Using the NIRS for analyzes of soil clay content

2020 ◽  
Author(s):  
Clémence Mariage ◽  
Gilles Colinet ◽  
Valérie Genot
Keyword(s):  
Near Infrared ◽  
Clay Content ◽  
Exchange Capacity ◽  
Simultaneous Estimation ◽  
Near Infrared Reflectance ◽  
Available Nutrients ◽  
Soil Clay ◽  
Cationic Exchange ◽  
Soil Clay Content ◽  
Non Destructive

<p>REQUASUD network (based in Wallonia, Belgium) consists of laboratories working directly with farmers, giving them soil fertility diagnostics and advice for a good management of soils and cultures. Therefore, the laboratories analyze, among others, available nutrients. But they need more information to correctly interpret the results and give fertility advice, like the cationic exchange capacity (CEC) and the clay content. However, analyzing CEC and clay content is expensive, time-consuming and requires the use of chemicals. To overpass this problem, the near-infrared reflectance spectroscopy (NIRS) has been developed and is now used routinely in the laboratories. This method is rapid, non-destructive and allows the simultaneous estimation of soil characteristics. Nowadays, the REQUASUD NIR database performances for clay content are the followings : RPD = 2,22% for cultures and RPD = 1,72% for grasslands. This method of analysis can be used for other purposes than fertility advice.</p>

scholarly journals Employing a Multi-Input Deep Convolutional Neural Network to Derive Soil Clay Content from a Synergy of Multi-Temporal Optical and Radar Imagery Data

2020 ◽  
Vol 12 (9) ◽  
pp. 1389 ◽  
Author(s):  
Nikolaos Tziolas ◽  
Nikolaos Tsakiridis ◽  
Eyal Ben-Dor ◽  
John Theocharis ◽  
George Zalidis
Keyword(s):  
Neural Network ◽  
Open Access ◽  
Convolutional Neural Network ◽  
Near Infrared ◽  
Clay Content ◽  
Google Earth ◽  
Spectral Information ◽  
Radar Images ◽  
Soil Clay ◽  
Soil Clay Content

Earth observation (EO) has an immense potential as being an enabling tool for mapping spatial characteristics of the topsoil layer. Recently, deep learning based algorithms and cloud computing infrastructure have become available with a great potential to revolutionize the processing of EO data. This paper aims to present a novel EO-based soil monitoring approach leveraging open-access Copernicus Sentinel data and Google Earth Engine platform. Building on key results from existing data mining approaches to extract bare soil reflectance values the current study delivers valuable insights on the synergistic use of open access optical and radar images. The proposed framework is driven by the need to eliminate the influence of ambient factors and evaluate the efficiency of a convolutional neural network (CNN) to effectively combine the complimentary information contained in the pool of both optical and radar spectral information and those form auxiliary geographical coordinates mainly for soil. We developed and calibrated our multi-input CNN model based on soil samples (calibration = 80% and validation 20%) of the LUCAS database and then applied this approach to predict soil clay content. A promising prediction performance (R2 = 0.60, ratio of performance to the interquartile range (RPIQ) = 2.02, n = 6136) was achieved by the inclusion of both types (synthetic aperture radar (SAR) and laboratory visible near infrared–short wave infrared (VNIR-SWIR) multispectral) of observations using the CNN model, demonstrating an improvement of more than 5.5% in RMSE using the multi-year median optical composite and current state-of-the-art non linear machine learning methods such as random forest (RF; R2 = 0.55, RPIQ = 1.91, n = 6136) and artificial neural network (ANN; R2 = 0.44, RPIQ = 1.71, n = 6136). Moreover, we examined post-hoc techniques to interpret the CNN model and thus acquire an understanding of the relationships between spectral information and the soil target identified by the model. Looking to the future, the proposed approach can be adopted on the forthcoming hyperspectral orbital sensors to expand the current capabilities of the EO component by estimating more soil attributes with higher predictive performance.

Near-infrared spectroscopy as a potential method for routine sediment analysis to improve rapidity and efficiency

1998 ◽  
Vol 37 (6-7) ◽  
pp. 181-188 ◽  
Author(s):  
Diane F. Malley
Keyword(s):  
Near Infrared ◽  
Organic Matter Content ◽  
Aggregate Size ◽  
Clay Content ◽  
Analytical Techniques ◽  
Matter Content ◽  
Near Infrared Reflectance ◽  
Organic C ◽  
Analytical Technique ◽  
Sediment Analysis

The potential for improvement in the rapidity, cost-effectiveness, and efficiency of sediment analysis by the application of near-infrared reflectance spectroscopy (NIRS) is recognized. The rapid (<2 min), non-chemical, non-destructive analytical technique of near-infrared (700–2500 nm) spectroscopy combines applied spectroscopy and complex statistics. It has been used for the experimental analysis of various constituents and functions of soils since the 1960s, and applications for the analysis of sediments are currently being explored. For application of NIRS, sediment samples require little preparation, other than drying, and the samples are not subject to the manipulations of conventional analytical techniques. The spectral information recorded in a 2 min scan can be used to predict numerous constituents and parameters on the samples once appropriate calibration equations have been prepared from sets of samples analyzed by both NIRS and conventional analytical techniques. Constituents and properties of soil and/or sediment analyzed by NIRS technology include moisture, organic matter content, organic C, CO3=, N, P, S, K, Ca, Mg, clay content, humic acids, lignin, cellulose, metal oxides, heavy metals, aggregate size, and inferred past pH of lakes. Several areas are identified where further research is needed to prepare for the application of NIRS to the routine analysis of sediments.

