  This study introduces an innovative Cumulative Link Modeling approach to
monitor crop progress over large areas using remote sensing data. The models
utilize the predictive attributes of calendar time, thermal time, and the
Normalized Difference Vegetation Index (NDVI). Two distinct issues are tackled:
real-time crop progress prediction, and completed season fitting. In the
context of prediction, the study presents two model variations, the standard
one based on the Multinomial distribution and a novel one based on the
Multivariate Binomial distribution. In the context of fitting, random effects
are incorporated to capture the inherent inter-seasonal variability, allowing
the estimation of biological parameters that govern crop development and
determine stage completion requirements. Theoretical properties in terms of
consistency, asymptotic normality, and distribution-misspecification are
reviewed. Model performance was evaluated on eight crops, namely corn, oats,
sorghum, soybeans, winter wheat, alfalfa, dry beans, and millet, using in-situ
data from Nebraska, USA, spanning a 20-year period. The results demonstrate the
wide applicability of this approach to different crops, providing real-time
predictions of crop progress worldwide, solely utilizing open-access data. To
facilitate implementation, an ecosystem of R packages has been developed and
made publicly accessible under the name Ages of Man.
