期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2010
卷号：XXXVIII - Part 7B
页码：612-617
出版社：Copernicus Publications
摘要：This research presents an alternative method to extrapolation land Surface Temperature (ST) through artificial neural network, using positional variables (UTM coordinates and altitude), temperature and air relative humidity. The study region was the Rio dos Sinos Hydrographic Basin (BHRS), in Rio Grande do Sul state, Brazil. For training the neural network was used a thermal image from NOAA satellite, with pixel size of 1X1 km, with known ST information referring to 12/06/2003. After training many network sets were done and one of them with the best performance and composed by a single intermediate layer (with 4 neurons and logistic sigmoid activation function) was selected. The training network was tested inside the BHRS where were collected 60 points of ST values supported by a portable laser sensor on date 3/18/2008. The average error provided by this model for ST measurement was 2.2oC and through executed statistical tests was possible to verify that not exist variation between average ST values accepted as true and the values provided by the neural model with a significance level of 5%
