Abi Saab, M. T., El Alam, R., Jomaa, I., Skaf, S., Fahed, S., Albrizio, R., & Todorovic, M. (2021). Coupling remote sensing data and Aquacrop model for simulation of winter wheat growth under rainfed and irrigated conditions in a Mediterranean environment. Agronomy, 11(11), 2265.
Abou-Ismail, O., (2004). Rice yield estimation by integrating remote sensing with rice growth simulation model. Pedosphere 14, 519–526.
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 Resources Management, 29(8), 2837-2853.
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).
Alexandratos, N., & Bruinsma, J. (2012). World Agriculture Towards 2030 / 2050: The 2012 Revision. Report.
Amiri, E., Rezaei, M., Eyshi Rezaei, E. & Bannayan, M. (2014). Evaluation of Ceres-Rice, Aquacrop and Oryza 2000 models in simulation of rice yield response to different irrigation and nitrogen management strategies. Journal of Plant Nutrition. 37(11): 1749-1769.
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. Agricultural Water Management, 100(1), 1-8.
Arvaneh, H., & Abbasi, F., (2014). Calibration and validation of the AquaCrop model for canola in the field. Iranian water research journal. 8(14). 9-17. (in persian).
Bach, H., & Mauser, W., (2003). Methods and examples for remote sensing data assimilation in land surface process modeling. IEEE Transaction on Geoscience and Remote sensing. 41, 1629–1637.
Badiehneshin, A., Noory, H., & Vazifedoust, M. (2014). Improving Crop Yield Estimation through SWAP Model Using Satellite Data. Iranian Journal of Soil and Water Research, 45(4), 379-388. doi: 10.22059/ijswr.2014.52590. (in persian).
Bastiaanssen, W.G., & Ali, S., (2003). A new crop yield forecasting model based on satellite measurements applied across the Indus Basin, Pakistan. Agriculture, Ecosystem and Environmental journal. 94, 321–340.
Battude, M., Al Bitar, A., Morin, D., Cros, J., Huc, M., Sicre, C.M., Le Dantec, V. & Demarez, V. (2016). Estimating maize biomass and yield over large areas using high spatial and temporal resolution Sentinel-2 like remote sensing data. Remote Sensing of Environment, 184, 668-681.
Battude, M., Al Bitar, A., Brut, A., Tallec, T., Huc, M., Cros, J., Weber, J.J., Lhuissier, L., Simonneaux, V. & Demarez, V. (2017). Modeling water needs and total irrigation depths of maize crop in the south west of France using high spatial and temporal resolution satellite imagery. Agricultural Water Management, 189, 123-136.
Beyene, A. N., Zeng, H., Wu, B., Zhu, L., Gebremicael, T. G., Zhang, M., & Bezabh, T. (2022). Coupling remote sensing and crop growth model to estimate national wheat yield in Ethiopia. Big Earth Data, 6(1), 18-35.
Bolten, J.D., Crow, W.T., Zhan, X., Jackson, T.J., & Reynolds, C.A., (2010). Evaluating the
utility of remotely sensed soil moisture retrievals for operational agricultural drought
monitoring. IEEE JSTAR 3(1), 57–66.
Bouman, B., (1995). Crop modelling and remote sensing for yield prediction. NJAS Wageningen Journal Life Science. 43, 143–161.
Busetto, L., Casteleyn, S., Granell, C., Pepe, M., Barbieri, M., Campos-Taberner, M., Casa, R., Collivignarelli, F., Confalonieri, R., Crema, A., García-Haro, F.J., Gatti, L., Gitas, I. Z., Gonz´alez-P´erez, A., Grau-Muedra, G., Guarneri, T., Holecz, F., Katsantonis, D., Minakou, C., Miralles, I., Movedi, E., Nutini, F., Pagani, V., Palombo, A., Di Paola, F., Pascucci, S., Pignatti, S., Stroppiana, D., Rampini, A., Ranghetti, L., Ricciardelli, E., Romano, F., Stavrakoudis, D. G., Viggiano M., & Boschetti, M., (2017). Downstream services for rice crop monitoring in Europe: From regional to local scale. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(12), 5423-5441.
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. Agricultural and Forest Meteorology 151, 101–115.
