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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. Source


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. Source


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. Source


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. Source


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. Source

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