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In-season biomass estimation of oilseed rape (Brassica napus L.) using fully polarimetric SAR imagery

Lookup NU author(s): Dr Hao Yang, Dr Rachel GaultonORCiD, Professor Zhenhong Li, Dr James Taylor



This is the authors' accepted manuscript of an article that has been published in its final definitive form by Springer, 2019.

For re-use rights please refer to the publisher's terms and conditions.


© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Accurate estimation of crop biophysical and biochemical parameters during crop growing seasons is essential for improving site-specific management and yield estimation. The potential ability of fully polarimetric synthetic aperture radar (SAR) data in estimating above-ground biomass of oilseed rape was investigated in this study. The temporal profile of different scattering intensity and polarimetric features during the entire growing season was identified with ground measurements. A polarimetric feature, relying on the polarimetric decomposition method, was put forward to estimate the biomass of oilseed rape. Validation results revealed great potential with a determination coefficient (R2) of 0.85, root mean squared error (RMSE) of 41.6 g/m2, and relative error (RE) of 28.5% for dry biomass, and an R2 of 0.76, RMSE of 527.4 g/m2 and RE of 28.6% for fresh biomass. Moreover, the use of full polarization SAR data was compared with single and dual polarization SAR data. The results suggest that when full polarization SAR data is available, a simpler model, higher saturation point and better accuracy can be achieved in biomass estimation of oilseed rape, which highlights the importance and value of polarimetry information in quantitative crop monitoring. This study provides guidelines for in-season monitoring of crop growth parameters with SAR data, which further improves crop monitoring capability in adverse weather conditions.

Publication metadata

Author(s): Yang H, Yang G, Gaulton R, Zhao C, Li Z, Taylor J, Wicks D, Minchella A, Chen E, Yang X

Publication type: Article

Publication status: Published

Journal: Precision Agriculture

Year: 2019

Volume: 20

Issue: 3

Pages: 630-648

Print publication date: 01/06/2019

Online publication date: 01/08/2018

Acceptance date: 02/04/2018

Date deposited: 08/09/2018

ISSN (print): 1385-2256

ISSN (electronic): 1573-1618

Publisher: Springer


DOI: 10.1007/s11119-018-9587-0

Notes: Highly cited paper in WoS.


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