Improving the Forecasting of Winter Wheat Yields in Northern China with Machine Learning–Dynamical Hybrid Subseasonal-to-Seasonal Ensemble Prediction

Junjun Cao; Huijing Wang; Jinxiao Li; Qun Tian; Dev Niyogi

doi:10.3390/rs14071707

Improving the Forecasting of Winter Wheat Yields in Northern China with Machine Learning–Dynamical Hybrid Subseasonal-to-Seasonal Ensemble Prediction

Junjun Cao
, Huijing Wang
, Jinxiao Li
, Qun Tian
, Dev Niyogi

Central China Normal University
Chinese Academy of Agricultural Sciences
CAS - Institute of Atmospheric Physics
China Meteorological Administration
University of Texas at Austin
Purdue University

Research output: Contribution to journal › Article › peer-review

Original language	English
Article number	1707
Journal	Remote Sensing
Volume	14
Issue number	7
DOIs	https://doi.org/10.3390/rs14071707
State	Published - Apr 1 2022
Externally published	Yes

ASJC Scopus Subject Areas

General Earth and Planetary Sciences

Keywords

climate variables
machine learning
subseasonal-to-seasonal prediction
winter wheat
yield forecasting

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10.3390/rs14071707

Cite this

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title = "Improving the Forecasting of Winter Wheat Yields in Northern China with Machine Learning–Dynamical Hybrid Subseasonal-to-Seasonal Ensemble Prediction",

keywords = "climate variables, machine learning, subseasonal-to-seasonal prediction, winter wheat, yield forecasting",

author = "Junjun Cao and Huijing Wang and Jinxiao Li and Qun Tian and Dev Niyogi",

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AU - Cao, Junjun

AU - Wang, Huijing

AU - Li, Jinxiao

AU - Tian, Qun

AU - Niyogi, Dev

N1 - Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

PY - 2022/4/1

Y1 - 2022/4/1

KW - climate variables

KW - machine learning

KW - subseasonal-to-seasonal prediction

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KW - yield forecasting

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Improving the Forecasting of Winter Wheat Yields in Northern China with Machine Learning–Dynamical Hybrid Subseasonal-to-Seasonal Ensemble Prediction

ASJC Scopus Subject Areas

Keywords

Access to Document

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