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An Improved UAV-Based ATI Method Incorporating Solar Radiation for Farm-Scale Bare Soil Moisture Measurement

The use of UAV-based remote sensing for soil moisture has developed rapidly in recent decades, with advantages such as high spatial resolution, flexible work arrangement, and ease of operation. In bare and low-vegetation-covered soils, the apparent thermal inertia (ATI) method, which adopts thermal...

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Published in:Remote sensing (Basel, Switzerland) Switzerland), 2023-08, Vol.15 (15), p.3769
Main Authors: Jia, Renhao, Liu, Jianli, Zhang, Jiabao, Niu, Yujie, Jiang, Yifei, Xuan, Kefan, Wang, Can, Ji, Jingchun, Ma, Bin, Li, Xiaopeng
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creator Jia, Renhao
Liu, Jianli
Zhang, Jiabao
Niu, Yujie
Jiang, Yifei
Xuan, Kefan
Wang, Can
Ji, Jingchun
Ma, Bin
Li, Xiaopeng
description The use of UAV-based remote sensing for soil moisture has developed rapidly in recent decades, with advantages such as high spatial resolution, flexible work arrangement, and ease of operation. In bare and low-vegetation-covered soils, the apparent thermal inertia (ATI) method, which adopts thermal infrared data from UAV-based remote sensing, has been widely used for soil moisture estimation at the field scale. However, the ATI method may not perform well under inconsistent weather conditions due to inconsistency of the intensity of the soil surface energy input. In this study, an improvement of the ATI method (ATI-R), considering the variation in soil surface energy input, was developed by the incorporation of solar radiation measurements. The performances of the two methods were compared using field experiment data during multiple heating processes under various weather conditions. It showed that on consistently sunny days, both ATI-R and ATI methods obtained good correlations with the volumetric water contents (VWC) (R2ATI-R = 0.775, RMSEATI-R = 0.023 cm3·cm−3 and R2ATI = 0.778, RMSEATI = 0.018 cm3·cm−3) on cloudy or a combination of sunny and cloudy days as long as there were significant soil-heating processes despite the different energy input intensities; the ATI-R method could perform better than the ATI method (cloudy: R2ATI-R = 0.565, RMSEATI-R = 0.024 cm3·cm−3 and R2ATI = 0.156, RMSEATI = 0.033 cm3·cm−3; combined: R2ATI-R = 0.673, RMSEATI-R = 0.028 cm3·cm−3 and R2ATI = 0.310, RMSEATI = 0.032 cm3·cm−3); and on overcast days, both the ATI-R and ATI methods could not perform satisfactorily (R2ATI-R = 0.027, RMSEATI-R = 0.024 cm3·cm−3 and R2ATI = 0.027, RMSEATI = 0.031 cm3·cm−3). The results indicate that supplemental solar radiation data could effectively expand applications of the ATI method, especially for inconsistent weather conditions.
