DOI: 10.1016/j.jag.2017.10.010
Scopus记录号: 2-s2.0-85036566043
论文题名: On the synergy of SMAP, AMSR2 AND SENTINEL-1 for retrieving soil moisture
作者: Santi E ; , Paloscia S ; , Pettinato S ; , Brocca L ; , Ciabatta L ; , Entekhabi D
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2018
卷: 65 起始页码: 114
结束页码: 123
语种: 英语
英文关键词: Electromagnetic models
; Neural networks
; Sentinel-1
; SMAP
; Soil moisture
Scopus关键词: algorithm
; artificial neural network
; image resolution
; microwave radiometer
; moisture content
; radiative transfer
; satellite data
; satellite sensor
; Sentinel
; soil moisture
; synthetic aperture radar
英文摘要: An algorithm for retrieving soil moisture content (SMC) from synergic use of both active and passive microwave acquisitions is presented. The algorithm takes advantage of the integration of microwave data from SMAP, Sentinel-1 and AMSR2 for overcoming the SMAP radar failure and obtaining a SMC product at enhanced resolution (0.1° × 0.1°) and improved accuracy with respect to the original SMAP radiometric SMC product. A disaggregation technique based on the Smoothing filter based intensity modulation (SFIM) allows combining the radiometric and SAR data. Disaggregated microwave data are used as inputs of an Artificial Neural Networks (ANN) based algorithm, which is able to exploit the synergy between active and passive acquisitions. The algorithm is defined, trained and tested using the SMEX02 experimental dataset and data simulated by forward electromagnetic models based on the Radiative Transfer Theory. Then the algorithm is adapted to satellite data and tested using one year of SMAP, AMSR2 and Sentinel-1 co-located data on a flat agricultural area located in the Po Valley, in northern Italy. Spatially distributed SMC values at 0.1° × 0.1° resolution generated by the Soil Water Balance Model (SWBM) are considered as reference for this purpose. The synergy of SMAP, Sentinel-1 and AMSR2 allowed increasing the correlation between estimated and reference SMC from R ≅ 0.68 of the SMAP based retrieval up to R ≅ 0.86 of the combination SMAP + Sentinel–1 + AMSR2. The corresponding Root Mean Square Error (RMSE) decreased from RMSE ≅ 0.04 m3/m3 to RMSE ≅ 0.024 m3/m3. © 2017 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79877
Appears in Collections: 气候变化事实与影响
There are no files associated with this item.
作者单位: Institute of Applied Physics - National Research Council, Florence, Italy; Research Institute for Geo-Hydrological Protection - National Research Council, Perugia, Italy; Parsons Laboratory, Massachusetts Institute of Technology, Cambridge, United States
Recommended Citation:
Santi E,, Paloscia S,, Pettinato S,et al. On the synergy of SMAP, AMSR2 AND SENTINEL-1 for retrieving soil moisture[J]. International Journal of Applied Earth Observation and Geoinformation,2018-01-01,65