Urban thermal anisotropies by local climate zones: An assessment using multi-angle land surface temperatures from ECOSTRESS
Bibliographic record
Abstract
Knowledge of anisotropy-induced spatial and temporal variations of land surface temperature (LST) is crucial for enhancing the quality of remote sensing products , refining land surface process modeling, and optimizing climate models. However, the limited availability of simultaneous multi-angle LST observations from space has hindered the exploration of this topic. NASA's latest ECOSTRESS sensor deployed on the International Space Station (ISS) generates multi-angle LST measurements at a 70-m spatial resolution for different times of day/night, providing a new avenue for investigating urban thermal anisotropy. In this study, we presented an initial examination of the performance of ECOSTRESS LST observations in unraveling the fine-grained urban thermal anisotropy, by taking the City of Phoenix, Arizona, United States, as the study area. We proposed a method to generate a quasi-simultaneous multi-angle ECOSTRESS LST dataset over the course of the diurnal cycle with the assistance of air temperature data from weather stations and hourly LST observations from a geostationary satellite , GOES-R. We then examined the thermal anisotropic patterns and their diurnal and seasonal variations across different Local Climate Zones (LCZs) at a spatial resolution of 200 m. Based on quasi-simultaneous multi-angle ECOSTRESS observations, Vinnikov and Vinnikov-RL models were employed to generate LCZ-scale anisotropy profiles of the study area to quantify and correct the LST directional effect. The results revealed that ECOSTRESS observations manifest unique angular patterns, featuring substantial variations in sensor viewing azimuth angles (VAA) and limited changes in sensor viewing zenith angles (VZA) within a 30° range. The angular effect led to notable variations in the observed LST, with potential deviations at the city scale of up to 10 K during winter and around 5 K during summer, relative to the nadir LST. Furthermore, the LST anisotropy exhibited distinct diurnal and seasonal patterns across LCZs, characterized by prominent variations in the intensity and width of hot/cold spots. LCZ 6, 9, and D typically displayed higher hotspot intensity and width than other LCZs at varying times of day in both summer and winter. In addition, the Vinnikov-RL model had good performance in simulating diurnal LST anisotropy over LCZs. This study reveals the potential of multi-angle ECOSTRESS LST observations in exploring urban thermal anisotropy, and contributes to better utilization of ECOSTRESS LST products. The integration of ECOSTRESS LST data with other satellite derived LST data have important implications for studying urban climate and improving long-term surface climate record, contributing to global climate studies.
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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".