Jones, J. W. et al. The DSSAT cropping system model. Eur. J. Agron. 18, 235–265 (2003).
Google Scholar
van Diepen, C. A., Wolf, J., van Keulen, H. & Rappoldt, C. WOFOST: a simulation model of crop production. Soil Use Manag. 5, 16–24 (1989).
Google Scholar
Cao, J. et al. Integrating multi-source data for rice yield prediction across China using machine learning and deep learning approaches. Agric. For. Meteorol. 297, 108275 (2021).
Google Scholar
Khanal, S., Kushal, K. C., Fulton, J. P., Shearer, S. & Ozkan, E. Remote sensing in agriculture—accomplishments, limitations, and opportunities. Remote Sens. 12, 3783 (2020).
Google Scholar
Maas, S. J. Parameterised model of gramineous crop growth: II. within-season simulation calibration. Agron. J. 85, 354–358 (1993).
Google Scholar
Nguyen, V., Jeong, S., Ko, J., Ng, C. & Yeom, J. Mathematical integration of remotely-sensed information into a crop modelling process for mapping crop productivity. Remote Sens. 11, 2131 (2019).
Google Scholar
Huang, J. et al. Assimilation of remote sensing into crop growth models: current status and perspectives. Agric. For. Meteorol. 276–277, 107609 (2019).
Google Scholar
Jin, X. et al. A review of data assimilation of remote sensing and crop models. Eur. J. Agron. 92, 141–152 (2018).
Google Scholar
Shawon, A. R. et al. Assessment of a proximal sensing-integrated crop model for simulation of soybean growth and yield. Remote Sens. 12, 410 (2020).
Google Scholar
Shawon, A. R. et al. Two-dimensional simulation of barley growth and yield using a model integrated with remote-controlled aerial imagery. Remote Sens. 12, 3766 (2020).
Google Scholar
Shin, T. et al. Simulation of wheat productivity using a model integrated with proximal and remotely controlled aerial sensing information. Front. Plant Sci. https://doi.org/10.3389/fpls.2021.649660 (2021).
Google Scholar
Huang, J. et al. Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation. Agric. For. Meteorol. 216, 188–202 (2016).
Google Scholar
Khaki, S., Wang, L. & Archontoulis, S. V. A CNN-RNN framework for crop yield prediction. Front. Plant Sci. https://doi.org/10.3389/fpls.2019.01750 (2020).
Google Scholar
Kim, N. et al. An artificial intelligence approach to prediction of corn yields under extreme weather conditions using satellite and meteorological data. Appl. Sci. 10, 3785 (2020).
Google Scholar
Kumar, P. et al. Comprehensive evaluation of soil moisture retrieval models under different crop cover types using C-band synthetic aperture radar data. Geocarto Int. 34, 1022–1041 (2019).
Google Scholar
Everingham, Y., Sexton, J., Skocaj, D. & Inman-Bamber, G. Accurate prediction of sugarcane yield using a random forest algorithm. Agron. Sustain. Dev. 36, 27 (2016).
Google Scholar
Feng, P., Wang, B., Li Liu, D., Waters, C. & Yu, Q. Incorporating machine learning with biophysical model can improve the evaluation of climate extremes impacts on wheat yield in south-eastern Australia. Agric. For. Meteorol. 275, 100–113 (2019).
Google Scholar
Shahhosseini, M., Hu, G., Huber, I. & Archontoulis, S. V. Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt. Sci. Rep. 11, 1606 (2021).
Google Scholar
Cai, Y. et al. Detecting in-season crop nitrogen stress of corn for field trials using UAV- and CubeSat-based multispectral sensing. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 12, 5153–5166 (2019).
Google Scholar
van Klompenburg, T., Kassahun, A. & Catal, C. Crop yield prediction using machine learning: a systematic literature review. Comput. Electron. Agric. 177, 105709 (2020).
Google Scholar
Kamilaris, A. & Prenafeta-Boldú, F. X. Deep learning in agriculture: a survey. Comput. Electron. Agric. 147, 70–90 (2018).
Google Scholar
Bui, D. T., Tsangaratos, P., Nguyen, V.-T., Liem, N. V. & Trinh, P. T. Comparing the prediction performance of a deep learning neural network model with conventional machine learning models in landslide susceptibility assessment. CATENA 188, 104426 (2020).
Google Scholar
Sahoo, A. K., Pradhan, C. & Das, H. Performance evaluation of different machine learning methods and deep-learning based convolutional neural network for health decision making. In Nature Inspired Computing for Data Science (eds Rout, M. et al.) (Springer International Publishing, 2020).
Jeong, S. et al. Development of Variable Threshold Models for detection of irrigated paddy rice fields and irrigation timing in heterogeneous land cover. Agric. Water Manag. 115, 83–91 (2012).
Google Scholar
Peng, D., Huete, A. R., Huang, J., Wang, F. & Sun, H. Detection and estimation of mixed paddy rice cropping patterns with MODIS data. Int. J. Appl. Earth Obs. Geoinf. 13, 13–23 (2011).
Google Scholar
Jeong, S., Ko, J. & Yeom, J.-M. Nationwide projection of rice yield using a crop model integrated with geostationary satellite imagery: a case study in South Korea. Remote Sens. 10, 1665 (2018).
Google Scholar
Xiao, X. et al. Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images. Remote Sens. Environ. 100, 95–113 (2006).
Google Scholar
Ozdogan, M. & Gutman, G. A new methodology to map irrigated areas using multi-temporal MODIS and ancillary data: an application example in the continental US. Remote Sens. Environ. 112, 3520–3537 (2008).