Chakrabarti, S., Bongiovanni, T., Judge, J., Zotarelli, L., & Bayer, C., (2014). Assimilation of SMOS soil moisture for quantifying drought impacts on crop yield in agricultural regions. IEEE JSTAR 7, 3867–3879.
Challinor, A.J., Smith, M.S., & Thornton, P., (2012). Use of agro-climate ensembles for quantifying uncertainty and informing adaptation. Agricultural and Forest Meteorology 170, 2–7.
Claverie, M., Demarez, V., Duchemin, B., Hagolle, O., Keravec, P., Marciel, B., Ceschia, E., Dejoux, J.F. & Dedieu, G. (2009). Spatialization of crop leaf area index and biomass by combining a simple crop model SAFY and high spatial and temporal resolutions remote sensing data. In Geoscience and Remote Sensing Symposium, 12-17 July 2009 Cape Town, South Africa IEEE International, IGARSS. 3, pp. III-478. IEEE.
Claverie, M., Demarez, V., Duchemin, B., Hagolle, O., Ducrot, D., Marais-Sicre, C., Dejoux, J.F., Huc, M., Keravec, P., Béziat, P. & Fieuzal, R. (2012). Maize and sunflower biomass estimation in southwest France using high spatial and temporal resolution remote sensing data. Remote Sensing of Environment, 124, 844-857.
Clevers, J. G. P. W., Büker, C., Van Leeuwen, H. J. C., & Bouman, B. A. M. (1994). A framework for monitoring crop growth by combining directional and spectral remote sensing information. Remote sensing of environment, 50(2), 161-170.
Clevers, J., Vonder, O. W., Jongschaap, R. E. E., Desprats, J. F., King, C., Prevot, L., & 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.
Coudron, W., De Frenne, P., Verheyen, K., Gobin, A., Boeckaert, C., De Cuypere, T., Lootens, P., Pollet, S. and De Swaef, T., (2023). Usefulness of cultivar-level calibration of AquaCrop for vegetables depends on the crop and data availability. Frontiers in Plant Science, 14.
Cui, Y., Liu, S., Li, X., Geng, H., Xie, Y., & He, Y. (2022). Estimating Maize Yield in the Black Soil Region of Northeast China Using Land Surface Data Assimilation: Integrating a Crop Model and Remote Sensing. Frontiers in Plant Science, 2073.
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. Agricultural and forest meteorology, 151(12), 1843-1855.
De Wit, A.D., & Van Diepen, C., (2007). Crop model data assimilation with the Ensemble
Kalman filter for improving regional crop yield forecasts. Agricultural and Forest Meteorology. 146(1-2),
38–56.
Delécolle, R., Maas, S. J., Guérif, M., & Baret, F. (1992). Remote sensing and crop production models: present trends. ISPRS Journal of Photogrammetry and Remote Sensing, 47(2-3), 145-161. doi: http://dx.doi.org/10.1016/0924-2716(92)90030-D.
Dente, L., Satalino, G., Mattia, F., & Rinaldi, M. (2008). Assimilation of leaf area index derived from ASAR and MERIS data into CERES-Wheat model to map wheat yield. Remote sensing of Environment, 112(4), 1395-1407.
Dong, Y., Zhao, C., Yang, G., Chen, L., Wang, J., & Feng, H., (2013). Integrating a very fast
simulated annealing optimization algorithm for crop leaf area index variational assimilation. Mathematical and Computer Modelling. 58(3-4), 877–885.
Dong, T., Liu, J., Qian, B., Zhao, T., Jing, Q., Geng, X., Wang, J., Huffman, T. & Shang, J., (2016). Estimating winter wheat biomass by assimilating leaf area index derived from fusion of Landsat-8 and MODIS data. International Journal of Applied Earth Observation and Geoinformation, 49, 63-74.
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. International journal of applied earth observation and geoinformation, 9(2), 165-193.
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. International Journal of Remote Sensing, 29(10), 3011-3032.
Fattori Jr, I. M., & Marin, F. R. (2023). Assessing the influence of crop model structure on the performance of data assimilation for sugarcane. Computers and Electronics in Agriculture, 209, 107848.
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. Ecological Application 21 (5), 1461–1473.
