Angular Effect Correction for Improved LAI and FVC Retrieval Using GF-1 Wide Field View Data
Bibliographic record
Abstract
Leaf area index (LAI) and fractional vegetation cover (FVC) are two essential vegetation parameters for ecological and climate studies. The Chinese Gaofen-1(GF-1) wide field view (WFV) satellite data is a valuable data source for LAI and FVC retrieval at high spatio-temporal resolution. Like its name, GF-1 WFV has very large view angle ranging from 0° to 48°, which can impact the accuracy of vegetation parameter retrieval. The primary aim of the study was to develop an angular effect correction (AFX-fix) method that can effectively normalize GF-1 WFV data. Our objective was to enhance the applicability of the corrected data in retrieving LAI and FVC. The AFX-fix method used angular index, anisotropy flat index (AFX), and a fixed set of bidirectional reflectance distribution function (BRDF) parameters. LAI and FVC were retrieved from the GF-1 WFV reflectance data using the PROSAIL model combined with a random forest method. Results showed that the accuracy of LAI and FVC retrieval in wheat and corn from angular corrected GF-1 WFV data was improved with a decrease in root mean square error (RMSE) by 0.66 for LAI and 0.03 for FVC compared to that based on the original data. We anticipate that this new method will help improve the performance of LAI retrieval of these crop types using WFV data.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".