doi:10.48130/tia-0026-0002

[1]

Donatelli M, Van Ittersum MK, Bindi M, Porter JR. 2002. Modelling cropping systems—highlights of the symposium and preface to the special issues. European Journal of Agronomy 18(1−2):1−11

doi: 10.1016/S1161-0301(02)00104-1

[2]

Pasquel D, Roux S, Richetti J, Cammarano D, Tisseyre B, et al. 2022. A review of methods to evaluate crop model performance at multiple and changing spatial scales. Precision Agriculture 23(4):1489−1513

doi: 10.1007/s11119-022-09885-4

[3]

Tian X, Engel BA, Qian H, Hua E, Sun S, et al. 2021. Will reaching the maximum achievable yield potential meet future global food demand? Journal of Cleaner Production 294:126285

doi: 10.1016/j.jclepro.2021.126285

[4]

Boonwichai S, Shrestha S, Babel MS, Weesakul S, Datta A. 2019. Evaluation of climate change impacts and adaptation strategies on rainfed rice production in Songkhram River Basin, Thailand. Science of The Total Environment 652:189−201

doi: 10.1016/j.scitotenv.2018.10.201

[5]

Xiao D, Liu DL, Wang B, Feng P, Bai H, et al. 2020. Climate change impact on yields and water use of wheat and maize in the North China Plain under future climate change scenarios. Agricultural Water Management 238:106238

doi: 10.1016/j.agwat.2020.106238

[6]

Cooper M, Tang T, Gho C, Hart T, Hammer G, et al. 2020. Integrating genetic gain and gap analysis to predict improvements in crop productivity. Crop Science 60(2):582−604

doi: 10.1002/csc2.20109

[7]

Hammer GL, McLean G, van Oosterom E, Chapman S, Zheng B, et al. 2020. Designing crops for adaptation to the drought and high-temperature risks anticipated in future climates. Crop Science 60(2):605−621

doi: 10.1002/csc2.20110

[8]

Donohue RJ, Lawes RA, Mata G, Gobbett D, Ouzman J. 2018. Towards a national, remote-sensing-based model for predicting field-scale crop yield. Field Crops Research 227:79−90

doi: 10.1016/j.fcr.2018.08.005

[9]

Han J, Zhang Z, Cao J, Luo Y, Zhang L, et al. 2020. Prediction of winter wheat yield based on multi-source data and machine learning in China. Remote Sensing 12(2):236

doi: 10.3390/rs12020236

[10]

Yun JI. 2003. Predicting regional rice production in South Korea using spatial data and crop-growth modeling. Agricultural Systems 77(1):23−38

doi: 10.1016/S0308-521X(02)00084-7

[11]

Mo X, Liu S, Lin Z, Xu Y, Xiang Y, et al. 2005. Prediction of crop yield, water consumption and water use efficiency with a SVAT-crop growth model using remotely sensed data on the North China Plain. Ecological Modelling 183(2−3):301−322

doi: 10.1016/j.ecolmodel.2004.07.032

[12]

Steinbuch L, Brus DJ, van Bussel LGJ, Heuvelink GBM. 2016. Geostatistical interpolation and aggregation of crop growth model outputs. European Journal of Agronomy 77:111−121

doi: 10.1016/j.eja.2016.03.007

[13]

de Wit AJW, van Diepen CA. 2008. Crop growth modelling and crop yield forecasting using satellite-derived meteorological inputs. International Journal of Applied Earth Observation and Geoinformation 10(4):414−425

doi: 10.1016/j.jag.2007.10.004

[14]

Ovando G, Sayago S, Bocco M. 2018. Evaluating accuracy of DSSAT model for soybean yield estimation using satellite weather data. ISPRS Journal of Photogrammetry and Remote Sensing 138:208−217

doi: 10.1016/j.isprsjprs.2018.02.015

[15]

Aghighi H, Azadbakht M, Ashourloo D, Shahrabi HS, Radiom S. 2018. Machine learning regression techniques for the silage maize yield prediction using time-series images of landsat 8 OLI. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11(12):4563−4577

doi: 10.1109/JSTARS.2018.2823361

[16]

de Wit A, Boogaard H, Fumagalli D, Janssen S, Knapen R, et al. 2019. 25 years of the WOFOST cropping systems model. Agricultural Systems 168:154−167

doi: 10.1016/j.agsy.2018.06.018

[17]

Huang Y, Ryu Y, Jiang C, Kimm H, Kim S, et al. 2018. BESS-Rice: a remote sensing derived and biophysical process-based rice productivity simulation model. Agricultural and Forest Meteorology 256:253−269

doi: 10.1016/j.agrformet.2018.03.014

[18]

