Soil moisture estimation using classification and regression trees and neural networks
نویسندگان
چکیده
In this paper, a soil moisture estimation model was developed to calculate the nationwide soil moisture fields using on site soil moisture observation, precipitation, surface temperature, MODIS NDVI and a data mining technique, Classification And Regression Tree (CART) algorithm and neural networks. The model was applied to the Yong-dam dam basin since the soil moisture observations of the Yong-dam dam basin were reliable. Soil moisture observations of 4 sites were used for the model calibration and that of a site was used for the model validation. Results showed that the soil moisture estimation using a data mining technique and ancillary data allow us to get reasonable soil moisture fields which are suitable for the hydrologic model application. Key-Words: Soil moisture, Neural Networks, CART, MODIS, Precipitation, NDVI, Surface Temperature
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