scholarly journals Evaluating the Precision and Accuracy of Proximal Soil vis–NIR Sensors for Estimating Soil Organic Matter and Texture

Soil Systems ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 48
Author(s):  
Nandkishor M. Dhawale ◽  
Viacheslav I. Adamchuk ◽  
Shiv O. Prasher ◽  
Raphael A. Viscarra Rossel
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Ex Situ ◽  
Soil Attributes ◽  
Significant Difference ◽  
Precision And Accuracy ◽  
Soil Reflectance

Measuring soil texture and soil organic matter (SOM) is essential given the way they affect the availability of crop nutrients and water during the growing season. Among the different proximal soil sensing (PSS) technologies, diffuse reflectance spectroscopy (DRS) has been deployed to conduct rapid soil measurements in situ. This technique is indirect and, therefore, requires site- and data-specific calibration. The quality of soil spectra is affected by the level of soil preparation and can be accessed through the repeatability (precision) and predictability (accuracy) of unbiased measurements and their combinations. The aim of this research was twofold: First, to develop a novel method to improve data processing, focusing on the reproducibility of individual soil reflectance spectral elements of the visible and near-infrared (vis–NIR) kind, obtained using a commercial portable soil profiling tool, and their direct link with a selected set of soil attributes. Second, to assess both the precision and accuracy of the vis–NIR hyperspectral soil reflectance measurements and their derivatives, while predicting the percentages of sand, clay and SOM content, in situ as well as in laboratory conditions. Nineteen locations in three agricultural fields were identified to represent an extensive range of soils, varying from sand to clay loam. All measurements were repeated three times and a ratio spread over error (RSE) was used as the main indicator of the ability of each spectral parameter to distinguish among field locations with different soil attributes. Both simple linear regression (SLR) and partial least squares regression (PLSR) models were used to define the predictability of % SOM, % sand, and % clay. The results indicated that when using a SLR, the standard error of prediction (SEP) for sand was about 10–12%, with no significant difference between in situ and ex situ measurements. The percentage of clay, on the other hand, had 3–4% SEP and 1–2% measurement precision (MP), indicating both the reproducibility of the spectra and the ability of a SLR to accurately predict clay. The SEP for SOM was only a quarter lower than the standard deviation of laboratory measurements, indicating that SLR is not an appropriate model for this soil property for the given set of soils. In addition, the MPs of around 2–4% indicated relatively strong spectra reproducibility, which indicated the need for more expanded models. This was apparent since the SEP of PLSR was always 2–3 times smaller than that of SLR. However, the relatively small number of test locations limited the ability to develop widely applicable calibration models. The most important finding in this study is that the majority of vis–NIR spectral measurements were sufficiently reproducible to be considered for distinguishing among diverse soil samples, while certain parts of the spectra indicate the capability to achieve this at α = 0.05. Therefore, the innovative methodology of evaluating both the precision and accuracy of DRS measurements will help future developers evaluate the robustness and applicability of any PSS instrument.

scholarly journals In situ near-infrared spectroscopy for soil organic matter prediction in paddy soil, Pasak watershed, Thailand

2018 ◽  
Vol 64 (No. 2) ◽  
pp. 70-75 ◽  
Author(s):  
Romsonthi Chutipong ◽  
Tawornpruek Saowanuch ◽  
Watana Sumitra
Keyword(s):  
Organic Matter ◽  
Infrared Spectroscopy ◽  
Soil Organic Matter ◽  
Soil Properties ◽  
Soil Quality ◽  
Near Infrared Spectroscopy ◽  
Spectral Reflectance ◽  
Near Infrared ◽  
Partial Least Square

Soil organic matter (SOM) is a major index of soil quality assessment because it is one of the key soil properties controlling nutrient budgets in agricultural production systems. The aim of the in situ near-infrared spectroscopy (NIRS) for SOM prediction in paddy area is evaluation of the potential of SOM and prediction of other soil properties. There are keys for soil fertility and soil quality assessments. A spectral reflectance of 130 soil samples was collected by field spectroradiometer in a region of near-infrared. Spectral reflectance collections were processed by the first derivative transformation with the Savitsky-Golay algorithms. Partial least square regression method was used to develop a calibration model between soil properties and spectral reflectance, which was used for prediction and validation processes. Finally, the results of this study demonstrate that NIRS is an effective method that can be used to predict SOM (R<sup>2</sup> = 0.73, RPD (ratio of performance to deviation) = 1.82) and total nitrogen (R<sup>2</sup> = 0.72, RPD = 1.78). Therefore, NIRS is a potential tool for soil properties predictions. The use of these techniques will facilitate the implementation of soil management with a decreasing cost and time of soil study in a large scale. However, further works are necessary to develop more accurate soil properties prediction and to apply this method to other areas.

