Monitoring autumn agriculture activities using Synthetic Aperture Radar (SAR) and coherence change detection
Bibliographic record
Abstract
Across Canada, farmers are encouraged to adopt beneficial management practices (BMPs) to protect soil heath, reduce green house gas emissions and mitigate off-site impacts from agriculture. Measuring the uptake of BMPs, including the implementation of conservation tillage, helps gauge the success of policies and programs to promote adoption. Satellites are one way to monitor BMP adoption and Synthetic Aperture Radars (SARs) are of particular interest given their all-weather data collection capability. This research investigated coherent change detection (CCD) to determine when farmers harvest and till their fields. A time series of both Sentinel-1 and RADARSAT Constellation Mission (RCM) images was acquired over a site in the Canadian Lake Erie basin, during the autumn of 2021, when farmers were harvesting and tilling fields of corn, soybeans and wheat. 16 CCD pairs were created and coherence values were interpreted based on observations collected for 101 fields. An m-chi decomposition was applied to the RCM data, and the Volume/Surface (V/S) ratio was calculated as an additional source of information to interpret results. Change events due to harvest, tillage, autumn seeding and chemical termination resulted in coherence values below 0.20. The mean and standard deviation for fields with observed change was 0.18 ± 0.03. Coherence values were 0.42 ± 0.15 for fields where no change was noted. Tests confirmed that the coherence associated with changed and unchanged fields was significantly different. Coherence values could also differentiate between some types of management events, including tillage and harvest. CCD could also separate harvest as a function of crop type (corn or soybeans). V/S ratios declined after tillage events but increased after both harvesting and chemical termination. Narrowing the date of harvest and tillage is as important as detecting change. To meet this requirement, Sentinel-1 and RCM CCD products with values below 0.20 (indicating change had occurred), were graphically overlaid. With this approach, the timing of corn harvest was identified as occurring within a 5-day window. The tilling of corn, soybeans and wheat was narrowed to a 4-day window. The results of this research confirmed that CCD can be used to capture change due to autumn agricultural activities, and this technique can also separate change due to harvest and tillage. Finally, this study demonstrated that when data from different SAR missions are combined in a virtual constellation, timing of harvest and tillage can be more precisely defined.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".