Time-variant error characterization of SMAP and ASCAT soil moisture using Triple Collocation Analysis

Abstract The error characterization of soil moisture products, for example, obtained from microwave remote sensing data, is a key requirement for using these products in applications like numerical weather prediction. The error variance and root-mean-square error are among the most popular metrics: they can be estimated consistently for three datasets using triple collocation (TC) without assuming any dataset to be free of errors. This technique can account for additive and multiplicative biases; that is, it assumes that the three products are linearly related. However, its susceptibility to nonlinear relations (e.g., due to sensor saturation and scale mismatch) has not been addressed. Here, a simulation study investigates the impact of quadratic relations on the TC error estimates [also when the products are first rescaled using the nonlinear cumulative distribution function (CDF) matching technique] and on those by two novel methods. These methods—based on error-in-variables regression and probabilistic factor analysis—extend standard TC by also accounting for nonlinear relations using quadratic polynomials. The relative differences between the error estimates of the ASCAT remotely sensed product by the quadratic and the linear methods are predominantly smaller than 10% in a case study based on remotely sensed, reanalysis, and in situ measured soil moisture over the contiguous United States. Exceptions with larger discrepancies indicate that nonlinear relations can pose a challenge to traditional TC analyses, as the simulations show they can introduce biases of either sign. In such cases, the use of nonlinear methods may complement traditional approaches for the error characterization of soil moisture products.

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Error Characterization of Soil Moisture Satellite Products: Retrieving Error Cross-Correlation Through Extended Quadruple Collocation

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2017.2714025 ◽

2017 ◽

Vol 10 (10) ◽

pp. 4522-4530 ◽

Cited By ~ 2

Author(s):

Nazzareno Pierdicca ◽

Fabio Fascetti ◽

Luca Pulvirenti ◽

Raffaele Crapolicchio

Keyword(s):

Soil Moisture ◽

Cross Correlation ◽

Error Characterization

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Error characterization of microwave satellite soil moisture data sets using Fourier analysis

Piantadosi, J., Anderssen, R.S. and Boland J. (eds) MODSIM2013, 20th International Congress on Modelling and Simulation ◽

10.36334/modsim.2013.l19.su ◽

2013 ◽

Keyword(s):

Soil Moisture ◽

Fourier Analysis ◽

Data Sets ◽

Soil Moisture Data ◽

Error Characterization

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Triple Collocation Analysis for Two Error-Correlated Datasets: Application to L-Band Brightness Temperatures over Land

Remote Sensing ◽

10.3390/rs12203381 ◽

2020 ◽

Vol 12 (20) ◽

pp. 3381

Author(s):

Verónica González-Gambau ◽

Antonio Turiel ◽

Cristina González-Haro ◽

Justino Martínez ◽

Estrella Olmedo ◽

...

Keyword(s):

Soil Moisture ◽

Interference Mitigation ◽

Prediction Models ◽

Spatial Scales ◽

Weather Prediction ◽

European Space Agency ◽

Error Characterization ◽

L Band ◽

New Formulation

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.

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Analytical framework for the characterization of the space-time variability of soil moisture

Advances in Water Resources ◽

10.1016/0309-1708(94)90022-1 ◽

1994 ◽

Vol 17 (1-2) ◽

pp. 35-45 ◽

Cited By ~ 78

Author(s):

Dara Entekhabi ◽

Ignacio Rodriguez-Iturbe

Keyword(s):

Soil Moisture ◽

Analytical Framework ◽

Space Time ◽

Time Variability

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Error Characterization of Sea Surface Salinity Products Using Triple Collocation Analysis

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2018.2810442 ◽

2018 ◽

Vol 56 (9) ◽

pp. 5160-5168 ◽

Cited By ~ 1

Author(s):

Nina Hoareau ◽

Marcos Portabella ◽

Wenming Lin ◽

Joaquim Ballabrera-Poy ◽

Antonio Turiel

Keyword(s):

Sea Surface ◽

Sea Surface Salinity ◽

Surface Salinity ◽

Error Characterization

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Combining Sentinel-1 and ALOS-2 observations for soil moisture retrieval

10.5194/egusphere-egu21-8660 ◽

2021 ◽

Author(s):

Anna Balenzano ◽

Giuseppe Satalino ◽

Francesco Lovergine ◽

Davide Palmisano ◽

Francesco Mattia ◽

...

Keyword(s):

Time Series ◽

Soil Moisture ◽

Temporal Resolution ◽

Surface Soil ◽

Earth Science ◽

Observation System ◽

Final Report ◽

Surface Soil Moisture ◽

Error Characterization ◽

L Band

One of the limitations of presently available Synthetic Aperture Radar (SAR) surface soil moisture (SSM) products is their moderated temporal resolution (e.g., 3-4 days) that is non optimal for several applications, as most user requirements point to a temporal resolution of 1-2 days or less. A possible path to tackle this issue is to coordinate multi-mission SAR acquisitions with a view to the future Copernicus Sentinel-1 (C&D and Next Generation) and L-band Radar Observation System for Europe (ROSE-L).In this respect, the recent agreement between the Japanese (JAXA) and European (ESA) Space Agencies on the use of SAR Satellites in Earth Science and Applications provides a framework to develop and validate multi-frequency and multi-platform SAR SSM products. In 2019 and 2020, to support insights on the interoperability between C- and L-band SAR observations for SSM retrieval, Sentinel-1 and ALOS-2 systematic acquisitions over the TERENO (Terrestrial Environmental Observatories) Selhausen (Germany) and Apulian Tavoliere (Italy) cal/val sites were gathered. Both sites are well documented and equipped with hydrologic networks.The objective of this study is to investigate the integration of multi-frequency SAR measurements for a consistent and harmonized SSM retrieval throughout the error characterization of a combined C- and L-band SSM product. To this scope, time series of Sentinel-1 IW and ALOS-2 FBD data acquired over the two sites will be analysed. The short time change detection (STCD) algorithm, developed, implemented and recently assessed on Sentinel-1 data [e.g., Balenzano et al., 2020; Mattia et al., 2020], will be tailored to the ALOS-2 data. Then, the time series of SAR SSM maps from each SAR system will be derived separately and aggregated in an interleaved SSM product. Furthermore, it will be compared against in situ SSM data systematically acquired by the ground stations deployed at both sites. The study will assess the interleaved SSM product and evaluate the homogeneous quality of C- and L-band SAR SSM maps.&#160;&#160;ReferencesBalenzano. A., et al., &#8220;Sentinel-1 soil moisture at 1km resolution: a validation study&#8221;, submitted to Remote Sensing of Environment (2020).Mattia, F., A. Balenzano, G. Satalino, F. Lovergine, A. Loew, et al., &#8220;ESA SEOM Land project on Exploitation of Sentinel-1 for Surface Soil Moisture Retrieval at High Resolution,&#8221; final report, contract number 4000118762/16/I-NB, 2020.

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