Gobron, N., Pinty, B., Verstraete, M.M., & Widlowski, J.L., (2000). Advanced vegetation indices optimized for up-coming sensors: design, performance, and applications. IEEE Transcation, Geoscience and Remote sensing. 38, 2489–2505.
Guerif, M., & Duke, C., (2000). Adjustment procedures of a crop model to the site specific
characteristics of soil and crop using remote sensing data assimilation. Agriculture, ecosystems & environment. 81(1), 57–69.
Hadria, R., Duchemin, B.I., Lahrouni, A., Khabba, S., Er Raki, S., Dedieu, G., Chehbouni,
A., & Olioso, A., (2006). Monitoring of irrigated wheat in a semi-arid climate using crop
modelling and remote sensing data: impact of satellite revisit time frequency. International Journal of Remote Sensing. 27(6), 1093–1117.
Hansen, J. W. & Jones, J. W. (2000). Scaling-up crop models for climate variability applications. Agricultural Systems, 65(1), 43-72. doi: 10.1016/s0308-521x(00)00025-1.
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. Agricultural Water Management, 176, 91-99.
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.
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. Agronomy Journal, 101(3), 448-459.
Huang, Y., Zhu, Y., Li, W., Cao, W., & Tian, Y., (2013). Assimilating remotely sensed information with the wheat grow model based on the ensemble square root filter for improving regional wheat yield forecasts. Plant Produce Sciences. 16, 352–364.
Huang, J., Tian, L., Liang, S., Ma, H., Becker-Reshef, I., Huang, Y., Su, W., Zhang, X., Zhu, D. & 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. Agricultural and Forest Meteorology, 204, 106-121.
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.
Hurtado, E., Artigao, M., & Caselles, V., (1994). Estimating Maize (Zea mays) cvapo-transpiration from NOAA-AVHRR thermal data in the Albacete area, Spain. International Journal of Remote Sensing. 15, 2023–2037.
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.
Jafari sayadi, F., (2021). Estimation of rice yield using satellite image integration algorithms and AquaCrop model. Thesis of PhD. Sari University of Agricultural Sciences and Natural Resources. Supervisr professor: Mohammad ali Gholami sefid kohi. (in persian).
Jégo, G., Pattey, E., & Liu, J. (2012). Using Leaf Area Index, retrieved from optical imagery, in the STICS crop model for predicting yield and biomass of field crops. Field crops research, 131, 63-74.
Jiang, Z., Chen, Z., Chen, J., Liu, J., Ren, J., Li, Z., Sun, L., & Li, H., (2014). Application of crop model data assimilation with a particle filter for estimating regional winter wheat yields. IEEE JSTAR 7, 4422–4431.
Jin, X.L., Diao, W.Y., Xiao, C.H., Wang, F.Y., Chen, B., Wang, K.R., & Li, S.K., (2013). Estimation of wheat agronomic parameters using new spectral indices. PLoS One 8, e72736.
Jin, M., Liu, X., Wu, L., & Liu, M. (2015a). An improved assimilation method with stress factors incorporated in the WOFOST model for the efficient assessment of heavy metal stress levels in rice. International Journal of Applied Earth Observation and Geoinformation, 41, 118-129.
Jin, X.L., Yang, G.J., Xu, X.G., Yang, H., Feng, H., Li, Z.H., Shen, J.X., Lan, Y.B., & Zhao, C.J., (2015b). Combined multi-temporal optical and radar parameters for estimating LAI and biomass in winter wheat using HJ and RADARSAR-2 data. Remote Sensing. 7, 13251–13272.
Jin, X., Kumar, L., Li, Z., Xu, X., Yang, G. & Wang, J., (2016a). Estimation of winter wheat biomass and yield by combining the Aquacrop model and field hyperspectral data. Remote Sensing, 8(12), p.972. 1-15. doi:10.3390/rs8120972.
Jin, X., Yang, G., Li, Z., Xu, X., Wang, J. & Lan, Y., (2016b). Estimation of water productivity in winter wheat using the AquaCrop model with field hyperspectral data. Precision Agriculture, 1-17.
