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At-Surface Reflectance and Albedo from Satellite for Operational Calculation of Land Surface Energy Balance

2008· article· en· W2061346772 on OpenAlexaff
Masahiro Tasumi, Richard G. Allen, Ricardo Trezza

Bibliographic record

VenueJournal of Hydrologic Engineering · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsKimberly-Clark (Canada)
Fundersnot available
KeywordsAlbedo (alchemy)Environmental scienceRemote sensingAtmospheric correctionSatelliteEvapotranspirationRadiative transferEarth's energy budgetMeteorologyEnergy balanceRadiationGeographyPhysics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.006

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.

Opus teacher head0.007
GPT teacher head0.184
Teacher spread0.177 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations235
Published2008
Admission routes1
Has abstractyes

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