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    Rights statement: This is the author’s version of a work that was accepted for publication in Physics Reports. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Remote Sensing of Environment 225, 2019, DOI: 10.1016/j.rse.2019.03.003

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A double instrumental variable method for geophysical product error estimation

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A double instrumental variable method for geophysical product error estimation. / Dong, Jianzhi ; Crow, Wade ; Duan, Zheng et al.
In: Remote Sensing of Environment, Vol. 225, 01.05.2019, p. 217-228.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Dong, J, Crow, W, Duan, Z, Wei, L & Lu, Y 2019, 'A double instrumental variable method for geophysical product error estimation', Remote Sensing of Environment, vol. 225, pp. 217-228. https://doi.org/10.1016/j.rse.2019.03.003

APA

Dong, J., Crow, W., Duan, Z., Wei, L., & Lu, Y. (2019). A double instrumental variable method for geophysical product error estimation. Remote Sensing of Environment, 225, 217-228. https://doi.org/10.1016/j.rse.2019.03.003

Vancouver

Dong J, Crow W, Duan Z, Wei L, Lu Y. A double instrumental variable method for geophysical product error estimation. Remote Sensing of Environment. 2019 May 1;225:217-228. doi: 10.1016/j.rse.2019.03.003

Author

Dong, Jianzhi ; Crow, Wade ; Duan, Zheng et al. / A double instrumental variable method for geophysical product error estimation. In: Remote Sensing of Environment. 2019 ; Vol. 225. pp. 217-228.

Bibtex

@article{da096845b9f744049f9f4ab5a2b82f42,
title = "A double instrumental variable method for geophysical product error estimation",
abstract = "The global validation of remotely sensed and/or modeled geophysical products is often complicated by a lack of suitable ground observations for comparison. By cross-comparing three independent collocated observations, triple collocation (TC) can solve for geophysical product errors in error-prone systems. However, acquiring three independent products for a geophysical variable of interest can be challenging. Here, a double instrumental variable based algorithm (IVd) is proposed as an extension of the existing single instrumental variable (IVs) approach to estimate product error standard deviation (σ) and product-truth correlation (R) using only two independent products - an easier requirement to meet in practice. An analytical examination of the IVd method suggests that it is less prone to bias and has reduced sampling errors relative to IVs. Results from an example application of the IVd method to precipitation product error estimation show that IVd-based σ and R are good approximations of reference values obtained from TC at the global extent. In addition to their spatial consistency, IVd estimated error metrics also have only marginal (less than 5%) relative biases versus a TC baseline. Consistent with our earlier analytical analysis, these empirical results are shown to be superior to those obtained by IVs. However, several caveats for the IVd approach should be acknowledged. As with TC and IVs, IVd estimates are less robust when the signal-to-noise ratio of geophysical products is very low. Additionally, IVd may be significantly biased when geophysical products have strongly contrasting error auto-correlations.",
keywords = "error estimation, Instrumental variable, triple collocation",
author = "Jianzhi Dong and Wade Crow and Zheng Duan and Lingna Wei and Yang Lu",
note = "This is the author{\textquoteright}s version of a work that was accepted for publication in Physics Reports. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Remote Sensing of Environment 225, 2019, DOI: 10.1016/j.rse.2019.03.003",
year = "2019",
month = may,
day = "1",
doi = "10.1016/j.rse.2019.03.003",
language = "English",
volume = "225",
pages = "217--228",
journal = "Remote Sensing of Environment",
issn = "0034-4257",
publisher = "Elsevier Inc.",

}

RIS

TY - JOUR

T1 - A double instrumental variable method for geophysical product error estimation

AU - Dong, Jianzhi

AU - Crow, Wade

AU - Duan, Zheng

AU - Wei, Lingna

AU - Lu, Yang

N1 - This is the author’s version of a work that was accepted for publication in Physics Reports. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Remote Sensing of Environment 225, 2019, DOI: 10.1016/j.rse.2019.03.003

PY - 2019/5/1

Y1 - 2019/5/1

N2 - The global validation of remotely sensed and/or modeled geophysical products is often complicated by a lack of suitable ground observations for comparison. By cross-comparing three independent collocated observations, triple collocation (TC) can solve for geophysical product errors in error-prone systems. However, acquiring three independent products for a geophysical variable of interest can be challenging. Here, a double instrumental variable based algorithm (IVd) is proposed as an extension of the existing single instrumental variable (IVs) approach to estimate product error standard deviation (σ) and product-truth correlation (R) using only two independent products - an easier requirement to meet in practice. An analytical examination of the IVd method suggests that it is less prone to bias and has reduced sampling errors relative to IVs. Results from an example application of the IVd method to precipitation product error estimation show that IVd-based σ and R are good approximations of reference values obtained from TC at the global extent. In addition to their spatial consistency, IVd estimated error metrics also have only marginal (less than 5%) relative biases versus a TC baseline. Consistent with our earlier analytical analysis, these empirical results are shown to be superior to those obtained by IVs. However, several caveats for the IVd approach should be acknowledged. As with TC and IVs, IVd estimates are less robust when the signal-to-noise ratio of geophysical products is very low. Additionally, IVd may be significantly biased when geophysical products have strongly contrasting error auto-correlations.

AB - The global validation of remotely sensed and/or modeled geophysical products is often complicated by a lack of suitable ground observations for comparison. By cross-comparing three independent collocated observations, triple collocation (TC) can solve for geophysical product errors in error-prone systems. However, acquiring three independent products for a geophysical variable of interest can be challenging. Here, a double instrumental variable based algorithm (IVd) is proposed as an extension of the existing single instrumental variable (IVs) approach to estimate product error standard deviation (σ) and product-truth correlation (R) using only two independent products - an easier requirement to meet in practice. An analytical examination of the IVd method suggests that it is less prone to bias and has reduced sampling errors relative to IVs. Results from an example application of the IVd method to precipitation product error estimation show that IVd-based σ and R are good approximations of reference values obtained from TC at the global extent. In addition to their spatial consistency, IVd estimated error metrics also have only marginal (less than 5%) relative biases versus a TC baseline. Consistent with our earlier analytical analysis, these empirical results are shown to be superior to those obtained by IVs. However, several caveats for the IVd approach should be acknowledged. As with TC and IVs, IVd estimates are less robust when the signal-to-noise ratio of geophysical products is very low. Additionally, IVd may be significantly biased when geophysical products have strongly contrasting error auto-correlations.

KW - error estimation

KW - Instrumental variable

KW - triple collocation

U2 - 10.1016/j.rse.2019.03.003

DO - 10.1016/j.rse.2019.03.003

M3 - Journal article

VL - 225

SP - 217

EP - 228

JO - Remote Sensing of Environment

JF - Remote Sensing of Environment

SN - 0034-4257

ER -