Paper Info
Title
First Application Of Regression Analysis To Retrieve Soil Moisture From Smap Brightness Temperature Observations Consistent With Smos
Abstract
In this study, we used a multilinear regression approach to retrieve surface soil moisture from NASA's Soil Moisture Active Passive (SMAP) satellite data to create a global dataset of surface soil moisture which is consistent with ESA's Soil Moisture and Ocean Salinity (SMOS) satellite retrieved surface soil moisture. This was achieved by calibrating coefficients of the regression model using SMOS soil moisture and horizontal and vertical brightness temperatures (TB), over the 2013 2014 period. Next, this model was applied to recent SMAP TB data from 31/03/201508/ 09/2015. The retrieved surface soil moisture from SMAP (referred here to as SMAP-reg) was compared to the operational SMAP L3 surface soil moisture retrieved using the single channel algorithm. Both exhibit comparable temporal dynamics with a good agreement of correlation (correlation coefficient R mostly > 0.8) between the SMAP-reg and the operational SMAP L3 surface soil moisture products.
Year
DOI
Venue
2016
10.1109/IGARSS.2016.7729417
2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS)
Keywords
Field
DocType
Soil moisture, statistical regression, SMOS, SMAP
Correlation coefficient,Moisture,Satellite,Brightness temperature,Computer science,Remote sensing,Salinity,Water content,Brightness,Calibration
Conference
ISSN
Citations 
PageRank 
2153-6996
0
0.34
References 
Authors
5
11
Name
Order
Citations
PageRank
A. Al-Yaari1298.23
Jean-Pierre Wigneron272077.00
Yann Kerr313630.53
Rodriguez-Fernandez, N.4238.09
Peggy O'Neill5102.58
Thomas J. Jackson61368247.01
G. De Lannoy751.82
Ahmad Al Bitar826126.44
Arnaud Mialon926626.28
Philippe Richaume1026930.37
Simon H. Yueh11686146.14