Articles | Volume 22, issue 8
Research article
23 Aug 2022
Research article |  | 23 Aug 2022

A dynamic hierarchical Bayesian approach for forecasting vegetation condition

Edward E. Salakpi, Peter D. Hurley, James M. Muthoka, Andrew Bowell, Seb Oliver, and Pedram Rowhani

Data sets

MCD43A4 MODIS/Terra+Aqua BRDF/Albedo Nadir BRDF Adjusted Ref Daily L3 Global - 500 m V006 C. Schaaf and Z. Wang

Model code and software

edd3x/Hierarchical-Bayesian-ARDL: Data and code (scripts) for forecasting Vegetation Condition (Drought) with a Hierarchical Bayesian Model (v1.0) E. E. Salakpi and A. Bowel

Short summary
The impact of drought may vary in a given region depending on whether it is dominated by trees, grasslands, or croplands. The differences in impact can also be the agro-ecological zones within the region. This paper proposes a hierarchical Bayesian model (HBM) for forecasting vegetation condition in spatially diverse areas. Compared to a non-hierarchical model, the HBM proved to be a more natural method for forecasting drought in areas with different land covers and agro-ecological zones.
Final-revised paper