Remote sensing of canopy water content: scaling from leaf data to MODIS
Bibliographic record
Abstract
The water in green vegetation is detectable using reflectances in the near infrared and shortwave infrared. Canopy water content is estimated from the product of leaf water content and leaf area index (LAI). The Normalized Difference Infrared Index [NDII = (R850 - R1650)/(R850 + R1650)] was found to be strongly related to canopy water content using various moderate resolution sensors (Landsat TM, ASTER, AWiFS) during the SMEX02, SMEX04, SMEX05, and OTTER experiments. With the high temporal resolution of MODIS, changes in canopy water content may perhaps be used to estimate plant water stress and wild-fire potential. However, the low spatial resolution of MODIS does not allow the relationship between NDII and canopy water content to be determined experimentally. The objective of this study is to validate the expected relationship of canopy water content with NDII by the standard LAI data product from MODIS; the quotient is the expected leaf water content which will vary by land-cover type. Maximum NDII for 2000-2007 was calculated from the MODIS standard surface reflectance data products and compared to maximum MODIS LAI for the same years. Mean leaf water content from MODIS was not significantly different from leaf data for most land cover types. However the large standard deviations indicated that canopy water content from NDII is not currently accurate for monitoring the incipient stages of plant water stress.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".