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Barcelona, Spain
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Sanchez-Ruiz S.,University of Salamanca | Piles M.,Polytechnic University of Catalonia | Piles M.,Barcelona Expert Center | Sanchez N.,University of Salamanca | And 5 more authors.
Journal of Hydrology | Year: 2014

Sensors in the range of visible and near-shortwave-thermal infrared regions can be used in combination with passive microwave observations to provide soil moisture maps at much higher spatial resolution than the original resolution of current radiometers. To do so, a new downscaling algorithm ultimately based on the land surface temperature (LST) - Normalized Difference Vegetation Index (NDVI) - Brightness Temperature (TB ) relationship is used, in which shortwave infrared indices are used as vegetation descriptors, instead of the more common near infrared ones. The theoretical basis of those indices, calculated as the normalized ratio of the 1240, 1640 and 2130 nm shortwave infrared (SWIR) bands and the 858 nm near infrared (NIR) band indicate that they are able to provide estimates of the vegetation water content. These so-called water indices extracted from MODIS products, have been used together with MODIS LST, and SMOS TB to improve the spatial resolution of ∼40 km SMOS soil moisture estimates. The aim was to retrieve soil moisture maps with the same accuracy as SMOS, but at the same resolution of the MODIS dataset, i.e., 500 m, which were then compared against in situ measurements from the REMEDHUS network in Spain. Results using two years of SMOS and MODIS data showed a similar performance for the four indices, with slightly better results when using the index derived from the first SWIR band. For the areal-average, a coefficient of correlation (R) of ∼0.61 and ∼0.72 for the morning and afternoon orbits, respectively, and a centered root mean square difference (cRMSD) of ∼0.04 m3 m-3 for both orbits was obtained. A twofold improvement of the current versions of this downscaling approach has been achieved by using more frequent and higher spatial resolution water indexes as vegetation descriptors: (1) the spatial resolution of the resulting soil moisture maps can be enhanced from ∼40 km up to 500 m, and (2) more accurate soil moisture maps (in terms of R and cRMSD) can be obtained, especially in periods of high vegetation activity. The results of this study support the use of high resolution LST and SWIR-based vegetation indices to disaggregate SMOS observations down to 500 m soil moisture maps, meeting the needs of fine-scale hydrological applications. © 2014 Elsevier B.V. All rights reserved.


Gourrion J.,CSIC - Institute of Marine Sciences | Gourrion J.,Barcelona Expert Center | Sabia R.,European Space Agency | Portabella M.,Barcelona Expert Center | And 4 more authors.
IEEE Geoscience and Remote Sensing Letters | Year: 2012

The Soil Moisture and Ocean Salinity (SMOS) mission was launched on November 2nd, 2009 aiming at providing sea surface salinity (SSS) estimates over the oceans with frequent temporal coverage. The detection and mitigation of residual instrumental systematic errors in the measured brightness temperatures are key steps prior to the SSS retrieval. For such purpose, the so-called ocean target transformation (OTT) technique is currently used in the SMOS operational SSS processor. In this paper, an assessment of the OTT is performed. It is found that, to compute a consistent and robust OTT, a large ensemble of measurements is required. Moreover, several effects are reported to significantly impact the OTT computation, namely, the apparent instrument (temporal) drift, forward model imperfections, auxiliary data (used by forward model) uncertainty and external error sources, such as galactic noise and Sun effects (among others). These effects have to be properly mitigated or filtered during the OTT computation, so as to successfully retrieve SSS from SMOS measurements. © 2012 IEEE.


