Next Article in Journal
Combining Satellite Remote Sensing Data with the FAO-56 Dual Approach for Water Use Mapping In Irrigated Wheat Fields of a Semi-Arid Region
Next Article in Special Issue
Comparing Accuracy of Airborne Laser Scanning and TerraSAR-X Radar Images in the Estimation of Plot-Level Forest Variables
Previous Article in Journal
Estimating Daily Land Surface Temperatures in Mountainous Environments by Reconstructed MODIS LST Data
Previous Article in Special Issue
Use of Soil Moisture Variability in Artificial Neural Network Retrieval of Soil Moisture
Remote Sens. 2010, 2(1), 352-374; doi:10.3390/rs2010352
Article

Analysis of a Least-Squares Soil Moisture Retrieval Algorithm from L-band Passive Observations

* ,
,
,
 and
Received: 25 November 2009; in revised form: 23 December 2009 / Accepted: 11 January 2010 / Published: 20 January 2010
(This article belongs to the Special Issue Microwave Remote Sensing)
Download PDF [306 KB, uploaded 19 June 2014]
Abstract: The Soil Moisture and Ocean Salinity (SMOS) mission of the European Space Agency (ESA), launched on November 2009, is an unprecedented initiative to globally monitor surface soil moisture using a novel 2-D L-band interferometric radiometer concept. Airborne campaigns and ground-based field experiments have proven that radiometers operating at L-band are highly sensitive to soil moisture, due to the large contrast between the dielectric constant of soil minerals and water. Still, soil moisture inversion from passive microwave observations is complex, since the microwave emission from soils depends strongly on its moisture content but also on other surface characteristics such as soil type, soil roughness, surface temperature and vegetation cover, and their contributions must be carefully de-coupled in the retrieval process. In the present study, different soil moisture retrieval configurations are examined, depending on whether prior information is used in the inversion process or not. Retrievals are formulated in terms of vertical (Tvv) and horizontal (Thh) polarizations separately and using the first Stokes parameter (TI ), over six main surface conditions combining dry, moist and wet soils with bare and vegetation-covered surfaces. A sensitivity analysis illustrates the influence that the geophysical variables dominating the Earth’s emission at L-band have on the precision of the retrievals, for each configuration. It shows that, if adequate constraints on the ancillary data are added, the algorithm should converge to more accurate estimations. SMOS-like brightness temperatures are also generated by the SMOS End-to-end Performance Simulator (SEPS) to assess the retrieval errors produced by the different cost function configurations. Better soil moisture retrievals are obtained when the inversion is constrained with prior information, in line with the sensitivity study, and more robust estimates are obtained using TI than using Tvv and Thh. This paper analyzes key issues to devise an optimal soil moisture inversion algorithm for SMOS and results can be readily transferred to the upcoming SMOS data to produce the much needed global maps of the Earth’s surface soil moisture.
Keywords: soil moisture; microwave radiometry; retrieval soil moisture; microwave radiometry; retrieval
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Export to BibTeX |
EndNote


MDPI and ACS Style

Piles, M.; Vall-llossera, M.; Camps, A.; Talone, M.; Monerris, A. Analysis of a Least-Squares Soil Moisture Retrieval Algorithm from L-band Passive Observations. Remote Sens. 2010, 2, 352-374.

AMA Style

Piles M, Vall-llossera M, Camps A, Talone M, Monerris A. Analysis of a Least-Squares Soil Moisture Retrieval Algorithm from L-band Passive Observations. Remote Sensing. 2010; 2(1):352-374.

Chicago/Turabian Style

Piles, María; Vall-llossera, Mercè; Camps, Adriano; Talone, Marco; Monerris, Alessandra. 2010. "Analysis of a Least-Squares Soil Moisture Retrieval Algorithm from L-band Passive Observations." Remote Sens. 2, no. 1: 352-374.


Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert