Abrahamsen, P., & Hansen, S. (2000). Daisy: an open soil-crop-atmosphere system model. Environmental modelling & software, 15(3), 313-330. https://doi.org/10.1016/S1364-8152(00)00003-7.
Adabi, V.; Azizian, A.; Ramezani Etedali, H.; Kaviani, A., & Ababaei, B. (2020). Local sensitivity analysis of AquaCrop model for wheat and maize in Qazvin Plain and Moghan Pars-Abad in Iran. Iranian J. Irrigation Drainage. 13(6), 1565-1579. Doi: 20.1001. 1.20087942.1398.13.6.3.9.
Ahmadi, S. H.; Mosallaeepour, E.; Kamgar-Haghighi, A. A., & Sepaskhah, A. R. (2015). Modeling maize yield and soil water content with AquaCrop under full and deficit irrigation managements. Water Resour. Manage. 29, 2837-2853. doi: 10.1007/s11269-015-0973-3.
Akbari E.; Darvishi Boloorani A.; Verrelst J.; Pignatti S.; Neysani Samany N.; Soufizadeh S. and Hamzeh S. (2024a). How global sensitive is the AquaCrop model to input parameters? A case study of silage maize yield on a regional scale. Front. Agron. 6: 1304611. doi: 10.3389/fagro.2024.1304611.
Akbari, E. (2020). Assimilation of remote sensing data in crop growth model for yield estimation. Thesis of PhD. Degree of remote sensing and GIS, remote sensing. University of Tehran. Geography faculty. Remote sensing and GIS group. Supervisr professor: Ali darvishi bolourani and Najme neisani samani. (in Persian with English Summary).
Akbari, E. (2023). A review of Remotely Sensed Data Assimilation into Crop Simulation Models. Iranian J. Soil Water Res. 54(5), 753-770, doi: 10.22059/ijswr.2023.357472. 669478. (in Persian with English Summary).
Akbari, E., Boloorani, A. D., Verrelst, J., & Pignatti, S. (2024b). Assimilating Satellite-Based Biophysical Variables Data into AquaCrop Model for Silage Maize Yield Estimation Using Water Cycle Algorithm. Remote Sensing, 16(24), 4665. https://doi.org/ 10.3390/rs16244665.
Akbari, E.; Boloorani, A. D.; Verrelst, J.; Pignatti, S.; Neysani Samany, N.; Soufizadeh, S., & Hamzeh, S. (2023). Biophysical Variable Retrieval of Silage Maize with Gaussian Process Regression and Hyperparameter Optimization Algorithms. Remote Sensing. 15(14), 3690. https://doi.org/10.3390/rs15143690.
Andarzian, B.; Bannayan, M.; Steduto, P.; Mazraeh, H.; Barati, M. E.; Barati, M. A., & Rahnama, A. (2011). Validation and testing of the AquaCrop model under full and deficit irrigated wheat production in Iran. Agric. Water Manage. 100(1), 1-8. Doi: 10.1016/j.agwat.2011.08.023.
Ashton, M. S., Tyrrell, M. L., Spalding, D., & Gentry, B. (Eds.). (2012). Managing forest carbon in changing climates. Springer Science and Business Media.
Attia, A.; El-Hendawy, S.; Al-Suhaibani, N.; Tahir, M.U.; Mubushar, M.; dos Santos Vianna, M.; Ullah, H.; Mansour, E. and Datta, A. (2021). Sensitivity of the DSSAT model in simulating maize yield and soil carbon dynamics in arid Mediterranean climate: Effect of soil, genotype and crop management. Field Crops Res. 260, 107981. Doi: 10.1016/j.fcr.2020.107981.
Brisson, N., Mary, B., Ripoche, D., Jeuffroy, M.H., Ruget, F., Nicoullaud, B., Gate, P., DevienneBarret, F., Antonioletti, R., Durr, C., Richard, G., Beaudoin, N., Recous, S., Tayot, X., Plenet, D., Cellier, P., Machet, J.M., Meynard, J.M., Delecolle, R., (1998). STICS: a generic model for the simulation of crops and their water and nitrogen balances. I. Theory and parameterization applied to wheat and corn. Agronomie, 18, 311-346. https://doi.org/10.1051/agro:19980501.
Cariboni, J.; Gatelli, D.; Liska, R., & Saltelli, A. (2007). The role of sensitivity analysis in ecological modelling. Ecol. Modell. 203(1-2), 167-182. Doi: 10.1016/j.ecolmodel.2005.10.045.
Casadebaig, P.; Zheng, B.; Chapman, S.; Huth, N.; Faivre, R., & Chenu, K. (2016). Assessment of the potential impacts of wheat plant traits across environments by combining crop modeling and global sensitivity analysis. PloS one 11(1), e0146385. https://doi.org/ 10.1371/journal. pone.0146385.
Ceglar, A.; Crepinsek, Z.; Kajfez-Bogataj, L.; Pogacar, T. (2011). The simulation of phenological development in dynamic crop model. The Bayesian comparison of different methods. Agric. For. Meteorol. 151, 101–115. https://doi.org/10.1016/j.agrformet. 2010.09.007.
Challinor, A.J.; Smith, M.S.; Thornton, P. (2012). Use of agro-climate ensembles for quantifying uncertainty and informing adaptation. Agric. For. Meteorol. 170, 2–7. https://doi.org/ 10.1016/j.agrformet.2012.09.007.
