scholarly journals Field Proximal Soil Sensor Fusion for Improving High-Resolution Soil Property Maps

Soil Systems ◽  
2020 ◽  
Vol 4 (3) ◽  
pp. 52
Author(s):  
Gustavo M. Vasques ◽  
Hugo M. Rodrigues ◽  
Maurício R. Coelho ◽  
Jesus F. M. Baca ◽  
Ricardo O. Dart ◽  
...  
Keyword(s):  
Soil Properties ◽  
Ordinary Kriging ◽  
Soil Property ◽  
Clay Content ◽  
Exchange Capacity ◽  
Organic C ◽  
Covariate Data ◽  
Independent Validation ◽  
Soil Sensors ◽  
Dense Grid

Mapping soil properties, using geostatistical methods in support of precision agriculture and related activities, requires a large number of samples. To reduce soil sampling and measurement time and cost, a combination of field proximal soil sensors was used to predict and map laboratory-measured soil properties in a 3.4-ha pasture field in southeastern Brazil. Sensor soil properties were measured in situ on a 10 × 10-m dense grid (377 samples) using apparent electrical conductivity meters, apparent magnetic susceptibility meter, gamma-ray spectrometer, water content reflectometer, cone penetrometer, and portable X-ray fluorescence spectrometer (pXRF). Soil samples were collected on a 20 × 20-m thin grid (105 samples) and analyzed in the laboratory for organic C, sum of bases, cation exchange capacity, clay content, soil volumetric moisture, and bulk density. Another 25 samples collected throughout the area were also analyzed for the same soil properties and used for independent validation of models and maps. To test whether the combination of sensors enhances soil property predictions, stepwise multiple linear regression (MLR) models of the laboratory soil properties were derived using individual sensor covariate data versus combined sensor data—except for the pXRF data, which were evaluated separately. Then, to test whether a denser grid sample boosted by sensor-based soil property predictions enhances soil property maps, ordinary kriging of the laboratory-measured soil properties from the thin grid was compared to ordinary kriging of the sensor-based predictions from the dense grid, and ordinary cokriging of the laboratory properties aided by sensor covariate data. The combination of multiple soil sensors improved the MLR predictions for all soil properties relative to single sensors. The pXRF data produced the best MLR predictions for organic C content, clay content, and bulk density, standing out as the best single sensor for soil property prediction, whereas the other sensors combined outperformed the pXRF sensor for the sum of bases, cation exchange capacity, and soil volumetric moisture, based on independent validation. Ordinary kriging of sensor-based predictions outperformed the other interpolation approaches for all soil properties, except organic C content, based on validation results. Thus, combining soil sensors, and using sensor-based soil property predictions to increase the sample size and spatial coverage, leads to more detailed and accurate soil property maps.

scholarly journals A Comparison, Validation, and Evaluation of the S-world Global Soil Property Database

Land ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 544
Author(s):  
Jetse J. Stoorvogel ◽  
Vera L. Mulder
Keyword(s):  
Comparative Analysis ◽  
Soil Properties ◽  
Soil Property ◽  
Evaluation Criteria ◽  
Clay Content ◽  
Soil Database ◽  
Global Environmental ◽  
Global Soil ◽  
Mean Square Difference ◽  
Methodological Evaluation

Despite the increased usage of global soil property maps, a proper review of the maps rarely takes place. This study aims to explore the options for such a review with an application for the S-World global soil property database. Global soil organic carbon (SOC) and clay content maps from S-World were studied at two spatial resolutions in three steps. First, a comparative analysis with an ensemble of seven datasets derived from five other global soil databases was done. Second, a validation of S-World was done with independent soil observations from the WoSIS soil profile database. Third, a methodological evaluation of S-world took place by looking at the variation of soil properties per soil type and short distance variability. In the comparative analysis, S-World and the ensemble of other maps show similar spatial patterns. However, the ensemble locally shows large discrepancies (e.g., in boreal regions where typically SOC contents are high and the sampling density is low). Overall, the results show that S-World is not deviating strongly from the model ensemble (91% of the area falls within a 1.5% SOC range in the topsoil). The validation with the WoSIS database showed that S-World was able to capture a large part of the variation (with, e.g., a root mean square difference of 1.7% for SOC in the topsoil and a mean difference of 1.2%). Finally, the methodological evaluation revealed that estimates of the ranges of soil properties for the different soil types can be improved by using the larger WoSIS database. It is concluded that the review through the comparison, validation, and evaluation provides a good overview of the strengths and the weaknesses of S-World. The three approaches to review the database each provide specific insights regarding the quality of the database. Specific evaluation criteria for an application will determine whether S-World is a suitable soil database for use in global environmental studies.

