land Surface Energy Balance
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Bibliographic record
Abstract
This paper presents a rapid, operational method for estimating at-surface albedo applicable to Landsat and MODIS satellite sensors for typical cloud-free, low-haze conditions and sensor view angles less than 20°. At-surface albedo estimates are required input to various surface energy balance models that are applied operationally. The albedo calculation method was developed using the SMARTS2 radiative transfer model and has been applied in recent versions of the University of Idaho METRIC model as a component of the surface energy balance for determining evapotranspiration. The albedo procedure uses atmospheric correction functions developed to require only general humidity data and a digital elevation model. The atmospheric correction functions have a reduced structure to enhance their operational applicability in routine instantaneous surface energy balances and to estimate evapotranspiration. The method does not require high levels of knowledge in atmospheric physics and radiation transfer processes, common to traditional radiation transfer models, which enhances their use by a broad range of agricultural and hydrologic scientists and engineers. The atmospheric correction and surface albedo estimation procedures are developed primarily for use with Landsat imagery, which does not have an official albedo product. However, the procedure is also applicable to MODIS imagery that has an official albedo product at the 1km scale, for situations where full broadband albedo having 500m resolution is needed, where albedo is needed for select days having small sensor view angles for reduction of pixel blurring, or where image striping or reflectance data fallout has occurred in the standard MODIS albedo product. Method results have been compared to literature values and independent data sets. Test applications against MODIS albedo products in New Mexico, Florida, and Idaho indicate that the expected error for actual albedo from the developed method is within the interval of −0.035 to +0.033 (95% confidence level), equivalent to a standard error of 0.017, over broad ranges in land surface elevation, humidity, and sun angle.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it