scholarly journals Remaining phosphorus and sodium fluoride pH in soils with different clay contents and clay mineralogies

2004 ◽  
Vol 39 (3) ◽  
pp. 241-246 ◽  
Author(s):  
Marcelo Eduardo Alves ◽  
Arquimedes Lavorenti
Keyword(s):  
Buffer Capacity ◽  
Ammonium Oxalate ◽  
Clay Mineralogy ◽  
Clay Content ◽  
Response Curves ◽  
Solution Ph ◽  
Ph Values ◽  
Soil Clay ◽  
Soil Clay Content ◽  
Discriminatory Capacity

The remaining phosphorus (Prem) has been used for estimating the phosphorus buffer capacity (PBC) of soils of some Brazilian regions. Furthermore, the remaining phosphorus can also be used for estimating P, S and Zn soil critical levels determined with PBC-sensible extractants and for defining P and S levels to be used not only in P and S adsorption studies but also for the establishment of P and S response curves. The objective of this work was to evaluate the effects of soil clay content and clay mineralogy on Prem and its relationship with pH values measured in saturated NaF solution (pH NaF). Ammonium-oxalate-extractable aluminum exerts the major impacts on both Prem and pH NaF, which, in turn, are less dependent on soil clay content. Although Prem and pH NaF have consistent correlation, the former has a soil-PBC discriminatory capacity much greater than pH NaF.

scholarly journals P-sorption and desorption in Savanna Brazilian soils as a support for phosphorus fertilizer management

2013 ◽  
Vol 37 (6) ◽  
pp. 521-530 ◽  
Author(s):  
Flávio Araújo Pinto ◽  
Edicarlos Damacena de Souza ◽  
Helder Barbosa Paulino ◽  
Nilton Curi ◽  
Marco Aurélio Carbone Carneiro
Keyword(s):  
Clay Content ◽  
Soil Characteristics ◽  
Fertilizer Management ◽  
Phosphorus Fertilizer ◽  
Phosphorus Adsorption ◽  
P Sorption ◽  
Brazilian Soils ◽  
Soil Clay ◽  
Sand Base ◽  
Soil Clay Content

Phosphorus (P) sorption by soils is a phenomenon that varies depending on soil characteristics, influencing its intensity and magnitude, which makes it a source or drain of P. The objective of this study was to determine the Maximum Phosphorus Adsorption Capacity (MPAC) and desorption of P from soils under native Savanna Brazilian and verify the correlation between MPAC and P Capacity Factor (PCF) with the chemical and physical properties of these soils. The study was conducted in seven soils under native Savannas. The Langmuir isotherms were adjusted from the values obtained in sorption assays, being evaluated the MPAC, the energy adsorption (EA) and PCF, which was calculated according to the levels of P-adsorbed and P-sorbed. Values of MPAC were classified as high in most soils, ranging from 283 up to 2635 mg kg-1 of P in the soil and were correlated with soil organic matter, clay, silt, sand, base saturation and pH. The PCF was higher in soils where the MPAC was also higher. The use of only one attribute of soil (clay content) as a criterion for the recommendation of phosphated fertilization, as routinely done, is susceptible to errors, needing the use of more attributes for a more accurate recommendation, as a function of the complexity of the interactions involved in the process.

Surface soil clay content mapping at large scales using multispectral (VNIR–SWIR) ASTER data

2018 ◽  
Vol 40 (4) ◽  
pp. 1506-1533
Author(s):  
Anis Gasmi ◽  
Cécile Gomez ◽  
Philippe Lagacherie ◽  
Hédi Zouari
Keyword(s):  
Surface Soil ◽  
Clay Content ◽  
Aster Data ◽  
Soil Clay ◽  
Soil Clay Content

scholarly journals Factors Affecting Microbial Formation of Nitrate-Nitrogen in Soil and Their Effects on Fertilizer Nitrogen Use Efficiency

2001 ◽  
Vol 1 ◽  
pp. 122-129 ◽  
Author(s):  
Alan Olness ◽  
Dian Lopez ◽  
David Archer ◽  
Jason Cordes ◽  
Colin Sweeney ◽  
...  
Keyword(s):  
Organic Matter ◽  
Decision Aid ◽  
Pore Space ◽  
Organic Matter Content ◽  
Clay Content ◽  
Matter Content ◽  
N Fertilizer ◽  
Nitrate N ◽  
Soil Clay ◽  
Soil Clay Content

Mineralization of soil organic matter is governed by predictable factors with nitrate-N as the end product. Crop production interrupts the natural balance, accelerates mineralization of N, and elevates levels of nitrate-N in soil. Six factors determine nitrate-N levels in soils: soil clay content, bulk density, organic matter content, pH, temperature, and rainfall. Maximal rates of N mineralization require an optimal level of air-filled pore space. Optimal air-filled pore space depends on soil clay content, soil organic matter content, soil bulk density, and rainfall. Pore space is partitioned into water- and air-filled space. A maximal rate of nitrate formation occurs at a pH of 6.7 and rather modest mineralization rates occur at pH 5.0 and 8.0. Predictions of the soil nitrate-N concentrations with a relative precision of 1 to 4 μg N g–1of soil were obtained with a computerized N fertilizer decision aid. Grain yields obtained using the N fertilizer decision aid were not measurably different from those using adjacent farmer practices, but N fertilizer use was reduced by >10%. Predicting mineralization in this manner allows optimal N applications to be determined for site-specific soil and weather conditions.

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