Profile Soil Property Estimation Using a VIS-NIR-EC-Force Probe

2017 ◽  
Vol 60 (3) ◽  
pp. 683-692 ◽  
Author(s):  
Yongjin Cho ◽  
Kenneth A. Sudduth ◽  
Scott T. Drummond
Keyword(s):  
Soil Properties ◽  
Water Content ◽  
Bulk Density ◽  
Soil Property ◽  
Soil Strength ◽  
Reflectance Spectra ◽  
Land Resource ◽  
Strength Data ◽  
Soil Sensors ◽  
Combining Data

Abstract. Combining data collected in-field from multiple soil sensors has the potential to improve the efficiency and accuracy of soil property estimates. Optical diffuse reflectance spectroscopy (DRS) has been used to estimate many important soil properties, such as soil carbon, water content, and texture. Other common soil sensors include penetrometers that measure soil strength and apparent electrical conductivity (ECa) sensors. Previous field research has related these sensor measurements to soil properties such as bulk density, water content, and texture. A commercial instrument that can simultaneously collect reflectance spectra, ECa, and soil strength data is now available. The objective of this research was to relate laboratory-measured soil properties, including bulk density (BD), total organic carbon (TOC), water content (WC), and texture fractions to sensor data from this instrument. At four field sites in mid-Missouri, profile sensor measurements were obtained to 0.9 m depth, followed by collection of soil cores at each site for laboratory measurements. Using only DRS data, BD, TOC, and WC were not well-estimated (R2 = 0.32, 0.67, and 0.40, respectively). Adding ECa and soil strength data provided only a slight improvement in WC estimation (R2 = 0.47) and little to no improvement in BD and TOC estimation. When data were analyzed separately by major land resource area (MLRA), fusion of data from all sensors improved soil texture fraction estimates. The largest improvement compared to reflectance alone was for MLRA 115B, where estimation errors for the various soil properties were reduced by approximately 14% to 26%. This study showed promise for in-field sensor measurement of some soil properties. Additional field data collection and model development are needed for those soil properties for which a combination of data from multiple sensors is required. Keywords: NIR spectroscopy, Precision agriculture, Reflectance spectra, Soil properties, Soil sensing.

scholarly journals Relationship of soil properties to fractionation, bioavailability and mobility of lead and zinc in soil

2008 ◽  
Vol 53 (No. 5) ◽  
pp. 225-238 ◽  
Author(s):  
N. Finžgar ◽  
P. Tlustoš ◽  
D. Leštan
Keyword(s):  
Organic Matter ◽  
Regression Analysis ◽  
Soil Organic Matter ◽  
Soil Properties ◽  
Multiple Regression ◽  
Contaminated Soils ◽  
Organic Matter Content ◽  
Clay Content ◽  
Exchange Capacity ◽  
Matter Content

Sequential extractions, metal uptake by <i>Taraxacum officinale</i>, Ruby&rsquo;s physiologically based extraction test (PBET) and toxicity characteristic leaching procedure (TCLP), were used to assess the risk of Pb and Zn in contaminated soils, and to determine relationships among soil characteristics, heavy metals soil fractionation, bioavailability and leachability. Regression analysis using linear and 2nd order polynomial models indicated relationships between Pb and Zn contamination and soil properties, although of small significance (<i>P</i> < 0.05). Statistically highly significant correlations (<i>P</i> < 0.001) were obtained using multiple regression analysis. A correlation between soil cation exchange capacity (CEC) and soil organic matter and clay content was expected. The proportion of Pb in the PBET intestinal phase correlated with total soil Pb and Pb bound to soil oxides and the organic matter fraction. The leachable Pb, extracted with TCLP, correlated with the Pb bound to carbonates and soil organic matter content (<i>R</i><sup>2</sup> = 69%). No highly significant correlations (<i>P</i> < 0.001) for Zn with soil properties or Zn fractionation were obtained using multiple regression.

