TY - GEN
T1 - Soil moisture estimation using synergy of optical, SAR, and topographic data with Gaussian Process Regression
AU - Stamenkovic, J.
AU - Notarnicola, C.
AU - Spindler, N.
AU - Cuozzo, G.
AU - Bertoldi, G.
AU - Della Chiesa, S.
AU - Niedrist, G.
AU - Greifeneder, F.
AU - Tuia, D.
AU - Borgeaud, M.
AU - Thiran, J.Ph.
PY - 2014/10/21
Y1 - 2014/10/21
N2 - In this work we address the synergy of optical, SAR (Synthetic Aperture Radar) and topographic data in soil moisture retrieval over an Alpine area. As estimation technique, we consider Gaussian Process Regression (GPR). The test area is located in South Tyrol, Italy where the main land types are meadows and pastures. Time series of ASAR Wide Swath - SAR, optical, topographic and ancillary data (meteorological information and snow cover maps) acquired repetitively in 2010 were examined. Regarding optical data, we used both, daily MODIS reflectances, and daily NDVI, interpolated from the 16-day MODIS composite. Slope, elevation and aspect were extracted from a 2.5 m DEM (Digital Elevation Model) and resampled to 10 m. Daily soil moisture measurements were collected in the three fixed stations (two located in meadows and one located in pasture). The snow maps were used to mask the points covered by snow. The best performance was obtained by adding MODIS band 6 at 1640 nm to SAR and DEM features. The corresponding coefficient of determination, R2, was equal to 0.848, and the root mean square error, RMSE, to 5.4 % Vol. Compared to the case when no optical data were considered, there was an increase of ca. 0.05 in R2 and a decrease in RMSE of ca. 0.7 % Vol. This work showed that the joint use of NDVI or water absorption reflectance with SAR and topographic data can improve the estimation of soil moisture in specific Alpine area and that GPR is an effective method for estimation.
AB - In this work we address the synergy of optical, SAR (Synthetic Aperture Radar) and topographic data in soil moisture retrieval over an Alpine area. As estimation technique, we consider Gaussian Process Regression (GPR). The test area is located in South Tyrol, Italy where the main land types are meadows and pastures. Time series of ASAR Wide Swath - SAR, optical, topographic and ancillary data (meteorological information and snow cover maps) acquired repetitively in 2010 were examined. Regarding optical data, we used both, daily MODIS reflectances, and daily NDVI, interpolated from the 16-day MODIS composite. Slope, elevation and aspect were extracted from a 2.5 m DEM (Digital Elevation Model) and resampled to 10 m. Daily soil moisture measurements were collected in the three fixed stations (two located in meadows and one located in pasture). The snow maps were used to mask the points covered by snow. The best performance was obtained by adding MODIS band 6 at 1640 nm to SAR and DEM features. The corresponding coefficient of determination, R2, was equal to 0.848, and the root mean square error, RMSE, to 5.4 % Vol. Compared to the case when no optical data were considered, there was an increase of ca. 0.05 in R2 and a decrease in RMSE of ca. 0.7 % Vol. This work showed that the joint use of NDVI or water absorption reflectance with SAR and topographic data can improve the estimation of soil moisture in specific Alpine area and that GPR is an effective method for estimation.
KW - Gaussian Proccess Regression
KW - MODIS reactance
KW - Soil moisture retrieval
KW - Synthetic Aperture Radar (SAR)
KW - Topographic features
U2 - 10.1117/12.2072828
DO - 10.1117/12.2072828
M3 - Conference paper
AN - SCOPUS:84922664593
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - SAR Image Analysis, Modeling, and Techniques XIV
A2 - Paloscia, Simonetta
A2 - Pierdicca, Nazzareno
A2 - Notarnicola, Claudia
PB - SPIE
T2 - SAR Image Analysis, Modeling, and Techniques XIV
Y2 - 24 September 2014 through 25 September 2014
ER -