Triple Collocation Analysis of Soil Moisture From Metop-A ASCAT and SMOS Against JRA-55 and ERA-Interim

Miyaoka, Kazuki; Gruber, A.; Ticconi, Francesca; Hahn, Sebastian; Wagner, Wolfgang; Figa-Saldaña, Julia; Anderson, Craig

doi:10.1109/jstars.2016.2632306

Cited by 28 publications

(18 citation statements)

References 38 publications

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“…For soil moisture, many geophysical applications such as hydrology and agriculture request high spatial resolution and there is no satellite mission proposed that fully meets all requirements. While instruments like SMOS and SMAP operate at a longer wavelength (L-band) to enhance their sensitivity to soil moisture under vegetation, as compared to C-band [50], [51], their spatial resolution remains rather coarse, leading to adverse error properties [52]. Also Sentinel-1, which like ASCAT provides C-band VV+VH backscatter observations, albeit at a much increased spatial resolution (20 m), is very promising [53].…”

Section: Spatial Resolutionmentioning

confidence: 99%

Scientific Developments and the EPS-SG Scatterometer

Stoffelen

Aaboe

Calvet

et al. 2017

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The second-generation exploitation of meteorological satellite polar system (EPS-SG) C-band-wavelength scatterometer instrument (called SCA), planned for launch in 2022, has a direct heritage from the successful advanced scatterometer (ASCAT) flown on the current EPS satellites. In addition, SCA will represent three major innovations with respect to ASCAT, namely: 1) Cross polarization and horizontal copolarization; 2) a nominal spatial resolution of 25 km; and 3) 20% greater spatial coverage than AS-CAT. The associated expected science and application benefits that led the SCA design are discussed with respect to ocean, land, and sea ice applications for near-real time, climate monitoring, and research purposes. Moreover, an option to implement an ocean Doppler capability to retrieve the ocean motion vector is briefly discussed as well. In conclusion, the SCA instrument innovations are well set to provide timely benefits in all the main application areas of the scatterometer (winds, soil moisture, sea ice) and can be expected to contribute to new and more sophisticated meteorological, oceanographic, land, sea ice, and climate services in the forthcoming SCA era.

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Section: Spatial Resolutionmentioning

confidence: 99%

Scientific Developments and the EPS-SG Scatterometer

Stoffelen

Aaboe

Calvet

et al. 2017

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Compared with ERA-40 SM product, ERA-Interim has improved initialization data set, prediction model, error correction, time scale, and other aspects (Dee et al, 2011), and adopted TESSEL land surface model, as well as 4D-VAR assimilation algorithm. But in most areas, ERA-Interim SM performs slightly wetter (~0.05 m 3 /m 3 ) (Miyaoka et al, 2017). But in most areas, ERA-Interim SM performs slightly wetter (~0.05 m 3 /m 3 ) (Miyaoka et al, 2017).…”

Section: Era-interim Smmentioning

confidence: 95%

“…The product has been verified and applied in the analysis of hydrology, meteorology, and numerical simulation (Materia et al, 2014;Dorigo et al, 2012;Xia et al, 2014). But in most areas, ERA-Interim SM performs slightly wetter (~0.05 m 3 /m 3 ) (Miyaoka et al, 2017). This study used the ERA-Interim SM product at the depth of 7 cm with the volumetric unit, m 3 /m 3 (Table 1).…”

Section: Era-interim Smmentioning

confidence: 99%

“…The observed SM from stations is generally considered accurate and regarded as the reference SM (Brocca, Melone, Moramarco, Wagner, & Hasenauer, 2010). Compared with the newly developed Triple Collation method based on hypotheses and strong sample size requirement that the sample number of each independent dataset is at least 100 (Miyaoka, et al, 2017), the application of in situ SM to validate other products is a simpler and more classical method. However, the strong spatial-temporal heterogeneity (James, Partel, Wilson, & Peltzer, 2003;Chaney, Roundy, Herrera-Estrada, & Wood, 2015;Gómez-Plaza, Alvarez-Rogel, Albaladejo, & Castillo, 2000) and discontinuity of SM data from stations limit its application in the research of spatial patterns and temporal evolution for SM (Clark, 2002;Wolf, 2011).…”

