Normalized Difference Vegetation Index Continuity of the Landsat 4-5 MSS and TM: Investigations Based on Simulation
Bibliographic record
Abstract
Landsat 4-5, built at the same time and with the same design, carrying the Multispectral Scanner System (MSS) and the Thematic Mapper (TM) simultaneously, jointly provided observation service for about 30 years (1982–2013). Considering the importance of data continuity for time series analyses, investigations on the continuity of the Landsat 4-5 MSS and TM are required. In this paper, characterization differences between the Landsat 4-5 MSS and TM were initially discussed using the synthesized reflectance records generated from a collection of Hyperion hyperspectral profiles which were well calibrated and widely distributed. The difference in near-infrared region mostly contributed to the difference in normalized difference vegetation index (NDVI) between MSS and TM, while the between-sensor difference in red spectrum was relatively minor. Models for transforming MSS NDVI to TM NDVI were proposed, and validated subsequently through cross-validation tests. Furthermore, effectiveness of the transformation models was investigated using eight synchronous observation pairs of the Landsat 5 MSS and TM. On average, the univariate models through ordinary least squares regression (OLS) regression resulted in a decrease about 10% of the median relative difference (MdRD). Meanwhile, the bivariate models improved the NDVI comparability in most cases, especially when the transformation models through ridge regression were implemented. The univariate model through OLS regression could be the only solution for cases when problems of data quality are encountered (e.g., problem in the MSS near-infrared channel (800–1000 nm)). In conclusion, the findings on NDVI transformation models from MSS to TM are valuable for reference, because of the collection of diverse Hyperion hyperspectral profiles used.
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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".