McColl K.A.,Massachusetts Institute of Technology | Entekhabi D.,Massachusetts Institute of Technology | Piles M.,Polytechnic University of Catalonia | Piles M.,Barcelona Expert Center
IEEE Transactions on Geoscience and Remote Sensing | Year: 2014

Simple functions of radar backscatter coefficients have been proposed as indices of soil moisture and vegetation, such as the radar vegetation index, i.e., RVI, and the soil saturation index, i.e., ms. These indices are ratios of noisy and potentially miscalibrated radar measurements and are therefore particularly susceptible to estimation errors. In this study, we consider uncertainty in satellite estimates of RVI and ms arising from two radar error sources: noise and miscalibration. We derive expressions for the variance and bias in estimates of RVI and ms due to noise. We also derive expressions for the sensitivity of RVI and ms to calibration errors. We use one year (September 1, 2011 to August 31, 2012) of Aquarius scatterometer observations at three polarizations (σ HH, σVV, and σHV) to map predicted error estimates globally, using parameters relevant to the National Aeronautics and Space Administration Soil Moisture Active and Passive satellite mission. We find that RVI is particularly vulnerable to errors in the calibration offset term over lightly vegetated regions, resulting in overestimates of RVI in some arid regions. ms is most sensitive to calibration errors over regions where the dynamic range of the backscatter coefficient is small, including deserts and forests. Noise induces biases in both indices, but they are negligible in both cases; however, it also induces variance, which is large for highly vegetated regions (for RVI) and areas with low dynamic range in backscatter values (for ms). We find that, with appropriate temporal and spatial averaging, noise errors in both indices can be reduced to acceptable levels. Areas sensitive to calibration errors will require masking. © 1980-2012 IEEE.


McColl K.A.,Massachusetts Institute of Technology | Vogelzang J.,KNMI | Konings A.G.,Massachusetts Institute of Technology | Entekhabi D.,Massachusetts Institute of Technology | And 3 more authors.
Geophysical Research Letters | Year: 2014

Calibration and validation of geophysical measurement systems typically require knowledge of the "true" value of the target variable. However, the data considered to represent the "true" values often include their own measurement errors, biasing calibration, and validation results. Triple collocation (TC) can be used to estimate the root-mean-square-error (RMSE), using observations from three mutually independent, error-prone measurement systems. Here, we introduce Extended Triple Collocation (ETC): using exactly the same assumptions as TC, we derive an additional performance metric, the correlation coefficient of the measurement system with respect to the unknown target, ρt;Xi . We demonstrate that ρt 2 ;Xi is the scaled, unbiased signal-to-noise ratio and provides a complementary perspective compared to the RMSE. We apply it to three collocated wind data sets. Since ETC is as easy to implement as TC, requires no additional assumptions, and provides an extra performance metric, it may be of interest in a wide range of geophysical disciplines. © 2014. American Geophysical Union.


Piles M.,Polytechnic University of Catalonia | Piles M.,Barcelona Expert Center | Sanchez N.,University of Salamanca | Vall-Llossera M.,Polytechnic University of Catalonia | And 6 more authors.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | Year: 2014

The ESA's Soil Moisture and Ocean Salinity (SMOS) mission is the first satellite devoted to measure the Earth's surface soil moisture. It has a spatial resolution of ∼ 40 km and a 3-day revisit. In this paper, a downscaling algorithm is presented as a new ability to obtain multiresolution soil moisture estimates from SMOS using visible-to-infrared remotely sensed observations. This algorithm is applied to combine 2 years of SMOS and MODIS Terra/Aqua data over the Iberian Peninsula into fine-scale (1 km) soil moisture estimates. Disaggregated soil moisture maps are compared to 0-5 cm ground-based measurements from the REMEDHUS network. Three matching strategies are employed: 1) a comparison at 40 km spatial resolution is undertaken to ensure SMOS sensitivity is preserved in the downscaled maps; 2) the spatiotemporal correlation of downscaled maps is analyzed through comparison with point-scale observations; and 3) high-resolution maps and ground-based observations are aggregated per land-use to identify spatial patterns related with vegetation activity and soil type. Results show that the downscaling method improves the spatial representation of SMOS coarse soil moisture estimates while maintaining temporal correlation and root mean squared differences with ground-based measurements. The dynamic range of in situ soil moisture measurements is reproduced in the highresolution maps, including stations with different mean soil wetness conditions. Downscaled maps capture the soil moisture dynamics of general land uses, with the exception of irrigated crops. This evaluation study supports the use of this downscaling approach to enhance the spatial resolution of SMOS observations over semi-arid regions such as the Iberian Peninsula. © 2008-2012 IEEE.


