Development of an ecohydrological model for coupled simulation of water and carbon fluxes, crop growth, and canopy spectra over croplands

Canopy spectral information, such as Sun-Induced chlorophyll Fluorescence (SIF) and hyperspectral reflectance, are closely associated with photosynthesis and canopy structure. These spectral indicators provide valuable insights into the actual growth status of crops, thereby guiding management practices in agricultural ecosystems. While considerable efforts have been devoted to simulating the processes of photosynthesis and crop growth, comprehensive and mechanistic modeling of canopy spectral information, integrated with these processes, remains underexplored in traditional crop models. Considering the recent advances in remote sensing observations which are mostly emitted or reflected signals, being able to accurately reproduce the canopy spectra is also advantageous to enhancing the model applicability. In this study, we propose an ecohydrological model (namely the Weishan model) with an integration of a water-carbon-energy fluxes module, a carbon allocation module, a reflectance spectrum module, and a SIF spectrum module for both C₃ (winter wheat) and C₄ crops (summer maize). Comprehensive model calibration and validation have been conducted based on the eddy covariance observations over a typical winter wheat-summer maize rotation cropping cropland in the North China Plain. Validation results highlight the capability and applicability of our ecohydrological model in reproducing the variation of water-carbon fluxes (i.e., evaporation, transpiration, averaged soil moisture, and gross primary productivity), crop growth variables (i.e., leaf area index and end-of-season crop yield), and canopy spectral information (i.e., top-of-canopy SIF, reflectance at near-infrared, red, and blue bands, and vegetation indices). Our model is capable of simulating canopy spectra through mechanistic representations of photosynthesis (e.g., utilizing the Farquhar biochemical model, the Ball-Berry stomatal model, and the energy balance model) and crop dynamics (e.g., phenology, leaf dynamics, carbon allocation and partitioning, biomass accumulation, and yield formation). This comprehensive framework enables the model to effectively disentangle the complex interactions among these processes within a changing environmental context. Furthermore, the model’s ability to accurately reproduce canopy spectra highlights its potential to leverage remote sensing observations to enhance the model performance. We emphasize the functionality and future applicability of our model in advancing ecohydrological and agricultural research.