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Lookup NU author(s): Professor Zhenhong Li
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© 2018 IEEE. Currently, most GPC prediction models by remote sensing are a little mechanism and tryin to expand at interannual and regional scales. The objective of this study is to use Hierarchical Linear Model (HLM) integrating spectral indices at anthesis and meteorological data to achieve GPC prediction at interannual scales. Results suggested that (1) spectral polygon vegetation index (SPVI) showed a high significance with GPC, with correlation coefficient (r) value of 0.38. (2) The linear model by SPVI performed low accuracy, and the determination coefficient (R2) and root mean square error (RMSE) values were 0.13 and 1.73%, respectively, while GPC model by SPVI showed poor robustness at interannual. (3) The estimation of GPC using HLM model with considering environmental variations yielded higher accuracy (R2 = 0.58 and RMSE = 1.21%) than the linear model, and the R2 and RMSE values of validation were 0.51 and 1.37%, respectively. A high consistency between the predicted GPC and the measured GPC was shown at different years. Overall, these results in this study have demonstrated the potential applicability of HLM model for GPC prediction at various years.
Author(s): Li Z, Wang J, Liu C, Song X, Xu X, Li Z
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: 2018 7th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2018
Year of Conference: 2018
Online publication date: 01/10/2018
Acceptance date: 06/08/2018
Publisher: IEEE
URL: https://doi.org/10.1109/Agro-Geoinformatics.2018.8476098
DOI: 10.1109/Agro-Geoinformatics.2018.8476098
Library holdings: Search Newcastle University Library for this item
ISBN: 9781538650387
