Quantifying systematic biases in satellite-based crop condition monitoring induced by inter-annual planting dynamics

The in-season monitoring of crop conditions via remote sensing is vital for assessing crop health and forecasting yields. However, accuracy suffers when static masks fail to capture annual changes in cropping patterns. Inter-annual fluctuations in crop type proportions alter regional vegetation index signals, leading to biased interpretations; however, despite their influence on regional monitoring accuracy, such dynamics are rarely considered. In this study, we developed a framework to identify and quantify the errors and biases arising from ignoring changes in annual crop patterns. Using U.S. Corn Belt Cropland Data Layer products (2018–2023), we created static masks and simulated eight scenarios with varying inter-annual crop proportions. By comparing the crop-specific NDVI from Sentinel-2 and MODIS with regional averages, we quantified the bias introduced by the static masks. Errors persisted throughout the season, peaking during early growth and senescence, with NDVI anomaly errors reaching 0.25 (42% of the absolute NDVI). Regions with high crop variability showed the largest biases, whereas stable areas had minimal errors. Both satellite datasets revealed consistent temporal error patterns, although MODIS produced lower values. In conclusion, incorporating annually updated crop type maps is essential for accurate crop condition monitoring because static masks introduce significant spatiotemporally variable biases.