Evaluating TerraSAR-X for the identification of tillage occurrence over an agricultural area in Canada
Bibliographic record
Abstract
Tillage practices can affect the long term sustainability of agricultural soils as well as a variety of soil processes that impact the environment. The benefits of reduced tillage and no-till practices over agriculture fields are well documented and include: (1) significant reductions in wind and water erosion mitigating nutrient and pesticide runoff into waterways; (2) increasing and/or maintaining soil organic matter; (3) increasing biological activity and improving soil structure; and (4) increasing soil carbon and its sequestration. Information on tillage activities assists in implementing policies and programs to promote beneficial management practices (BMPs), and in monitoring the success of these initiatives. Agriculture and Agri-Food Canada supports environmentally responsible agriculture and has identified this as one of their priorities. Thus, tillage information requirements have become increasingly important to a number of programs and policies within the department. Rapid, accurate and objective methods are required to map and monitor tillage activities. Earth observing satellites can assist with targeting and monitoring land management activities. For the last decade, research has clearly demonstrated that complementary information provided by both optical and radar satellite sensors are fundamental in developing an agricultural land management monitoring system. Launched in June 2007, the TerraSAR-X is a radar satellite acquiring data at the X-band frequency (9.6 GHz). The application of TerraSAR-X data for conservation tillage mapping has been somewhat limited, and thus this study investigates its use in determining tillage occurrence. An HH-HV TerraSAR-X image was acquired on November 4, 2009 and ground data were also collected characterizing tillage conditions at the time of acquisition. Backscatter responses were analyzed to identify tillage occurrence and to differentiate between untilled, chiseled and moldboard ploughed fields. Preliminary analysis showed that HH polarization can better contribute to tillage discrimination than compared to HV polarization and that the backscatter response can be used to discriminate untilled fields from ones that are moldboard ploughed. However, chiseled fields were often confused with highroughness (rms height~1.30 cm) untilled fields and moldboard ploughed fields. Fully polarimetric X-band radar datasets could potentially contribute more information to mapping tillage conditions.
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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 | 0.001 |
| 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 | 0.001 | 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".