Developing a process-based and remote sensing driven crop yield model for maize (PRYM–Maize) and its validation over the Northeast China Plain

• Published in: Journal of Integrative Agriculture Vol. 20; no. 2; pp. 408 - 423
• Main Authors: ZHANG, Sha, BAI, Yun, ZHANG, Jia-hua, ALI, Shahzad
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2021; College of Computer Science and Technology, Qingdao University, Qingdao 266071, P.R.China; University of Chinese Academy of Sciences, Beijing 100049, P.R.China%School of Automation, Qingdao University, Qingdao 266071, P.R.China; School of Automation, Qingdao University, Qingdao 266071, P.R.China; Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, P.R.China%College of Computer Science and Technology, Qingdao University, Qingdao 266071, P.R.China; Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, P.R.China; Elsevier
• Subjects: development stage; grain filling; harvest index; maize yield simulation; process-based and remote sensing model; maize yield simulation; development stage; grain filling; harvest index; process-based and remote sensing model

Spatial dynamics of crop yield provide useful information for improving the production. High sensitivity of crop growth models to uncertainties in input factors and parameters and relatively coarse parameterizations in conventional remote sensing (RS) approaches limited their applications over broad...