000170407 001__ 170407
000170407 005__ 20260420103354.0
000170407 0247_ $$2doi$$a10.3389/sjss.2026.14694
000170407 0248_ $$2sideral$$a148865
000170407 037__ $$aART-2026-148865
000170407 041__ $$aeng
000170407 100__ $$aSampériz Sarvisé, Manuel$$uUniversidad de Zaragoza
000170407 245__ $$aRemote sensing for within-plot soil variability assessment using NDVI dispersion metrics
000170407 260__ $$c2026
000170407 5060_ $$aAccess copy available to the general public$$fUnrestricted
000170407 5203_ $$aWhile within-plot soil variability strongly influences crop development and agronomic management efficiency, practical and transferable methods for characterizing this variability remain limited. This study proposes a remote sensing framework to assess soil-driven crop heterogeneity using Normalized Difference Vegetation Index (NVDI)-derived parameters and phenological analysis. Crop dynamics were monitored using Sentinel-2 imagery to identify key phenological stages (start of season, maximum development, and senescence), while high-resolution PlanetScope imagery was used to analyze within-plot spatial variability. NDVI dispersion metrics were used as proxies for spatial variability in crop development across soil units and under homogeneous management conditions to isolate soil effects. Results showed that NDVI standard deviation effectively captured spatial heterogeneity in crop development, revealing greater variability in Glacis Slope soils compared with the more homogeneous Platform unit. Crop senescence emerged as the most informative stage for detecting soil-driven variability, while emergence patterns were influenced by sowing dates and early establishment conditions. Analyses under homogeneous management confirmed that soil properties controlling soil moisture dynamics govern spatial variability in crop response. The proposed methodology provides a robust, low-cost, and transferable approach for identifying within-plot variability and supports precision agriculture by enabling site-specific management decisions based on satellite-derived indicators.
000170407 536__ $$9info:eu-repo/grantAgreement/ES/UZ-OTRI2021-0314
000170407 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000170407 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000170407 700__ $$0(orcid)0000-0002-0139-0843$$aUsón Murillo, Asunción$$uUniversidad de Zaragoza
000170407 7102_ $$15011$$2705$$aUniversidad de Zaragoza$$bDpto. CC.Agrar.y Medio Natural$$cÁrea Producción Vegetal
000170407 773__ $$g16 (2026), [14 pp.]$$pSpan. j. soil sci.$$tSpanish journal of soil science$$x2253-6574
000170407 8564_ $$s6939914$$uhttps://zaguan.unizar.es/record/170407/files/texto_completo.pdf$$yVersión publicada
000170407 8564_ $$s2204683$$uhttps://zaguan.unizar.es/record/170407/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000170407 909CO $$ooai:zaguan.unizar.es:170407$$particulos$$pdriver
000170407 951__ $$a2026-04-18-10:48:59
000170407 980__ $$aARTICLE