The application of proximal visible and near-infrared spectroscopy to estimate soil organic matter on the Triffa Plain of Morocco

Lazaar, Ayoub; Mouazen, Abdul Mounem; Hammouti, Kamal El; Fullen, Michael A.; Pradhan, Biswajeet; Memon, Muhammad Sohail; Andich, Karim; Monir, Abdelilah

doi:10.1016/j.iswcr.2020.04.005

Cited by 27 publications

(17 citation statements)

References 57 publications

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“…Apart from the difference in costs of the used instruments and the level of portability, there was no obvious benefit of vis-NIR system over mid-IR, or vice versa. The stated error estimates reported in this study and in earlier studies ( Table 1 ) [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 39 , 40 ] are higher than those corresponding to traditional analysis, where the laboratory method has a detection limit of 20 g kg −1 for sand and clay (dry basis), and generally, is reproducible to within ±80 g kg −1 , Ref. [ 49 ].…”

Section: Resultssupporting

confidence: 66%

“…These portable instruments are built using semiconductor technologies and they are relatively inexpensive, robust, and simpler to use when compared to laboratory-based bench-top instruments. They are suitable for deployment in the field for proximal sensing [ 1 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ]. Numerous studies support their use for the simultaneous assessment of primary and secondary soil properties on the go [ 34 , 35 , 36 ].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Two Portable Hyperspectral-Sensor-Based Instruments to Predict Key Soil Properties in Canadian Soils

Dhawale

Adamchuk

Prasher

et al. 2022

Sensors

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In contrast with classic bench-top hyperspectral (multispectral)-sensor-based instruments (spectrophotometers), the portable ones are rugged, relatively inexpensive, and simple to use; therefore, they are suitable for field implementation to more closely examine various soil properties on the spot. The purpose of this study was to evaluate two portable spectrophotometers to predict key soil properties such as texture and soil organic carbon (SOC) in 282 soil samples collected from proportional fields in four Canadian provinces. Of the two instruments, one was the first of its kind (prototype) and was a mid-infrared (mid-IR) spectrophotometer operating between ~5500 and ~11,000 nm. The other instrument was a readily available dual-type spectrophotometer having a spectral range in both visible (vis) and near-infrared (NIR) regions with wavelengths ranging between ~400 and ~2220 nm. A large number of soil samples (n = 282) were used to represent a wide variety of soil textures, from clay loam to sandy soils, with a considerable range of SOC. These samples were subjected to routine laboratory soil analysis before both spectrophotometers were used to collect diffuse reflectance spectroscopy (DRS) measurements. After data collection, the mid-IR and vis-NIR spectra were randomly divided into calibration (70%) and validation (30%) sets. Partial least squares regression (PLSR) was used with leave one out cross-validation techniques to derive the spectral calibrations to predict SOC, sand, and clay content. The performances of the calibration models were reevaluated on the validation set. It was found that sand content can be predicted more accurately using the portable mid-IR spectrophotometer and clay content is better predicted using the readily available dual-type vis-NIR spectrophotometer. The coefficients of determination (R2) and root mean squared error (RMSE) were determined to be most favorable for clay (0.82 and 78 g kg−1) and sand (0.82 and 103 g kg−1), respectively. The ability to predict SOC content precisely was not particularly good for the dataset of soils used in this study with an R2 and RMSE of 0.54 and 4.1 g kg−1. The tested method demonstrated that both portable mid-IR and vis-NIR spectrophotometers were comparable in predicting soil texture on a large soil dataset collected from agricultural fields in four Canadian provinces.

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Section: Resultssupporting

confidence: 66%

Section: Introductionmentioning

confidence: 99%

Evaluation of Two Portable Hyperspectral-Sensor-Based Instruments to Predict Key Soil Properties in Canadian Soils

Dhawale

Adamchuk

Prasher

et al. 2022

Sensors

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“…According to [5,15,16] the band near 1400, 1930, 2200 can be associated to the Kaolinite, illite and smectite clay minerals. The bands appear at 2260 nm and 2340 nm are related to Fe-OH and Mg-OH, respectively [17][18][19]. From this discussion, we conclude that many clay minerals are characterized by same absorption bands for example 1400, 1900 and 2200 nm can be attributed to the kaolinite or smectite or illite.…”

Section: Vis-nir Spectroscopy In Clay Mineral Identificationmentioning

confidence: 63%

Physicochemical and Mineralogical Characterization of the Mediterranean Soils of Triffa Plain (Northeast of Morocco) by the Physicochemical Analysis, X-ray Diffraction and Vis-NIR Spectroscopy

Lazaar

Hammouti

Andich³

et al. 2020

Proceedings of the 4th Edition of International Conference on Geo-It and Water Resources 2020, Geo-It and Water Resources 2020

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“…Finally, little correlation was found with organic matter based on the characteristic wavelengths [55].…”

Section: Ga-generated Characteristic Wavelengthsmentioning

confidence: 93%

Detecting Arsenic Contamination Using Satellite Imagery and Machine Learning

Agrawal¹,

Petersen

2021

Toxics

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Arsenic, a potent carcinogen and neurotoxin, affects over 200 million people globally. Current detection methods are laborious, expensive, and unscalable, being difficult to implement in developing regions and during crises such as COVID-19. This study attempts to determine if a relationship exists between soil’s hyperspectral data and arsenic concentration using NASA’s Hyperion satellite. It is the first arsenic study to use satellite-based hyperspectral data and apply a classification approach. Four regression machine learning models are tested to determine this correlation in soil with bare land cover. Raw data are converted to reflectance, problematic atmospheric influences are removed, characteristic wavelengths are selected, and four noise reduction algorithms are tested. The combination of data augmentation, Genetic Algorithm, Second Derivative Transformation, and Random Forest regression (R2=0.840 and normalized root mean squared error (re-scaled to [0,1]) = 0.122) shows strong correlation, performing better than past models despite using noisier satellite data (versus lab-processed samples). Three binary classification machine learning models are then applied to identify high-risk shrub-covered regions in ten U.S. states, achieving strong accuracy (=0.693) and F1-score (=0.728). Overall, these results suggest that such a methodology is practical and can provide a sustainable alternative to arsenic contamination detection.

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The application of proximal visible and near-infrared spectroscopy to estimate soil organic matter on the Triffa Plain of Morocco

Cited by 27 publications

References 57 publications

Evaluation of Two Portable Hyperspectral-Sensor-Based Instruments to Predict Key Soil Properties in Canadian Soils

Evaluation of Two Portable Hyperspectral-Sensor-Based Instruments to Predict Key Soil Properties in Canadian Soils

Physicochemical and Mineralogical Characterization of the Mediterranean Soils of Triffa Plain (Northeast of Morocco) by the Physicochemical Analysis, X-ray Diffraction and Vis-NIR Spectroscopy

Detecting Arsenic Contamination Using Satellite Imagery and Machine Learning

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