Crop condition mapping and yield loss detection are highly relevant scientific fields
due to their economic importance. Here, we report a new, robust six-category crop
condition mapping methodology based on five vegetation indices (VIs) using Sentinel-2
imagery at a 10 m spatial resolution. We focused on maize, the most drought-affected
crop in the Carpathian Basin, using three selected years of data (2017, 2022, and
2023). Our methodology was validated at two different spatial scales against independent
reference data. At the parcel level, we used harvester-derived precision yield data
from six maize parcels. The agreement between the yield category maps and those predicted
from the crop condition time series by our Random Forest model was 84.56%, while the
F1 score was 0.74 with a two-category yield map. Using a six-category yield map, the
accuracy decreased to 48.57%, while the F1 score was 0.42. The parcel-level analysis
corroborates the applicability of the method on large scales. Country-level validation
was conducted for the six-category crop condition map against official county-scale
census data. The proportion of areas with the best and worst crop condition categories
in July explained 64% and 77% of the crop yield variability at the county level, respectively.
We found that the inclusion of the year 2022 (associated with a severe drought event)
was important, as it represented a strong baseline for the scaling. The study’s novelty
is also supported by the inclusion of damage claims from the Hungarian Agricultural
Risk Management System (ARMS). The crop condition map was compared with these claims,
with further quantitative analysis confirming the method’s applicability. This method
offers a cost-effective solution for assessing damage claims and can provide early
yield loss estimates using only remote sensing data.