Cumulative link mixed-effects models in the service of remote sensing crop progress monitoring.

Abstract

This study introduces an innovative cumulative link modeling (CLM) approach to monitor crop progress over large areas using remote sensing data. Two distinct models are developed, a fixed-effects CLM and a mixed-effects one that incorporates annual random effects to capture the inherent inter-seasonal variability. Inference is based on partial-likelihood with two law variations, the standard CLM based on the multinomial distribution and a novel one based on the product binomial distribution. Model performance is evaluated on eight crops, namely corn, oats, sorghum, soybeans, winter wheat, alfalfa, dry beans, and millet, using in-situ data from Nebraska, USA, spanning 20 years. The models utilize the predictive attributes of calendar time, thermal time, and the normalized difference vegetation index. The results demonstrate the wide applicability of this approach to different crops, providing large-scale predictions of crop progress and allowing the estimation of important agronomic parameters. To facilitate reproducibility, an ecosystem of R packages has been developed and made publicly accessible under the name Ages of Man. The packages can be utilized to implement the presented methodology in any area with this type of data, including the USA.