Applications of remote sensing for crop residue cover mapping
Bibliographic record
Abstract
• Crop residue is critical for soil and crop health, and it is valuable to monitor • Many previous studies used remote sensing to quantify crop residue cover (CRC) • This literature review emphasizes remote sensing platforms, sensors, and analytical methods estimation • Recent advancements in these techniques can further support residue management Crop residue is critical for the health of soils and crops as it can maintain soil moisture, reduce soil erosion, support soil nutrient cycling, and increase soil carbon sequestration. Monitoring crop residue cover (CRC) is thus essential for understanding the distribution and amount of crop residues in the field and for developing corresponding management strategies. Remote sensing is a powerful geospatial technique that enables the collection of images covering large areas repeatedly, which can contribute greatly to CRC mapping. This paper reviews the use of remote sensing in estimating CRC, focusing on different remote sensing platforms (e.g., satellites and drones), sensors (e.g., multispectral, hyperspectral, non-optical) and analytical methods (e.g., spectral unmixing, image classification). A total of 101 studies were selected based on their relevance to the scope of this review. The review found that while remote sensing technologies have shown great potential in accurately monitoring CRC, challenges remain in data integration, sensor selection, and computational demands, pointing to the need for ongoing research to optimize crop residue monitoring. This review is expected to bring more insights to agricultural researchers and practitioners and promote developing effective techniques for CRC mapping and management.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".