Using an unmanned aerial vehicle for soil moisture remote sensing by means of ultra-wideband electromagnetic impulses

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Abstract

Under long-time experiments, the possibility of remote sensing of soil moisture with ultra-wideband (UWB) electromagnetic impulses from an unmanned aerial vehicle (UAV) was investigated. The soil surface of test sites with varying degrees of roughness was under fallow in conditions of natural moisture, drying, and periodic harrowing. The soil moisture was found by inverse problem solving, while minimizing the norm of discrepancy between the module of reflection coefficients, which were calculated using the Fresnel formula (for dielectrically homogeneous half-space) and the average values, measured at different hovering heights of the UAV over the sensing test sites. During the experiments from June 12 to September 28, 2022, the achievability of practically significant accuracy of remote sensing of volumetric soil moisture on the test sites in a 6—7 cm topsoil with a standard deviation of less than 4 % (relative to the weighted-drying measurements of soil samples, in-situ collected) was demonstrated. As a result, it was shown that in the frequency range of the sensing impulse from 456 MHz to 1014 MHz, the influence of diffuse scattering of waves on random soil surface roughness (standard deviations of the heights of roughness less than 2 cm) can be neglected within the above-mentioned error in the soil moisture retrieval.

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About the authors

K. V. Muzalevskiy

Kirensky Institute of Physics Federal Research Center KSC Siberian Branch Russian Academy of Sciences

Author for correspondence.
Email: rsdkm@ksc.krasn.ru
Russian Federation, Akademgorodok 50, bld. 38, Krasnoyarsk, 660036

S. V. Fomin

Kirensky Institute of Physics Federal Research Center KSC Siberian Branch Russian Academy of Sciences

Email: rsdkm@ksc.krasn.ru
Russian Federation, Akademgorodok 50, bld. 38, Krasnoyarsk, 660036

A. Yu. Karavayskiy

Kirensky Institute of Physics Federal Research Center KSC Siberian Branch Russian Academy of Sciences

Email: rsdkm@ksc.krasn.ru
Russian Federation, Akademgorodok 50, bld. 38, Krasnoyarsk, 660036

Z. Ruzicka

Kirensky Institute of Physics Federal Research Center KSC Siberian Branch Russian Academy of Sciences

Email: rsdkm@ksc.krasn.ru
Russian Federation, Akademgorodok 50, bld. 38, Krasnoyarsk, 660036

Yu. A. Leskova

Kirensky Institute of Physics Federal Research Center KSC Siberian Branch Russian Academy of Sciences

Email: rsdkm@ksc.krasn.ru
Russian Federation, Akademgorodok 50, bld. 38, Krasnoyarsk, 660036

A. G. Lipshin

Krasnoyarsk Research Institute of Agriculture Federal Research Center KSC Siberian Branch Russian Academy of Sciences

Email: rsdkm@ksc.krasn.ru
Russian Federation, Svobodny Prosp., 66, Krasnoyarsk

V. N. Romanov

Krasnoyarsk Research Institute of Agriculture Federal Research Center KSC Siberian Branch Russian Academy of Sciences

Email: rsdkm@ksc.krasn.ru
Russian Federation, Svobodny Prosp., 66, Krasnoyarsk

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Supplementary files

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2. Fig. 1. Location of test sites in the area of ​​the settlement Minino: coordinates of site 1 - 56.0644 N, 92.6967 E; site 2 - 56.0888 N, 92.6660 E; site 3 - 56.0951 N, 92.6654 E.

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3. Fig. 2. Top view of three prepared areas with different degrees of roughness in test section 2.

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4. Fig. 3. External appearance of the UAV reflectometer.

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5. Fig. 4. Envelope (1), time shape (2) and normalized spectrum (3) of the synthesized UWB pulse. The distance between the phase center of the antenna and the reflective screen (brass mesh) is approximately 87 cm.

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6. Fig. 5. Propagation time tpropag (1) and maximum envelope smet,max(dlid) (2) of synthesized UWB pulse reflected from metal screen (brass mesh) at different heights dlid of position of antenna phase center above screen. Approximating dependences and corresponding R2 and RMS: curve 3 — tpropag = (0.18 ± 0.03)+(6.54 ± 0.01) dlid, R2 = 0.999 and RMS = 0.15 ns; curve 4 — smet, max(dlid) = (1.017 ± 4.910–3)/(2dlid), R2 = 0.986 and RMS = 0.014 m–1.

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7. Fig. 6. Characteristics of small-scale irregularities of the soil surface of the test plots: a) distribution of the heights of irregularities hш, soil surface pизм(hш), measured by the photogrammetric method on 09/21/2022 on test plot 2, site 1, with R2 = 0.981; pтеор(hш) is the corresponding approximation by the Gaussian function; b) correlation between the measured and approximated (Gaussian function) distributions of hш for all test plots during all experiments (1), linear regression (2), with R2 = 0.964; c, d) distribution of the standard deviation σш and the correlation length lк of the heights of small-scale irregularities of the soil surface measured by the photogrammetric method as a whole for all test plots during all experiments.

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8. Fig. 7. Distribution of volumetric soil moisture values: a) measured on 21.09.2022 pmeas(W) at test site 2, site 1 with R2 = 0.869, ptheor(W) – corresponding approximation by a Gaussian function; b) correlation between measured and approximated (Gaussian function) distributions of W for all test sites during all experiments (1), linear regression (2) with R2 = 0.895.

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9. Fig. 8. Time shapes s(t, dlid) (solid lines) and envelopes sogib(t, dlid) (dashed lines) of UWB pulses measured on September 11, 2022 at test site 1, site 1 at σsh = 0.5 cm, lh = 7.5 cm, W = 25.5% and different UAV hovering heights: dlid = 1.01 (1), 1.63 (2), 2.33 (3), 3.20 (4), 4.17 (5) and 5.11 m (6).

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10. Fig. 9. Delay time (a) and maxima of the envelopes (b) of UWB pulses reflected from the soil cover: a – measurements were carried out on September 11 and 17, 2022 at test site 1, respectively, at sites 1 (1) and 2 (2); b – approximations of the measured values ​​of ssoil, max(dlead) (symbols 1 and 2) by linear dependencies are designated 4 and 5, respectively. Linear dependencies 3 were obtained during calibration (see Section 3B).

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11. Fig. 10. Dependence of the reflection coefficient modulus normalized to the roughness factor, measured by the UAV reflectometer (1), on the volumetric soil moisture in-situ; the reflection coefficient modulus 2 is calculated using formula (5).

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12. Fig. 11. Dependence of the roughness factor Sш(σш), measured (1) on test sections, on the standard deviation of the heights of soil surface irregularities, σш; the Gaussian distribution function (2) approximating (1); the roughness factor (3), calculated using model (4).

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13. Fig. 12. Dependence of the values ​​of volumetric soil moisture found during the solution of the inverse problem and measured in situ: solution of the inverse problem with correction (1) and without correction (2) of the reflection coefficient due to the soil surface roughness factor and the corresponding regression lines (3, 4).

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