Chen, Y., & Cournède, P. H. (2014). Data assimilation to reduce uncertainty of crop model prediction with convolution particle filtering. Ecol. Modell. 290, 165-177. https://doi.org/10.1016/j.ecolmodel.2014.01.030.
Chenu, K.; Casadebaig, P., & Christopher, J. (2016). From a global sensitivity analysis of a crop model to wheat improvement in the field. In iCROPM2016 International Crop Modelling Symposium" Crop Modelling for Agriculture and Food Security under Global Change". International Crop Modelling Symposium iCROPM2016, Berlin, Germany, 15-17 March 2016. Germany: AgMIP.
Claverie, M.; Demarez, V.; Duchemin, B.; Hagolle, O.; Ducrot, D.; Marais-Sicre, C.; Dejoux, J.F.; Huc, M.; Keravec, P.; Béziat, P. and Fieuzal, R. (2012). Maize and sunflower biomass estimation in southwest France using high spatial and temporal resolution remote sensing data. Remote Sens. Environ. 124, 844-857. https://doi.org/10.1016/j.rse. 2012.04.005.
Clevers, J.; Vonder, O. W.; Jongschaap, R. E. E.; Desprats, J. F.; King, C.; Prevot, L., and Bruguier, N. (2002). Using SPOT data for calibrating a wheat growth model under Mediterranean conditions. Agronomie. 22(6), 687-694. doi: 10.1051/agro:2002038.
Colombi, A., Bancheri, M., Acutis, M., Basile, A., Botta, M., & Perego, A. (2024). A sound understanding of a cropping system model with the global sensitivity analysis. Environmental Modelling & Software, 173, 105932. https://doi.org/10.1016/j.envsoft. 2023. 105932.
Colombi, A.; Acutis, M.; Bancheri, M.; Basile, A.; Botta, M., & Perego, A. (2022). A Two-Step Global Sensitivity Analysis of the Crop-Growth Armosa Model. Available at SSRN 4234834. Available at SSRN: https://ssrn.com/abstract=4234834 or http://dx.doi.org/10.2139/ssrn.4234834.
Confalonieri, R.; Acutis, M.; Bellocchi, G.; Cerrani, I.; Tarantola, S.; Donatelli, M., & Genovese, G. (2006). Exploratory sensitivity analysis of CropSyst, WARM and WOFOST: a case-study with rice biomass simulations. RIVISTA ITALIANA DI AGROMETEORO-LOGIA. 11(3), 17-25. JRC35589.
Confalonieri, R.; Bellocchi, G.; Bregaglio, S.; Donatelli, M., & Acutis, M. (2010a). Comparison of sensitivity analysis techniques: a case study with the rice model WARM. Ecol. Modell. 221(16), 1897-1906. https://doi.org/ 10.1016/j.ecolmodel.2010.04.021.
Confalonieri, R.; Bellocchi, G.; Tarantola, S.; Acutis, M.; Donatelli, M., & Genovese, G. (2010b). Sensitivity analysis of the rice model WARM in Europe: exploring the effects of different locations, climates and methods of analysis on model sensitivity to crop parameters. Environ. Modell. Soft. 25(4), 479-488. https://doi.org/10.1016/j. envsoft.2009. 10.005.
Corbeels, M.; Chirat, G.; Messad, S., & Thierfelder, C. (2016). Performance and sensitivity of the DSSAT crop growth model in simulating maize yield under conservation agriculture. Eur. J. Agron. 76, 41-53. https://doi.org/10.1016/j.eja.2016.02.001.
Curnel, Y.; de Wit, A.J.; Duveiller, G.; Defourny, P. (2011). Potential performances of remotely sensed LAI assimilation in WOFOST model based on an OSS Experiment. Agric. For. Meteorol. 151, 1843–1855. https://doi.org/ 10.1016/ j.agrformet. 2011.08.002.
de Souza, J. L. M.; de Oliveira, C. T.; Rosa, S. L. K., & Tsukahara, R. Y. (2022). Sensitivity analysis of the AquaCrop model for maize crop in Southern Brazil. Agric. Eng. Inter.: CIGR Journal. 24(3).
DeJonge, K. C.; Ascough II, J. C.; Ahmadi, M.; Andales, A. A., & Arabi, M. (2012). Global sensitivity and uncertainty analysis of a dynamic agroecosystem model under different irrigation treatments. Ecol. Modell. 231, 113-125. https://doi.org/10.1016/j.ecolmodel. 2012. 01.024.
Dlamini, L.; Crespo, O.; van Dam, J., & Kooistra, L. (2023). A Global Systematic Review of Improving Crop Model Estimations by Assimilating Remote Sensing Data: Implications for Small-Scale Agricultural Systems. Remote Sensing. 15(16), 4066. https://doi.org/10.3390/rs15164066.
Dorigo, W. A.; Zurita-Milla, R.; de Wit, A. J.; Brazile, J.; Singh, R., & Schaepman, M. E. (2007). A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling. Int. J. Appl. Earth Obs. Geoinf. 9(2), 165-193. DOI:10.1016/ j.jag.2006.05.003.
Fang, H.; Liang, S.; Hoogenboom, G.; Teasdale, J., & Cavigelli, M. (2008). Corn‐yield estimation through assimilation of remotely sensed data into the CSM‐CERES‐Maize model. Int. J. Remote Sens. 29(10), 3011-3032. https://doi.org/ 10.1080/01431160701408386.
