Bridging the Gap Between Lidar, Thermal, and Multispectral Remote Sensing for Irrigation Scheduling Applications
Bibliographic record
Abstract
Irrigation reduces the soil moisture deficit in crop production; however, the Canadian Prairies is a semi-arid landscape with limited water resource availability, requiring careful application of water management practices. This thesis studies methods to reduce irrigation water consumption in agricultural fields with complex soil texture dynamics using the water deficit index (WDI) to indirectly detect crop water stress and measure root zone soil moisture. This index is an extension of the crop water stress index (CWSI) that uses remotely sensed surface temperature (Ts) in addition to the fraction of vegetation (fc) to estimate the crop evaporative fraction through manipulation of the available energy balance equation. Seasonal and spatial relationships between WDI and volumetric water content (VWC) over a wheat and pea crop were observed at a study site with heterogeneous soil textures over two growing seasons; wheat was planted in the first growing season, and pea was planted in the second. Ten ground-based stations were used to observe average daily fluctuations in WDI by measuring Ts and deriving fc using the normalized difference vegetation index (NDVI). Results indicated that deep layers of sandier soils are more likely to cause high variations of WDI during dry-down events. Remotely sensed WDI agrees with measured eddy covariant energy fluxes at the beginning to middle of the growing season; however, NDVI is impacted by leaf senescence after seed fill for both crops, reducing the accuracy of WDI later in the growing season because of errors in fc. Light detection and ranging (lidar) is introduced as a more sophisticated approach to obtain fc and is used as a method to validate WDI obtained using NDVI canopy fraction using unpiloted aerial vehicle (UAV) imagery during the pea growing season. Canopy fraction obtained using NDVI UAV imagery produced WDI values that agreed with canopy fraction derived using lidar demonstrating that NDVI provides accurate fc for the calculation of WDI. A technical analysis was performed to assess the accuracy of crop height models obtained using Structure from Motion (SfM) photogrammetry techniques compared to lidar. Photogrammetry crop height models were obtained using high-quality red-green-blue (RGB) imagery with accurate real-time kinematic (RTK) positioning, or RGB, multispectral and thermal imagery georeferenced using 3D ground control points (3D-GCPs); thermal and RGB SfM crop height models georeferenced using 3D-GCPs were inaccurate when compared to lidar crop heights. Further analysis was performed on identifying the empirical relationship that existed between lidar-derived fc and crop height for wheat and pea crops. The ability to track seasonal and spatial relationships between WDI and VWC, and the ability to obtain crop height models using multispectral imagery provides exciting progress at bridging the gap between thermal, multispectral and lidar remote sensing for irrigation scheduling applications.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".