Triple Collocation Analysis for Two Error-Correlated Datasets: Application to L-Band Brightness Temperatures over Land
Open Access
- 16 October 2020
- journal article
- research article
- Published by MDPI AG in Remote Sensing
- Vol. 12 (20), 3381
- https://doi.org/10.3390/rs12203381
Abstract
The error characterization of satellite observations is crucial for blending observations from multiple platforms into a unique dataset and for assimilating them into numerical weather prediction models. In the last years, the triple collocation (TC) technique has been widely used to assess the quality of many geophysical variables acquired with different instruments and at different scales. This paper presents a new formulation of the triple collocation (Correlated Triple Collocation (CTC)) for the case of three datasets that resolve similar spatial scales, with two of them being error-correlated datasets. Besides, the formulation is designed to ensure fast convergence of the error estimators. This approach is of special interest in cases such that finding more than three datasets with uncorrelated errors is not possible and the amount of data is limited. First, a synthetic experiment has been carried out to assess the performance of CTC formulation. As an example of application, the error characterization of three collocated L-band brightness temperature (TB) measurements over land has been performed. Two of the datasets come from ESA (European Space Agency) SMOS (Soil Moisture and Ocean Salinity) mission: one is the reconstructed TB from the operational L1B v620 product, and the other is the reconstructed TB from the operational L1B v620 product resulting from application of an RFI (Radio Frequency Interference) mitigation technique, the nodal sampling (NS). The third is an independent dataset, the TB acquired by a NASA (National Aeronautics and Space Administration) SMAP (Soil Moisture Active Passive) radiometer. Our analysis shows that the application of NS leads to TB error reduction with respect to the current version of SMOS TB in 80% of the points in the global map, with an average reduction of approximately 1 K over RFI-free regions and approximately 1.45 K over strongly RFI-contaminated areas.Funding Information
- European Space Agency (SMOS Expert Support Laboratories Level 1 (SMOS-P7-DME-COM-CCN05-E-R) and the CCI+ Sea Surface Salinity project)
This publication has 31 references indexed in Scilit:
- Validation practices for satellite soil moisture retrievals: What are (the) errors?Remote Sensing of Environment, 2020
- Error Characterization of Sea Surface Salinity Products Using Triple Collocation AnalysisIEEE Transactions on Geoscience and Remote Sensing, 2018
- Error Characterization of Soil Moisture Satellite Products: Retrieving Error Cross-Correlation Through Extended Quadruple CollocationIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017
- On mesoscale analysis and ASCAT ambiguity removalQuarterly Journal of the Royal Meteorological Society, 2016
- Estimating error cross‐correlations in soil moisture data sets using extended collocation analysisJournal of Geophysical Research: Atmospheres, 2016
- Nodal Sampling: A New Image Reconstruction Algorithm for SMOSIEEE Transactions on Geoscience and Remote Sensing, 2015
- Quadruple Collocation Analysis for Soil Moisture Product AssessmentIEEE Geoscience and Remote Sensing Letters, 2015
- Assessment of Satellite-Derived Sea Surface Salinity in the Indian OceanIEEE Geoscience and Remote Sensing Letters, 2012
- Error characterisation of global active and passive microwave soil moisture datasetsHydrology and Earth System Sciences, 2010
- Toward the true near‐surface wind speed: Error modeling and calibration using triple collocationJournal of Geophysical Research: Oceans, 1998