Fernández-Guisuraga, J. M., Verrelst, J., Calvo, L., & Suárez-Seoane, S. (2021). Hybrid inversion of radiative transfer models based on high spatial resolution satellite reflectance data improves fractional vegetation cover retrieval in heterogeneous ecological systems after fire. Remote sensing of environment, 255, 112304. https://doi.org/ 10.1016/ j.rse.2021.112304.
Franczyk, A. (2019). Using the Morris sensitivity analysis method to assess the importance of input variables on time-reversal imaging of seismic sources. Acta Geophys. 67(6), 1525-1533. https://doi.org/ 10.1007/s11600-019-00356-5.
Gao, C.; Wang, H.; Weng, E.S.; Lakshmivarahan, S.; Zhang, Y.F.; Luo, Y.Q., (2011). Assimilation of multiple data sets with the ensemble Kalman filter to improve forecasts of forest carbon dynamics. Ecol. Appl. 21 (5), 1461–1473. https://doi.org/10.1890/09-1234.1.
Gao, Y., Sun, C., Ramos, T.B., Tan, J., Oliveira, A.R., Huang, Q., Huang, G. and Xu, X. (2024). Global Sensitivity Analysis of the Advanced ORYZA-N Model with Different Rice Types and Irrigation Regimes. Plants, 13(02). https://doi.org/10.3390/plants13020262.
Gasanov, M.; Petrovskaia, A.; Nikitin, A.; Matveev, S.; Tregubova, P.; Pukalchik, M., & Oseledets, I. (2020). Sensitivity analysis of soil parameters in crop model supported with high-throughput computing. In Computational Science–ICCS 2020: 20th International Conference, Amsterdam, The Netherlands, June 3–5, 2020, Proceedings, Part VII 20 (pp. 731-741). Springer International Publishing. DOI: 10.1007/978-3-030-50436-6_54.
Ge, H.; Ma, F.; Li, Z.; Du, C. (2021). Global Sensitivity Analysis for CERES-Rice Model under Different Cultivars and Specific-Stage Variations of Climate Parameters. Agron. J. 11, 2446. https://doi.org/10.3390/ agronomy 11122446.
Guo, D.; Zhao, R.; Xing, X., & Ma, X. (2019). Global sensitivity and uncertainty analysis of the AquaCrop model for maize under different irrigation and fertilizer management conditions. Arch. Agron. Soil Sci. 1-19. https://doi.org/10.1080/03650340. 2019.1657845.
Hansen, J. W. and Jones, J. W. (2000). Scaling-up crop models for climate variability applications. Agric. Syst. 65(1), 43-72. doi: 10.1016/s0308-521x (00)00025-1.
Hao, S., Ryu, D., Western, A. W., Perry, E., Bogena, H., & Franssen, H. J. H. (2024). Global sensitivity analysis of APSIM-wheat yield predictions to model parameters and inputs. Ecological Modelling, 487, 110551. https://doi.org/10.1016/j.ecolmodel.2023.110551.
Hao, S.; Ryu, D.; Western, A.; Perry, E.; Bogena, H., & Hendricks Franssen, H. J. (2021). Sensitivity Analysis of APSIM Wheat Yield Predictions. In AGU Fall Meeting Abstracts (Vol. 2021, pp. H53C-01).
Haruna, M. M.; Ismail, H.; Bashir, A. U., & Dibal, J. M. (2023). Local sensitivity analysis of the AquaCrop model outputs for wheat under Semi-Arid water stress condition. Algerian J. Eng. Tech. 8(1), 22-30.
Hassanli, M.; Ebrahimian, H.; Mohammadi, E.; Rahimi, A., & Shokouhi, A. (2016). Simulating maize yields when irrigating with saline water, using the AquaCrop, SALTMED, and SWAP models. Agric. Water Manage. 176, 91-99. Doi: 10.1016/j.agwat. 2016.05.003.
Hassanpour, R.; Majnooni-Heris, A.; Fakheri Fard, A.; Verrelst, J. (2024). Monitoring Biophysical Variables (FVC, LAI, LCab, and CWC) and Cropland Dynamics at Field Scale Using Sentinel-2 Time Series. Remote Sens. 16, 2284. https://doi.org/ 10.3390/ rs16132284.
Hastie, T., Tibshirani, R., Friedman, J.H. (2009). The elements of statistical learning: data mining, inference, and prediction (2nd ed.). New York: Springer-Verlag.
Hatfield, J. L., & Dold, C. (2019). Water-use efficiency: advances and challenges in a changing climate. Frontiers in plant science, 10, 103. https://doi.org/10.3389/fpls.2019.00103.
Hoefsloot, P.; Ines, A. V.; van Dam, J.; Duveiller, G.; Kayitakire, F., & Hansen, J. (2012). Combining crop models and remote sensing for yield prediction: Concepts, applications and challenges for heterogeneous smallholder environments. Report of CCFAS-JRC Workshop at Joint Research Centre, Ispra, Italy, June 13-14, 2012. Joint Research Center Technical Report. Luxembourg: Publications Office of the European Union. doi:10.2788/72447.
Hsiao, T. C.; Heng, L.; Steduto, P.; Rojas-Lara, B.; Raes, D., & Fereres, E. (2009). AquaCrop—the FAO crop model to simulate yield response to water: III. Parameterization and testing for maize. Agron. J. 101(3), 448-459. Doi: 10.2134/agronj2008.0218s.
