Invention:
This is an open-source machine-learning based method of estimating errors associated with global rainfall erosivity calculations based on IMERG (Integrated Multi-satellite Retrievals for Global precipitation measurement) data. The error estimation is developed by first analyzing IMERG-based erosivity data against reference datasets based on measurements from in-ground gauges. Then, datasets of auxiliary variables are used to attribute error sources and drive machine learning models to correct the errors. Ultimately, this error estimation and correction will be used to develop a tool for the real-time error corrected calculation of global rainfall erosivity from IMERG data.
Background:
Global rainfall erosivity measurements are crucial to understanding and modeling soil erosion, which is important in land management, environmental conservation, and climate resilience planning. Traditional methods for estimating global rainfall erosivity are based on the use of ground-based gauges, which often suffer from spatial sparsity and data gaps. Conversely, IMERG data has a high spatiotemporal resolution and is highly accurate. However, using IMERG data to estimate global rainfall erosivity introduces new errors that must be accounted for to obtain accurate estimations. This new method aims to estimate and correct these errors to enable real-time, high-resolution global rainfall erosivity estimates.
Applications:
- Global rainfall erosivity measurement
- Soil erosion modeling
- Environmental monitoring
- Land management
- Conservation
- Climate resilience
Advantages:
- Increased resolution of data
- Enhanced accuracy
- Real-time assessment
