Winter Wheat Yield Prediction at County Level and Uncertainty Analysis in Main Wheat-Producing Regions of China with Deep Learning Approaches
暂无分享,去创建一个
Jianxi Huang | Xinlei Wang | Quanlong Feng | Dongqin Yin | Jianxi Huang | D. Yin | Xinlei Wang | Quanlong Feng
[1] Jian Sun,et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[2] Peng Gao,et al. Detrending crop yield data for spatial visualization of drought impacts in the United States, 1895–2014 , 2017 .
[3] Claudia Notarnicola,et al. Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data , 2015, Remote. Sens..
[4] S. Quiring,et al. An evaluation of agricultural drought indices for the Canadian prairies , 2003 .
[5] Zhang Zha. Spatio-temporal changes of agrometrorological disasters for wheat production across China since 1990 , 2013 .
[6] Pierre-Majorique Léger,et al. The early explanatory power of NDVI in crop yield modelling , 2008 .
[7] M. Bindi,et al. A simple model of regional wheat yield based on NDVI data , 2007 .
[8] Zhonghu He,et al. Wheat cropping systems and technologies in China , 2009 .
[9] Stefano Ermon,et al. Deep Transfer Learning for Crop Yield Prediction with Remote Sensing Data , 2018, COMPASS.
[10] S. S. Sidhu,et al. Pre-harvest wheat yield and production estimation for the Punjab, India , 1994 .
[11] William J. Davies,et al. An analysis of China's grain production: looking back and looking forward , 2014 .
[12] Jim W. Hall,et al. Assessing the Impacts of Extreme Agricultural Droughts in China Under Climate and Socioeconomic Changes , 2018 .
[13] Shusen Wang,et al. Crop yield forecasting on the Canadian Prairies using MODIS NDVI data , 2011 .
[14] Kadambot H. M. Siddique,et al. Wheat yield improvements in China: Past trends and future directions , 2015 .
[15] Michael J. Roberts,et al. Nonlinear Effects of Weather on Corn Yields , 2006 .
[16] Kah Phooi Seng,et al. Big data and machine learning for crop protection , 2018, Comput. Electron. Agric..
[17] A. Kiureghian,et al. Aleatory or epistemic? Does it matter? , 2009 .
[18] B. Holben. Characteristics of maximum-value composite images from temporal AVHRR data , 1986 .
[19] Michael Dixon,et al. Google Earth Engine: Planetary-scale geospatial analysis for everyone , 2017 .
[20] Joachim Denzler,et al. Deep learning and process understanding for data-driven Earth system science , 2019, Nature.
[21] Philip Lewis,et al. Evaluation of regional estimates of winter wheat yield by assimilating three remotely sensed reflectance datasets into the coupled WOFOST–PROSAIL model , 2019, European Journal of Agronomy.
[22] C. Justice,et al. A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data , 2010 .
[23] Yang Shao,et al. An analysis of cropland mask choice and ancillary data for annual corn yield forecasting using MODIS data , 2015, Int. J. Appl. Earth Obs. Geoinformation.
[24] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[25] Stefano Ermon,et al. Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data , 2017, AAAI.
[26] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[27] Nari Kim,et al. Machine Learning Approaches to Corn Yield Estimation Using Satellite Images and Climate Data :A Case of Iowa State , 2016 .
[28] Anuj Karpatne,et al. Physics Guided RNNs for Modeling Dynamical Systems: A Case Study in Simulating Lake Temperature Profiles , 2018, SDM.
[29] Qi Jing,et al. Crop Yield Estimation Using Time-Series MODIS Data and the Effects of Cropland Masks in Ontario, Canada , 2019, Remote. Sens..
[30] Jianxi Huang,et al. Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation , 2016 .
[31] Philip Lewis,et al. Assimilation of remote sensing into crop growth models: Current status and perspectives , 2019, Agricultural and Forest Meteorology.
[32] Jianxi Huang,et al. Improving the timeliness of winter wheat production forecast in the United States of America, Ukraine and China using MODIS data and NCAR Growing Degree Day information , 2015 .
[33] Marco A. S. Netto,et al. A Scalable Machine Learning System for Pre-Season Agriculture Yield Forecast , 2018, 2018 IEEE 14th International Conference on e-Science (e-Science).
[34] Yarin Gal,et al. Uncertainty in Deep Learning , 2016 .
[35] Jianxi Huang,et al. Assimilating Soil Moisture Retrieved from Sentinel-1 and Sentinel-2 Data into WOFOST Model to Improve Winter Wheat Yield Estimation , 2019, Remote. Sens..
[36] Marvin N. Wright,et al. SoilGrids250m: Global gridded soil information based on machine learning , 2017, PloS one.
[37] A. J. Stern,et al. Crop Yield Assessment from Remote Sensing , 2003 .
[38] Zoubin Ghahramani,et al. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning , 2015, ICML.
[39] Alex Kendall,et al. What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? , 2017, NIPS.
[40] Jianhua Gong,et al. Urban Flood Mapping Based on Unmanned Aerial Vehicle Remote Sensing and Random Forest Classifier—A Case of Yuyao, China , 2015 .
[41] Jianhua Gong,et al. UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis , 2015, Remote. Sens..
[42] Dehai Zhu,et al. Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model , 2015 .
[43] Ryosuke Shibasaki,et al. ESTIMATING CORN YIELD IN THE UNITED STATES WITH MODIS EVI AND MACHINE LEARNING METHODS , 2016 .
[44] G. Heuvelink,et al. SoilGrids1km — Global Soil Information Based on Automated Mapping , 2014, PloS one.
[45] Jie He,et al. Improving land surface temperature modeling for dry land of China , 2011 .
[46] A. Schut,et al. Improved wheat yield and production forecasting with a moisture stress index, AVHRR and MODIS data. , 2009 .
[47] Leo Breiman,et al. Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author) , 2001 .
[48] Liangzhi You,et al. Patterns of Cereal Yield Growth across China from 1980 to 2010 and Their Implications for Food Production and Food Security , 2016, PloS one.
[49] C. Field,et al. Crop yield gaps: their importance, magnitudes, and causes. , 2009 .
[50] Michele Meroni,et al. Estimating and understanding crop yields with explainable deep learning in the Indian Wheat Belt , 2020, Environmental Research Letters.
[51] Rafael Rieder,et al. Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review , 2018, Comput. Electron. Agric..
[52] Douglas K. Bolton,et al. Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics , 2013 .
[53] Chao Liu,et al. Predicting County Level Corn Yields Using Deep Long Short Term Memory Models , 2018, ArXiv.
[54] Bernhard Scholkopf. Causality for Machine Learning , 2019 .
[55] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[56] Jonathan P. Resop,et al. Random Forests for Global and Regional Crop Yield Predictions , 2016, PloS one.
[57] Jie He,et al. On downward shortwave and longwave radiations over high altitude regions: Observation and modeling in the Tibetan Plateau , 2010 .
[58] Geoffrey E. Hinton,et al. Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.
[59] David Makowski,et al. Comparison of Statistical Models for Analyzing Wheat Yield Time Series , 2013, PloS one.
[60] Catherine Champagne,et al. Effect of using crop specific masks on earth observation based crop yield forecasting across Canada , 2019, Remote Sensing Applications: Society and Environment.
[61] Liangliang Zhang,et al. Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China , 2020, Remote. Sens..
[62] Dehai Zhu,et al. Integrating Multitemporal Sentinel-1/2 Data for Coastal Land Cover Classification Using a Multibranch Convolutional Neural Network: A Case of the Yellow River Delta , 2019, Remote. Sens..