The Normalized Difference Vegetation Index (NDVI) is a widely used indicator of vegetation vigor, yet its reliability in agricultural time series is limited by atmospheric and radiometric inconsistencies. This study evaluates two local correction methods using pseudo-invariant reference tarps to reduce noise in Planet Scope NDVI measurements for maize fields in Ithaca, New York during the 2023 growing season. The first approach, the Satellite Tarp Residual Method, applied residuals from linear regressions of satellite-derived tarp NDVI to adjust field NDVI. The second approach, the Spectral–Satellite Tarp Correction, predicted spectral tarp NDVI for satellite acquisition dates via linear models, computed correction terms, and applied them to field NDVI. Validation against spectral ground-truth NDVI from Elementary Measurement Areas (EMAs) employed Dynamic Time Warping (DTW) to assess temporal profile similarity. Results indicate that the Satellite Tarp Residual Method provided the smallest DTW distance (0.6064) compared to the uncorrected NDVI (0.6091), with the best performance achieved by subtracting red tarp residuals. The Spectral–Satellite Tarp Correction yielded minimal improvement and, in some cases, degraded temporal agreement due to interpolation errors. Findings suggest that temporally coincident satellite-based reference measurements offer more effective short-term corrections than predicted spectral values when spectral data are temporally sparse. Future work should integrate satellite temporal coverage with spectral spectral precision through hybrid or non-linear correction frameworks to improve NDVI time series reliability for precision agriculture.