Improving the Forecasting of Winter Wheat Yields in Northern China with Machine Learning–Dynamical Hybrid Subseasonal-to-Seasonal Ensemble Prediction
出版年份 2022 全文链接
标题
Improving the Forecasting of Winter Wheat Yields in Northern China with Machine Learning–Dynamical Hybrid Subseasonal-to-Seasonal Ensemble Prediction
作者
关键词
-
出版物
Remote Sensing
Volume 14, Issue 7, Pages 1707
出版商
MDPI AG
发表日期
2022-04-02
DOI
10.3390/rs14071707
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt
- (2021) Mohsen Shahhosseini et al. Scientific Reports
- Performance of 13 crop simulation models and their ensemble for simulating four field crops in Central Europe
- (2021) M. Kostková et al. JOURNAL OF AGRICULTURAL SCIENCE
- Corn yield prediction and uncertainty analysis based on remotely sensed variables using a Bayesian neural network approach
- (2021) Yuchi Ma et al. REMOTE SENSING OF ENVIRONMENT
- Cereal Yield Forecasting with Satellite Drought-Based Indices, Weather Data and Regional Climate Indices Using Machine Learning in Morocco
- (2021) El houssaine Bouras et al. Remote Sensing
- Crop yield forecasting and associated optimum lead time analysis based on multi-source environmental data across China
- (2021) Linchao Li et al. AGRICULTURAL AND FOREST METEOROLOGY
- Is satellite Sun-Induced Chlorophyll Fluorescence more indicative than vegetation indices under drought condition?
- (2021) Junjun Cao et al. SCIENCE OF THE TOTAL ENVIRONMENT
- Dynamical Seasonal Prediction of Tropical Cyclone Activity Using the FGOALS-f2 Ensemble Prediction System
- (2021) Jinxiao Li et al. WEATHER AND FORECASTING
- Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China
- (2020) Jichong Han et al. Remote Sensing
- Dynamic wheat yield forecasts are improved by a hybrid approach using a biophysical model and machine learning technique
- (2020) Puyu Feng et al. AGRICULTURAL AND FOREST METEOROLOGY
- Building sustainable science partnerships between early-career researchers to better understand and predict East Asia water cycle extremes
- (2020) Xiangbo Feng et al. BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY
- Identifying the spatiotemporal changes of annual harvesting areas for three staple crops in China by integrating multi-data sources
- (2020) Yuchuan Luo et al. Environmental Research Letters
- Combining Multi-Source Data and Machine Learning Approaches to Predict Winter Wheat Yield in the Conterminous United States
- (2020) Yumiao Wang et al. Remote Sensing
- Winter Wheat Yield Prediction at County Level and Uncertainty Analysis in Main Wheat-Producing Regions of China with Deep Learning Approaches
- (2020) Xinlei Wang et al. Remote Sensing
- Crop yield prediction using machine learning: A systematic literature review
- (2020) Thomas van Klompenburg et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- Predicting county-scale maize yields with publicly available data
- (2020) Zehui Jiang et al. Scientific Reports
- Integrated phenology and climate in rice yields prediction using machine learning methods
- (2020) Yahui Guo et al. ECOLOGICAL INDICATORS
- Evaluation of Different Crop Models for Simulating Rice Development and Yield in the U.S. Mississippi Delta
- (2020) Sanai Li et al. Agronomy-Basel
- Integrating Multi-Source Data for Rice Yield Prediction across China using Machine Learning and Deep Learning Approaches
- (2020) Juan Cao et al. AGRICULTURAL AND FOREST METEOROLOGY
- Evaluation of FAMIL2 in Simulating the Climatology and Seasonal‐to‐Interannual Variability of Tropical Cyclone Characteristics
- (2019) Jinxiao Li et al. Journal of Advances in Modeling Earth Systems
- Deep learning and process understanding for data-driven Earth system science
- (2019) Markus Reichstein et al. NATURE
- Extreme Weather Events in Agriculture: A Systematic Review
- (2019) Alessia Cogato et al. Sustainability
- The China Multi-Model Ensemble Prediction System and Its Application to Flood-Season Prediction in 2018
- (2019) Hong-Li Ren et al. Journal of Meteorological Research
- Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches
- (2019) Yaping Cai et al. AGRICULTURAL AND FOREST METEOROLOGY
- Deep learning for multi-year ENSO forecasts
- (2019) Yoo-Geun Ham et al. NATURE
- The central trend in crop yields under climate change in China: A systematic review
- (2019) Yuan Liu et al. SCIENCE OF THE TOTAL ENVIRONMENT
- Combining Optical, Fluorescence, Thermal Satellite, and Environmental Data to Predict County-Level Maize Yield in China Using Machine Learning Approaches
- (2019) Liangliang Zhang et al. Remote Sensing
- Seasonal climate forecasts provide more definitive and accurate crop yield predictions
- (2018) Jaclyn N. Brown et al. AGRICULTURAL AND FOREST METEOROLOGY
- Probabilistic maize yield prediction over East Africa using dynamic ensemble seasonal climate forecasts
- (2018) Geoffrey E.O. Ogutu et al. AGRICULTURAL AND FOREST METEOROLOGY
- Satellite sun-induced chlorophyll fluorescence detects early response of winter wheat to heat stress in the Indian Indo-Gangetic Plains
- (2018) Lian Song et al. GLOBAL CHANGE BIOLOGY
- Benefits of Seasonal Climate Prediction and Satellite Data for Forecasting U.S. Maize Yield
- (2018) Bin Peng et al. GEOPHYSICAL RESEARCH LETTERS
- Effects of the Madden–Julian Oscillation on 2-m air temperature prediction over China during boreal winter in the S2S database
- (2018) Yang Zhou et al. CLIMATE DYNAMICS
- The prospects for China's food security and imports: Will China starve the world via imports?
- (2017) Ji-kun HUANG et al. Journal of Integrative Agriculture
- Evaluation of the Integrated Canadian Crop Yield Forecaster (ICCYF) model for in-season prediction of crop yield across the Canadian agricultural landscape
- (2015) Aston Chipanshi et al. AGRICULTURAL AND FOREST METEOROLOGY
- 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) B. Franch et al. REMOTE SENSING OF ENVIRONMENT
- Climate variation explains a third of global crop yield variability
- (2015) Deepak K. Ray et al. Nature Communications
- Improved maize cultivated area estimation over a large scale combining MODIS–EVI time series data and crop phenological information
- (2014) Jiahua Zhang et al. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- Corn Yield Forecasting in Northeast China Using Remotely Sensed Spectral Indices and Crop Phenology Metrics
- (2014) Meng WANG et al. Journal of Integrative Agriculture
- Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics
- (2013) Douglas K. Bolton et al. AGRICULTURAL AND FOREST METEOROLOGY
- Standardized precipitation evapotranspiration index (SPEI) revisited: parameter fitting, evapotranspiration models, tools, datasets and drought monitoring
- (2013) Santiago Beguería et al. INTERNATIONAL JOURNAL OF CLIMATOLOGY
- Remote sensing-based global crop monitoring: experiences with China's CropWatch system
- (2013) Bingfang Wu et al. International Journal of Digital Earth
- Winter wheat area estimation from MODIS-EVI time series data using the Crop Proportion Phenology Index
- (2012) Yaozhong Pan et al. REMOTE SENSING OF ENVIRONMENT
- Global food demand and the sustainable intensification of agriculture
- (2011) D. Tilman et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Support Vector Machines for classification and regression
- (2009) Richard G. Brereton et al. ANALYST
Add your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload NowCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now