Piles M.,Polytechnic University of Catalonia | Piles M.,Barcelona Expert Center | Piles M.,University of Melbourne | Camps A.,Polytechnic University of Catalonia | And 9 more authors.
IEEE Transactions on Geoscience and Remote Sensing | Year: 2011

A downscaling approach to improve the spatial resolution of Soil Moisture and Ocean Salinity (SMOS) soil moisture estimates with the use of higher resolution visible/infrared (VIS/IR) satellite data is presented. The algorithm is based on the so-called "universal triangle" concept that relates VIS/IR parameters, such as the Normalized Difference Vegetation Index (NDVI), and Land Surface Temperature (Ts), to the soil moisture status. It combines the accuracy of SMOS observations with the high spatial resolution of VIS/IR satellite data into accurate soil moisture estimates at high spatial resolution. In preparation for the SMOS launch, the algorithm was tested using observations of the UPC Airborne RadIomEter at L-band (ARIEL) over the Soil Moisture Measurement Network of the University of Salamanca (REMEDHUS) in Zamora (Spain), and LANDSAT imagery. Results showed fairly good agreement with ground-based soil moisture measurements and illustrated the strength of the link between VIS/IR satellite data and soil moisture status. Following the SMOS launch, a downscaling strategy for the estimation of soil moisture at high resolution from SMOS using MODIS VIS/IR data has been developed. The method has been applied to some of the first SMOS images acquired during the commissioning phase and is validated against in situ soil moisture data from the OZnet soil moisture monitoring network, in South-Eastern Australia. Results show that the soil moisture variability is effectively captured at 10 and 1 km spatial scales without a significant degradation of the root mean square error. © 2011 IEEE.


Wu L.,Polytechnic University of Catalonia | Wu L.,Barcelona Expert Center | Wu L.,Institute Destudis Espacials Of Catalonia Ieec | Torres F.,Polytechnic University of Catalonia | And 7 more authors.
IEEE Geoscience and Remote Sensing Letters | Year: 2013

This work has been conducted in the framework of several projects devoted to assess the performance of the Soil Moisture and Ocean Salinity (SMOS) mission full-pol measurement mode. Since its launch in November 2009, SMOS is producing dual-polarization brightness temperature synthesized images that are yielding a high scientific return. However, these images are affected by a non-negligible spatial amplitude error, the so-called spatial bias (SB), that degrades geophysical parameter retrieval. This effect is particularly detrimental in SMOS polarimetric images where spatial bias is masking the polarimetric physical signature to a large extend. This paper presents a method to mitigate SMOS spatial bias by taking into account the co-and cross-polar antenna patterns in the image reconstruction algorithm through the, so called, full-pol G-matrix (FPG). The method is validated by producing spatial bias maps out of the comparison between SMOS full-pol images and an accurate polarimetric brightness temperature model of the ocean. This model has been provided to SMOS ESLs (Expert Support Laboratories) by LOCEAN (Laboratoire d'Océanographie et du Climat, France) as a test bench to validate and improve SMOS Level 1 (L1) data. Finally, a radiometric performance summary table comparing spatial bias and radiometric sensitivity between this new FPG approach and SMOS current co-polar G-matrix approach (CPG) is provided. This paper presents the best quality SMOS polarimetric images, which may lead a breakthrough in the science returns of the mission. © 2013 IEEE.


