Abstract—This paper describes the selection of a state-space estimation method for application to the emerging research domain of agrometeorology. The work comes from a wider geocomputational research programme that relates to climate and environment monitoring and subsequent data analysis. In particular, the data currently being collected refers to meso-micro climates in vineyards across eight countries. It is terrestrial in kind, being in the context of near-ground truth continuous data. The time-related nature of the data, being continuous across a geo-spatial plane, gives rise to the need for mathematical models that are intrinsically spatio-temporal and while effective in their robust adequacy, are also computationally efficient. State-space models are considered a class of model within the time-series literature but they have some uniquely distinguishing features for continuous multivariate data representation. Ensemble Kalman Filter models are Bayesian based estimators of multiple realisations of state-spaces over time, so are proposed here as applicable to this analytical process domain.
Index Terms—Geocomputation; estimation; agronomy; meteorology; sensors; monitoring telemetry.
P Sallis is with Geoinformatics Research Centre (GRC), School of Computing and Mathematical Sciences, Auckland University of Technology (AUT), Auckland, New Zealand (e-mail: philip.sallis@aut.ac.nz).
S. Hernández is with Laboratorio de Procesamiento, de Información Geoespacial,Universidad Católica del Maule, Talca, Chile. (email:shernandez@ucm.cl ).
S Shanmuganthan is with Geoinformatics Research Centre, School of Computing and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand. (e-mail: subana.shanmuganthan@aut.ac.nz).
Cite:Philip Sallis, Sergio Hernández, and Subana Shanmuganathan, "Dynamic Multivariate Continuous Data State-Space Estimation for Agrometeorological Event Anticipation," International Journal of Machine Learning and Computing vol.2, no. 5, pp. 672-676, 2012.