Desorption of native and added zinc from a range of New Zealand soils in relation to soil properties

Soil Research ◽  
1997 ◽  
Vol 35 (6) ◽  
pp. 1253 ◽  
Author(s):  
D. Singh ◽  
R. G. McLaren ◽  
K. C. Cameron
Keyword(s):  
Regression Analysis ◽  
Soil Properties ◽  
Exchange Capacity ◽  
Organic C ◽  
Soil Variables ◽  
Dominant Soil ◽  
Linear Regressions ◽  
Mn Oxides ◽  
Extractable Zn ◽  
Different Soils

Compared with zinc (Zn) sorption, there is very little information on the effect of soil properties on Zn desorption from soils. In this study, desorption of native and added Zn from 7 Canterbury (NZ) soils was determined using a technique involving repeated equilibration of soil in 0·01 M Ca(NO3)2. The concentrations and patterns of desorption of both native and added Zn varied between the different soils. Greater concentrations of native Zn were desorbed from surface soils than from subsoils, and greater concentrations of added Zn were desorbed from subsoils than from their corresponding surface horizons. Correlation analysis showed that cation exchange capacity (CEC) and organic carbon (C) were the dominant soil variables contributing towards sorption or desorption of Zn. However, simple linear regressions involving CEC or organic C explained only 48–62% of the total variation in Zn sorption or desorption from the different soils. Multiple regression analysis indicated that cumulative native Zn desorption (expressed as percentage of DTPA-extractable Zn) was strongly related to CEC and the content of Mn oxides, which in combination explained 80% of the variability between soils. Regression analysis also showed that CEC plus Mn oxides and pH explained 91% of the variability in Zn sorption between the soils; whereas for added Zn desorbed (%), CEC plus pH and crystalline Al oxides explained 93% of variability in added Zn desorption.

scholarly journals Development of a composite soil degradation assessment index for cocoa agroecosystems in southwestern Nigeria

Solid Earth ◽  
2017 ◽  
Vol 8 (4) ◽  
pp. 827-843 ◽  
Author(s):  
Sunday Adenrele Adeniyi ◽  
Willem Petrus de Clercq ◽  
Adriaan van Niekerk
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Soil Properties ◽  
Cation Exchange ◽  
Cation Exchange Capacity ◽  
Soil Degradation ◽  
Clay Content ◽  
Exchange Capacity ◽  
Southwestern Nigeria ◽  
Assessment Index

Abstract. Cocoa agroecosystems are a major land-use type in the tropical rainforest belt of West Africa, reportedly associated with several ecological changes, including soil degradation. This study aims to develop a composite soil degradation assessment index (CSDI) for determining the degradation level of cocoa soils under smallholder agroecosystems of southwestern Nigeria. Plots where natural forests have been converted to cocoa agroecosystems of ages 1–10, 11–40, and 41–80 years, respectively representing young cocoa plantations (YCPs), mature cocoa plantations (MCPs), and senescent cocoa plantations (SCPs), were identified to represent the biological cycle of the cocoa tree. Soil samples were collected at a depth of 0 to 20 cm in each plot and analysed in terms of their physical, chemical, and biological properties. Factor analysis of soil data revealed four major interacting soil degradation processes: decline in soil nutrients, loss of soil organic matter, increase in soil acidity, and the breakdown of soil textural characteristics over time. These processes were represented by eight soil properties (extractable zinc, silt, soil organic matter (SOM), cation exchange capacity (CEC), available phosphorus, total porosity, pH, and clay content). These soil properties were subjected to forward stepwise discriminant analysis (STEPDA), and the result showed that four soil properties (extractable zinc, cation exchange capacity, SOM, and clay content) are the most useful in separating the studied soils into YCP, MCP, and SCP. In this way, we have sufficiently eliminated redundancy in the final selection of soil degradation indicators. Based on these four soil parameters, a CSDI was developed and used to classify selected cocoa soils into three different classes of degradation. The results revealed that 65 % of the selected cocoa farms are moderately degraded, while 18 % have a high degradation status. The numerical value of the CSDI as an objective index of soil degradation under cocoa agroecosystems was statistically validated. The results of this study reveal that soil management should promote activities that help to increase organic matter and reduce Zn deficiency over the cocoa growth cycle. Finally, the newly developed CSDI can provide an early warning of soil degradation processes and help farmers and extension officers to implement rehabilitation practices on degraded cocoa soils.