Soltani A, Alimagham SM, Nehbandani A, Torabi B, Zeinali E, et al. 2020. SSM-iCrop2: a simple model for diverse crop species over large areas. Agricultural Systems 182:102855

doi: 10.1016/j.agsy.2020.102855

[19]

Zhao C, Liu B, Xiao L, Hoogenboom G, Boote KJ, et al. 2019. A SIMPLE crop model. European Journal of Agronomy 104:97−106

doi: 10.1016/j.eja.2019.01.009

[20]

Ramirez-Villegas J, Koehler AK, Challinor AJ. 2017. Assessing uncertainty and complexity in regional-scale crop model simulations. European Journal of Agronomy 88:84−95

doi: 10.1016/j.eja.2015.11.021

[21]

Huang J, Gómez-Dans JL, Huang H, Ma H, Wu Q, et al. 2019. Assimilation of remote sensing into crop growth models: Current status and perspectives. Agricultural and Forest Meteorology 276−277:107609

doi: 10.1016/j.agrformet.2019.06.008

[22]

Amani M, Ghorbanian A, Ali Ahmadi S, Kakooei M, Moghimi A, et al. 2020. Google earth engine cloud computing platform for remote sensing big data applications: a comprehensive review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13:5326−5350

doi: 10.1109/JSTARS.2020.3021052

[23]

Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, et al. 2017. Google earth engine: planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18−27

doi: 10.1016/j.rse.2017.06.031

[24]

Manivasagam VS, Rozenstein O. 2020. Practices for upscaling crop simulation models from field scale to large regions. Computers and Electronics in Agriculture 175:105554

doi: 10.1016/j.compag.2020.105554

[25]

Wang X, Huang J, Feng Q, Yin D. 2020. Winter wheat yield prediction at county level and uncertainty analysis in main wheat-producing regions of China with deep learning approaches. Remote Sensing 12(11):1744

doi: 10.3390/rs12111744

[26]

Cao J, Zhang Z, Luo Y, Zhang L, Zhang J, et al. 2021. Wheat yield predictions at a county and field scale with deep learning, machine learning, and google earth engine. European Journal of Agronomy 123:126204

doi: 10.1016/j.eja.2020.126204

[27]

Lobell DB, Thau D, Seifert C, Engle E, Little B. 2015. A scalable satellite-based crop yield mapper. Remote Sensing of Environment 164:324−333

doi: 10.1016/j.rse.2015.04.021

[28]

Jaafar H, Mourad R. 2021. GYMEE: a global field-scale crop yield and ET mapper in google earth engine based on landsat, weather, and soil data. Remote Sensing 13(4):773

doi: 10.3390/rs13040773

[29]

Liu B, Martre P, Ewert F, Porter JR, Challinor AJ, et al. 2019. Global wheat production with 1.5 and 2.0 °C above pre-industrial warming. Global Change Biology 25(4):1428−1444

doi: 10.1111/gcb.14542

[30]

Ye Z, Qiu X, Chen J, Cammarano D, Ge Z, et al. 2020. Impacts of 1.5 °C and 2.0 °C global warming above pre-industrial on potential winter wheat production of China. European Journal of Agronomy 120:126149

doi: 10.1016/j.eja.2020.126149

[31]

Tang Y, Zhou R, He P, Yu M, Zheng H, et al. 2023. Estimating wheat grain yield by assimilating phenology and LAI with the WheatGrow model based on theoretical uncertainty of remotely sensed observation. Agricultural and Forest Meteorology 339:109574

doi: 10.1016/j.agrformet.2023.109574

[32]

Cao W, Moss DN. 1997. Modelling phasic development in wheat: a conceptual integration of physiological components. The Journal of Agricultural Science 129(2):163−172

doi: 10.1017/s0021859697004668

[33]

Yan M, Cao W, Luo W, Jiang H. 2000. A mechanistic model of phasic and phenological development of wheat. I. Assumption and description of the model. The Journal of Applied Ecology 11(3):355−359

[34]

Liu TM, Cao WX, Luo WH, Wang SH, Guo WS, et al. 2001. Quantitative simulation on dry matter partitioning dynamic in wheat organs. Acta Tritical Crops 21(1):25−31 (in Chinese)

[35]

Pan J, Zhu Y, Jiang D, Dai T, Li Y, et al. 2006. Modeling plant nitrogen uptake and grain nitrogen accumulation in wheat. Field Crops Research 97(2−3):322−336

doi: 10.1016/j.fcr.2005.11.006

[36]

