Integration of satellite imagery and in situ soil moisture data for estimating irrigation water requirements
Bibliographic record
Abstract
Remote sensing images provide a reliable approach to monitor crop physiological development and can be used to assess crop evapotranspiration (ETc) and irrigation water requirements (IWR). This study compared the suitability of multispectral images acquired from Unmanned Aerial Vehicles (UAV-MSI), PlanetScope, and Sentinel-2A & 2B satellite platforms for estimating ETc. It integrated this ETc data with in situ soil moisture data to estimate IWR of field gown tomato crops (Lycopersicum esculentum) in southeastern Canada. The experimental field was divided into three (3) blocks, and irrigation scheduling consisted of 100, 80, and 60% of soil's field capacity, corresponding to three irrigation regimes. Plants were selected from each of the three blocks, through a systematic grid sampling approach. The sampled plants were georeferenced and identified in the images. Normalized difference vegetation indices (NDVI) obtained from the remote sensing platforms were evaluated for estimating the crop consumptive coefficient and ETc. The ETc predicted from satellite images were compared with estimates of ETc obtained from the FAO 56 Penman-Monteith module of the AquaCrop model. ETc maps from Sentinel-2 were combined with soil moisture data to predict IWR. The results indicate a significant difference in average NDVI values obtained from the UAV-MSI (0.87 ± 0.03) and satellite platforms (0.71 ± 0.03 and 0.82 ± 0.05, for PlanetScope and Sentinel-2, respectively), which suggests that the UAV-MSI overestimated the field NDVI values. There was a good agreement between Kc and NDVI values extracted from satellite images, with R2 = 0.98, p < 0.001, for Sentinel-2 and R2 = 0.78, p < 0.001, for PlanetScope. ETc values estimated from Sentinel-2 satellite platform were closely corroborated with AquaCrop model (with R2 = 0.94; p < 0.01), which shows the suitability of Sentinel-2 imagery for assessing crop canopy cover and IWR at the field scale. The amount of irrigation water that the grower applied using micro-drip irrigation system (342 and 416 mm in 2017 and 2018 growing seasons, respectively) exceeded the estimated IWR (165 and 199 mm in 2017 and 2018 growing seasons, respectively), which suggests that the field was over-irrigated. This study has shown the practicality of integrating soil moisture measurements and remotely sensed crop parameters for mapping actual irrigation requirements. It indicates a significant progress towards the development of a near real-time approach for supporting precision irrigation.
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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 | 0.000 |
| 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 | 0.001 |
| 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".