Oliva R.,European Space Agency | Martin-Neira M.,European Space Agency | Corbella I.,Polytechnic University of Catalonia | Corbella I.,Barcelona Expert Center | And 6 more authors.
IEEE Transactions on Geoscience and Remote Sensing | Year: 2013

This paper summarizes the rationale for the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission routine calibration plan, including the analysis of the calibration parameter annual variability, and the performances and stability of SMOS images after one year of data. SMOS spends 1.68 $\%$ of the total observation time in calibration. The instrument performs well within expectations with regard to accuracy and radiometric sensitivity, although spatial ripples are present in SMOS images. Several mechanisms are currently used or under investigation to mitigate this problem. Also, a loss antenna model has recently been introduced to correct for physical temperature-induced effects. This antenna model successfully corrects observed orbital variations, but has difficulties in correcting brightness temperature long-term drifting, as assessed using relatively well-known targets other than the external calibration region-cold space. © 2012 IEEE.


Sa nchez-Ruiz S.,University of Salamanca | Piles M.,University of Barcelona | Piles M.,Barcelona Expert Center | Sa nchez N.,University of Salamanca | And 5 more authors.
Journal of Hydrology | Year: 2014

Sensors in the range of visible and near-shortwave-thermal infrared regions can be used in combination with passive microwave observations to provide soil moisture maps at much higher spatial resolution than the original resolution of current radiometers. To do so, a new downscaling algorithm ultimately based on the land surface temperature (LST) - Normalized Difference Vegetation Index (NDVI) - Brightness Temperature (TB) relationship is used, in which shortwave infrared indices are used as vegetation descriptors, instead of the more common near infrared ones. The theoretical basis of those indices, calculated as the normalized ratio of the 1240, 1640 and 2130nm shortwave infrared (SWIR) bands and the 858nm near infrared (NIR) band indicate that they are able to provide estimates of the vegetation water content. These so-called water indices extracted from MODIS products, have been used together with MODIS LST, and SMOS TB to improve the spatial resolution of ~40km SMOS soil moisture estimates. The aim was to retrieve soil moisture maps with the same accuracy as SMOS, but at the same resolution of the MODIS dataset, i.e., 500m, which were then compared against in situ measurements from the REMEDHUS network in Spain. Results using two years of SMOS and MODIS data showed a similar performance for the four indices, with slightly better results when using the index derived from the first SWIR band. For the areal-average, a coefficient of correlation (R) of ~0.61 and ~0.72 for the morning and afternoon orbits, respectively, and a centered root mean square difference (cRMSD) of ~0.04m3m-3 for both orbits was obtained. A twofold improvement of the current versions of this downscaling approach has been achieved by using more frequent and higher spatial resolution water indexes as vegetation descriptors: (1) the spatial resolution of the resulting soil moisture maps can be enhanced from ~40km up to 500m, and (2) more accurate soil moisture maps (in terms of R and cRMSD) can be obtained, especially in periods of high vegetation activity. The results of this study support the use of high resolution LST and SWIR-based vegetation indices to disaggregate SMOS observations down to 500m soil moisture maps, meeting the needs of fine-scale hydrological applications. © 2014 Elsevier B.V.


Mecklenburg S.,European Space Agency | Drusch M.,European Space Agency | Kerr Y.H.,CNRS Center for the Study of the Biosphere from Space | Font J.,CSIC - Institute of Marine Sciences | And 8 more authors.
IEEE Transactions on Geoscience and Remote Sensing | Year: 2012

The European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission was launched on the 2nd of November 2009. The first six months after launch, the so-called commissioning phase, were dedicated to test the functionalities of the spacecraft, the instrument, and the ground segment including the data processors. This phase was successfully completed in May 2010, and SMOS has since been in the routine operations phase and providing data products to the science community for over a year. The performance of the instrument has been within specifications. A parallel processing chain has been providing brightness temperatures in near-real time to operational centers, e.g., the European Centre for Medium-Range Weather Forecasts. Data quality has been within specifications; however, radio-frequency interference (RFI) has been detected over large parts of Europe, China, Southern Asia, and the Middle East. Detecting and flagging contaminated observations remains a challenge as well as contacting national authorities to localize and eliminate RFI sources emitting in the protected band. The generation of Level 2 soil moisture and ocean salinity data is an ongoing activity with continuously improved processors. This article will summarize the mission status after one year of operations and present selected first results. © 2012 IEEE.

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