Section: Introductionmentioning

confidence: 99%

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Comparison of soil moisture products from microwave remote sensing, land model, and reanalysis using global ground observations

Deng

Wang

Bai

et al. 2019

Hydrological Processes

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High-quality soil moisture (SM) datasets are in great demand for climate, hydrology, and other fields, but detailed evaluation of SM products from various sources is scarce. Thus, using 670 SM stations worldwide, we evaluated and compared SM products from microwave remote sensing [Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) (C-and X-bands) and European Space Agency's Climate Change Initiative (ESA CCI)], land surface model [Global Land Data Assimilation System (GLDAS)], and reanalysis data [ECMWF Re-Analysis-Interim (ERA-Interim) and National Centers for Environmental Prediction (NCEP)] under different time scales and various climates and land covers. We find that: (a) ESA CCI and GLDAS have the closest values to the in situ SM on the annual scale, whereas others overestimate the SM; ERA-Interim (averaged R = 0.58) and ESA CCI (averaged R = 0.54) correlate best with the in situ data, while GLDAS performs worst. (b) Overall, the deviations of each product vary in seasons. ESA CCI and ERA-Interim products are closer to the in situ SM at seasonal scales, and AMSR-E and NCEP perform worst in December-February and June-August, respectively. (c) Except for NCEP and ERA-Interim, others can well reflect the intermonthly variation of the in situ SM. (d) Under various climates and land covers, AMSR-E products are less effective in cold climates, whereas GLDAS and NCEP products perform poorly in arid or temperate and dry climates. Moreover, the Bias and R of each SM product differobviously under different forest types, especially the AMSR-E products. In summary, SM from ESA CCI is the best, followed by ERA-Interim product, and precipitation is an important auxiliary data for selecting high-quality SM stations and improving the accuracy of SM from GLDAS. These results can provide a reference for improving the accuracy of the above SM products. K E Y W O R D S

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“…One of the most common applications of TC is to estimate the error structures of remote sensing data products when it is not feasible to collect in situ data at the scale of a satellite pixel [e.g., Scipal et al, 2010;Chen et al, 2017;Yilmaz and Crow, 2014;Miyaoka et al, 2017;Roebeling et al, 2012]. TC requires that all measurement sources are related to the ''true'' signal up to a scaling constant plus some additive error that is independent of truth.…”

Section: Motivationmentioning

confidence: 99%

Nonparametric triple collocation

Nearing

Yatheendradas

Crow

et al. 2017

Water Resources Research

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Triple collocation has found widespread application in the hydrological sciences because it provides information about the errors in our measurements without requiring that we have any direct access to the true value of the variable being measured. Triple collocation derives variance‐covariance relationships between three or more independent measurement sources and an indirectly observed truth variable in the case where the measurement operators are additive. We generalize that theory to arbitrary observation operators by deriving nonparametric analogues to the total error and total correlation statistics as integrations of divergences from conditional to marginal probability ratios. The nonparametric solution to the full measurement problem is underdetermined, and we therefore retrieve conservative bounds on the theoretical total nonparametric error and correlation statistics. We examine the application of both linear and nonlinear triple collocation to synthetic examples and to a real‐data test case related to evaluating space‐borne soil moisture retrievals using sparse monitoring networks and dynamical process models.

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Triple Collocation Analysis of Soil Moisture From Metop-A ASCAT and SMOS Against JRA-55 and ERA-Interim

Cited by 28 publications

References 38 publications

Scientific Developments and the EPS-SG Scatterometer

Scientific Developments and the EPS-SG Scatterometer

Comparison of soil moisture products from microwave remote sensing, land model, and reanalysis using global ground observations

Nonparametric triple collocation

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