Maize is an important crop used as food, feed, and industrial raw material. Therefore,
it is critical to maximize maize yield on available land by using optimal inputs and
adapting to challenges posed by climate change. The Leaf Area Index (LAI) is a key
parameter that provides significant assistance in forecasting maize yields. This study
focuses on modeling the Leaf Area Index for maize. Specifically, it compiles and systematizes
the main findings of papers published over the past approximately 10–15 years. Our
results are organized and presented based on the five most commonly used models: CERES-Maize,
AquaCrop, WOFOST, APSIM, and RZWQM2. The limitations of these models’ applicability
are also discussed. We present the limitations of these models and compare their minimum
climate input requirements. Additionally, we evaluate the performance of the models
across different climate zones, explore how the integration of remote sensing data
sources can enhance model estimation accuracy, and examine the potential for spatial
scalability in maize LAI modeling.