Huang, J., Ma, H., Su, W., Zhang, X., Huang, Y., Fan, J., & Wu, W., (2015b). Jointly assimilating MODIS LAI and ET products into the SWAP model for winter wheat yield estimation. IEEE JSTAR. 8, 4060–4071. DOI: 10.1109/JSTARS.2015.2403135.
Huang, J.; Gómez-Dans, J.L.; Huang, H.; Ma, H.; Wu, Q.; Lewis, P.E.; Liang, S.; Chen, Z.; Xue, J.H.; Wu, Y. and Zhao, F. (2019). Assimilation of remote sensing into crop growth models: Current status and perspectives. Agric. For. Meteorol. 276, 107609. https://doi.org/ 10.1016/j.agrformet.2019.06.008.
Huang, J.; Tian, L.; Liang, S.; Ma, H.; Becker-Reshef, I.; Huang, Y.; Su, W.; Zhang, X.; Zhu, D. and Wu, W. (2015a). Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model. Agric. For. Meteorol. 204, 106-121. https://doi.org/ 10.1016/j.agrformet.2015.02.001.
Ines, A.V., Das, N.N., Hansen, J.W. & Njoku, E.G., (2013). Assimilation of remotely sensed soil moisture and vegetation with a crop simulation model for maize yield prediction. Remote Sensing of Environment. 138, 149-164. https://doi.org/10.1016/j.rse. 2013.07.018.
Iooss, B., & Lemaître, P. (2015). A review on global sensitivity analysis methods. Uncertainty management in simulation-optimization of complex systems: algorithms and applications, Part of the Operations Research/Computer Science Interfaces Series book series (ORCS, volume 59). 101-122.
Jin, X.; Kumar, L.; Li, Z.; Feng, H.; Xu, X.; Yang, G., & Wang, J. (2018a). A review of data assimilation of remote sensing and crop models. Eur. J. Agron. 92, 141-152. Doi: 10.1016/j.eja.2017.11.002.
Jin, X.; Li, Z.; Nie, C.; Xu, X.; Feng, H.; Guo, W., & Wang, J. (2018b). Parameter sensitivity analysis of the AquaCrop model based on extended fourier amplitude sensitivity under different agro-meteorological conditions and application. Field Crops Res. 226, 1-15. https://doi.org/10.1016/j.fcr.2018.07.002.
Jin, X.; Li, Z.; Yang, G.; Yang, H.; Feng, H.; Xu, X.; Wang, J.; Li, X. and Luo, J. (2017). Winter wheat yield estimation based on multi-source medium resolution optical and radar imaging data and the AquaCrop model using the particle swarm optimization algorithm. ISPRS J. Photogramm. Remote Sens. 126, 24-37. Doi: 10.1016/j.isprsjprs.2017.02.001.
Jin, X.L.; Feng, H.K.; Zhu, X.K.; Li, Z.H.; Song, S.N.; Song, X.Y.; Yang, G.J.; Xu, X.G. and Guo, W.S. (2014). Assessment of the AquaCrop model for use in simulation of irrigated winter wheat canopy cover, biomass, and grain yield in the North China Plain. PloS one. 9(1), e86938. https://doi.org/10.1371/ journal.pone.0086938.
Jones, J.W., Tsuji, G.Y., Hoogenboom, G., Hunt, L.A., Thornton, P.K., Wilkens, P.W., Imamura, D.T., Bowen, W.T. and Singh, U. (1998). Decision support system for agrotechnology transfer: DSSAT v3. In Understanding options for agricultural production (pp. 157-177). Dordrecht: Springer Netherlands. https://doi.org/10.1007/ 978-94-017-3624-4_8.
Krishnan, P.; Maity, P. P., & Kundu, M. (2021). Sensitivity analysis of cultivar parameters to simulate wheat crop growth and yield under moisture and temperature stress conditions. Heliyon. 7(7). https://doi.org/10.1016/j.heliyon. 2021.e07602.
Lamboni, M.; Makowski, D.; Lehuger, S.; Gabrielle, B., & Monod, H. (2009). Multivariate global sensitivity analysis for dynamic crop models. Field Crops Res. 113(3), 312-320. https://doi.org/10.1016/ j.fcr.2009.06.007.
Launay, M., and Guerif, M. (2005). Assimilating remote sensing data into a crop model to improve predictive performance for spatial applications. Agric. Ecosyst. Environ. 111(1-4), 321-339. doi: http://dx.doi.org/10.1016/ j.agee.2005.06.005.
Li, W., Song, R., Awais, M., Ji, L., Li, S., Liu, M., Lang, T. and Qi, H. (2024b). Global Sensitivity Analysis of Crop Parameters Based on AquaCrop Model. Water Resources Management. 38(6), 2039-2058. https://doi.org/ 10.1007/s11269-024-03740-z.
Li, X.; Tan, J.; Li, H.; Wang, L.; Niu, G., & Wang, X. (2023). Sensitivity Analysis of the WOFOST Crop Model Parameters Using the EFAST Method and Verification of Its Adaptability in the Yellow River Irrigation Area, Northwest China. Agron. J. 13(9). 2294. https://doi.org/ 10.3390/agronomy13092294.
Li, Y., Zou, C., Feng, L., Yao, X., Li, T., Chen, X., & Zhao, J. (2024a). Estimation of winter wheat yield in Hebi city based on crop models and remote sensing data assimilation. In Journal of Physics: Conference Series (Vol. 2791, No. 1, p. 012079). IOP Publishing. DOI=10.1088/ 1742-6596/2791/1/012079.
