Geographically weighted modelling of surface salinity in Florida Bay using Landsat TM data

An effective remote-sensing approach is needed for surface salinity monitoring in Florida Bay, a typical estuarine and coastal ecosystem (ECE). Yet, the non-stationary nature of surface salinity makes it difficult to model with conventional regression methods. A geographically weighted regression (GWR) approach was proposed to model surface salinity from Landsat Thematic Mapper (TM) imagery in this study. The models were constructed and validated with spatiotemporally matched field-surveyed salinity and TM imagery collected in February 1999. The GWR models reported high coefficient of determination (R-2) values and low root mean square errors (RMSEs) in validation. A 1999 model was also used to hindcast the surface salinity with TM imagery collected in December 1998 and validated with surface salinity collected at that time. The validation reported a reasonably low RMSE. It suggests a GWR approach, with field survey and remotely sensed data, may be useful in modelling and predicting the spatial variation pattern of surface salinity in Florida Bay, and could potentially serve as a less costly alternative or a supplement to field survey currently undertaken for salinity monitoring in the coastal areas of the Greater Everglades.