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In bare and low-vegetation-covered soils, the apparent thermal inertia (ATI) method, which adopts thermal infrared data from UAV-based remote sensing, has been widely used for soil moisture estimation at the field scale. However, the ATI method may not perform well under inconsistent weather conditions due to inconsistency of the intensity of the soil surface energy input. In this study, an improvement of the ATI method (ATI-R), considering the variation in soil surface energy input, was developed by the incorporation of solar radiation measurements. The performances of the two methods were compared using field experiment data during multiple heating processes under various weather conditions. It showed that on consistently sunny days, both ATI-R and ATI methods obtained good correlations with the volumetric water contents (VWC) (R2ATI-R = 0.775, RMSEATI-R = 0.023 cm3·cm−3 and R2ATI = 0.778, RMSEATI = 0.018 cm3·cm−3) on cloudy or a combination of sunny and cloudy days as long as there were significant soil-heating processes despite the different energy input intensities; the ATI-R method could perform better than the ATI method (cloudy: R2ATI-R = 0.565, RMSEATI-R = 0.024 cm3·cm−3 and R2ATI = 0.156, RMSEATI = 0.033 cm3·cm−3; combined: R2ATI-R = 0.673, RMSEATI-R = 0.028 cm3·cm−3 and R2ATI = 0.310, RMSEATI = 0.032 cm3·cm−3); and on overcast days, both the ATI-R and ATI methods could not perform satisfactorily (R2ATI-R = 0.027, RMSEATI-R = 0.024 cm3·cm−3 and R2ATI = 0.027, RMSEATI = 0.031 cm3·cm−3). The results indicate that supplemental solar radiation data could effectively expand applications of the ATI method, especially for inconsistent weather conditions.</description><identifier>ISSN: 2072-4292</identifier><identifier>EISSN: 2072-4292</identifier><identifier>DOI: 10.3390/rs15153769</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>apparent thermal inertia (ATI) ; Energy ; Experiments ; Flexible work hours ; Flying-machines ; Heat ; Heating ; Imaging systems ; Measurement ; Methods ; Moisture content ; Moisture effects ; Precipitation ; Radiation ; Radiation measurement ; Remote sensing ; Soil moisture ; Soil surfaces ; Solar radiation ; Spatial discrimination ; Spatial resolution ; Surface energy ; Surface properties ; thermal infrared ; UAV-based remote sensing ; Weather</subject><ispartof>Remote sensing (Basel, Switzerland), 2023-08, Vol.15 (15), p.3769</ispartof><rights>COPYRIGHT 2023 MDPI AG</rights><rights>2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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In bare and low-vegetation-covered soils, the apparent thermal inertia (ATI) method, which adopts thermal infrared data from UAV-based remote sensing, has been widely used for soil moisture estimation at the field scale. However, the ATI method may not perform well under inconsistent weather conditions due to inconsistency of the intensity of the soil surface energy input. In this study, an improvement of the ATI method (ATI-R), considering the variation in soil surface energy input, was developed by the incorporation of solar radiation measurements. The performances of the two methods were compared using field experiment data during multiple heating processes under various weather conditions. It showed that on consistently sunny days, both ATI-R and ATI methods obtained good correlations with the volumetric water contents (VWC) (R2ATI-R = 0.775, RMSEATI-R = 0.023 cm3·cm−3 and R2ATI = 0.778, RMSEATI = 0.018 cm3·cm−3) on cloudy or a combination of sunny and cloudy days as long as there were significant soil-heating processes despite the different energy input intensities; the ATI-R method could perform better than the ATI method (cloudy: R2ATI-R = 0.565, RMSEATI-R = 0.024 cm3·cm−3 and R2ATI = 0.156, RMSEATI = 0.033 cm3·cm−3; combined: R2ATI-R = 0.673, RMSEATI-R = 0.028 cm3·cm−3 and R2ATI = 0.310, RMSEATI = 0.032 cm3·cm−3); and on overcast days, both the ATI-R and ATI methods could not perform satisfactorily (R2ATI-R = 0.027, RMSEATI-R = 0.024 cm3·cm−3 and R2ATI = 0.027, RMSEATI = 0.031 cm3·cm−3). The results indicate that supplemental solar radiation data could effectively expand applications of the ATI method, especially for inconsistent weather conditions.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/rs15153769</doi><orcidid>https://orcid.org/0000-0001-5671-0931</orcidid><orcidid>https://orcid.org/0000-0002-6800-648X</orcidid><orcidid>https://orcid.org/0000-0003-1685-6455</orcidid><oa>free_for_read</oa></addata></record>
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subjects apparent thermal inertia (ATI)
Energy
Experiments
Flexible work hours
Flying-machines
Heat
Heating
Imaging systems
Measurement
Methods
Moisture content
Moisture effects
Precipitation
Radiation
Radiation measurement
Remote sensing
Soil moisture
Soil surfaces
Solar radiation
Spatial discrimination
Spatial resolution
Surface energy
Surface properties
thermal infrared
UAV-based remote sensing
Weather
title An Improved UAV-Based ATI Method Incorporating Solar Radiation for Farm-Scale Bare Soil Moisture Measurement
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