Pan J, Zhu Y, Cao W. 2007. Modeling plant carbon flow and grain starch accumulation in wheat. Field Crops Research 101(3):276−284

doi: 10.1016/j.fcr.2006.12.005

[37]

Hu J, Cao W, Jiang D, Luo W. 2004. Quantification of water stress factor for crop growth simulation: I. Effects of drought and waterlogging stress on photosynthesis, transpiration and dry matter partitioning in winter wheat. Acta Agronomica Sinica 30:315−321 (in Chinese)

[38]

Liu L, Wang E, Zhu Y, Tang L, Cao W. 2013. Effects of warming and autonomous breeding on the phenological development and grain yield of double-rice systems in China. Agriculture, Ecosystems & Environment 165:28−38

doi: 10.1016/j.agee.2012.11.009

[39]

Liu H, Wu Y, Lu J, Gou T, Zhao S, et al. 2025. Predictive modeling study on the critical nitrogen concentration and accumulation in cut Chrysanthemum Based on the cumulative photo-thermal effect. Horticulturae 11(11):1313

doi: 10.3390/horticulturae11111313

[40]

Dong J, Fu Y, Wang J, Tian H, Fu S, et al. 2020. Early-season mapping of winter wheat in China based on Landsat and Sentinel images. Earth System Science Data 12(4):3081−3095

doi: 10.5194/essd-12-3081-2020

[41]

Muñoz Sabater J. 2019. ERA5-Land monthly averaged data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), the European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK. doi: 10.24381/cds.68d2bb30

[42]

Huang X, Han S, Shi C. 2021. Multiscale assessments of three reanalysis temperature data systems over China. Agriculture 11(12):1292

doi: 10.3390/agriculture11121292

[43]

Araghi A, Martinez CJ, Olesen JE, Hoogenboom G. 2022. Assessment of nine gridded temperature data for modeling of wheat production systems. Computers and Electronics in Agriculture 199:107189

doi: 10.1016/j.compag.2022.107189

[44]

Monteith JL. 1972. Solar radiation and productivity in tropical ecosystems. Journal of Applied Ecology 9(3):747−766

doi: 10.2307/2401901

[45]

McCree KJ. 1966. A solarimeter for measuring photosynthetically active radiation. Agricultural Meteorology 3(5−6):353−366

doi: 10.1016/0002-1571(66)90017-3

[46]

Saxton KE, Rawls WJ. 2006. Soil water characteristic estimates by texture and organic matter for hydrologic solutions. Soil Science Society of America Journal 70(5):1569−1578

doi: 10.2136/sssaj2005.0117

[47]

Feng S, Qiu J, Crow WT, Mo X, Liu S, et al. 2023. Improved estimation of vegetation water content and its impact on L-band soil moisture retrieval over cropland. Journal of Hydrology 617:129015

doi: 10.1016/j.jhydrol.2022.129015

[48]

Yao N, Li Y, Liu Q, Zhang S, Chen X, et al. 2022. Response of wheat and maize growth-yields to meteorological and agricultural droughts based on standardized precipitation evapotranspiration indexes and soil moisture deficit indexes. Agricultural Water Management 266:107566

doi: 10.1016/j.agwat.2022.107566

[49]

Hou M, Li Y, Biswas A, Chen X, Xie L, et al. 2024. Concurrent drought threatens wheat and maize production and will widen crop yield gaps in the future. Agricultural Systems 220:104056

doi: 10.1016/j.agsy.2024.104056

[50]

Wu Z, Feng H, He H, Zhou J, Zhang Y. 2021. Evaluation of soil moisture climatology and anomaly components derived from ERA5-land and GLDAS-2.1 in China. Water Resources Management 35(2):629−643

doi: 10.1007/s11269-020-02743-w

[51]

Kang Y, Özdoğan M. 2019. Field-level crop yield mapping with Landsat using a hierarchical data assimilation approach. Remote Sensing of Environment 228:144−163

doi: 10.1016/j.rse.2019.04.005

[52]

Teluguntla P, Thenkabail PS, Xiong J, Gumma MK, Giri C, et al. 2015. Global food security support analysis data at nominal 1 km (GFSAD1km) derived from remote sensing in support of food security in the twenty-first century: current achievements and future possibilities. In Land Resources Monitoring, Modeling, and Mapping with Remote Sensing. Boca Raton: CRC Press. pp.167−196 doi: 10.1201/b19322-8

[53]

