Impact of root water content on root biomass estimation using ground penetrating radar: evidence from forward simulations and field controlled experiments

Li Guo1, Henry Lin2, Bihang Fan1, Xihong Cui1, Jin Chen1

1 State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China;

2 Department of Ecosystem Science and Management, The Pennsylvania State University, University Park, PA 16802, USA.

Abstract：Background and aims The GPR indices used for predicting root biomass are measures of root radar reflectance. However, root radar reflectance is highly correlated with root water content. The objectives of this study are to assess the impact of root water content on GPR-based root biomass estimation and to develop more reliable approaches to quantify root biomass using GPR.

Methods Four hundred nine roots of five plant species in a sandy area of northern China were examined to determine the general water content range of roots in sandy soils. Two sets of GPR simulation scenarios (including 492 synthesized radargrams in total) were then conducted to compare the changes of root radar signal and the accuracies of root biomass estimation by GPR at different root gravimetric water content levels. In the field, GPR transects were scanned for Ulmus pumila roots buried in sandy soils with three antenna center frequencies (0.5, 0.9, and 2.0 GHz). The performance of two new GPR-based root biomass quantification approaches (one using time interval GPR index and the other using a non-linear regression model) was then tested.

Results All studied roots exhibited a broad range of gravimetric water content (>125 %), with the water contents of most roots ranging from 90 % to 150 %. Both field experiments and forward simulations indicated that 1) waveforms of root radar reflection, radar-reflectance related GPR indices, and root biomass estimation accuracy were all affected by root water content; and 2) using time interval index and establishing a nonlinear regression model of root biomass on GPR indices improved the accuracy of root biomass estimation, decreasing the prediction error (RMSE) by 4 to 30 % under field conditions.

Conclusions The magnitude of GPR indices depends on both root biomass and root water content, and root water content affects root biomass estimation using GPR indices. Using a linear regression model of root biomass on radar-reflectance related GPR index for root biomass estimation would only be feasible for roots with a relative narrow range of water content (e.g., when gravimetric water contents of studied roots vary within 20 %). Appropriate GPR index and regression models should be selected based on the water content range of roots. The new protocol of root biomass quantification by GPR presented in this study improves the accuracy of root biomass estimation.

Keywords： Ground penetrating radar; Noninvasive root investigation; Root biomass estimation; Root water content; Forward simulation; Controlled field experiment.