A comprehensive review of meteorological and remote sensing indices for drought monitoring and their in situ application in India
Bibliographic record
Abstract
Drought is one of the most elaborate and destructive natural calamities to affect agriculture, water resource management, and socio-economic stability. Being a rainfed country, India is highly prone to drought-related hazards in the agricultural sector, placing soil moisture and crop productivity at a rapid fall, reporting a loss of approximately 33% or more from the 35 million hectares of cropped land between 2016–17 and 2021–22. This review provides a comprehensive analysis of the meteorological and remote sensing indices used for drought monitoring, with a specific focus on their application in India. Meteorological indices derived from historical climate data, along with remote sensing technologies, enhance drought assessment by identifying severity and spatial extent through satellite imagery and vegetation indices. The limitation with each procedure, however, lies in its requirement for data availability as well as resolution concerns. This review also alludes to the possibility of integrating machine learning and artificial intelligence that might monitor and enhance drought management. However, this is not yet free from regional disparities in data gathering and access to technology in under-resourced areas. For instance, the more developed states like Punjab, Tamil Nadu, Karnataka, benefit from dense meteorological networks, observation stations, and detailed agricultural records, enabling a more robust drought monitoring and assessment. In contrast, majority of the northeastern states often lacks sufficient ground-based stations and consistent historical data, making reliable drought monitoring more difficult. This disparity affects the effectiveness of advanced techniques, such as machine learning and artificial intelligence, which rely on robust datasets for accurate drought prediction and management. This could be addressed by interdisciplinary collaboration and international best practices-India can improve its preparedness and drought resilience.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".