Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Triple Collocation Analysis for Two Error-Correlated Datasets: Application to L-Band Brightness Temperatures over Land
Special issue Accuracy Assessment and Validation of Remotely Sensed Data and Products.-- 32 pages, 17 figures, appendix.-- This work is a contribution to CSIC Thematic Exploitation Platform TELEDETECT ; 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 ; This research was funded by the Spanish Ministry of Economy and Competitiveness, through the National R+D Plan under L-Band Project ESP2017-89463-C3-1-R and by previous grants from the European Space Agency through the contract of SMOS Expert Support Laboratories Level 1 (SMOS-P7-DME-COM-CCN05-E-R) and the CCI+ Sea Surface Salinity project ; Peer reviewed
Triple Collocation Analysis for Two Error-Correlated Datasets: Application to L-Band Brightness Temperatures over Land
Special issue Accuracy Assessment and Validation of Remotely Sensed Data and Products.-- 32 pages, 17 figures, appendix.-- This work is a contribution to CSIC Thematic Exploitation Platform TELEDETECT ; 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 ; This research was funded by the Spanish Ministry of Economy and Competitiveness, through the National R+D Plan under L-Band Project ESP2017-89463-C3-1-R and by previous grants from the European Space Agency through the contract of SMOS Expert Support Laboratories Level 1 (SMOS-P7-DME-COM-CCN05-E-R) and the CCI+ Sea Surface Salinity project ; Peer reviewed
Triple Collocation Analysis for Two Error-Correlated Datasets: Application to L-Band Brightness Temperatures over Land
González Gambau, Verónica (Autor:in) / Turiel, Antonio (Autor:in) / González-Haro, Cristina (Autor:in) / Martínez, Justino (Autor:in) / Olmedo, Estrella (Autor:in) / Oliva, R. (Autor:in) / Martín-Neira, Manuel (Autor:in) / Ministerio de Economía y Competitividad (España) / European Space Agency / Ministerio de Ciencia, Innovación y Universidades (España)
01.10.2020
2072-4292
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
DDC:
710
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