  Carbon sequestration and water filtering are two examples of the several
ecosystem services provided by wetlands. Open water mapping is an effective
means to measure any wetland extension as these are comprised of many open
water bodies. An economical, though indirect, approach towards mapping open
water bodies is through applying geo-computational methods to satellite images.
In this work we propose the flexible Beta regression (FBR) model to predict
open water fraction from measurements of a water index. We focus on
observations derived from two MODIS images acquired during the dry season of
2008 in Marismas Nacionales, a wetland located in the north-western Pacific
coast of Mexico. A Bayesian estimation procedure is presented to estimate the
FBR model; in particular, we provide details of a nested Metropolis-Hastings
and Gibbs sampling algorithm to carry out parameter estimation. Our results
show that the FBR model produces valid predictors of water fraction unlike the
standard model. Our work is complemented by software developed in the R
language and available through a GitHub repository.
