Triple Collocation Analysis for Two Error-Correlated Datasets: Application to L-Band Brightness Temperatures over Land
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 qual...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/20/3381 |
| _version_ | 1797550812458647552 |
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| author | Verónica González-Gambau Antonio Turiel Cristina González-Haro Justino Martínez Estrella Olmedo Roger Oliva Manuel Martín-Neira |
| author_facet | Verónica González-Gambau Antonio Turiel Cristina González-Haro Justino Martínez Estrella Olmedo Roger Oliva Manuel Martín-Neira |
| author_sort | Verónica González-Gambau |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T15:34:48Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T15:34:48Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-a9febffd8faf408b91b0a0b33cda089f2023-11-20T17:18:57ZengMDPI AGRemote Sensing2072-42922020-10-011220338110.3390/rs12203381Triple Collocation Analysis for Two Error-Correlated Datasets: Application to L-Band Brightness Temperatures over LandVerónica González-Gambau0Antonio Turiel1Cristina González-Haro2Justino Martínez3Estrella Olmedo4Roger Oliva5Manuel Martín-Neira6Department of Physical Oceanography, Institute of Marine Sciences, CSIC and Barcelona Expert Center, Passeig Maritim de la Barceloneta, 37-49, 08003 Barcelona, SpainDepartment of Physical Oceanography, Institute of Marine Sciences, CSIC and Barcelona Expert Center, Passeig Maritim de la Barceloneta, 37-49, 08003 Barcelona, SpainDepartment of Physical Oceanography, Institute of Marine Sciences, CSIC and Barcelona Expert Center, Passeig Maritim de la Barceloneta, 37-49, 08003 Barcelona, SpainDepartment of Physical Oceanography, Institute of Marine Sciences, CSIC and Barcelona Expert Center, Passeig Maritim de la Barceloneta, 37-49, 08003 Barcelona, SpainDepartment of Physical Oceanography, Institute of Marine Sciences, CSIC and Barcelona Expert Center, Passeig Maritim de la Barceloneta, 37-49, 08003 Barcelona, SpainZenithal Blue Technologies S.L.U. for the European Space Agency, 08023 Barcelona, SpainEuropean Space Research and Technology Centre, European Space Agency, 2200 AG Noordwijk, The NetherlandsThe 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.https://www.mdpi.com/2072-4292/12/20/3381triple collocationerror characterizationerror cross-correlationL-band brightness temperaturesSMOS (Soil Moisture and Ocean Salinity)SMAP (Soil Moisture Active Passive) |
| spellingShingle | Verónica González-Gambau Antonio Turiel Cristina González-Haro Justino Martínez Estrella Olmedo Roger Oliva Manuel Martín-Neira Triple Collocation Analysis for Two Error-Correlated Datasets: Application to L-Band Brightness Temperatures over Land Remote Sensing triple collocation error characterization error cross-correlation L-band brightness temperatures SMOS (Soil Moisture and Ocean Salinity) SMAP (Soil Moisture Active Passive) |
| title | Triple Collocation Analysis for Two Error-Correlated Datasets: Application to L-Band Brightness Temperatures over Land |
| title_full | Triple Collocation Analysis for Two Error-Correlated Datasets: Application to L-Band Brightness Temperatures over Land |
| title_fullStr | Triple Collocation Analysis for Two Error-Correlated Datasets: Application to L-Band Brightness Temperatures over Land |
| title_full_unstemmed | Triple Collocation Analysis for Two Error-Correlated Datasets: Application to L-Band Brightness Temperatures over Land |
| title_short | Triple Collocation Analysis for Two Error-Correlated Datasets: Application to L-Band Brightness Temperatures over Land |
| title_sort | triple collocation analysis for two error correlated datasets application to l band brightness temperatures over land |
| topic | triple collocation error characterization error cross-correlation L-band brightness temperatures SMOS (Soil Moisture and Ocean Salinity) SMAP (Soil Moisture Active Passive) |
| url | https://www.mdpi.com/2072-4292/12/20/3381 |
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