15 citations as recorded by crossref.

1. Water footprints and crop water use of 175 individual crops for 1990–2019 simulated with a global crop model O. Mialyk et al. https://doi.org/10.1038/s41597-024-03051-3
2. AquaCrop.jl: A Process-Based Model of Crop Growth G. Díaz Iturry et al. https://doi.org/10.21105/joss.07944
3. Regionalising a global gridded crop model using satellite-based phenology for water footprint analysis in the Upper Syr Darya Basin G. Baviskar et al. https://doi.org/10.1016/j.ejrh.2026.103768
4. Remote sensing analysis on primary productivity and forest cover dynamics: A Western Ghats India case study M. Baldo et al. https://doi.org/10.1016/j.ecoinf.2022.101922
5. Soil Moisture Prediction: A Review of Models, Principles, Applications and Future Needs D. Kikoyo et al. https://doi.org/10.3390/environments13080441
6. On the gap between crop and land surface models: comparing irrigation and other land surface estimates from AquaCrop and Noah-MP over the Po Valley L. Busschaert et al. https://doi.org/10.5194/hess-30-2579-2026
7. Optimisation of AquaCrop backscatter simulations using Sentinel-1 observations S. de Roos et al. https://doi.org/10.1016/j.rse.2023.113621
8. Net irrigation requirement under different climate scenarios using AquaCrop over Europe L. Busschaert et al. https://doi.org/10.5194/hess-26-3731-2022
9. AquaCrop Model Performance Under Various Reference Evapotranspiration Equations for Simulating the Growth and Yield of Wheat A. Shabani et al. https://doi.org/10.1002/ird.70033
10. Assessment of AquaCrop Inputs from ERA5-Land and Sentinel-2 for Soil Water Content Estimation and Durum Wheat Yield Prediction: A Case Study in a Tunisian Field H. Ghazouani et al. https://doi.org/10.3390/w17243522
11. Towards collective intelligence in agriculture: Deep reinforcement learning and digital twins for efficient management of collective irrigation water distribution systems C. El Hachimi et al. https://doi.org/10.1016/j.nexus.2025.100599
12. SpatialAquaCrop, an R Package for Raster-Based Implementation of the AquaCrop Model V. Barros et al. https://doi.org/10.3390/plants11212907
13. Impacts of Climate Change and Adaptation Strategies for Rainfed Barley Production in the Almería Province, Spain F. Saretto et al. https://doi.org/10.3390/atmos15050606
14. Advancing crop modeling and data assimilation using AquaCrop v7.2 in NASA's Land Information System Framework v7.5 G. De Lannoy et al. https://doi.org/10.5194/gmd-19-2551-2026
15. Future projections of European maize yields using AquaCrop with an adaptive growing season L. Busschaert et al. https://doi.org/10.1016/j.eja.2025.127920