2020
DOI: 10.7717/peerj.9087
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Estimation of soil salt content by combining UAV-borne multispectral sensor and machine learning algorithms

Abstract: Soil salinization is a global problem closely related to the sustainable development of social economy. Compared with frequently-used satellite-borne sensors, unmanned aerial vehicles (UAVs) equipped with multispectral sensors provide an opportunity to monitor soil salinization with on-demand high spatial and temporal resolution. This study aims to quantitatively estimate soil salt content (SSC) using UAV-borne multispectral imagery, and explore the deep mining of multispectral data. For this purpose, a total … Show more

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Cited by 32 publications
(26 citation statements)
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References 56 publications
(64 reference statements)
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“…Different VIs and SIs are calculated from two or more bands, so the selection of appropriate VIs and SIs is crucial. It has been shown that Spectral indices such as NDVI and CRSI are highly sensitive to SSC (Wei et al, 2020;Qi et al, 2020). We selected six widely used VIs and four SIs for this study, and their calculation equations are given in Table 2, where, R, B, G, NIR and RE are the T A B L E 1 UAV imagery and ground measurements with respect to the day of year (DOY) and the number of ground sampling points (GSP) in each study area: Ground parameters include SSC, LAI, CHL and H…”
Section: Vegetation Indices Calculationmentioning
confidence: 99%
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“…Different VIs and SIs are calculated from two or more bands, so the selection of appropriate VIs and SIs is crucial. It has been shown that Spectral indices such as NDVI and CRSI are highly sensitive to SSC (Wei et al, 2020;Qi et al, 2020). We selected six widely used VIs and four SIs for this study, and their calculation equations are given in Table 2, where, R, B, G, NIR and RE are the T A B L E 1 UAV imagery and ground measurements with respect to the day of year (DOY) and the number of ground sampling points (GSP) in each study area: Ground parameters include SSC, LAI, CHL and H…”
Section: Vegetation Indices Calculationmentioning
confidence: 99%
“…Hu et al (2019) used the random Forest (RF) regression method to quantitatively estimate SSC and drew soil distribution maps of three different vegetation coverage based on UAV hyperspectral imagery. Wei et al (2020) established an estimation model of SSC by using UAV multispectral imagery and three machine learning algorithms and concluded that the quantitative estimation of SSC by UAV multispectral remote sensing is feasible. Chen et al (2020) established estimation models of SSC in sunflower fields at the budding and flowering stages and at different soil depths by using UAV multispectral data.…”
Section: Introductionmentioning
confidence: 99%
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“…Nevertheless, they can also be integrated into semi-empirical or physical models for quantitative prediction of the salinity content classes in the soil (Al-Ali et al, 2021). To select the most informative soil salinity index, comparative studies have been completed by applying regression analyses between EC-Lab and SI derived from spectral measurements, satellite, airborne and drone images (Allbed et al, 2014;Bannari et al, 2018;Peng et al, 2019;Hu et al, 2019;Wei et al, 2020;Milewski et al, 2020;Gopalakrishnan and Kumar, 2020). Often, obtained results vary depending on the spectral wavebands integrated in the equation of each index.…”
Section: Spectra and Image Data Processingmentioning
confidence: 99%
“…Mohammad et al [14] implemented the monitoring of soil salinity in a large area of Qom County, Qom Province, Iran, based on Sentinel-2A data. Wei et al [15] took advantage of UAV equipped with Micro-MCA multispectral sensors to obtain images and realized the estimation of soil salinity in a small area of Hetao Irrigation District. Wang et al [16] utilized a portable spectrometer ASD to obtain soil hyperspectral to construct a soil salinity inversion model in Baidunzi Basin, China, and achieved high model accuracy.…”
Section: Introductionmentioning
confidence: 99%