This article applies a nonlinear machine learning method, support vector regression (SVR), to construct empirical models retrieving water quality variables using remote sensing images. Based on in situ measurements and high-resolution multispectral SPOT-5 (Satellite Pour l'Observation de la Terre) data, a fittest nonlinear function between input and output was obtained from this method, and SVR model parameters were selected automatically using a genetic algorithm (GA). The relationship between water quality variables – permanganate index (CODMₙ), ammonia-nitrogen (NH₃–N) and chemical oxygen demand (COD) – and spectral components of SPOT-5 data for the Weihe River in China was constructed by the proposed method. Spatial distribution maps for the three water quality variables were also developed. The results show that SVR can implement any nonlinear mapping, and produce better predictions than the traditional statistical multiple regression method, especially when samples are limited. With further testing, SVR can also be extended to hyperspectral remote sensing applications in the management of land and water resources.
Authors and Publishers
Taylor & Francis Group publishes books for all levels of academic study and professional development, across a wide range of subjects and disciplines.
Taylor & Francis Group publishes quality peer-reviewed journals under the Routledge and Taylor & Francis imprints. The newest part of the group, Cogent OA, offers a purely open access program.
Note from Land Portal:
Taylor & Francis Online contains many publications related to land issues, though mostly at the charge of a fee.