Liu, J.; Liu, Z.; Zhu, A. X.; Shen, F.; Lei, Q., & Duan, Z. (2019). Global sensitivity analysis of the APSIM-Oryza rice growth model under different environmental conditions. Sci. Total Environ. 651, 953-968. https://doi.org/10.1016/ j.scitotenv.2018.09.254.
Lizumi, T.; Yokozawa, M.; Nishimori, M. (2009). Parameter estimation and uncertainty analysis of a large-scale crop model for paddy rice: Application of a Bayesian approach. Agric. For. Meteorol. 149 (2), 333–348. https://doi.org/ 10.1016/j.agrformet. 2008.08.015.
Lu, Y.; Chibarabada, T. P.; McCabe, M. F.; De Lannoy, G. J., & Sheffield, J. (2021). Global sensitivity analysis of crop yield and transpiration from the FAO-AquaCrop model for dryland environments. Field Crops Res. 269, 108182. https://doi.org/ 10.1016/ j.fcr.2021.108182.
Ma, H.; Wang, J.; Liu, T.; Guo, Y.; Zhou, Y.; Yang, T.; Zhang, W. & Sun, C. (2023). Time series global sensitivity analysis of genetic parameters of CERES-maize model under water stresses at different growth stages. Agric. Water Manage. 275, 108027. https://doi.org/10.1016/j.agwat.2022.108027.
Martínez-Ruiz, A.; Ruiz-García, A.; Prado-Hernández, J. V.; López-Cruz, I. L.; Valencia-Islas, J. O., & Pineda-Pineda, J. (2021). Global sensitivity analysis and calibration by differential evolution algorithm of HORTSYST crop model for fertigation management. Water J. 13(5), 610. https://doi.org/10.3390/w13050610.
McCown, R. L., Hammer, G. L., Hargreaves, J. N. G., Holzworth, D. P., & Freebairn, D. M. (1996). APSIM: a novel software system for model development, model testing and simulation in agricultural systems research. Agricultural systems, 50(3), 255-271. https://doi.org/10.1016/0308-521X(94)00055-V.
Meng, Y.; Qiu, X.; Liu, L.; Liu, B.; Zhu, Y.; Cao, W.; Tang, L. (2021). Sensitivity Analysis of Genetic Parameters of RiceGrow Model. Journal of Agricultural Big Data. 3(3): 23-32. doi: 10.19788/j.issn.2096-6369.210303.
Morris, M.D. (1991). Factorial sampling plans for preliminary computational experiments. Technometrics. 33, 161-174. https://doi.org/ 10.2307/1269043.
Müller, K. R., Mika, S., Tsuda, K., & Schölkopf, K. (2018). An introduction to kernel-based learning algorithms. In Handbook of neural network signal processing (pp. 4-1). CRC Press.
Nehbandani, A.; Soltani, A.; Nourbakhsh, F., & Dadrasi, A. (2020). Estimating crop model parameters for simulating soybean production in Iran conditions. OCL. 27, 58. https://doi.org/10.1051/ocl/2020057.
Nendel, C., Berg, M., Kersebaum, K.C., Mirschel, W., Specka, X., Wegehenkel, M., Wenkel, K.O. and Wieland, R., (2011). The MONICA model: Testing predictability for crop growth, soil moisture and nitrogen dynamics. Ecological Modelling, 222(9), 1614-1625. https://doi.org/10.1016/j.ecolmodel.2011.02.018.
Niu, X.Z.; Easterling, W.; Hays, C.J.; Jacobs, A.; Mearns, L., (2009). Reliability and input data induced uncertainty of the EPIC model to estimate climate change impact on sorghum yields in the U.S. Great Plains. Agric. Ecosyst. Environ. 129 (1–3), 268–276. https://doi.org/ 10.1016/j.agee.2008.09.012.
Oulaid, B., Milne, A. E., Waine, T., El Alami, R., Rafiqi, M., & Corstanje, R. (2024). Stepwise model parameterization using satellite imagery and hemispherical photography: Tuning AquaCrop sensitive parameters for improved winter wheat yield predictions in semi-arid regions. Field Crops Research. 309, 109327. https://doi.org/10.1016/j.fcr. 2024.109327.
Paleari, L. and Confalonieri, R. (2016). Sensitivity analysis of a sensitivity analysis: We are likely overlooking the impact of distributional assumptions. Ecol. Modell. 340, 57-63. https://doi.org/10.1016/ j.ecolmodel. 2016.09.008.
Paleari, L.; Movedi, E.; Zoli, M.; Burato, A.; Cecconi, I.; Errahouly, J.; Pecollo, E.; Sorvillo, C. and Confalonieri, R. (2021). Sensitivity analysis using Morris: Just screening or an effective ranking method? Ecol. Modell. 455, 109648. https://doi.org/ 10.1016/j.ecolmodel.2021.109648.
Pathak, T. B.; Fraisse, C. W.; Jones, J. W.; Messina, C. D., & Hoogenboom, G. (2007). Use of global sensitivity analysis for CROPGRO cotton model development. Trans. ASABE. 50(6), 2295-2302. doi: 10.13031/ 2013.24082.