Soil properties and lodgepole pine growth on rehabilitated landings in northeastern British Columbia

2003 ◽  
Vol 83 (4) ◽  
pp. 465-474 ◽  
Author(s):  
C. E. Bulmer ◽  
M. Krzic
Keyword(s):  
British Columbia ◽  
Soil Properties ◽  
Lodgepole Pine ◽  
Clay Content ◽  
Exchange Capacity ◽  
Soil Productivity ◽  
Mechanical Resistance ◽  
Average Height ◽  
Water Storage Capacity ◽  
Pine Trees

We determined post-establishment tree growth and soil properties on rehabilitated log landings and forest plantation sites with medium texture in northeastern British Columbia. Six years after rehabilitation treatments were applied, 60% of rehabilitated landing plots had more than 1000 stems ha-1, while 17% had fewer than 600 stems ha-1. The average height of undamaged lodgepole pine trees on rehabilitated landings was consistently lower than for trees of the same age on plantations. Surface (0–7 cm) and subsurface (10–17 cm) soil bulk densities were higher for rehabilitated landings than for adjacent plantations. Rehabilitated landing and plantation soils had similar values of total and aeration porosity. Plantation soils had higher available water storage capacity (AWSC) than rehabilitated soils. Soil mechanical resistance after landing rehabilitation was often higher than for plantation soils at the same depth. Soils on both rehabilitated landings and plantations showed an increase in mechanical resistance from June to September 2001. With the exception of June 2001, soil mechanical resistance after landing rehabilitation was often higher than 2500 kPa. For surface mineral soils, there were no differences in total C, N, or cation exchange capacity (CEC) between rehabilitated landings and plantations. Rehabilitated landing soils had significantly higher total C and N at 10–17 cm depth than plantation soils, which coincided with higher clay content for the landing subsoils. Key words: Forest soil rehabilitation, soil degradation, soil productivity, soil conservation

scholarly journals USING LEGACY SOIL DATA FOR STANDARDIZING PREDICTIONS OF TOPSOIL CLAY CONTENT OBTAINED FROM VNIR/SWIR HYPERSPECTRAL AIRBORNE IMAGES

2015 ◽  
Vol XL-3/W3 ◽  
pp. 439-444
Author(s):  
C. Gomez ◽  
A. Gholizadeh ◽  
L. Borůvka ◽  
P. Lagacherie
Keyword(s):  
Soil Properties ◽  
Spectral Index ◽  
Near Infrared ◽  
Soil Property ◽  
Clay Content ◽  
Short Wave ◽  
Soil Samples ◽  
Spectral Bands ◽  
Large Sets ◽  
The One

Mapping of topsoil properties using Visible, Near-Infrared and Short Wave Infrared (VNIR/SWIR) hyperspectral imagery requires large sets of ground measurements for calibrating the models that estimate soil properties. To avoid collecting such expensive data, we proposed a procedure including two steps that involves only legacy soil data that were collected over and?or around the study site: <i>1)</i> estimation of a soil property using a spectral index of the literature and <i>2)</i> standardisation of the estimated soil property using legacy soil data. This approach was tested for mapping clay contents in a Mediterranean region in which VNIR/SWIR AISA-DUAL hyperspectral airborne data were acquired. The spectral index was the one proposed by Levin et al (2007) using the spectral bands at 2209, 2133 and 2225 nm. Two legacy soil databases were tested as inputs of the procedure: the <i>Focused-Legacy</i> database composed of 67 soil samples collected in 2000 over the study area, and the No-Focused-Legacy database composed of 64 soil samples collected between 1973 and 1979 around but outside of the study area. The results were compared with those obtained from 120 soil samples collected over the study area during the hyperspectral airborne data acquisition, which were considered as a reference. <br><br> Our results showed that: <i>1)</i> the spectral index with no further standardisation offered predictions with high accuracy in term of coefficient of correlation <i>r</i> (0.71), but also high <i>bias</i> (&minus;414 g/kg) and <i>SEP</i> (439 g/kg), <i>2)</i> the standardisation using both legacy soil databases allowed an increase of accuracy (<i>r</i> = 0.76) and a reduction of <i>bias</i> and <i>SEP</i> and <i>3)</i> a better standardisation was obtained by using the <i>Focused-Legacy</i> database rather than the <i>No-Focused-Legacy</i> database. Finally, the clay predicted map obtained with standardisation using the <i>Focused-Legacy</i> database showed pedologically-significant soil spatial structures with clear short-scale variations of topsoil clay contents in specific areas. <br><br> This study, associated with the coming availability of a next generation of hyperspectral VNIR/SWIR satellite data for the entire globe, paves the way for inexpensive methods for delivering high resolution soil properties maps.