scholarly journals EXPLORE THE CHARACTER OF SOIL SPECTRAL REFLECTANCE RELATE TO THE SOIL ORGANIC MATTER CONTENT

2019 ◽  
Vol 8 (3) ◽  
pp. 214
Author(s):  
Sari Virgawati ◽  
Muhjidin Mawardi ◽  
Lilik Sutiarso ◽  
Sakae Shibusawa ◽  
Hendrik Segah ◽  
...  
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Spectral Reflectance ◽  
Near Infrared ◽  
Diffuse Reflectance Spectroscopy ◽  
Low Cost ◽  
Organic Matter Content ◽  
Nir Spectroscopy ◽  
Matter Content ◽  
Strong Negative Correlation

ABSTRACTThe visible and near-infrared (Vis-NIR) diffuse reflectance spectroscopy has emerged as a rapid and low-cost tool for extensive investigation of soil properties. The objective of this research was to explore how significant the relationship between the soil spectral reflectance and soil organic matter (SOM) content. Some soil samples in Yogyakarta were taken for SOM content and spectroscopy measurement. The SOM was analyzed using Walkley and Black method, while the spectral reflectance was determined using ASD Field-spectrophotometer by scanned the sample with Vis-NIR spectrum. Pearson’s coefficient showed that there was a strong negative correlation between SOM and soil spectral of certain wavelengths. Soil with less organic matter content performed high reflectance. Keywords: Soil organic matter; Vis-NIR spectroscopy; soil reflectance; Pearson’s correlation coefficient.

scholarly journals In situ acoustic analysis of two twentieth-century heritage carillons

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Scott Allan Orr
Keyword(s):  
Twentieth Century ◽  
Acoustic Analysis ◽  
Mechanical Action ◽  
Ex Situ ◽  
Physical Acoustics ◽  
Acoustic Characteristics ◽  
Dynamic Level ◽  
Significant Difference ◽  
And Function

AbstractCarillons are a diverse and global form of musical and civic heritage: musical instruments comprised of a series of 23 or more bells, typically hung in a tower-like structure, tuned chromatically and played from a touch-sensitive manual and pedal console using an elaborate mechanical action. Carillon bells have a distinct series of musical overtones which should be accurately tuned to one another and with other bells they sound alongside. Although these overtones have been previously studied ex situ, this study assesses the acoustic characteristics of two early-twentieth century carillons in Toronto, Canada as a combination of structure, bells, and mechanical action. Thus, the instrument and its context are considered holistically, more accurately reflecting the musical sensitivity of a carillonist. Spectral analysis of audio samples of each bell at different musical dynamic levels enabled the analysis of the acoustic qualities of the bells and the mechanical action of the instruments. The tuning of bells in the instruments varied; most importantly, there was a significant difference between the audial intensity of the bell tones produced by the instruments, demonstrating the importance of the mechanical action as part of the ‘carillon system’. This was represented with a resistive power-law model, that represents the sensitivity of intensity to carillonist musical dynamic level. A discussion of the implications for artistic and heritage practice follows. Understanding the in situ physical acoustics of the carillon as a holistic instrument in its context informs performers, arrangers, and composers of how they can best embrace the instrument’s unique qualities to improve artistic pursuits and support the appreciation of carillons as heritage instruments and function as civic voices.

Estimation of soil organic matter using proximal and satellite sensors after a wildfire in Mediterranean Croatia

2021 ◽  
Author(s):  
Iva Hrelja ◽  
Ivana Šestak ◽  
Igor Bogunović
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Spectral Data ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Nir Spectroscopy ◽  
Soil Samples ◽  
Least Squares Regression ◽  
Macro Scale ◽  
Sentinel 2

