Multi-Sensor NDVI Fusion for Daily Crop Evapotranspiration Mapping: A Six-Year Irrigated Maize Assessment Using...

Accurate crop evapotranspiration (ET_c) estimation at high spatial and temporal resolution remains a major challenge for precision irrigation. This study presents a multi-sensor data fusion framework combining daily MODIS (250 m), Sentinel-2 (10 m), and Landsat 8/9 (30 m) imagery with FAO-56 Penman-Monteith reference evapotranspiration (ET₀) to generate pixel-wise daily ET_c maps for irrigated maize (Zea mays L.) near Nyírbátor, Hungary, over six growing seasons (2020-2025). The proposed Median Time Series Model exploits field-scale MODIS NDVI as a temporal backbone and derives pixel-wise linear transfer functions to reconstruct daily NDVI at 10-30 m resolution. Three gap-filling strategies were compared; the median approach yielded the highest agreement (NDVI reconstruction R² = 0.81; RMSE = 0.19 (NDVI units); pixel-wise correlation 0.70-0.85) and effectively suppressed sub-pixel spectral mixture artefacts. Sentinel-2 consistently outperformed Landsat 8/9 (pixel-wise R² = 0.36-0.78 vs. 0.001-0.91). A nonlinear power crop coefficient model (K_c = a · NDVI^b) proved more robust than linear rescaling (mean validation R² of 0.80 (power) vs. 0.71 (rescale) across Sentinel-2 seasons; both methods were positive in all six seasons after correcting an unconstrained-fit artefact). Seasonal ET_c ranged from 313 to 545 mm, with cumulative water deficits reaching -334 mm during the 2021 drought. Six-year mean seasonal ET_c (428-483 mm for Sentinel-2) falls within the 400-600 mm range published for irrigated maize under comparable continental conditions, with season-integrated ET_c/ET₀ ratios (rescale method mean 0.86; power method mean 0.84) consistent with expected FAO-56 K_c trajectories. Cross-validation against an independent MATLAB implementation confirmed algorithmic consistency (reference ET₀ (R² = 0.88-0.91, Pearson r = 0.97-1.00)) and daily ET_c while identifying meteorological input as the dominant source of absolute ET_c uncertainty (estimated at ±15-30% through first-order error propagation). Plausibility assessment was limited to comparison with published seasonal benchmarks and an independent algorithmic implementation; no eddy covariance or lysimeter measurements were available for direct ET_c validation.