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Mediterranean grasslands are strongly constrained by water availability and exhibit pronounced seasonal variability in productivity. Remote sensing provides valuable tools for vegetation monitoring, with the Normalized Difference Vegetation Index (NDVI) being one of the most widely used indicators. However, in Mediterranean environments the relationship between NDVI and actual aboveground biomass is often complex due to spectral saturation, summer senescence, and a decoupling between greenness and structural biomass. Identifying when NDVI reliably represents grassland production is therefore essential for its application in grazing management and climate impact assessments.This study was conducted in three representative grassland areas of the Community of Madrid (central Spain): Piñuecar, Colmenar Viejo, and Tielmes, spanning a gradient from humid mountain environments to semi-arid lowlands. NDVI time series were derived from the MODIS MOD09Q1 product for the period 2000–2025, with 250 m spatial resolution and an 8-day temporal frequency. Aboveground biomass was estimated using the SIMPAST predictive grassland model, driven by climate data from six global climate models from the CMIP6 ensemble. NDVI was analysed both as instantaneous values and as temporally accumulated NDVI using simple integration. The relationship between NDVI and biomass was evaluated through linear regressions and coefficients of determination (R²) at annual, seasonal, and phenological scales.Instantaneous NDVI showed almost no explanatory power for biomass variability, with R² values close to zero (≈ 0.00–0.03), indicating that punctual greenness indicators fail to represent accumulated grassland production. In contrast, temporally accumulated NDVI exhibited a strong relationship with annual biomass, with R² values ranging from approximately 0.60 to 0.75 across sites. Seasonal analyses revealed that the highest correlations occurred during autumn and spring, coinciding with periods of active growth. During summer and winter, NDVI–biomass relationships weakened considerably due to senescence, and reduced metabolic activity.Segmenting the annual cycle into five eco-physiological periods further improved the coherence between the spectral signal and actual growth dynamics, reaching maximum R² values of up to 0.74–0.75 during peak growth phases. Piñuecar showed the strongest NDVI–biomass coupling, while Tielmes achieved high correlations during episodic humid pulses despite its generally arid conditions. Colmenar Viejo exhibited greater interannual variability, likely linked to heterogeneous water stress.These results confirm that temporal integration of NDVI is essential to represent productivity in Mediterranean grasslands. Phenological segmentation allows identification of time windows in which NDVI acts as a reliable proxy for real growth, providing operational criteria for grassland monitoring under climatic variability..ReferencesAragón Pizarro, M., Díaz-Ambrona, C. G. H., Tarquis, A. M., Almeida-Ñauñay, A. F., and Sanz, E.: Modelling Biomass Projections in Grasslands of Central Spain Under Climate Change Scenarios, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10928, https://doi.org/10.5194/egusphere-egu25-10928, 2025.Iglesias, E., Báez, K., H. Diaz-Ambrona, C. Assessing drought risk in Mediterranean Dehesa grazing lands. Agricultural Systems, 149, 65-74, 2016. https://doi.org/10.1016/j.agsy.2016.07.017AcknowledgementsThe first author acknowledges the support of Project “Garantía Juvenil” scholarship from Comunidad de Madrid. This research was partially supported by Universidad Politécnica de Madrid under project “Clasificación de Pastizales Mediante Métodos Supervisados – SANTOS” (RP220220C024).