Spatio-Temporal Sensitivity of MODIS Land Surface Temperature Anomalies Indicates High Potential for Large-Scale Land Cover Change Detection in Permafrost Landscapes
Bibliographic record
Abstract
The accelerated warming Arctic climate may alter the surface energy balance locally and regionally of which a changing land surface temperature (LST) is a key indicator. Modelling current and anticipated changes of the surface energy balance requires an understanding of the spatio-temporal interactions between LST and land cover. This paper investigated the accuracy of MODIS LST V5 1 km level 3 product and its spatio-temporal sensitivity to land cover properties in a Canadian High Arctic permafrost landscape. Land cover ranged from fully vegetated moss/segde grass tundra to sparsely vegetated bare soil and barren areas. Daily mean MODIS LST were compared to in-situ radiometer measurements over wet tundra for three summers and two winters in 2008, 2009, and 2010. MODIS LST showed an accuracy of 1.8°C and a RMSE of 3.8°C in the total observation period including both summer and winter. Agreement was lowest during summer 2009 and freeze-back periods which were associated with prevailing overcast conditions. A multi-year anomaly analysis revealed robust spatio-temporal patterns taking into account the found uncertainty and different atmospheric conditions. Summer periods with regional mean LST larger than 5°C showed highest spatial diversity with four distinct anomaly classes. Dry ridge areas heated up most whereas wetland areas and dry barren surfaces with high albedo were coolest. Mean inter-annual differences of LST anomalies for different land cover classes were less than 1°C. However, spatial pattern showed fewer positive anomalies in 2010 suggesting differences in surface moisture due to interannual differences in the amount of end-of-winter snow. Presented summer LST anomalies might serve as a baseline against which to evaluate past and future changes in land surface properties with regard to the surface energy balance. Sub-temporal heterogeneity due to snow or ice on/off as well as the effect of subpixel water bodies has to be taken into account. A multi-sensor approach combining thermal satellite measurements with high-resolution optical and radar imagery therefore promises to be an effective tool for a dynamic, process-based ecosystem monitoring scheme.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".