Drought Monitoring and Vegetation Response Assessment
Summary
Drought monitoring and vegetation response assessment integrate meteorological, hydrological and ecological observations to detect, characterise and forecast water deficits and their impacts on plant communities. Central to this field are remote sensing techniques that derive vegetation indices from satellite imagery, ground‐based soil moisture measurements and climate indicators such as precipitation and temperature anomalies. Advances in data fusion, machine learning and process-based modelling enable the synthesis of multi‐source information into composite drought indicators that track onset, severity and recovery of stress at regional to global scales. Such tools support early warning systems, guide agricultural management and inform ecosystem conservation by linking physical drought drivers with observed changes in photosynthetic activity, canopy structure and crop yield.
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Drought Monitoring and Vegetation Response Assessment publication trend
The graph below shows the total number of articles in drought monitoring and vegetation response assessment across all publications each year (not limited to Nature Index journals).
Technical terms
Standardized Precipitation Index (SPI): A normalised indicator of precipitation anomaly over a specified accumulation period, used to characterise meteorological drought.
Vegetation Health Index (VHI): A combined metric of vegetation greenness and temperature stress derived from satellite observations, employed for large‐scale drought detection.
Normalized Difference Vegetation Index (NDVI): A measure of canopy greenness based on the ratio of reflected near-infrared and red light, widely used to assess plant health and photosynthetic activity.
Soil Moisture Anomaly: The deviation of observed soil moisture from its long-term average, indicating hydrological drought conditions.
Shapley Additive Explanations (SHAP): A game‐theoretic approach to interpret complex machine-learning models by quantifying the contribution of each input feature to model outputs.
References
- Investigating agricultural drought in Northern Italy through explainable Machine Learning: Insights from the 2022 drought. Computers and Electronics in Agriculture (2024).
- An improved global vegetation health index dataset in detecting vegetation drought. Scientific Data (2023).
- How well do meteorological indicators represent agricultural and forest drought across Europe?. Environmental Research Letters (2018).
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