Jin, X., Li, Z., Yang, G., Yang, H., Feng, H., Xu, X., Wang, J., Li, X. & 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 Journal of Photogrammetry and Remote Sensing, 126, 24-37.
Jin, X., Kumar, L., Li, Z., Feng, H., Xu, X., Yang, G., & Wang, J. (2018). A review of data assimilation of remote sensing and crop models. European Journal of Agronomy, 92, 141-152.
Jin, X., Li, Z., Feng, H., Ren, Z., & Li, S. (2020). Estimation of maize yield by assimilating biomass and canopy cover derived from hyperspectral data into the AquaCrop model. Agricultural Water Management, 227, 105846.
Jin, N., Tao, B., Ren, W., He, L., Zhang, D., Wang, D., & Yu, Q. (2022). Assimilating remote sensing data into a crop model improves winter wheat yield estimation based on regional irrigation data. Agricultural Water Management, 266, 107583.
Jongschaap, R.E., & Schouten, L.S., (2005). Predicting wheat production at regional scale by integration of remote sensing data with a simulation model. Agronomy for Sustainable Development. 25, 481–489.
khoshravesh, M., Ghadamifiroozabadi, A., Shirazi, P., & Najafimood, M. (2015). Cotton Response Simulation to Dry and Salinity Stress by using of AquaCrop Model. Irrigation and Water Engineering, 6(21), 166-179. (in persian).
Kim, D., & Kaluarachchi, J. (2015). Validating FAO AquaCrop using Landsat images and regional crop information. Agricultural Water Management, 149, 143-155.
Kivi, M., Vergopolan, N., & Dokoohaki, H. (2022). A comprehensive assessment of in situ and remote sensing soil moisture data assimilation in the APSIM model for improving agricultural forecasting across the US Midwest. Hydrology and Earth System Sciences Discussions, 1-33.
Kivi, M., Vergopolan, N., & Dokoohaki, H. (2023). A comprehensive assessment of in situ and remote sensing soil moisture data assimilation in the APSIM model for improving agricultural forecasting across the US Midwest.
HESS, 27(5), 1173–1199.
https://doi.org/10.5194/hess-27-1173-2023.
Launay, M., & Guerif, M. (2005). Assimilating remote sensing data into a crop model to improve predictive performance for spatial applications. Agriculture, Ecosystems & Environment, 111(1-4), 321-339. doi: http://dx.doi.org/10.1016/j.agee.2005.06.005.
Li, R., Li, C.J., Dong, Y.Y., Feng, L., Wang, J.H., Yang, X.D., & Pan, Y.C., (2011). Assimilation
of remote sensing and crop model for LAI estimation based on ensemble Kalman
Filter. Agricultural sciences in China. 10(10), 1595–1602.
Li, Y., Zhou, G.Q., Zhou, J., Zhang, G.F., Chen, C., & Wang, J., (2014). Assimilating remote
sensing information into a coupled hydrology-crop growth model to estimate regional
maize yield in arid regions. Ecological modelling. 291, 15–27.
Li, Z., Jin, X., Zhao, C., Wang, J., Xu, X., Yang, G., Li, C. & Shen, J., (2015). Estimating wheat yield and quality by coupling the DSSAT-CERES model and proximal remote sensing. European Journal of Agronomy, 71, 53-62.
Liang, S., Li, X., Xie, X., (2013). Land Surface Observation, Modeling and Data Assimilation World Scientific. World scientific publishing company. 1st edition.
Liao, C., Wang, J., Dong, T., Shang, J., Liu, J., & Song, Y. (2019). Using spatio-temporal fusion of Landsat-8 and MODIS data to derive phenology, biomass and yield estimates for corn and soybean. Science of The Total Environment, 650, 1707-1721.
Liu, C., Gao, W., Liu, P., & Sun, Z., (2014). Assimilation of remote sensing data into crop
growth model to improve the estimation of regional winter wheat yield, SPIE optical
engineering + applications. In Remote Sensing and Modeling of Ecosystems for Sustainability XI. 9221. 10-18. SPIE.
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. Agricultural and Forest Meteorology 149 (2), 333–348.
Lu, Y., Chibarabada, T. P., Ziliani, M. G., Onema, J. M. K., McCabe, M. F., & Sheffield, J. (2021). Assimilation of soil moisture and canopy cover data improves maize simulation using an under-calibrated crop model. Agricultural Water Management, 252, 106884.