&lt;p&gt;Spectral data obtained from optical spaceborne sensors are being recognized as a valuable source of data that show promising results in assessing soil properties on medium and macro scale. Combining this technique with laboratory Visible-Near Infrared (VIS-NIR) spectroscopy methods can be an effective approach to perform robust research on plot scale to determine wildfire impact on soil organic matter (SOM) immediately after the fire. Therefore, the objective of this study was to assess the ability of Sentinel-2 superspectral data in estimating post-fire SOM content and comparison with the results acquired with laboratory VIS-NIR spectroscopy.&lt;/p&gt;&lt;p&gt;The study is performed in Mediterranean Croatia (44&amp;#176; 05&amp;#8217; N; 15&amp;#176; 22&amp;#8217; E; 72 m a.s.l.), on approximately 15 ha of fire affected mixed &lt;em&gt;Quercus ssp.&lt;/em&gt; and &lt;em&gt;Juniperus ssp.&lt;/em&gt; forest on Cambisols. A total of 80 soil samples (0-5 cm depth) were collected and geolocated on August 22&lt;sup&gt;nd&lt;/sup&gt; 2019, two days after a medium to high severity wildfire. The samples were taken to the laboratory where soil organic carbon (SOC) content was determined via dry combustion method with a CHNS analyzer. SOM was subsequently calculated by using a conversion factor of 1.724. Laboratory soil spectral measurements were carried out using a portable spectroradiometer (350-1050 nm) on all collected soil samples. Two Sentinel-2 images were downloaded from ESAs Scientific Open Access Hub according to the closest dates of field sampling, namely August 31&lt;sup&gt;st&lt;/sup&gt; and September 5&lt;sup&gt;th &lt;/sup&gt;2019, each containing eight VIS-NIR and two SWIR (Short-Wave Infrared) bands which were extracted from bare soil pixels using SNAP software. Partial least squares regression (PLSR) model based on the pre-processed spectral data was used for SOM estimation on both datasets. Spectral reflectance data were used as predictors and SOM content was used as a response variable. The accuracy of the models was determined via Root Mean Squared Error of Prediction (RMSE&lt;sub&gt;p&lt;/sub&gt;) and Ratio of Performance to Deviation (RPD) after full cross-validation of the calibration datasets.&lt;/p&gt;&lt;p&gt;The average post-fire SOM content was 9.63%, ranging from 5.46% minimum to 23.89% maximum. Models obtained from both datasets showed low RMSE&lt;sub&gt;p &lt;/sub&gt;(Spectroscopy dataset RMSE&lt;sub&gt;p&lt;/sub&gt; = 1.91; Sentinel-2 dataset RMSE&lt;sub&gt;p&lt;/sub&gt; = 0.99). RPD values indicated very good predictions for both datasets (Spectrospcopy dataset RPD = 2.72; Sentinel-2 dataset RPD = 2.22). Laboratory spectroscopy method with higher spectral resolution provided more accurate results. Nonetheless, spaceborne method also showed promising results in the analysis and monitoring of SOM in post-burn period.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords:&lt;/strong&gt; remote sensing, soil spectroscopy, wildfires, soil organic matter&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Acknowledgment: &lt;/strong&gt;This work was supported by the Croatian Science Foundation through the project &quot;Soil erosion and degradation in Croatia&quot; (UIP-2017-05-7834) (SEDCRO). Aleksandra Per&amp;#269;in is acknowledged for her cooperation during the laboratory work.&lt;/p&gt;

Metal contamination of urban soils in Cork, Ireland

2021 ◽  
Author(s):  
Hannah Binner ◽  
Timothy Sullivan ◽  
Maria E. Mc Namara
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Metal Contamination ◽  
Urban Soils ◽  
Soil Samples ◽  
Ex Situ ◽  
City Centre ◽  
Metal Concentrations ◽  
Ph Values ◽  
Background Levels

