Using multispectral imagery to characterise weathering: A case study of moraines in the Central Andes
Bibliographic record
Abstract
There is growing recognition that multispectral satellite imagery can be used to characterise the weathering state of material exposed at the Earth’s surface. However, it remains unclear if and how satellite-derived weathering indices can be linked, in a universal way, to the fundamental controls on weathering (e.g., surface age and composition, temperature, moisture availability). Here, we use Landsat-8 Operational Land Imager (OLI) multispectral imagery to characterise the reflectance of 75 dated moraines, distributed throughout the Central Andes between 7 and 27 °S. As imaged from space, these moraines represent ageing surfaces composed of sediment that is undergoing physical and chemical weathering. The moraines present an ideal opportunity to explore the controls on weathering over 103-105 year timescales, because they span major gradients in age (1-112 kyr), temperature (2-9 °C), precipitation (100-1000 mm/yr), and also lithological composition.From contrast-enhanced Landsat-8 imagery, we derive a simple band ratio that has been demonstrated to act as a weathering index, scaling with the extent of chemical weathering and the presence of secondary minerals. At every location, we observe a non-linear increase in the weathering index with moraine age. Older moraines exhibit a systematic shift in brightness from visible wavelengths to the short-wave infrared, driven by mineralogical changes during weathering. We also detect subtler variations in the rates and magnitudes of changes in the weathering index that relate to lithological composition. Moraines with mineralogically-diverse compositions (e.g., intrusives and volcanics) display faster and larger increases in the weathering index compared to moraines with mineralogically-simple lithologies (e.g., quartzite and carbonates). Next, we derive the rates of change of the weathering index as a function of moraine age, and compare with past climate. Precipitation emerges as a key control on the weathering index, with faster weathering coinciding with wetter conditions in both time and space. The weathering index increases faster during known episodes of wet climate in the past, registering both 23 kyr precessional cycles and abrupt, 1 kyr Heinrich events. The weathering index also exhibits significantly larger increases in the northern Central Andes, where the climate is much wetter, compared to the southern Central Andes where the climate is drier. Temperature exerts an inverse control on the weathering index, which we speculate reflects the role frost-shattering plays in facilitating chemical weathering.This work demonstrates that freely-available Landsat-8 imagery can be used to reconstruct the weathering of sedimentary landforms across time and space. Furthermore, our results hint at the presence of fundamental relationships between weathering state, as detected by multispectral sensors, and primary variables such as time, substrate composition, and climate.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".