Pedological assessments along an arid and semi-arid transect using soil spectral behavior analysis

Taghdis, Sahar; Farpoor, Mohammad Hady; Mahmoodabadi, Majid

doi:10.1016/j.catena.2022.106288

Cited by 7 publications

(6 citation statements)

References 82 publications

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“…Zhu, Li, et al (2022) and Zhu, Liang, et al (2022) used multiple pedotransfer functions as efficient methods to predict K through the utilization of easily measurable soil properties. An alternative to these conventional analyses, the prediction of soil properties by Vis–NIR‐SWIR spectroscopy (visible, near‐infrared, and shortwave infrared spectroscopy: 350–2500 nm) has lately gained prominence (Mina et al, 2021; Taghdis et al, 2022; Tiruneh et al, 2022), as it is easier, faster, and cheaper to measure.…”

Section: Introductionmentioning

confidence: 99%

Near‐infrared spectroscopy as an alternative tool for predicting soil erodibility in a watershed under desertification

Teixeira,

da Silva,

Barbosa

et al. 2023

Land Degrad Dev

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Near‐infrared spectroscopy (NIR) has become an alternative to conventional techniques for assessing soil properties that are difficult to measure, especially at sites under intense erosion. Thus, the aim of this study was to assess near‐infrared spectroscopy as an alternative tool for predicting soil erodibility in a watershed under desertification. Two hundred and fourteen composite soil samples were collected, where 100 samples were analyzed for physical and chemical properties to obtain K (soil erodibility), Ki (interrill erodibility), and Kr (rill erodibility) values and the remaining 114 composite soil samples were only used to obtain spectral signatures in the NIR range. Then, such database was used to predict the erodibility parameters and soil physicochemical properties. Partial least squares regression was used for modeling the K, Ki, Kr indices and other soil properties. The highest erodibility indices were observed in Acrisols and Luvisols (K = 33.59 × 10−3 t h MJ−1 mm−1, R2 = 0.61, square root of the mean error (RMSE) = 2.62 and residual prediction deviation (RPD) = 1.62; Ki = 5.45 × 10−6 kg s m−4, R2 = 0.49, RMSE = 0.21 and RPD = 1.47; Kr = 21.78 × 10−3 s m−1, R2 = 0.54, RMSE = 0.66 and RPD = 1.68). Erodibility prediction using spectral data was found to be an acceptable alternative and can be applied in other areas struggling with the same phenomena to acquire new robust databases and to improve the understanding of soil erosion and desertification process. Also, our results can extend studies with universal soil‐loss equation and revised universal soil‐loss equation, since erodibility is one of the most important and expensive parameters of these equations, mainly in developing countries where financial support is scarce.

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Section: Introductionmentioning

confidence: 99%

Near‐infrared spectroscopy as an alternative tool for predicting soil erodibility in a watershed under desertification

Teixeira,

da Silva,

Barbosa

et al. 2023

Land Degrad Dev

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“…Moreover, SS can strongly vary in both vertical and horizontal space, and the spatial and temporal variation of SS is more pronounced especially in saline soils [11]. The high spatial and temporal variability of SS introduces difficulty to traditional measuring methods for obtaining SS in a time-sensitive and rapid manner, especially on a large spatial scale.…”

Section: Introductionmentioning

confidence: 99%

“…Soil Vis-NIR spectral are strongly influenced by water vapor in the air. It can produce strong noise in the absorption band of the spectrum near 1400 and 1900 nm, which can seriously interfere with the spectral data [11]. Several studies have proposed that preprocessing methods can effectively reduce spectral noise and can eliminate the effects of some interfering factors [10].…”

Section: Introductionmentioning

confidence: 99%

“…Although ELM techniques have been widely used in many fields, less research has been conducted on the application of SS monitoring [16]. To select the best salinity inversion model, many researchers have also compared multiple models [11,17,18], but no uniform results have been obtained. For conventional prediction models, the main difficulty in extracting useful information from Vis-NIR spectra depends on the amount of data and data dimensionality [19].…”

Section: Introductionmentioning

confidence: 99%

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Desert Soil Salinity Inversion Models Based on Field In Situ Spectroscopy in Southern Xinjiang, China

