When the Comparison Is the Problem: Spatial Resolution and Validation Bias in InSAR-Derived Coastal Subsidence Assessments Along the U.S. Gulf Coast

Manoochehr Shirzaei, Leonard Ohenhen, Carmen Atkins, Oluwaseyi Dasho, Nitheshnirmal Sadhasivam, Nivedita P. Kamaraj, Florence Onyike, Olasunkanmi Olorunsaye, Esther O. Oyedele, Susanna Werth

Li et al. (2026) compare two InSAR-derived surface-elevation change datasets for the central U.S. Gulf Coast and conclude that InSAR is unreliable in vegetated coastal settings for rates below 5 mm/yr. While InSAR reproducibility is a timely and consequential question, we demonstrate that the paper's principal conclusions rest on three methodological decisions that critically undermine the comparison: (1) spatial aggregation of the O24 dataset from its native 50 m to 1 km prior to comparison, a ~400x reduction in pixel density that destroys the sub-kilometer spatial structure for which the dataset was designed; (2) a progressively filtered GNSS validation network of only ~20 stations concentrated in atypical stable Pleistocene upland settings, contrasting with O24's original validation across 157 stations spanning the full coastal domain; and (3) a 5 mm/yr caution threshold derived from inter-product disagreement between two methodologically dissimilar datasets rather than from principled uncertainty quantification. We validate O24 at its native 50 m resolution against 88 GNSS stations from the Nevada Geodetic Laboratory within the Li et al. study domain, obtaining a residual standard deviation of 1.6 mm/yr, consistent with Ohenhen et al. (2024) and directly contradicting the paper's characterization of O24 performance. We call on the InSAR community to prioritize coordinated benchmarking and invest in methodological literacy around resolution, coherence, and uncertainty quantification, so that inter-product disagreement is neither conflated with measurement failure nor permitted to drive policy-relevant conclusions without rigorous independent validation.