TY - JOUR
T1 - Retrieving soil moisture from simulated brightness temperatures by a neural network
AU - Liou, Yuei An
AU - Liu, Shou Fang
AU - Wang, Wen June
N1 - Funding Information:
Manuscript received September 30, 2000; revised April 20, 2001. This work was supported by National Science Council, R.O.C., Grant 89-2111-M-008-025-AP3. Y.-A. Liou is with the Center for Space and Remote Sensing Research, National Central University, Chung-Li 320, Taiwan, R.O.C. (e-mail: [email protected]). S.-F. Liu and W.-J. Wang are with the Department of Electrical Engineering, National Central University, Chung-Li 320, Taiwan, R.O.C. S.-F. Liu is with the Department of Industrial Design, Oriental Institute of Technology, Taipei 220, Taiwan, R.O.C. Publisher Item Identifier S 0196-2892(01)06677-3.
PY - 2001/9
Y1 - 2001/9
N2 - We present the retrievals of surface soil moisture (SM) from simulated brightness temperatures by a newly developed error propagation learning back propagation (EPLBP) neural network. The frequencies of interest include 6.9 and 10.7 GHz of the advanced microwave scanning radiometer (AMSR) and 1.4 GHz (L-band) of the soil moisture and ocean salinity (SMOS) sensor. The land surface process/radiobrightness (LSP/R) model is used to provide lime series of both SM and brightness temperatures at 6.9 and 10.7 GHz for AMSRs viewing angle of 55°, and at L-band for SMOS's multiple viewing angles of 0°, 10°, 20°, 30°, 40°, and 50° for prairie grassland with a column density of 3.7 km/m 2. These multiple frequencies and viewing angles allow us to design a variety of observation modes to examine their sensitivity to SM. For example, L-band brightness temperature at any single look angle is regarded as an L-band one-dimensional (1-D) observation mode. Meanwhile, it can be combined with either the observation at the other angles to become an L-band two-dimensional (2-D) or a multiple dimensional observation mode, or with the observation at 6.9 or 10.7 GHz to become a multiple frequency/dimensional observation mode. In this paper, it is shown that the sensitivity of radiobrightness at AMSR channels to SM is increased by incorporating L-band radiobrightness. In addition, the advantage of an L-band 2-D or a multiple dimensional observation mode over an L-band 1-D observation mode is demonstrated.
AB - We present the retrievals of surface soil moisture (SM) from simulated brightness temperatures by a newly developed error propagation learning back propagation (EPLBP) neural network. The frequencies of interest include 6.9 and 10.7 GHz of the advanced microwave scanning radiometer (AMSR) and 1.4 GHz (L-band) of the soil moisture and ocean salinity (SMOS) sensor. The land surface process/radiobrightness (LSP/R) model is used to provide lime series of both SM and brightness temperatures at 6.9 and 10.7 GHz for AMSRs viewing angle of 55°, and at L-band for SMOS's multiple viewing angles of 0°, 10°, 20°, 30°, 40°, and 50° for prairie grassland with a column density of 3.7 km/m 2. These multiple frequencies and viewing angles allow us to design a variety of observation modes to examine their sensitivity to SM. For example, L-band brightness temperature at any single look angle is regarded as an L-band one-dimensional (1-D) observation mode. Meanwhile, it can be combined with either the observation at the other angles to become an L-band two-dimensional (2-D) or a multiple dimensional observation mode, or with the observation at 6.9 or 10.7 GHz to become a multiple frequency/dimensional observation mode. In this paper, it is shown that the sensitivity of radiobrightness at AMSR channels to SM is increased by incorporating L-band radiobrightness. In addition, the advantage of an L-band 2-D or a multiple dimensional observation mode over an L-band 1-D observation mode is demonstrated.
KW - Brightness temperature
KW - Neural network
KW - Soil moisture
UR - http://www.scopus.com/inward/record.url?scp=0035446304&partnerID=8YFLogxK
U2 - 10.1109/36.942544
DO - 10.1109/36.942544
M3 - 期刊論文
AN - SCOPUS:0035446304
SN - 0196-2892
VL - 39
SP - 1662
EP - 1672
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
IS - 8
ER -