Remote sensing-based spatiotemporal dynamics of agricultural drought on Prince Edward Island using Google Earth engine
Bibliographic record
Abstract
Climate change is a primary factor contributing to widespread drought conditions worldwide. Therefore, assessing agricultural drought's spatial and temporal extent is crucial. This study explicitly applies remote sensing techniques to monitor drought in the cropland area of Prince Edward Island, Canada, with a particular emphasis on potato crops. The long-term drought was evaluated using MODIS for 2012–2022, while the seasonal drought at the field scale was calculated using Landsat-8 OLI/TIRS for the 2021 and 2022 crop growth seasons. The computed remote sensing drought indices include Vegetation Condition Index (VCI), Vegetation Health Index (VHI), and Temperature Condition Index (TCI), which are derived using the Google Earth Engine platform. Examining long-term drought by MODIS revealed that 2020 was the most dominant drought year, according to all three drought indices. However, the seasonal variations of VCI, TCI, and VHI at the field scale observed in the three fields in 2021 and 2022 demonstrated that June went through considerable drought in both years. August was the second critical month following June for drought conditions. CHIRPS data indicated significant rainfall anomalies relative to the long-term seasonal average for the 2021 crop season, specifically in June (−38.5 %) and August (−38.2 %), while the rainfall in the crop season in 2022 exceeded the seasonal average. Based on Pearson correlation analysis, VHI correlated strongly with VCI (CC = 0.87 for 2021 and 0.93 for 2022) and moderately with rainfall (CC = 0.68 for 2021 and 0.63 for 2022). The spatial autocorrelation analysis revealed substantial positive autocorrelation of drought for 2019, 2020, 2021 and 2022. However, 2020 has the highest spatial autocorrelation, with Moran's I of 0.54 and a z-score of 24.8. Hence, this study will optimize irrigation, decrease crop loss, sustain crop yields, and enhance food security. • Remote sensing-derived drought indices were used to examine agricultural drought. • VCI, TCI and VHI were combined to investigate the seasonal and long-term droughts. • Utilized Landsat 8 OLI/TIRS and MODIS datasets to compute drought indices. • CHIRPS data was utilized to evaluate rainfall deviation from the long-term average. • Based on drought indices, 2020 was characterized by significant drought conditions.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".