Modeling the reflection of Photosynthetically active radiation in a monodominant floodable forest in the Pantanal of Mato Grosso State using multivariate statistics and neural networks.

The study of radiation entrance and exit dynamics and energy consumption in a system is important for understanding the environmental processes that rule the biosphere-atmosphere interactions of all ecosystems. This study provides an analysis of the interaction of energy in the form of photosynthetically active radiation (PAR) in the Pantanal, a Brazilian wetland forest, by studying the variation of PAR reflectance and its interaction with local rainfall. The study site is located in Private Reserve of Natural Heritage, Mato Grosso State, Brazil, where the vegetation is a monodominant forest of Vochysia divergens Phol. The results showed a high correlation between the reflection of visible radiation and rainfall; however, the behavior was not the same at the three heights studied. An analysis of the hourly variation of the reflected waves also showed the seasonality of these phenomena in relation to the dry and rainy seasons. A predictive model for PAR was developed with a neural network that has a hidden layer, and it showed a determination coefficient of 0.938. This model showed that the Julian day and time of measurements had an inverse association with the wind profile and a direct association with the relative humidity profile.