Chapter 11
Crop Growth Modeling and Yield
Forecasting
Haizhu Pan and Zhongxin Chen
Abstract In the past decades, crop growth modeling and yield forecasting have
attracted increasing attention in both scientific researches and agricultural practices.
Many scientific studies have been carried out to improve the capabilities of crop
growth modeling and yield forecasting by using various data sources and methods
like statistical models, crop growth simulation models, and remote sensing. In this
chapter, four categories of crop growth models were reviewed. Firstly, the traditional
crop modeling and forecasting methods were introduced: statistical modeling and
crop growth models. Then remote sensing models mainly based on spectral indices
and quantitative products were introduced. The quality of remote sensing data is
critical for crop modeling and yield forecasting. Finally, the widely used data
assimilation of crops was described. More research is necessary for the full use of
the value of remote sensing and crop growth model in crop growth monitoring and
yield forecasting at a regional scale.
Keywords Crop growth · Modeling · Yield forecast · Remote sensing · Data
assimilation
11.1 Introduction
Crop growth and yield are very important information in crop management and agricultural policy-making at farm and regional scales. Under the challenge of climate
change and increasing population stresses to agricultural sustainability, the interests
in crop growth modeling and yield forecasting have increased quickly in the last
H. Pan
Breeding Base for State Key Laboratory of Land Degradation and Ecological Restoration in
Northwest China, Ningxia University, Yinchuan, China
e-mail: panhaizhu@nxu.edu.cn
Z. Chen (*)
Food and Agriculture Organization of the United Nations, Rome, Italy
e-mail: zhongxin.chen@fao.org
© Springer Nature Switzerland AG 2021
L. Di, B. Üstündağ (eds.), Agro-geoinformatics, Springer Remote Sensing/
Photogrammetry, https://doi.org/10.1007/978-3-030-66387-2_11
205
Crop Growth Modeling and Yield
Forecasting
Haizhu Pan and Zhongxin Chen
Abstract In the past decades, crop growth modeling and yield forecasting have
attracted increasing attention in both scientific researches and agricultural practices.
Many scientific studies have been carried out to improve the capabilities of crop
growth modeling and yield forecasting by using various data sources and methods
like statistical models, crop growth simulation models, and remote sensing. In this
chapter, four categories of crop growth models were reviewed. Firstly, the traditional
crop modeling and forecasting methods were introduced: statistical modeling and
crop growth models. Then remote sensing models mainly based on spectral indices
and quantitative products were introduced. The quality of remote sensing data is
critical for crop modeling and yield forecasting. Finally, the widely used data
assimilation of crops was described. More research is necessary for the full use of
the value of remote sensing and crop growth model in crop growth monitoring and
yield forecasting at a regional scale.
Keywords Crop growth · Modeling · Yield forecast · Remote sensing · Data
assimilation
11.1 Introduction
Crop growth and yield are very important information in crop management and agricultural policy-making at farm and regional scales. Under the challenge of climate
change and increasing population stresses to agricultural sustainability, the interests
in crop growth modeling and yield forecasting have increased quickly in the last
H. Pan
Breeding Base for State Key Laboratory of Land Degradation and Ecological Restoration in
Northwest China, Ningxia University, Yinchuan, China
e-mail: panhaizhu@nxu.edu.cn
Z. Chen (*)
Food and Agriculture Organization of the United Nations, Rome, Italy
e-mail: zhongxin.chen@fao.org
© Springer Nature Switzerland AG 2021
L. Di, B. Üstündağ (eds.), Agro-geoinformatics, Springer Remote Sensing/
Photogrammetry, https://doi.org/10.1007/978-3-030-66387-2_11
205