Pereira, R. A. D. A., Vianna, M. S., & Marin, F. R. (2023). Sample size, range of parameters and time-dependent effects on global sensitivity analysis in sugarcane modelling. The Journal of Agricultural Science. 161(6), 817-834. DOI: https://doi.org/10.1017/ S0021859624000030.
Pianosi, F.; Beven, K.; Freer, J.; Hall, J. W.; Rougier, J.; Stephenson, D. B., & Wagener, T. (2016). Sensitivity analysis of environmental models: A systematic review with practical workflow. Environ. Model. Softw. 79, 214-232. https://doi.org/10.1016/j.envsoft. 2016. 02.008.
QIN, G.X.; WU, J.; LI, C.B.; SHEN, S.J.; LI, H.H.; YANG, D.H.; JIAO, M.R. & QI, Q. (2022). Sensitivity analysis of WOFOST model crop parameters in different grassland types. Acta Prataculturae Sinica. 31(5), 13. (in Chinese with English Summary). DOI: https://doi.org/10.11686/ cyxb2021391.
Quaife, T.; Lewis, P.; De Kauwe, M.; Williams, M.; Law, B.E.; Disney, M.; Bowyer, P. (2008). Assimilating canopy reflectance into an ecosystem model with an ensemble Kalman filter. Rem. Sens. Environ. 112, 1347–1364. Doi: 10.1016/j.rse.2007.05.020.
Raes, D., Steduto, P., Hsiao, T., Fereres, E. (2017). AquaCrop Version 6.0 Reference Manual. Annex I. Food Agric. Organ. United Nations, Rome, Italy. http//www.fao.org/land-water/ databases-and-software/aquacrop/en/.
Rahimikhoob, H.; Sohrabi, T., and Delshad, M. (2023). Variance-based global sensitivity analysis and uncertainty quantification of the AquaCrop model parameters for basil (Ocimum basilicum L.) under different nitrogen fertilizer rates. J. Plant Nutr. 1-18. https://doi.org/10.1080/01904167.2023.2280142.
Ranghetti, M., Boschetti, M., Ranghetti, L., Tagliabue, G., Panigada, C., Gianinetto, M., Verrelst, J. and Candiani, G., (2023). Assessment of maize nitrogen uptake from PRISMA hyperspectral data through hybrid modelling. European journal of remote sensing, 56(1), 2117650. https://doi.org/ 10.1080/22797254.2022.2117650.
Rosa, S. L. K.; Souza, J. L. M. D.; Tsukahara, R. Y., & Kochinski, E. G. (2023). Sensitivity analysis of the AquaCrop model for wheat crop in Campos Gerais region, Paraná. Revista Ceres. 70, 32-41. https://doi.org/ 10.1590/ 0034-737X202370010004.
Ruan, S., Cao, H., Wu, S., Ma, Y., Li, W., Jin, Y., Deng, H., Chen, G., Wu, W. and Yang, P. (2024). Rape Yield Estimation Considering Non-Foliar Green Organs Based on the General Crop Growth Model. Plant Phenomics. 6, 0253. https://doi.org/10.34133/ plantphenomics.0253.
Rutjens, R. J., Evers, J. B., Band, L. R., Jones, M. D., & Owen, M. R. (2024). Are we focusing on the right parameters? Insights from Global Sensitivity Analysis of a Functional-Structural Plant Model. in silico Plants. 6(2), diae011. https://doi.org/10.1093/insilicoplants/ diae014.
Sahoo, R.N., Rejith, R.G., Gakhar, S., Verrelst, J., Ranjan, R., Kondraju, T., Meena, M.C., Mukherjee, J., Dass, A., Kumar, S. and Kumar, M., (2024). Estimation of wheat biophysical variables through UAV hyperspectral remote sensing using machine learning and radiative transfer models. Computers and Electronics in Agriculture, 221, 108942. https://doi.org/ 10.1016/ j.compag.2024.108942.
Salmerón-Miranda, F., Båth, B., Eckersten, H., Forkman, J., & Wivstad, M. (2007). Aboveground nitrogen in relation to estimated total plant uptake in maize and bean. Nutrient Cycling in Agroecosystems, 79(2), 125-139. https://doi.org/10.1007/ s10705-007-9102-x.
Saltelli, A.; Annoni, P. (2010). How to avoid a perfunctory sensitivity analysis. Environ. Model. Softw. 25, 1508-1517. https://doi.org/ 10.1016/j.envsoft.2010.04.012.
Saltelli, A.; Ratto, M.; Andres, T.; Campolongo, F.; Cariboni, J.; Gatelli, D.; Saisana, M. & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. New York: Wiley.
Saltelli, A.; Tarantola, S.; Chan, K.P.S. (1999). A quantitative model-independent method for global sensitivity analysis of model output. Technometrics. 41, 39-56. https://doi.org/ 10.1080/00401706.1999.10485594.
Sarangi, A.; Bandyopadhyay, K. K.; Samal, A., & Pathan, A. (2016). Evaluation of FAO AquaCrop model for wheat under different irrigation regimes. J. App. Nat. Sci. 8(1), 473-480. DOI:10.31018/jans.v8i1.820.
Senna, M. C., Costa, M. H., & Shimabukuro, Y. E. (2005). Fraction of photosynthetically active radiation absorbed by Amazon tropical forest: A comparison of field measurements, modeling, and remote sensing. Journal of Geophysical Research: Biogeosciences, 110(G1). https://doi.org/10.1029/2004JG000005.