Lu, Y., Wei, C., McCabe, M. F., & Sheffield, J. (2022). Multi-variable assimilation into a modified AquaCrop model for improved maize simulation without management or crop phenology information.
Agricultural Water Management, 266, 107576.
https://doi.org/10.1016/j.agwat.2022.107576.
Ma, G., Huang, J., Wu, W., Fan, J., Zou, J. & Wu, S., (2013). Assimilation of MODIS-LAI into the WOFOST model for forecasting regional winter wheat yield. Mathematical and Computer Modelling, 58(3), 634-643.
Ma, C., Liu, M., Ding, F., Li, C., Cui, Y., Chen, W., & Wang, Y. (2022a). Wheat growth monitoring and yield estimation based on remote sensing data assimilation into the SAFY crop growth model. Scientific Reports, 12(1), 5473.
Ma, B., Wang, Q., Xue, B., Hou, Z., Jiang, Y., & Cai, W. (2022b). Application of UAV Remote Sensing in Monitoring Water Use Efficiency and Biomass of Cotton Plants Adjacent to Shelterbelt. Frontiers in Plant Science, 13.
Maas, S.J., (1988). Use of remotely-sensed information in agricultural crop growth models.
Ecological modelling. 41(3-4), 247–268.
Mirsafi, Z. S., Sepaskhah, A. R., Ahmadi, S. H., & Kamgar-Haghighi, A. A. (2016). Assessment of AquaCrop model for simulating growth and yield of saffron (Crocus sativus L.). Scientia horticulturae, 211, 343-351.
Mokhtari, S., Pirmoradian, N., Vazifehdoost, M., & Davatgar, N. (2013). Increasing accuracy of regional rice yield estimation by improvement of spatial resolution of leaf area index maps in VSM vegetative model. Cereal Research, 2(3), 209-221. (in persian).
Mokhtari, A., Noory, H., & Vazifedoust, M. (2018). Improving crop yield estimation by assimilating LAI and inputting satellite-based surface incoming solar radiation into SWAP model. Agricultural and Forest Meteorology, 250, 159-170.
Morel, J., Martiné, J.-F., Bégué, A., Todoroff, P., & Petit, M., (2012). A comparison of two
coupling methods for improving a sugarcane model yield estimation with a NDVI derived variable, In Remote sensing for agriculture, ecosystems, and hydrology XIV . 8531. 93-102. SPIE.
Morel, J., Bégué, A., Todoroff, P., Martiné, J. F., Lebourgeois, V., & Petit, M. (2014a). Coupling a sugarcane crop model with the remotely sensed time series of fIPAR to optimise the yield estimation. European journal of agronomy, 61, 60-68.
Morel, J., Todoroff, P., Bégué, A., Bury, A., Martiné, J.F., & Petit, M., (2014b). Toward a satellite-based system of sugarcane yield estimation and forecasting in smallholder farming conditions: a case study on Reunion Island. Remote Sensing. 6, 6620–6635.
Nearing, G.S., Crow, W.T., Thorp, K.R., Moran, M.S., Reichle, R.H., & Gupta, H.V., (2012). Assimilating remote sensing observations of leaf area index and soil moisture for wheat yield estimates: an observing system simulation experiment. Water Resources Research. 48 W05525.
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. Agriculture, Ecosystems & Environment 129 (1–3), 268–276.
Nouvellon, Y., Moran, M.S., Seen, D.L., Bryant, R., Rambal, S., Ni, W., Bégué, A., Chehbouni, A., Emmerich, W.E., & Heilman, P., (2001). Coupling a grassland ecosystem model with Landsat imagery for a 10-year simulation of carbon and water budgets. Remote Sensing Environment. 78, 131–149.
Orlova, Y., & Linker, R. (2023). Data assimilation with sensitivity-based particle filter: A simulation study with AquaCrop. Computers and Electronics in Agriculture, 204, 107538.
Panday, S.G., (2014). Use of Remote Sensing Data in a Crop Growth Model to Estimate Actual Crop Yields: Testing Aquacrop with NDVI Time Series for Two Crops in Spain. University of Twente Faculty of Geo-Information and Earth Observation (ITC). Master of science in Geo information science and earth observation.