Liu L, Wallach D, Li J, Liu B, Zhang L, et al. 2018. Uncertainty in wheat phenology simulation induced by cultivar parameterization under climate warming. European Journal of Agronomy 94:46−53

doi: 10.1016/j.eja.2017.12.001

[54]

Liu T, Cao W, Luo W, Wang S, Yin J. 2001. A simulation model of photosynthetic production and dry matter accumulation in wheat. Journal of Triticeae Crops 21(3):26−30

doi: 10.7606/j.issn.1009-1041.2001.03.062

[55]

Huang J, Tian L, Liang S, Ma H, Becker-Reshef I, et al. 2015. 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

doi: 10.1016/j.agrformet.2015.02.001

[56]

Banerjee K, Dutta S, Das S, Sadhukhan R. 2025. Crop simulation models as decision tools to enhance agricultural system productivity and sustainability – a critical review. Technology in Agronomy 5(1):e002

doi: 10.48130/tia-0024-0032

[57]

Ma C, Liu M, Ding F, Li C, Cui Y, et al. 2022. Wheat growth monitoring and yield estimation based on remote sensing data assimilation into the SAFY crop growth model. Scientific Reports 12:5473

doi: 10.1038/s41598-022-09535-9

[58]

Kasampalis DA, Alexandridis TK, Deva C, Challinor A, Moshou D, et al. 2018. Contribution of remote sensing on crop models: a review. Journal of Imaging 4(4):52

doi: 10.3390/jimaging4040052

[59]

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

doi: 10.3390/rs15164066

[60]

Cao J, Zhang Z, Zhang L, Luo Y, Li Z, et al. 2020. Damage evaluation of soybean chilling injury based on Google Earth Engine (GEE) and crop modelling. Journal of Geographical Sciences 30(8):1249−1265

doi: 10.1007/s11442-020-1780-1

[61]

Azzari G, Jain M, Lobell DB. 2017. Towards fine resolution global maps of crop yields: testing multiple methods and satellites in three countries. Remote Sensing of Environment 202:129−141

doi: 10.1016/j.rse.2017.04.014

[62]

Amin NU, Islam F, Umar M, Muhammad W, Rahman SU, et al. 2025. Evaluation of crop phenology using remote sensing and decision support system for agrotechnology transfer. Scientific Reports 15:11582

doi: 10.1038/s41598-025-95109-4

[63]

Ronanki S, Pavlík J, Masner J, Jarolímek J, Stočes M, et al. 2022. An APSIM-powered framework for post-rainy sorghum-system design in India. Field Crops Research 277:108422

doi: 10.1016/j.fcr.2021.108422

[64]

Jang WS, Lee Y, Neff JC, Im Y, Ha S, et al. 2019. Development of an EPIC parallel computing framework to facilitate regional/global gridded crop modeling with multiple scenarios: a case study of the United States. Computers and Electronics in Agriculture 158:189−200

doi: 10.1016/j.compag.2019.02.004

[65]

Zhang Z, Li Z, Chen Y, Zhang L, Tao F. 2020. Improving regional wheat yields estimations by multi-step-assimilating of a crop model with multi-source data. Agricultural and Forest Meteorology 290:107993

doi: 10.1016/j.agrformet.2020.107993

[66]

Engels AM, Gaiser T, Ewert F, Grahmann K, Hernández-Ochoa I. 2025. Simulating soil moisture dynamics in a diversified cropping system under heterogeneous soil conditions. Agronomy 15(2):407

doi: 10.3390/agronomy15020407

[67]

Takoutsing B, Heuvelink GBM, Aynekulu E, Shepherd KD. 2024. Modelling and mapping maize yields and making fertilizer recommendations with uncertain soil information. Precision Agriculture 26(1):5

doi: 10.1007/s11119-024-10200-6

[68]

Zhuo W, Fang S, Gao X, Wang L, Wu D, et al. 2022. Crop yield prediction using MODIS LAI, TIGGE weather forecasts and WOFOST model: a case study for winter wheat in Hebei, China during 2009–2013. International Journal of Applied Earth Observation and Geoinformation 106:102668

doi: 10.1016/j.jag.2021.102668

[69]

Feng P, Wang B, Liu DL, Waters C, Xiao D, et al. 2020. Dynamic wheat yield forecasts are improved by a hybrid approach using a biophysical model and machine learning technique. Agricultural and Forest Meteorology 285:107922

doi: 10.1016/j.agrformet.2020.107922

[70]

Leng G, Hall JW. 2020. Predicting spatial and temporal variability in crop yields: an inter-comparison of machine learning, regression and process-based models. Environmental Research Letters 15(4):044027

doi: 10.1088/1748-9326/ab7b24

[71]