Servin-Palestina, M.; López-Cruz, I. L.; Zegbe-Domínguez, J. A.; Ruiz-García, A.; Salazar-Moreno, R., & Medina-García, G. (2022). Spatiotemporal Uncertainty and Sensitivity Analysis of the SIMPLE Model Applied to Common Beans for Semi-Arid Climate of Mexico. Agron. J. 12(8). 1813. https://doi.org/ 10.3390/agronomy12081813.
Shafiei, M.; Ghahreman, B.; Saghafian, B.; Davari, K., & Vazifeh Doost, M. (2018). Global sensitivity analysis of WOFOST model parameters for maize and wheat yield simulation. Iranian J. Soil. Water. Res. 49(4), 831-839. (in Persian with English Summary). doi: 10.22059/ijswr.2018.133882.667317.
Shan, Y., Huang, M., Harris, P., & Wu, L. (2021). A sensitivity analysis of the SPACSYS model. Agriculture. 11(7), 624. https://doi.org/ 10.3390/agriculture11070624.
Silvestro, P. C.; Pignatti, S.; Yang, H.; Yang, G.; Pascucci, S.; Castaldi, F., & Casa, R. (2017). Sensitivity analysis of the Aquacrop and SAFYE crop models for the assessment of water limited winter wheat yield in regional scale applications. PloS one. 12(11), e0187485. Doi: 10.3390/rs9050509.
Song, M. D.; Feng, H.; Li, Z. P., & Gao, J. E. (2014). Global Sensitivity Analyses of DSSAT–CERES–Wheat Model Using Morris and EFAST Methods. Transactions of the Chinese Society for Agricultural Machinery. 45(10), 124-131. DOI:10.6041/ j.issn.1000-1298.2014.10.020.
Specka, X.; Nendel, C., & Wieland, R. (2019). Temporal sensitivity analysis of the MONICA model: Application of two global approaches to analyze the dynamics of parameter sensitivity. Agric. 9(2), 37. https://doi.org/ 10.3390/agriculture9020037.
Steduto, P.; Hsiao, T.C.; Raes, D.; Fereres, E. (2009). AquaCrop—The FAO crop model to simulate yield response to water: I. Concepts and underlying principles. Agron. J. 101, 426–437. https://doi.org/10.2134/ agronj2008. 0139s.
Sun, H., Feng, M., Yang, W., Bi, R., Sun, J., Zhao, C., Xiao, L., Wang, C. and Kubar, M.S. (2022). Monitoring leaf nitrogen accumulation with optimized spectral index in winter wheat under different irrigation regimes. Frontiers in Plant Science, 13, 913240. https://doi.org/ 10.3389/ fpls.2022.913240.
Sun, X.Y.; Newham, L.T.H.; Croke, B.F.W.; Norton, J.P. (2012). Three complementary methods for sensitivity analysis of a water quality model. Environ. Model. Softw. 37, 19-29. https://doi.org/10.1016/j.envsoft.2012.04.010.
Tavakoli, A. R.; Moghadam, M. M., & Sepaskhah, A. R. (2015). Evaluation of the AquaCrop model for barley production under deficit irrigation and rainfed condition in Iran. Agric. Water Manage. 161, 136-146. https://doi.org/ 10.1016/j.agwat. 2015.07.020.
Upreti, D.; Pignatti, S.; Pascucci, S.; Tolomio, M.; Li, Z.; Huang, W., & Casa, R. (2020). A Comparison of Moment-Independent and Variance-Based Global Sensitivity Analysis Approaches for Wheat Yield Estimation with the Aquacrop-OS Model. Agron. J. 10(4), 607. https://doi.org/10.3390/agronomy 10040607.
Van Diepen, C. V., Wolf, J. V., Van Keulen, H., & Rappoldt, C. (1989). WOFOST: a simulation model of crop production. Soil use and management, 5(1), 16-24. https://doi.org/ 10.1111/j.1475-2743.1989.tb00755.x.
Vanuytrecht, E.; Raes, D., & Willems, P. (2014). Global sensitivity analysis of yield output from the water productivity model. Environ. Modell. Software. 51, 323-332. Doi: 10.1016/j.envsoft.2013.10.017.
Varella, H.; Buis, S.; Launay, M., & Guérif, M. (2012). Global sensitivity analysis for choosing the main soil parameters of a crop model to be determined. Agric. Sci. 3(7). DOI: 10.4236/as.2012.37116.
Varella, H.; Guérif, M., & Buis, S. (2010). Global sensitivity analysis measures the quality of parameter estimation: The case of soil parameters and a crop model. Environ. Modell. Soft. 25(3), 310-319. https://doi.org/ 10.1016/j.envsoft.2009.09.012.
Verrelst, J., Alonso, L., Camps-valls, G., Delegido, J., Moreno, J. (2012a). Retrieval of Vegetation Biophysical Parameters Using Gaussian Process Techniques. IEEE Trans. Geosci. Remote Sens, 50(5), 1832–1843. doi: 10.1109/TGRS.2011.2168962.
Verrelst, J., Dethier, S., Rivera, J. P., Munoz-Mari, J., Camps-Valls, G., & Moreno, J. (2016). Active learning methods for efficient hybrid biophysical variable retrieval. IEEE Geoscience and Remote Sensing Letters, 13(7), 1012-1016. Doi: 10.1109/LGRS.2016. 2560799.
