Remote sensing activities in Southern Ontario in NRCan/ESS Groundwater Geoscience Program
Bibliographic record
Abstract
Water resources and their sustainability/vulnerability are determined by climate, physiography conditions of land surface and aquifers, and human activities. Satellite remote sensing can contribute to a better understanding of water resources in various ways. In this talk, three activities associated with remote sensing in the NRCan/ESS Groundwater Geoscience Program will be discussed. The first activity (1) is water cycle modelling and water budget assessment. This activity involves modelling the various water fluxes and storages in the atmosphere-vegetation-soil-aquifer system. It relies on the ESS land surface model EALCO and remote sensing products, as well as a number of other datasets for climate, soil and aquifers. Major outputs include evapotranspiration, surface runoff, snow cover, soil water, diffuse recharge and discharge of groundwater, etc. The model provides a platform to integrate the physical water processes with satellite observations, and to study water sustainability/vulnerability issues associated with climate change and human disturbances. The second activity (2) is soil moisture mapping. This activity aims at downscaling SMOS/SMAP soil moisture products (40-50km) using Radarsat-2 data to produce soil moisture map at a higher resolution (5-10km). The method includes removing the effect of vegetation using the water-cloud model and the effect of soil surface roughness using multi-temporal Radarsat-2 data. The wavelet transform is combined with the water-cloud model in soil moisture downscaling. The third activity (3) is characterising water storage variations using Radarsat-2 InSAR data and microgravity measurements. InSAR has been proven to be an efficient technique for measuring surface deformation. This activity investigates the potential of using an inversion model of surface deformation to characterise water storage variations. Field microgravity and GPS measurements over the Waterloo Moraine were also conducted to investigate the potential for using gravity signal to characterise water storage. Activity (1) aims at modelling the water cycle through integrating multiple remote sensing products that are available. Activities (2) and (3) are expected to generate new/improved water-related variables from remote sensing, which can be used in Activity (1) to further constrain the water modelling so that our understanding of the water can be improved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".