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

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Cited articles

Adede, C., Oboko, R., Wagacha, P. W., and Atzberger, C.: A Mixed Model Approach to Vegetation Condition Prediction Using Artificial Neural Networks (ANN): Case of Kenya's Operational Drought Monitoring, Remote Sens., 11, 1099,, 2019. a
Asaad, A.-A. B. and Magadia, J. C.: Stochastic Gradient Hamiltonian Monte Carlo on Bayesian Time Series Modeling, in: 14th National Convention on Statistics Crowne Plaza Manila Galleria, 1–3 October 2019, Ortigas Center, Quezon City, 2019. a
Ayugi, B. O., Wen, W., and Chepkemoi, D.: Analysis of Spatial and Temporal Patterns of Rainfall Variations over Kenya, 6, (last access: 3 October 2021), 2016. a
Barrett, A. B., Duivenvoorden, S., Salakpi, E. E., Muthoka, J. M., Mwangi, J., Oliver, S., and Rowhani, P.: Forecasting vegetation condition for drought early warning systems in pastoral communities in Kenya, Remote Sens. Environ., 248, 111886,, 2020. a, b, c
Ben Taieb, S. and Hyndman, R. J.: Recursive and direct multi-step forecasting: the best of both worlds, (last access: 14 August 2022), 2014. a
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