Generalizability of gene expression programming and random forest methodologies in estimating cropland and grassland leaf area index

چکیده مقاله

Leaf Area Index (LAI) is a very important structural attribute of ecosystems which affects the energy, water and
carbon exchanges between the land surface and atmosphere. Direct measurement of LAI is costly and time
consuming so indirect measurement approaches have been developed for determining its magnitude. The present paper aimed at modeling LAI in cropland and grassland sites using the available meteorological data
through two heuristic data driven techniques, namely, gene expression programming (GEP) and random forest
(RF). Different data set organizations were designed using local (temporal) and external (spatial) norms to
provide a thoroughgoing data scanning strategy. The results showed that the external GEP and RF models (EGEP
and ERF) might be suitable approaches for modeling LAI by average scatter index (SI) values of 0.275 and 0.270
(for cropland) and 0.273 and 0.279 (for grassland) when compared to the local GEP and RF models with average
SI values of 0.207 and 0.204 (cropland), and 0.249 and 0.204 (grassland), respectively. The presented methodology allowed the evaluation in each site of models developed (trained) using local patterns and the models
developed using the exogenous data (patterns from ancillary sites).