Wang

Xie

et al. 2022

Remote Sensing

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Soil salinization is prominent environmental issue in arid and semi-arid regions, such as Xinjiang in Northwest China. Salinization severely restricts economic and agricultural development and would lead to ecosystem degradation. Finding a method of rapidly and accurately determining soil salinity (SS) is one of the main challenges in salinity evaluation, saline soil development, and utilization. In situ visible and near infrared (Vis-NIR) spectroscopy has proven to be a promising technique for detecting soil properties since it can realize real-time, rapid detection of SS. However, it still remains challenging whether Vis-NIR in situ spectroscopy can invert SS with high accuracy due to the interference of environmental factors (e.g., light, water vapor, solar altitude angle, etc.) on the spectral in the field. To fill this knowledge gap, we collected Vis-NIR in situ spectral and lab-measured SS data from 135 surface soil samples in the Kongterik Pasture Nature Reserve (KPNR) in the desert oasis ecotone of southern Xinjiang, China. We used genetic algorithm (GA), particle swarm optimization (PSO), and simulated annealing (SA) algorithms to select the feature bands of SS. Subsequently, we combined extreme learning machines (ELM), back-propagation neural networks (BPNN), and convolutional neural networks (CNN) to build inversion models of SS. The results showed that different feature bands selection methods could improve the Vis-NIR in situ spectral prediction model accuracy. Either SS inversion models were built using full-band spectral data or feature-band spectral data. Compared with the full-band (401–2400 nm) spectral modeling, the validation set R2 of ELM, BPNN, and CNN models built selected feature bands selected by PSO, GA, and SA, respectively, were improved by more than 0.06. The accuracy of predicting SS varied widely among modeling methods. The accuracy of CNN model was obviously higher than that of BPNN and ELM models. The optimal hybrid model for predicting SS constructed in this study is SA-CNN model (R2 = 0.79, RMSE = 9.41 g kg−1, RPD = 1.81, RPIQ = 2.37). This study showed that the spectral feature bands selection methods can reduce the influence of environmental factors on in situ spectroscopy and significantly enhance the inversion accuracy of SS. The present study provided that estimating SS using in situ Vis-NIR spectral is feasible.

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“…Soil moisture and its spatiotemporal variation are crucial quantitative methods to reflect the spatial pattern of vegetation (Legates et al, 2011), while soil salinity is related to variations in soil moisture (Wang et al, 2020a;Shah et al, 2021). In addition, quantitative relationships between soil properties and reflectance have been reported in the literature (Davari et al, 2021;Mzid et al, 2022;Taghdis et al, 2022). Wu et al, (2021), who examined the SWC and EC of soil samples and combined remote sensing with mathematical models to develop predictive models for determining soil salinity.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal dynamics of soil water-salt and its topographic gradient effects on Alpine area over the Northeastern Tibetan Plateau, China

Zhang

Nian

Adingo

et al. 2022

Front. Environ. Sci.

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Soil water–salt variations at different spatiotemporal scales and their influencing factors are essential for regional vegetation restoration and management. However, the study of soil water–salt of the alpine area has not taken into account the effect of topography, especially in the long time series. This work investigated the spatiotemporal characteristics of water and salt in topsoil from 2000 to 2021, using regression analysis of on-site data and MODIS products. The heterogeneity and trends of soil water–salt in various topographic factors (elevation, slope, and aspect) were also explored by selecting the maximum area of each gradient factor as the constrained condition with the help of ArcGIS software. The results revealed that 1) the models applied to SWC (soil water content), EC (electrical conductivity), and pH utilizing vegetation indices were suitable for estimating the spatiotemporal variations of soil water–salt. 2) Soil water–salt exhibited local aggregation characteristics with an upward trend in SWC and a downward trend in EC and pH as a whole. 3) Mean annual SWC peaked at 3,400–3,800 m with increasing elevation and trended downward with increasing slope. The aspect of mean annual SWC was characterized by lower values in the southwest and south and higher values in the north, while the results for pH were reversed. The slope had a greater influence on EC than on SWC and pH. 4) The trend change of SWC, EC, and pH fluctuated at 2000–2,400 m and >4,600 m with a clear inflection point at 4°–8° and 20°–24°, respectively. In summary, the effects of topography responded to the distribution and trends of soil water–salt. The results have significant implications for implementing ecological improvements in regions with complicated topography and can serve as a reference for formulating future ecological policies.

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Pedological assessments along an arid and semi-arid transect using soil spectral behavior analysis

Cited by 7 publications

References 82 publications

Near‐infrared spectroscopy as an alternative tool for predicting soil erodibility in a watershed under desertification

Near‐infrared spectroscopy as an alternative tool for predicting soil erodibility in a watershed under desertification

Desert Soil Salinity Inversion Models Based on Field In Situ Spectroscopy in Southern Xinjiang, China

Spatiotemporal dynamics of soil water-salt and its topographic gradient effects on Alpine area over the Northeastern Tibetan Plateau, China

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