Soil Moisture Inversion Based on AMSR-E and MODIS Data Fusion: A Case Study of Huaihe River Basin

Abstract:

In this paper, the correlation between fused data and original data, the measured soil and the precipitation data over Huaihe river basin by exploring the inversion of soil moisture from the time and space based on the method of multi-source remote sensing data fusion has been studied. In order to fuse the AMSR-E data which is all-day and all-weather and can penetrate the earth surface to some extent, with the MODIS data that can reflect the surface condition and temperature characteristics, the method of wavelet fusion was carried out in MATLAB. The conclusions of this study are listed as follows: (1) the inversion result of the fused data based on AMSE-E and MODIS is much better than a single remote sensing data inversion; (2) the fused data based on AMSE-E and MODIS is sensitive to soil moisture change trend when the seasons alternated every year, especially in the spring, summer and autumn.

• Periodical: Applied Mechanics and Materials (Volumes 716-717)
• Pages: 1064-1067
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.716-717.1064
• Online since: December 2014
• Authors: Jing Wen Xu*, Yu Peng Wang, Jun Fang Zhao, Fei Yu Pu, Peng Wang
• Keywords: Data Fusion, Huaihe River Basin, Soil Moisture

References

[1] Waltz E, Llinas J. Multisensor data fusion[M]. Boston: Artech house, (1990).

[2] Njoku E G, Chan S K. Vegetation and surface roughness effects on AMSR-E land observations[J]. Remote Sensing of environment, 2006, 100(2): 190-199.

DOI: 10.1016/j.rse.2005.10.017

[3] Njoku E G, Jackson T J, Lakshmi V, et al. Soil moisture retrieval from AMSR-E[J]. Geoscience and Remote Sensing, IEEE Transactions on, 2003, 41(2): 215-229.

DOI: 10.1109/tgrs.2002.808243

[4] Njoku E G, Jackson T J, Lakshmi V, et al. Soil moisture retrieval from AMSR-E[J]. Geoscience and Remote Sensing, IEEE Transactions on, 2003, 41(2): 215-229.

DOI: 10.1109/tgrs.2002.808243

[5] Comiso J C, Cavalieri D J, Markus T. Sea ice concentration, ice temperature, and snow depth using AMSR-E data[J]. Geoscience and Remote Sensing, IEEE Transactions on, 2003, 41(2): 243-252.

DOI: 10.1109/tgrs.2002.808317

[6] Suits G., Malila W., and Weller T. Procedures for using signals from one sensor as substitutes for signals of another[J]. Remote Sensing of Environment, 2009, 25: 395-408.

DOI: 10.1016/0034-4257(88)90111-3

[7] Burt P.J. Adelson E.H. The Laplacian Pyramid as a Compaet Image Code[J]. IEEE Transactions on Communications; 2010, Vol. COMM-31(4): 532-540.

DOI: 10.1109/tcom.1983.1095851

[8] Clausi D. A. Improved texture recognition of SAR sea ice imagery by data fusion of MRF features with traditional methods[A]. In Proeeedings of IEEE Intemational Geoscience and Remote Sensing SymPosium[C], 2001, 3: 1170-1172.

DOI: 10.1109/igarss.2001.976781

[9] Uekie D. G. Synergism of SAR and visible/inftared data for forest type discriminationt[J]. Photogrammetric Engineering and Remote Sensing, 1990, 56: 1237-1246.