A Comparison of Land Surface Temperature, Derived from AMSR-2, Landsat and ASTER Satellite Data
Bibliographic record
Abstract
Land surface parameters are highly integrated and have a direct effect on water and energy balance and weather predictions. Due to the difficulties in correcting the influences of the atmosphere absorbability and the earth surface emissivity diversification, the retrieval of land surface temperature (LST) from satellite data is a challenging task. To retrieve microwave land emissivity, infrared surface skin temperatures have been used as surface physical temperature. However, passive microwave emissions originate from deeper layers with respect to the skin temperature. So, this inconsistency in sensitivity depths between skin temperatures and microwave temperatures may introduce a discrepancy in the determined emissivity. In this research work, 6 sample sites were chosen on the globe for 2013 and 2014 and then derive land surface temperature from AMSR-2, Landsat and ASTER brightness temperature values. The algorithm is developed from a surface brightness temperature dataset, which uses a range of surface parameters and atmospheric quantities as inputs. The retrieved LST is compared within AMSR-2, Landsat and ASTER for the same period and area. Maximum time ASTER shows highest temperature than other data and AMSR-2 has lowest temperature on same area. Landsat and ASTER is closer to ground measured temperature than AMSR-2 data. It will be interesting to see how the satellite-derived surface temperature will behave in an assimilation scheme in a follow-up study.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".