Advancing crop modeling and data assimilation using AquaCrop v7.2 in NASA's Land Information System Framework v7.5

Abstract

This paper introduces the open-source AquaCrop v7.2 model as a new process-based crop model within NASA's Land Information System Framework (LISF) v7.5. The LISF enables high-performance crop modeling with efficient geospatial data handling, and paves the way for scalable satellite data assimilation into AquaCrop. Through three exploratory showcases, we demonstrate the current capabilities of AquaCrop in the LISF, along with topics for future development. First, coarse-scale (0.1°) generic crop growth simulations with various crop parameterizations are performed over Europe. Satellite-based estimates of land surface phenology are used to inform spatially variable crop parameters. These parameters improve canopy cover simulations in growing degree days compared to using uniform crop parameters in calendar days. Second, ensembles of coarse-scale simulations over Europe are created by perturbing meteorological forcings and soil moisture. The resulting uncertainties in root-zone soil moisture and biomass are often greater in water-limited regions than elsewhere. The third showcase aims to improve fine-scale (1/112°) winter wheat simulations through satellite data assimilation. Fine-scale canopy cover observations are assimilated with an ensemble Kalman filter to update the crop state in the Piedmont region of Italy. The state updating is beneficial for the intermediary biomass estimates, but leads to only small improvements in yield estimates relative to reference data. The spatiotemporal variability of simulated yield is poor due to strong model (parameter) constraints, and the assimilated satellite-based canopy cover observations are not sufficiently informative of yield. Furthermore, the exact fields of the reference yield data are unknown and thus hard to compare to simulations. The showcases highlight pathways to advance future crop estimates, e.g. through crop parameter updating and multi-sensor and multi-variate data assimilation.