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

机译:探地雷达对根系水分含量对根系生物量估计的影响:前向模拟和现场控制实验的证据

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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. 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. 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. 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.
机译:用于预测根生物量的GPR指数是根雷达反射率的量度。但是,根雷达反射率与根水含量高度相关。这项研究的目的是评估根水含量对基于GPR的根生物量估计的影响,并开发更可靠的方法来使用GPR量化根生物量。在中国北方的一片沙质地区,对五种植物的490根进行了研究,以确定沙质土壤中根的一般水分含量范围。然后进行了两组GPR模拟场景(总共包括492个合成雷达图),以比较根雷达信号的变化和在不同根重水分含量水平下GPR估算根生物量的准确性。在野外,对GPR断面进行扫描,以发现埋在沙质土壤中的榆树根(Ulmus pumila)根具有三个天线中心频率(0.5、0.9和2.0 GHz)。然后测试了两种新的基于GPR的根生物量量化方法(一种使用时间间隔GPR指数,另一种使用非线性回归模型)的性能。所有研究过的根均显示出较重的水分含量范围(> 125%),大多数根的水分含量在90%至150%之间。现场实验和正演模拟均表明:1)根部含水量影响根雷达反射的波形,与雷达反射有关的GPR指数以及根生物量估计的准确性; 2)利用时间间隔指数,在GPR指数上建立根生物量的非线性回归模型,提高了根生物量估计的准确性,在田间条件下将预测误差(RMSE)降低了4%至30%。 GPR指数的大小取决于根系生物量和根系水分含量,根系水分含量会影响使用GPR指数进行的根系生物量估算。将根生物量的线性回归模型用于与雷达反射率相关的GPR指数进行根生物量估计仅对水分含量相对较窄的根可行(例如,当所研究根的重量水分含量在20%以内时)。应根据根的水分含量范围选择适当的GPR指数和回归模型。本研究提出了一种新的利用GPR进行根生物量定量的方案,提高了根生物量估算的准确性。

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