Mapping crop evapotranspiration with high-resolution imagery and meteorological data: insights into sustainable agriculture in Prince Edward Island
Bibliographic record
Abstract
Soil moisture variability caused by soil erosion, weather extremes, and spatial variations in soil health is a limiting factor for crop growth and productivity. Crop evapotranspiration (ET) is significant for irrigation water management systems. The variability in crop water requirements at various growth stages is a common concern at a global level. In Canada’s Prince Edward Island (PEI), where agriculture is particularly prominent, this concern is predominantly evident. The island’s most prominent business, agriculture, finds it challenging to predict agricultural water needs due to shifting climate extremes, weather patterns, and precipitation patterns. Thus, accurate estimations for irrigation water requirements are essential for water conservation and precision farming. This work used a satellite-based normalized difference vegetation index (NDVI) technique to simulate the crop coefficient (K c ) and crop evapotranspiration (ET c ) for field-scale potato cultivation at various crop growth stages for the growing seasons of 2021 and 2022. The standard FAO Penman–Monteith equation was used to estimate the reference evapotranspiration (ET r ) using weather data from the nearest weather stations. The findings showed a statistically significant ( p < 0.05) positive association between NDVI and tabulated K c values extracted from all three satellites (Landsat 8, Sentinel-2A, and Planet) for the 2021 season. However, the correlation weakened in the subsequent year, particularly for Sentinel-2A and Planet data, while the association with Landsat 8 data became statistically insignificant ( p > 0.05). Sentinel-2A outperformed Landsat 8 and Planet overall. The K c values peaked at the halfway stage, fell before the maturity period, and were at their lowest at the start of the season. A similar pattern was observed for ET c (mm/day), which peaked at midseason and decreased with each developmental stage of the potato crop. Similar trends were observed for ET c (mm/day), which peaked at the mid-stage with mean values of 4.0 (2021) and 3.7 (2022), was the lowest in the initial phase with mean values of 1.8 (2021) and 1.5 (2022), and grew with each developmental stage of the potato crop. The study’s ET maps show how agricultural water use varies throughout a growing season. Farmers in Prince Edward Island may find the applied technique helpful in creating sustainable growth plans at different phases of crop development. Integrating high-resolution imagery with soil health, yield mapping, and crop growth parameters can help develop a decision support system to tailor sustainable management practices to improve profit margins, crop yield, and quality.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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 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".