Peng, X., Han, W., Ao, J., & Wang, Y. (2021). Assimilation of LAI derived from UAV multispectral data into the SAFY model to estimate maize yield. Remote Sensing, 13(6), 1094.
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. Remote Sensing Environment. 112, 1347–1364.
Raes, D., Steduto, P., Hsiao, T. C. and Fereres, E. (2017). AquaCrop. FAO Crop – Water productivity model to simulate yield response to water. Reference Manual. Ver. 6. FAO. Land and Water Division. Rome. Italy.
Razzaghi, F., Zhou, Z., Andersen, M. N., & Plauborg, F. (2017). Simulation of potato yield in temperate condition by the AquaCrop model. Agricultural water management, 191, 113-123.
Ren, J., Yu, F., Du, Y., Qin, J., & Chen, Z. (2009). Assimilation of field measured LAI into crop growth model based on SCE-UA optimization algorithm. In Geoscience and Remote Sensing Symposium, 12- 17 July 2009 Cape Town, South Africa IEEE International, IGARSS 2009.. 3. III-573. IEEE.
Ren, J., Yu, F., Qin, J., Chen, Z., & Tang, H. (2010). Integrating remotely sensed LAI with EPIC model based on global optimization algorithm for regional crop yield assessment. In Geoscience and Remote Sensing Symposium (IGARSS), 25-30 July Honolulu, HI, USA 2010 IEEE International. 2147-2150. IEEE.
Sadooghi, L., Homaee, M., Noroozi, A., & Asadi Kapourchal, S. (2017). Estimating rice yield using VSM model and satellite images in Guilan province. Cereal Research, 6(3), 397-410. (in persian).
Salemi, H. R., Andarzian, B., & Govanji, S., (2015). Assessment of Yield Optimization and Water Use for Local Rice under Water Deficit Conditions in Nekuabad Irrigation Network in Isfahan, Iran. Agricultural Mechanization and Systems Research, 16(2), 25-40. doi: 10.22092/erams.2015.102851. ( in persian).
Silvestro, Paolo Cosmo, Stefano Pignatti, Simone Pascucci, Hao Yang, Zhenhai Li, Guijun Yang, Wenjiang Huang, & Casa R. (2017). Estimating Wheat Yield in China at the Field and District Scale from the Assimilation of Satellite Data into the Aquacrop and Simple Algorithm for Yield (SAFY) Models. Remote Sensing, 9(5), 509.
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. Agricultural Water Management, 161, 136-146.
Tewes, A., Montzka, C., Nolte, M., Krauss, G., Hoffmann, H., & Gaiser, T. (2020). Assimilation of sentinel-2 estimated LAI into a crop model: Influence of timing and frequency of acquisitions on simulation of water stress and biomass production of winter wheat. Agronomy, 10(11), 1813.
Thorp, K.R., Hunsaker, D.J., & French, A.N., (2010). Assimilating leaf area index estimates
from remote sensing into the simulations of a cropping systems model. Transactions of the ASABE,
53 (1), 251–262
Trépos, R., Champolivier, L., Dejoux, J. F., Al Bitar, A., Casadebaig, P., & Debaeke, P. (2020). Forecasting sunflower grain yield by assimilating leaf area index into a crop model. Remote Sensing, 12(22), 3816.
Tripathy, R., Chaudhari, K.N., Mukherjee, J., Ray, S.S., Patel, N., Panigrahy, S., & Parihar,
J.S., (2013). Forecasting wheat yield in Punjab state of India by combining crop simulation model WOFOST and remotely sensed inputs. Remote sensing letters, 4(1), 19–28.
Trombetta, A., Iacobellis, V., Tarantino, E., & Gentile, F. (2016). Calibration of the AquaCrop model for winter wheat using MODIS LAI images. Agricultural Water Management, 164, 304-316.
Vazifedoust, M., van Dam, J. C., Bastiaanssen, W. G. M., & Feddes, R. A. (2009). Assimilation of satellite data into agrohydrological models to improve crop yield forecasts, International Journal of Remote Sensing, 30(10), 2523-2545.
