2026-05-30T03:55:05Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/399012026-02-03T11:22:01Zcom_10630_2254col_10630_37953

False discovery rate estimation and control in remote sensing: reliable statistical significance in spatially dependent gridded data. Gutiérrez-Hernández, Oliver García, Luís V. Estructuras de datos (Informática) Ficheros de datos Tablas de contingencia 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 2025-09-15T09:35:23Z 2025-09-15T09:35:23Z 2025-04-04 journal article 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 eng open access Taylor & Francis