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Gutiérrez-Hernández, Oliver García, Luís V. 2025-09-15T09:35:23Z 2025-09-15T09:35:23Z 2025-04-04 Gutiérrez-Hernández, O., & García, L. (2025). False Discovery Rate Estimation and Control in Remote Sensing: Reliable Statistical Significance in Spatially Dependent Gridded Data. Remote Sensing Letters, 16(5), 537–548. https://hdl.handle.net/10630/39901 10.1080/2150704X.2025.2478664 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 eng open access Estructuras de datos (Informática) Ficheros de datos Tablas de contingencia False discovery rate estimation and control in remote sensing: reliable statistical significance in spatially dependent gridded data. journal article