Estimation of soil moisture dynamics using a recurrent dynamic learning neural network

Author

Tzeng, Y.C. ; Fan, K.T. ; Lin, C.Y. ; Lee, Y.J. ; Chen, K.S.

Author_Institution

Dept. of Electron. Eng., Nat. United Univ., Maioli, Taiwan

fYear

2012

fDate

22-27 July 2012

Firstpage

1251

Lastpage

1253

Abstract

Knowing the temporal features of soil moisture dynamics is essential for proper water resource management, fertilization management, and crop production. This paper proposes a recurrent dynamic learning neural network (RDLNN) to estimate soil moisture evolution by rainfall forcing. Long-term measurements of rainfall and soil moisture content were gathered. Soil moisture contents estimated from daily and/or hourly precipitation by RDLNN, were compared with ground measurements. Experimental results suggested that RDLNN is a promising tool for estimating soil moisture from hourly precipitation.

Keywords

agriculture; geophysics computing; hydrological techniques; learning (artificial intelligence); moisture; neural nets; rain; remote sensing; soil; water resources; RDLNN; crop production; fertilization management; long term rainfall measurements; long term soil moisture content measurements; rainfall forcing; recurrent dynamic learning neural network; soil moisture dynamics estimation; soil moisture dynamics temporal features; soil moisture evolution; water resource management; Land surface temperature; MODIS; Moisture measurement; Neural networks; Soil measurements; Soil moisture; Water resources; recurrent dynamic learning neural network; soil moisture;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International

Conference_Location

Munich

ISSN

2153-6996

Print_ISBN

978-1-4673-1160-1

Electronic_ISBN

2153-6996

Type

conf

DOI

10.1109/IGARSS.2012.6351314

Filename

6351314