All-sky hourly estimation over East Asia using Himawari-8 AHI and multi-source data: investigating the main climatic drivers of afternoon depression and intraday variability in gross primary productivity
Why this work is in the frame
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Bibliographic record
Abstract
Intraday observations from geostationary satellites provide key information for estimating terrestrial productivity and analyzing environmental drivers, but cloud cover often hinders continuous monitoring. In this study, we addressed this limitation by combining multi-source data and a data-driven approach to develop hourly, all-sky, regional-scale gross primary productivity (GPP). Our all-sky GPP showed strong consistency with ground measurements in East Asia (coefficient of determination (R2) = 0.86, root mean squared error = 2.4 μmol CO₂/m²/s) and outperformed conventional hourly GPP products derived from the observations of International Space Station (ISS) sensors and polar-orbiting satellites. To investigate how the importance of input variables differs between clear-sky and cloudy-sky conditions, we applied Shapely Additive exPlanation (SHAP) analysis. Notably, the Himawari-8-derived features contributed most in both clear- and cloudy-sky models. Under heat stress in clear-sky conditions, water-content features exhibited increasing impact compared to normal conditions, while latent heat flux demonstrated high contribution beneath the clouds. By capturing these regime shifts in feature importance, our all-sky GPP effectively captured the widespread afternoon depression in summer across East Asia. Regional analysis by land cover, using the Pearson correlation coefficient (r), revealed that vapor pressure deficit drove afternoon depression under clear skies (r = -0.519), whereas surface latent heat flux was the primary driver under cloudy conditions (r = -0.785). These findings highlight the synergistic use of high-frequency geostationary observations and the detailed spatial information from polar-orbiting satellites, which enhances our understanding of the shifting environmental drivers of GPP across regimes.
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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 | 0.003 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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