Wagner, M. P., Slawig, T., Taravat, A., & Oppelt, N. (2020). Remote Sensing Data Assimilation in Dynamic Crop Models Using Particle Swarm Optimization. ISPRS International Journal of Geo-Information, 9(2), 105.
Wang, J., Li, X., Lu, L. & Fang, F., (2013). Estimating near future regional corn yields by integrating multi-source observations into a crop growth model. European journal of agronomy, 49, 126-140.
Wang, H., Zhu, Y., Li, W., Cao, W., & Tian, Y. (2014). Integrating remotely sensed leaf area index and leaf nitrogen accumulation with RiceGrow model based on particle swarm optimization algorithm for rice grain yield assessment. Journal of Applied Remote Sensing, 8(1), 083674-083674.
Wang, Y., Huang, D., Zhao, L., Shen, H., Xing, X., & Ma, X. (2022). The distributed CERES-Maize model with crop parameters determined through data assimilation assists in regional irrigation schedule optimization. Computers and Electronics in Agriculture, 202, 107425.
Wu, S., Huang, J., Liu, X., Fan, J., Ma, G., & Zou, J., (2011). Assimilating MODIS-LAI into
Crop Growth Model with EnKF to Predict Regional Crop Yield, In Computer and Computing Technologies in Agriculture V: 5th IFIP TC 5/SIG 5.1 Conference, CCTA 2011, Beijing, China, October 29-31, 2011, Proceedings, Part III 5. 410-418. Springer Berlin Heidelberg.
Wu, S., Yang, P., Ren, J., Chen, Z., & Li, H. (2021). Regional winter wheat yield estimation based on the WOFOST model and a novel VW-4DEnSRF assimilation algorithm. Remote Sensing of Environment, 255, 112276.
Xu, X., Shen, S., Gao, F., Wang, J., Ma, X., Xiong, S., & Fan, Z. (2022). Considering different water supplies can improve the accuracyof the WOFOST crop model and remote sensing assimilation in predicting wheat yield
. International Agrophysics. 36(4): 337–349.
https://doi.org/10.31545/intagr/154892.
Yao, F., Tang, Y., Wang, P., & Zhang, J., (2015). Estimation of maize yield by using a process based model and remote sensing data in the Northeast China Plain. Physics Chemistry Earth Parts A/B/C 87, 142–152.
Zabihi, A., Darzi-Naftchali, A., & khoshravesh, M. (2015). Rice yield under surface and subsurface drainage managements and evaluation of AquaCrop model. Irrigation and Water Engineering, 5(20), 150-163. (in persian).
Zare, H., Weber, T. K., Ingwersen, J., Nowak, W., Gayler, S., & Streck, T. (2022). Combining Crop Modeling with Remote Sensing Data Using a Particle Filtering Technique to Produce Real-Time Forecasts of Winter Wheat Yields under Uncertain Boundary Conditions. Remote Sensing, 14(6), 1360.
Zhang, Y., Yang, B., Liu, X., & Wang, C. (2017). Estimation of rice grain yield from dual-polarization Radarsat-2 SAR data by integrating a rice canopy scattering model and a genetic algorithm. International Journal of Applied Earth Observation and Geoinformation, 57, 75-85.
Zhang, T., Su, J., Liu, C., & Chen, W. H. (2021). State and parameter estimation of the AquaCrop model for winter wheat using sensitivity informed particle filter. Computers and Electronics in Agriculture, 180, 105909.
Zhao, Y., Chen, S., & Shen, S., (2013). Assimilating remote sensing information with crop
model using Ensemble Kalman Filter for improving LAI monitoring and yield estimation. Ecological modelling. 270, 30–42.
Zhou, G., Liu, X., & Liu, M. (2019). Assimilating remote sensing phenological information into the WOFOST model for rice growth simulation. Remote Sensing, 11(3), 268.
Zhuo, W., Huang, J., Xiao, X., Huang, H., Bajgain, R., Wu, X., Gao, X., Wang, J., Li, X. & Wagle, P., (2022). Assimilating remote sensing-based VPM GPP into the WOFOST model for improving regional winter wheat yield estimation. European Journal of Agronomy, 139, 126556.
)