Mohammadzadeh A, Azimzadeh Y, Lotfi R, Zadhasan E, Alizadeh K, et al. 2025. Analyzing the rainfed wheat yield gap in Northwest Iran. Farming System 3(1):100126

doi: 10.1016/j.farsys.2024.100126

[72]

Müller C, Elliott J, Kelly D, Arneth A, Balkovic J, et al. 2019. The Global Gridded Crop Model Intercomparison phase 1 simulation dataset. Scientific Data 6:50

doi: 10.1038/s41597-019-0023-8

[73]

Sadeh Y, Zhu X, Chenu K, Dunkerley D. 2019. Sowing date detection at the field scale using CubeSats remote sensing. Computers and Electronics in Agriculture 157:568−580

doi: 10.1016/j.compag.2019.01.042

[74]

Sun C, Tao Y, Liu S, Wang S, Xu H, et al. 2024. Automatic mapping of winter wheat planting structure and phenological phases using time-series sentinel data. Scientific Reports 14:17886

doi: 10.1038/s41598-024-68960-0

[75]

Tamiminia H, Salehi B, Mahdianpari M, Quackenbush L, Adeli S, et al. 2020. Google Earth Engine for geo-big data applications: a meta-analysis and systematic review. ISPRS Journal of Photogrammetry and Remote Sensing 164:152−170

doi: 10.1016/j.isprsjprs.2020.04.001

[76]

Kumhálová J, Kumhála F, Kroulík M, Matějková Š. 2011. The impact of topography on soil properties and yield and the effects of weather conditions. Precision Agriculture 12(6):813−830

doi: 10.1007/s11119-011-9221-x

[77]

Gao XS, Xiao Y, Deng LJ, Li QQ, Wang CQ, et al. 2019. Spatial variability of soil total nitrogen, phosphorus and potassium in Renshou County of Sichuan Basin, China. Journal of Integrative Agriculture 18(2):279−289

doi: 10.1016/S2095-3119(18)62069-6

[78]

Xu H, Zhang XH, Qiu XL, Zhu Y, Cao W. 2020. Design and implementation of gridded simulation and prediction system for wheat growth. Transactions of the Chinese Society of Agricultural Engineering 36(15):167−172 (in Chinese)

[79]

Amani M, Kakooei M, Moghimi A, Ghorbanian A, Ranjgar B, et al. 2020. Application of google earth engine cloud computing platform, sentinel imagery, and neural networks for crop mapping in Canada. Remote Sensing 12(21):3561

doi: 10.3390/rs12213561

[80]

Yang G, Li X, Xiong Y, He M, Zhang L, et al. 2025. Annual winter wheat mapping for unveiling spatiotemporal patterns in China with a knowledge-guided approach and multi-source datasets. ISPRS Journal of Photogrammetry and Remote Sensing 225:163−179

doi: 10.1016/j.isprsjprs.2025.04.031

[81]

Pan L, Xia H, Yang J, Niu W, Wang R, et al. 2021. Mapping cropping intensity in Huaihe basin using phenology algorithm, all Sentinel-2 and Landsat images in Google Earth Engine. International Journal of Applied Earth Observation and Geoinformation 102:102376

doi: 10.1016/j.jag.2021.102376

[82]

He T, Zhang M, Xiao W, Zhai G, Fang K, et al. 2025. Trend and potential enhancement of cropping intensity. Computers and Electronics in Agriculture 229:109777

doi: 10.1016/j.compag.2024.109777

[83]

Ray DK, West PC, Clark M, Gerber JS, Prishchepov AV, et al. 2019. Climate change has likely already affected global food production. PLoS One 14(5):e0217148

doi: 10.1371/journal.pone.0217148

[84]

Zhao C, Liu B, Piao S, Wang X, Lobell DB, et al. 2017. Temperature increase reduces global yields of major crops in four independent estimates. Proceedings of the National Academy of Sciences of the United States of America 114(35):9326−9331

doi: 10.1073/pnas.1701762114

[85]

Jägermeyr J, Müller C, Ruane AC, Elliott J, Balkovic J, et al. 2021. Climate impacts on global agriculture emerge earlier in new generation of climate and crop models. Nature Food 2(11):873−885

doi: 10.1038/s43016-021-00400-y

[86]

Jin X, Kumar L, Li Z, Feng H, Xu X, et al. 2018. A review of data assimilation of remote sensing and crop models. European Journal of Agronomy 92:141−152

doi: 10.1016/j.eja.2017.11.002