Soil Moisture Inversion Using AMSR-E Remote Sensing Data: An Artificial Neural Network Approach

Xing Mei Xie; Jing Wen Xu; Jun Fang Zhao; Shuang Liu; Peng Wang

doi:10.4028/www.scientific.net/AMM.501-504.2073

Paper Titles

Analysis of the Characteristics of Discharged Sediment in Yangtze River Estuary
p.2049

Study on Hydraulic Fracturing Affected by Natural Fractures
p.2056

Cnoidal Wave-Induced Seepage Forces on Large Porous Vertical Circular Cylinder Resting on Porous Elastic Seabed
p.2060

The Dynamic Response of the Platform Mooring Conditions
p.2067

Soil Moisture Inversion Using AMSR-E Remote Sensing Data: An Artificial Neural Network Approach
p.2073

The Application and Improvement of MESIS in Chongqing
p.2077

The Effect of Suction on the Swirling Flow of Non-Newtonian Fluid
p.2081

Study on the New Determined Method of Profile Inversion of Polymer Flooding
p.2085

Nitrate Removal of Fluidized-Bed Biofilm Reactors Using Starch / Polyvinyl Alcohol Blends as Carbon Source and Carrier Simultaneously at Low Temperature
p.2089

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 501-504Soil Moisture Inversion Using AMSR-E Remote...

Soil Moisture Inversion Using AMSR-E Remote Sensing Data: An Artificial Neural Network Approach

Abstract:

In this work artificial neural network with a back-propagation learning algorithm (BPNN) is employed to solve soil moisture retrieval for Sichuan Middle Hilly Area in China. Eighteen kinds of BPNN models have been developed using AMSR-E observations to retrieve soil moisture. The results show that the 18.7GHz band has some positive effect on improving soil moisture estimation accuracy while the 36.5GHz may interfere with deriving soil moisture, and vertical brightness temperature has a closer relationship with observed near-surface soil moisture than horizontal TB. The BPNN model driven by vertical and horizontal TB dataset at 6.9GHz and 10.7GHz frequency has the best performance of all the BPNN models withr value of 0.4968 and RMSE 10.2976%. Generally, the BPNN model is more suitable for soil moisture estimation than NASA product for the study area and can provide significant soil moisture information due to its ability of capturing non-linear and complex relationship.

You might also be interested in these eBooks

Advances in Civil and Structural Engineering III

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 501-504)

Pages:

2073-2076

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.501-504.2073

Citation:

Cite this paper

Online since:

January 2014

Authors:

Xing Mei Xie, Jing Wen Xu*, Jun Fang Zhao, Shuang Liu, Peng Wang

Keywords:

AMSR-E, Artificial Neural Network Approach, Soil Moisture Retrieval

Export:

RIS, BibTeX

Price:

Permissions:

Request Permissions

* - Corresponding Author

References

[1] S. Ahmad, A. Kalra, and H. Stephen, Estimating soil moisture using remote sensing data: A machine learning approach, Advances in Water Resources, vol. 33 (2010), pp.69-80.

DOI: 10.1016/j.advwatres.2009.10.008

Google Scholar

[2] E. G. Njoku, T. J. Jackson, V. Lakshmi, et al., Soil moisture retrieval from AMSR-E, IEEE Trans Geosci Remote Sens, vol. 41 (2003), pp.215-229.

DOI: 10.1109/tgrs.2002.808243

Google Scholar

[3] C. S. Draper, J. P. Walker, P. J. Steinle, et al., An evaluation of AMSR-E derived soil moisture over Australia, Remote Sensing Of Environment, vol. 113 Apr 15 (2009), pp.703-710.

DOI: 10.1016/j.rse.2008.11.011

Google Scholar

[4] J. P. Wigneron, J. C. Calvet, T. Pellarin, et al., Retrieving near-surface soil moisture from microwave radiometric observations: Current status and future plans, Remote Sensing Of Environment, vol. 85 (2003), pp.489-506.

DOI: 10.1016/s0034-4257(03)00051-8

Google Scholar

[5] X. -z. Chen, S. -s. Chen, R. -f. Zhong, et al., A semi-empirical inversion model for assessing surface soil moisture using AMSR-E brightness temperatures, Journal of Hydrology, vol. 456–457 (2012), pp.1-11.

DOI: 10.1016/j.jhydrol.2012.05.022

Google Scholar

[6] A. Pandey, S. K. Jha, J. K. Srivastava, et al., Artificial neural network for the estimation of soil moisture and surface roughness, Russian Agricultural Sciences, vol. 36 (2010), pp.428-432.

DOI: 10.3103/s106836741006011x

Google Scholar

[7] X. -q. H. and L. -h. L., Preliminary Study on Modernization and Cultural Tourism Development of Mountainous Areas: the Case of the Hilly Central Sichuan Basin, Journal of Mountain Science, vol. 26 (2008), pp.244-252.

Google Scholar

[8] A. P. Markopoulos, D. E. Manolakos, and N. M. Vaxevanidis, Artificial neural network models for the prediction of surface roughness in electrical discharge machining, Journal Of Intelligent Manufacturing, vol. 19 (2008), pp.283-292.

DOI: 10.1007/s10845-008-0081-9

Google Scholar

[9] J. XU, X. ZHU, W. ZHANG, et al., Daily streamflow forecasting by artificial neural network in a large-scale basin, in Proc. - IEEE Youth Conf. Inf., Comput. Telecommun., YC-ICT, 2009, pp.487-490.

DOI: 10.1109/ycict.2009.5382453

Google Scholar