False discovery rate estimation and control in remote sensing: reliable statistical significance in spatially dependent gridded data. - Gutiérrez-Hernández, Oliver; García, Luís V. (Taylor & Francis, 2025)
In remote sensing, analysing statistical significance (expressed in terms of p-values) in gridded datasets with thousands of pixels requires addressing the multiple testing problem, which increases the risk of false positives. The false discovery rate (FDR) provides a flexible alternative to traditional correction procedures, yet its application in remote sensing remains underexplored. This research combines FDR estimation via the location-based estimator (LBE) with FDR control using the Benjamini-Hochberg (BH) procedure to enhance the reliability of statistical inference in spatially gridded data. These methods were applied to gridded p-values (p-value map) derived from spatiotemporal Contextual Mann-Kendall (CMK) trend tests using the global MODIS NDVI (Moderate Resolution Imaging Spectroradiometer – Normalized Difference Vegetation Index) MOD13C2 product, highlighting their applicability to scenarios requiring p-value-based corrections. Our findings highlight the complementary strengths of FDR estimation and control, offering a robust framework for addressing large-scale multiple testing challenges in remote sensing under spatial dependence
False discovery rate estimation and control in remote sensing
Keywords: Estructuras de datos (Informática); Ficheros de datos; Tablas de contingencia; FDR; FDR control; FDR estimation; Multiplicity; Raster data