Comparative analysis of SPOT, Landsat, MODIS, and AVHRR normalized difference vegetation index data on the estimation of leaf area index in a mixed grassland ecosystem
Why this work is in the frame
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Bibliographic record
Abstract
Many grassland studies have depended on or are currently depending on the Landsat series of satellite sensors for monitoring work. However, given the identified gaps in Landsat data, alternatives to Landsat imagery need to be tested in an operational environment. In this study, normalized difference vegetation index (NDVI) values are derived from a Système Pour l'Observation de la Terre (SPOT), Moderate Resolution Imaging Spectroradiometer (MODIS), and Advanced Very High Resolution Radiometer (AVHRR) image and compared to the NDVI values from a Landsat image for LAI estimation in a semi-arid heterogeneous grassland. Results indicate a high agreement between Landsat and SPOT data with R2 over 85% at all buffer levels (100, 250, and 1000 m), and a significant but lower agreement between MODIS and Landsat with R2 around 28% at 250 m buffer level to 37% at 100 m buffer level. Based on in situ measurements of LAI in 22 homogeneous sites, the relationships established between LAI and NDVI show that SPOT and Landsat could predict LAI with acceptable accuracy, but MODIS and AVHRR cannot quantify the spatial variation in LAI measurements. Data fusion or blending techniques that combine the spectral information of high spatial/low temporal resolution data with low spatial/high temporal resolution data may be considered to study semi-arid heterogeneous grasslands.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it