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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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
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 -