Verrelst, J., Muñoz, J., Alonso, L., Delegido, J., Pablo Rivera, J., Camps-valls, G., Moreno, J. (2012b). Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and -3. Remote Sens. Environ. 118, 127–139. doi: 10.1016/ j.rse.2011.11.002.
Verrelst, J., Schaepman, M.E., Malenovský, Z., Clevers, J.G.P.W. (2010). Effects of woody elements on simulated canopy reflectance: Implications for forest chlorophyll content retrieval. Remote Sens. Environ 2010, 114(3), 647–656. https://doi.org/10.1016/j.rse.2009. 11.004.
Verrelst, J.; Rivera, J. P.; Veroustraete, F.; Muñoz-Marí, J.; Clevers, J. G.; Camps-Valls, G., & Moreno, J. (2015). Experimental Sentinel-2 LAI estimation using parametric, non-parametric and physical retrieval methods–A comparison. ISPRS J. Photogramm. Remote Sens. 108, 260-272. https://doi.org/10.1016/j.isprsjprs.2015.04.013.
Wang, G.; Mehmood, F.; Zain, M.; Hamani, A. K. M.; Xue, J.; Gao, Y., & Duan, A. (2022). AquaCrop Model Evaluation for Winter Wheat under Different Irrigation Management Strategies: A Case Study on the North China Plain. Agron. J. 12(12), 3184. https://doi.org/ 10.3390/agronomy12123184.
Wang, J., Wang, Y., & Qi, Z. (2024). Remote Sensing Data Assimilation in Crop Growth Modeling from an Agricultural Perspective: New Insights on Challenges and Prospects. Agron. J. 14(9), 1920. https://doi.org/10.3390/ agronomy14091920.
Wang, J.; Li, X.; Lu, L. and Fang, F. (2013a). Estimating near future regional corn yields by integrating multi-source observations into a crop growth model. Eur. J. Agron. 49, 126-140. https://doi.org/10.1016/j.eja.2013.03.005.
Wang, J.; Li, X.; Lu, L., & Fang, F. (2013b). Parameter sensitivity analysis of crop growth models based on the extended Fourier Amplitude Sensitivity Test method. Environ. Modell. Soft. 48, 171-182. https://doi.org/ 10.1016/j.envsoft.2013.06.007.
WEI, X., & NIE, Z. (2024). Sensitivity analysis and optimization of leaf area index related parameters of dryland wheat based on APSIM model. Chinese Journal of Eco-Agriculture. 32(1), 119-129. DOI: 10.12357/ cjea.20230345.
XING, H. M.; XU, X. G.; LI, Z. H.; CHEN, Y. J.; FENG, H. K.; YANG, G. J., & CHEN, Z. X. (2017). Global sensitivity analysis of the AquaCrop model for winter wheat under different water treatments based on the extended Fourier amplitude sensitivity test. J. Integr. Agric. 16(11), 2444-2458. https://doi.org/ 10.1016/S2095-3119(16)61626-X.
Xing, H.; Xu, X.; Yang, F.; Feng, H., & Yang, G. (2016). Global sensitivity analysis of winter wheat yield and process-based variable with AquaCrop model. In Geoscience and Remote Sensing Symposium (IGARSS), 2016 IEEE International (pp. 5929-5932). IEEE. DOI: 10.1109/IGARSS.2016.7730549.
Xiong, W. (2022). In-Season Crop Yield Forecasting in Africa by Coupling Remote Sensing and Crop Modeling: A Systematic Literature Review. Available online: https://cgspace.cgiar.org/server/api/core/bitstreams/30d1f3cc-065d-40cd-8730-de6d87004c 51/content (accessed on 10 July 2022).
Xu, M.; Wang, C.; Ling, L.; Batchelor, W. D.; Zhang, J., & Kuai, J. (2021). Sensitivity analysis of the CROPGRO-Canola model in China: A case study for rapeseed. Plos one. 16(11), e0259929. https://doi.org/ 10.1371/ journal.pone.0259929.
Xu, X.; Sun, C.; Huang, G., & Mohanty, B. P. (2016). Global sensitivity analysis and calibration of parameters for a physically-based agro-hydrological model. Environ. Modell. Soft. 83, 88-102. https://doi.org/ 10.1016/j.envsoft.2016.05.013.
Zeng, W.; Lei, G.; Zha, Y.; Fang, Y.; Wu, J., & Huang, J. (2018). Sensitivity and uncertainty analysis of the HYDRUS-1D model for root water uptake in saline soils. Crop Pasture Sci. 69(2), 163-173.
https://doi.org/10.1071/ CP17020.
Zhang, M.; Gao, Y.M.; Zhang, Y.Q. (2023). Sensitivity analysis and calibration of the APSIM next-generation model under different irrigation and sowing density in wheat. Chinese Journal of Eco-Agriculture. 31(1): 102−112 doi: 10.12357/cjea.20220384. (in Chinese with English Summary).
Zhang, X.; Guo, P.; Guo, S.; Zhang, F., & Zhang, C. (2022). An integrated model to optimize irrigation amount and time in shallow groundwater area under drought conditions. J. Contam. Hydrol. 246, 103956. https://doi.org/ 10.1016/j.jconhyd.2022.103956.
Zhao, G.; Bryan, B. A., & Song, X. (2014). Sensitivity and uncertainty analysis of the APSIM-wheat model: Interactions between cultivar, environmental, and management parameters. Ecol. Modell. 279, 1-11. https://doi.org/10.1016/j.ecolmodel.2014.02.003.