&lt;p&gt;Soil contamination is widespread across Europe. In particular, contamination of urban soils by metals is poorly characterised. This is a major environmental concern, especially given that urban recreational amenities may be located on former industrial sites and/or may possess ex situ soils derived from industrial areas. We surveyed soils from nine urban recreational sites (15 samples per site) in Cork city in order to assess the degree of metal contamination. The results show that Pb concentrations exceed national background levels in all soil samples from all sites by a mean of 600 % and at least 140 %. Mn, Fe and Zn are enriched above background levels in all soil samples from three (Mn and Fe) to five (Zn) of the sites and, at the remaining sites, show 7 &amp;#8211; 14 localised hotspots. Similar hotspots characterise Cu, Rb and Sr, which each exceed background levels at eight or more sampling locations at four sites. Co, Ni, As and Sn concentrations exceed background levels in at least three hotspots at each of three to six sites. Overall, metal concentrations are highest in the sites closest to the city centre, reflecting diverse sources that potentially include traffic and current and historical domestic coal burning and industry. At each urban site, the element grouping Zn and Pb recurs in 50 to 80 % of locations and enrichment in the element grouping Mn, Fe, Cu, Zn and Pb recurs in approx. 50 % of locations; Ni and As recur in approx. 10 % of the locations. At three sites, elevated concentrations of Mn, Fe, Cu, Zn and Pb are associated with high LOI (Loss-on-ignition) values &amp;#8211; a proxy for the amount of soil organic matter present &amp;#8211; and near-neutral pH values. Conversely, low LOI and acidic pH values are associated with lower concentrations of these elements. This indicates that soil metal concentrations are influenced by the amount of organic matter present and by pH. &amp;#160;Future analyses and experiments will further investigate links between soil organic matter and metal concentrations.&lt;/p&gt;

In situ photo-polymerization of soil organic matter by heterogeneous nano-TiO2 and biomimetic metal-porphyrin catalysts

2016 ◽  
Vol 52 (4) ◽  
pp. 585-593 ◽  
Author(s):  
Assunta Nuzzo ◽  
Elisa Madonna ◽  
Pierluigi Mazzei ◽  
Riccardo Spaccini ◽  
Alessandro Piccolo
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Metal Porphyrin ◽  
Nano Tio2 ◽  
Photo Polymerization

Effective carbon sequestration in Italian agricultural soils by in situ polymerization of soil organic matter under biomimetic photocatalysis

2018 ◽  
Vol 29 (3) ◽  
pp. 485-494 ◽  
Author(s):  
Alessandro Piccolo ◽  
Riccardo Spaccini ◽  
Vincenza Cozzolino ◽  
Assunta Nuzzo ◽  
Marios Drosos ◽  
...  
Keyword(s):  
Organic Matter ◽  
Carbon Sequestration ◽  
Soil Organic Matter ◽  
In Situ Polymerization ◽  
Agricultural Soils

Quantifying in situ and modeling net nitrogen mineralization from soil organic matter in arable cropping systems

2017 ◽  
Vol 111 ◽  
pp. 44-59 ◽  
Author(s):  
Hugues Clivot ◽  
Bruno Mary ◽  
Matthieu Valé ◽  
Jean-Pierre Cohan ◽  
Luc Champolivier ◽  
...  
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Nitrogen Mineralization ◽  
Cropping Systems ◽  
Net Nitrogen Mineralization ◽  
Quantifying In

DNA Sequence and Community Structure Diversity of Multi-Year Soil Fungi in Grape of Xinjiang

2021 ◽  
Author(s):  
Tong Liu ◽  
Feng Xue
Keyword(s):  
Organic Matter ◽  
Community Structure ◽  
Soil Organic Matter ◽  
Soil Fungi ◽  
Rhizosphere Soil ◽  
Middle Layer ◽  
Fungal Communities ◽  
Seedless Grape ◽  
Significant Difference ◽  
Vineyard Soil

Abstract This study is designed to understand the community structure and diversity of fungi in the rhizosphere soil of grape. As the sample for this study, the rhizosphere soil of Crimson seedless grape with different planting years was collected from Shihezi in Xinjiang to carry out high-throughput sequencing, by which the complete sequence of soil fungi DNA was identified, and accordingly, the richness and diversity index of fungi were determined. The results showed that the dominant phyla of fungi in the grape rhizosphere soil with different planting years were Ascomycota and Basidiomycota, and the dominant classes of fungi were Sordariomycetes and Dothideomycetes. Soil organic matter, total potassium, total nitrogen and available phosphorus were the main soil fertility factors affecting the abundance and diversity of soil fungal communities, among which soil organic matter had the most significant influence. In addition, the fungal diversity and richness were highest in the middle layer (20-35 cm) of the grape rhizosphere soil with 12 planting years and lowest in the lower layer (35-50 cm) of the grape rhizosphere soil with 5 planting years. Linear discriminant analysis suggested that there were more biomarkers in the vineyard rhizosphere soil with 10 planting years, which meant there were more fungal communities with significant difference in the soil, especially in the middle layer (20-35). The results of this study can provide data reference and theoretical basis for improving vineyard soil quality, evaluating soil microecological effects and improving ecological environment of vineyard soil.

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