A new technique of measuring sap flow that accurately measures a wide range of sap flow rates

Accurate estimation of sap flow is vital for plant models and significantly impacts existing management policies, particularly when scaling from the plant level to plot levels. The heat pulse method (HPM) is widely used for measuring sap flow in plants because of its added benefits compared to other traditional methods. In HPM, there are many approaches, all of which follow the Marshall theory. The existing HPM fails to measure a wide range of sap flow rates in a single approach. The literature suggests that those limitations may be because of factors such as wounding, sensor resolution, and others. However, these reasons apply only within specific heat pulse velocity ranges. These methods typically rely on 1-3 data points for sap flow estimation. In some methods, the data points for particular flow rates may be susceptible to noise, resulting errors in sap flow estimates. While a combination of different methods could potentially address this issue, they often require different probe configurations, additional probes, and complex switching algorithms. However, none of the existing techniques have successfully measured the full range of sap flow rates. In this study, we present a new approach capable of measuring a wide range of sap flow rates by minimizing the sum of square errors between modeled and observed temperature data points, utilizing 180 data points. Additionally, we demonstrate that the signal-to-noise ratio as an explanatory framework shows the limitations of existing methods within specific heat pulse velocity ranges. We show that the signal-to-noise ratio can be increased by utilizing all available data points. The Sum of Square Errors Minimization method can accurately measure a wide range of sap flow rates without the need to change probe configurations, contributing to improved scaling from plant level to plot levels.