A remote sensing framework to map potential toxic elements in agricultural soils in the humid tropics

Mendes, Wanderson de Sousa; Demattê, José Alexandre Melo; Resende, M.; Ruiz, Luis Fernando Chimelo; Mello, Danilo César de; Rosas, Jorge Tadeu Fim; Silvero, Nélida Elizabet Quiñonez; Alleoni, Luís Reynaldo Ferracciú; Colzato, Marina; Rosin, Nícolas Augusto; Campos, Lucas Rabelo

doi:10.1016/j.envpol.2021.118397

Cited by 11 publications

(6 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sampling was conducted at a depth of 0-20 cm [17]. The collected soil samples were crushed and mixed, then spread out into a square shape.…”

Section: Soil Sample Collectionmentioning

confidence: 99%

Estimation of Surface Soil Nutrient Content in Mountainous Citrus Orchards Based on Hyperspectral Data

Jiao,

Liu,

Wang

et al. 2024

Agriculture

View full text Add to dashboard Cite

Monitoring soil conditions is of great significance for guiding fruit tree production and increasing yields. Achieving a rapid determination of soil physicochemical properties can more efficiently monitor soil conditions. Traditional sampling and survey methods suffer from slow detection speeds, low accuracy, limited coverage, and require a large amount of manpower and resources. In contrast, the use of hyperspectral technology enables the precise and rapid monitoring of soil physicochemical properties, playing an important role in advancing precision agriculture. Yuxi City, Yunnan Province, was selected as the study area; soil samples were collected and analyzed for soil organic matter (SOM), total nitrogen (TN), total phosphorus (TP), and available nitrogen (AN) contents. Additionally, soil spectral reflectance was obtained using a portable spectroradiometer. Hyperspectral characteristic bands for soil nutrients were selected from different spectral preprocessing methods, and different models were used to predict soil nutrient content, identifying the optimal modeling approach. For SOM prediction, the second-order differentiation-multiple stepwise regression (SD-MLSR) model performed exceptionally well, with an R2 value of 0.87 and RMSE of 6.61 g·kg−1. For TN prediction, the logarithm of the reciprocal first derivative-partial least squares regression (LRD-PLSR) model had an R2 of 0.77 and RMSE of 0.37 g·kg−1. For TP prediction, the logarithmic second-order differentiation-multiple stepwise regression (LTSD-MLSR) model had an R2 of 0.69 and RMSE of 0.04 g·kg−1. For AN prediction, the logarithm of the reciprocal second derivative-partial least squares regression (LRSD-PLSR) model had an R2 of 0.83 and RMSE of 24.12 mg·kg−1. The results demonstrate the high accuracy of these models in predicting soil nutrient content.

show abstract

“…Sampling was conducted at a depth of 0-20 cm [17]. The collected soil samples were crushed and mixed, then spread out into a square shape.…”

Section: Soil Sample Collectionmentioning

confidence: 99%

Estimation of Surface Soil Nutrient Content in Mountainous Citrus Orchards Based on Hyperspectral Data

Jiao,

Liu,

Wang

et al. 2024

Agriculture

View full text Add to dashboard Cite

show abstract

“…Remote spectral detection has now been implemented for more than three decades, with the broadband optical methods having demonstrated much success at remote detection of foliage, minerals, and industrial materials, for example. [1][2][3][4] There now exist multitudes of studies using infrared standoff for remote detection of crops, plants, soil, minerals, and other naturally occurring substances, as well as for anthropogenic sources or industrial contamination. [5][6][7] However, one of the seldom-tested and more challenging applications for standoff detection is the ability to detect thin layers of analytes on various substrates.…”

Section: Introductionmentioning

confidence: 99%

Using synthetic infrared spectra derived from n/k optical constants for standoff detection of chemical deposits

Lonergan

Erickson

Kelly-Gorham

et al. 2023

Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XXIV

View full text Add to dashboard Cite

We report results from a recent field experiment to test the validity of using physics-based synthetic infrared spectra to serve as endmembers in a spectral database targeted at chemical deposits. Specifically, the optical constants n and k, (the real and imaginary part of the refractive index) were used to first model infrared reflectance spectra for different thicknesses of chemical layers (e.g. acetaminophen, methylphosphonic acid -MPA, etc.) on various conducting and insulating substrates such as aluminum, wood, and glass. In the experimental portion of the research, thin films of the solid and liquid analytes were deposited onto such substrates to form micron-thick layers of the analytes at different thicknesses: Standoff data from an imaging instrument were then recorded and analyzed to not only identify the different analytes, but also quantify the layer/deposit thickness. To gauge success, the detection results using the synthetic data were compared to the results from laboratory hemispherical reflectance (HRF) spectra that were collected for the same sample planchets measured in the field via standoff methods. Preliminary results indicate good agreement between the synthetic reference data as compared to the lab-measured HRF data in terms of their ability to quantitatively reduce longwave infrared data. Specifically, modeled IR spectra for acetaminophen on an aluminum planchet at various thicknesses (1, 2, 5, 10, 15, and 20 μm) were synthesized and compared with standoff field reflectance data as well as HRF laboratory reflectance spectra for two samples: a 5.2 μm-and 12.8 μm-thick layer of acetaminophen on aluminum. Using a first-order approximation, analysis of the field data estimates the thicknesses of the samples to be 2 and 10 μm for the two samples, respectively, while the HRF laboratory data yields thickness estimates of between 5-10 μm and 10 μm, respectively. Both yield reasonable estimates, with the uncertainty most likely due to factors yet to be accounted for in the synthetic spectra such as light scattering.

show abstract

“…As long as it has an atomic number larger than 20 and has an atomic density greater than 5 gcm-3, any found naturally metal or metalloid is considered to be high-mass. Chromium, cobalt, and zinc are all members of the platinum group (de Sousa Mendes and Demattê, 2022). Other metals in the platinum group involve iron, cadmium, cadmium, cadmium, cadmium, lead, arsenic, cadmium, and platinum.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid IOT Based Artificial Intelligence Algorithm for Toxicity Prediction In The Environment And Its Effect On Human Health

2023

Global NEST: The International Journal

View full text Add to dashboard Cite

<p>Salem Algarni Mechanical Engineering Department, College of Engineering, King Khalid University, Abha 61421, Asir, Kingdom of Saudi Arabia Vineet Tirth Mechanical Engineering Department, College of Engineering, King Khalid University, Abha 61421, Asir, Kingdom of Saudi Arabia Talal Alqahtani Mechanical Engineering Department, College of Engineering, King Khalid University, Abha 61421, Asir, Kingdom of Saudi Arabia Pravin R. Kshirsagar Professor, Department of Artificial Intelligence, G.H. Raisoni College of Engineering, Nagpur, India</p>

show abstract

A remote sensing framework to map potential toxic elements in agricultural soils in the humid tropics

Cited by 11 publications

References 73 publications

Estimation of Surface Soil Nutrient Content in Mountainous Citrus Orchards Based on Hyperspectral Data

Estimation of Surface Soil Nutrient Content in Mountainous Citrus Orchards Based on Hyperspectral Data

Using synthetic infrared spectra derived from n/k optical constants for standoff detection of chemical deposits

A Novel Hybrid IOT Based Artificial Intelligence Algorithm for Toxicity Prediction In The Environment And Its Effect On Human Health

Contact Info

Product

Resources

About