Google Scholar
Yeom, J.-M., Jeong, S., Deo, R. C. & Ko, J. Mapping rice area and yield in northeastern Asia by incorporating a crop model with dense vegetation index profiles from a geostationary satellite. GISci. Remote Sens. 58, 1–27 (2021).
Google Scholar
Yeom, J.-M. et al. Monitoring paddy productivity in North Korea employing geostationary satellite images integrated with GRAMI-rice model. Sci. Rep. 8, 16121 (2018).
Google Scholar
Jeong, S., Ko, J., Choi, J., Xue, W. & Yeom, J.-M. Application of an unmanned aerial system for monitoring paddy productivity using the GRAMI-rice model. Int. J. Remote Sens. 39, 2441–2462 (2018).
Google Scholar
Jeong, S. et al. Geographical variations in gross primary production and evapotranspiration of paddy rice in the Korean Peninsula. Sci. Total Environ. 714, 136632 (2020).
Google Scholar
Roger, P., Vermote, E. & Ray, J. MODIS Surface Reflectance User’s Guide. Collection 6 (2015).
Scharlemann, J. P. W. et al. Global data for ecology and epidemiology: a novel algorithm for temporal Fourier processing MODIS data. PLoS ONE 3, e1408 (2008).
Google Scholar
Pede, T. & Mountrakis, G. An empirical comparison of interpolation methods for MODIS 8-day land surface temperature composites across the conterminous Unites States. ISPRS J. Photogramm. Remote Sens. 142, 137–150 (2018).
Google Scholar
Kilibarda, M. et al. Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution. J. Geophys. Res. Atmos. 119, 2294–2313 (2014).
Google Scholar
Nunez, M. The development of a satellite-based insolation model for the tropical western Pacific Ocean. Int. J. Climatol. 13, 607–627 (1993).
Google Scholar
Otkin, J. A., Anderson, M. C., Mecikalski, J. R. & Diak, G. R. Validation of GOES-based insolation estimates using data from the U.S. Climate reference network. J. Hydrometeorol. 6, 460–475 (2005).
Google Scholar
Pinker, R. & Laszlo, I. Modeling surface solar irradiance for satellite applications on a global scale. J. Appl. Meteorol. 31, 194–211 (1992).
Google Scholar
Kawamura, H., Tanahashi, S. & Takahashi, T. Estimation of insolation over the Pacific Ocean off the Sanriku coast. J. Oceanogr. 54, 457–464 (1998).
Google Scholar
Yeom, J.-M., Seo, Y.-K., Kim, D.-S. & Han, K.-S. Solar radiation received by slopes using COMS imagery, a physically based radiation model, and GLOBE. J. Sens. 2016, 1–15 (2016).
Google Scholar
Yeom, J.-M., Han, K.-S. & Kim, J.-J. Evaluation on penetration rate of cloud for incoming solar radiation using geostationary satellite data. Asia-Pac. J. Atmos. Sci. 48, 115–123 (2012).
Google Scholar
Kawai, Y. & Kawamura, H. Validation and improvement of satellite-derived surface solar radiation over the Northwestern Pacific Ocean. J. Oceanogr. 61, 79–89 (2005).
Google Scholar
Tanahashi, S., Kawamura, H., Matsuura, T., Takahashi, T. & Yusa, H. A system to distribute satellite incident solar radiation in real-time. Remote Sens. Environ. 75, 412–422 (2001).
Google Scholar
Elbern, H., Schmidt, H., Talagrand, O. & Ebel, A. 4D-variational data assimilation with an adjoint air quality model for emission analysis. Environ. Model. Softw. 15, 539–548 (2000).
Google Scholar
Press, W. H., Teukolsky, S. A., Vetterling, W. T. & Flannery, B. P. Numerical Recipes: The Art of Scientific Computing (Cambridge University Press, 1992).
Google Scholar
Ko, J. et al. Simulation and mapping of rice growth and yield based on remote sensing. J. Appl. Remote Sens. 9, 096067 (2015).
Google Scholar
Emami Javanmard, M., Ghaderi, S. F. & Hoseinzadeh, M. Data mining with 12 machine learning algorithms for predict costs and carbon dioxide emission in integrated energy-water optimization model in buildings. Energy Convers. Manag. 238, 114153 (2021).
Google Scholar
Diebold, F. X. & Shin, M. Machine learning for regularized survey forecast combination: partially-egalitarian LASSO and its derivatives. Int. J. Forecast. 35, 1679–1691 (2019).
Google Scholar
Khosla, E., Dharavath, R. & Priya, R. Crop yield prediction using aggregated rainfall-based modular artificial neural networks and support vector regression. Environ. Dev. Sustain. 22, 5687–5708 (2020).
Google Scholar
Wang, S., Azzari, G. & Lobell, D. B. Crop type mapping without field-level labels: random forest transfer and unsupervised clustering techniques. Remote Sens. Environ. 222, 303–317 (2019).
Google Scholar
Ustuner, M. & Balik, S. F. Polarimetric target decompositions and light gradient boosting machine for crop classification: a comparative evaluation. ISPRS Int. J. Geo Inf. 8, 97 (2019).
Google Scholar
Jeong, S., Ko, J. & Yeom, J.-M. Predicting rice yield at pixel scale through synthetic use of crop and deep learning models with satellite data in South and North Korea. Sci. Total Environ. 802, 149726 (2022).
Google Scholar
Nash, J. E. & Sutcliffe, J. V. River flow forecasting through conceptual models part I: a discussion of principles. J. Hydrol. 10, 282–290 (1970).
Google Scholar
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