A remote sensing-based scheme to improve regional crop model calibration at sub-model component level

Jing Zhang, Yi Chen and Zhao Zhang

Agricultural Systems, 2020, vol. 181, issue C

Abstract: Parameter calibration is an importantly preliminary step before using a crop model to simulate crop growth and final yield. Compared with the traditionally accepted calibration method parameterizing the whole model simultaneously (called as “Global Scheme”), the Sub-Model Component (SMC) Scheme emphasizes on parameterizing different functional modules in a crop model sequentially. However, the SMC Scheme receives less attention, especially at regional scales. Therefore, this study led a performance evaluation of the two calibration schemes through using them to incorporate remote sensing data into a crop model (MCWLA-Rice) independently in Northeast China. We found the SMC Scheme reduced root mean square error (RMSE) on average by 4 days for heading date and 2 days for harvest date. Using the Pearson correlation coefficient (R) to assess the similarity between time series of modelled LAI and remotely-sensed LAI, the SMC Scheme decreased LAI estimation error by 0.04. Finally, the SMC Scheme greatly decreased relative RMSE (RRMSE) for yield by 11%. In addition, temperature and topography could affect the performance of SMC Scheme. Our findings demonstrated that the SMC Scheme calibrated the crop model more effectively and reliably, suggesting its potentially wide application in other regions and crops.

Keywords: Calibration; Crop model; MCWLA; SMC scheme; Remote sensing (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0308521X19305931
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:agisys:v:181:y:2020:i:c:s0308521x19305931

DOI: 10.1016/j.agsy.2020.102814

Access Statistics for this article

Agricultural Systems is currently edited by J.W. Hansen, P.K. Thornton and P.B.M. Berentsen

More articles in Agricultural Systems from Elsevier
Bibliographic data for series maintained by Catherine Liu ().