The Impacts of Heating Strategy on Soil Moisture Estimation Using Actively Heated Fiber Optics

Abstract

Several recent studies have highlighted the potential of Actively Heated Fiber Optics (AHFO) for high resolution soil moisture mapping. In AHFO, the soil moisture can be calculated from the cumulative temperature ( T cum ), the maximum temperature ( T max ), or the soil thermal conductivity determined from the cooling phase after heating ( λ ). This study investigates the performance of the T cum , T max and λ methods for different heating strategies, i.e., differences in the duration and input power of the applied heat pulse. The aim is to compare the three approaches and to determine which is best suited to field applications where the power supply is limited. Results show that increasing the input power of the heat pulses makes it easier to differentiate between dry and wet soil conditions, which leads to an improved accuracy. Results suggest that if the power supply is limited, the heating strength is insufficient for the λ method to yield accurate estimates. Generally, the T cum and T max methods have similar accuracy. If the input power is limited, increasing the heat pulse duration can improve the accuracy of the AHFO method for both of these techniques. In particular, extending the heating duration can significantly increase the sensitivity of T cum to soil moisture. Hence, the T cum method is recommended when the input power is limited. Finally, results also show that up to 50% of the cable temperature change during the heat pulse can be attributed to soil background temperature, i.e., soil temperature changed by the net solar radiation. A method is proposed to correct this background temperature change. Without correction, soil moisture information can be completely masked by the background temperature error.

Keywords: active DTS; heating strategy; soil moisture; soil temperature.

Figure 1

Particle size distribution of the sand used for testing AHFO soil moisture estimation.

Figure 2

An illustrative diagram of the experiment setup.

Figure 3

Soil temperature measurement during a heat pulse before (a) and after (b) background temperature correction. Red dots in (a) are the temperature measurements used for estimating the background temperature (red solid line) during the heat pulse.

Figure 4

${\mathrm{T}}_{\mathrm{cum}}$ (first column), ${\mathrm{T}}_{\max }$ (second column), and the estimated soil thermal conductivity ($\lambda$) (third column) as a function of EC5 measured soil moisture. The solid lines in the first two columns, are the fitted ${\mathrm{T}}_{\mathrm{cum}}$ and ${\mathrm{T}}_{\max }$ to soil moisture relationship, in which 2.5 cm measurements were not considered. The solid lines in the third column represents the measured soil thermal conductivity curve using KD2Pro heat-pulse sensor. Each plot represents heat pulse and soil moisture data collected from all four depths.

Figure 5

The sensitivity ${\mathrm{T}}_{\mathrm{cum}}$ (a) and the ${\mathrm{T}}_{\max }$ (b) to soil moisture when different heating strategies were used. The sensitivity curves are derived from the fitted ${\mathrm{T}}_{\mathrm{cum}}$ and the ${\mathrm{T}}_{\max }$ to soil moisture relationship in Figure 4.

Figure 6

Comparison of the observed and the estimated soil moisture using the ${\mathrm{T}}_{\mathrm{cum}}$ method (a–c) and ${\mathrm{T}}_{\max }$ method (d–f). Soil moisture measurements at 2.5 cm were not included.

Figure 7

Same as Figure 4 but without background temperature correction.