scholarly journals Characteristics of Soils Developing from Gabbro, Phyllite and Chert Parent Rock in Karangsambung District

2019 ◽  
Vol 4 (3) ◽  
pp. 131
Author(s):  
Ratna Taher ◽  
Makruf Nurudin ◽  
Eko Hanudin
Keyword(s):  
Cation Exchange ◽  
Cation Exchange Capacity ◽  
Particle Density ◽  
Chemical Properties ◽  
Clay Content ◽  
Exchange Capacity ◽  
Parent Rock ◽  
Soil Morphology ◽  
Total N ◽  
Organic C

Understanding the nature of the soil is very important to know the potential and the proper management of the soil. This study aimed to determine the differences in morphological, physical, and chemical properties of the soils developing from gabbro, phylitte and chert parent materials. The soil profile was made to represent each parent rock of gabbro, phyllite and chert located on the upper and middle slopes with pine-dominated vegetation and mixed gardens. Observation in the field is a professional description to observe soil morphology. Soil samples were taken at each horizon to analyze soil physical properties (bulk density, particle density, and texture), soil chemical properties (pH, exchanged cations, cation exchange capacity, available P, organic C, and total N). Texture analysis results showed that clay content of the soil developing from parent rock of Gabro 1 is the highest, followed by the soil clay content from  Chert 1, Phyllite 1, Chert 2, Phyllite 2, and Gabbro 2, respectively. The order of soil acidity level (pH) is Gabbro 2 > Gabbro 1> Chert 1 ~ Chert 2 > Phyllite 1 ~ Phyllite 2. Meanwhile, the order of the cation exchange capacity is Gabbro 1> Gabbro 2> Phyllite 1> Chert 1> Phyllite 2> Chert 2, and the order of the base saturation is Chert 2> Gabbro 2> Chert 1> Phyllite 2 > Phyllite1> Gabbro 1.

scholarly journals Zinc Adsorption in Relation to Soil Properties, Analysed with Langmuir and Freundlich Models in Soils of Kashmir Valley

2019 ◽  
Vol 7 (1) ◽  
pp. 68-73
Author(s):  
Syed Sadat ◽  
N. Z. Rehman ◽  
M. A. Bhat ◽  
M.A. Wani
Keyword(s):  
Soil Properties ◽  
Chemical Properties ◽  
Clay Content ◽  
Exchange Capacity ◽  
Sorption Data ◽  
Average Value ◽  
Physico Chemical ◽  
Zinc Concentrations ◽  
Langmuir And Freundlich Equations ◽  
Zinc Adsorption

The phenomenon of fixation of added zinc in soils considerably affects the availability and efficiency of applied zinc. Pertaining to this situation, different land-use soil samples across the valley were analysed for various physico-chemical properties and adsorption capacities. The results showed that the soils were slightly acidic to alkaline in reaction and differ far and wide in other soil properties. Cation exchange capacity (CEC) of the soils showed little variation between the samples and varied from13.3 to 17.2 cmol(p+) kg-1 with an average value of 15.1 cmol(p+) kg-1of soil. The maximum of zinc adsorption were greatly influenced by soil organic matter, clay content and CEC of the soils. The data was fitted to Langmuir and Freundlich equations and the results yielded that the Freundlich equation showed better fit to the sorption data at higher zinc concentrations. However, both the models were having satisfactory results for the obtained data.

Modelling of soil texture and its verification with related soil properties

Soil Research ◽  
2018 ◽  
Vol 56 (4) ◽  
pp. 421 ◽  
Author(s):  
M. Shahadat Hossain ◽  
G. K. M. Mustafizur Rahman ◽  
M. Saiful Alam ◽  
M. Mizanur Rahman ◽  
A. R. M. Solaiman ◽  
...  
Keyword(s):  
Electrical Conductivity ◽  
Soil Properties ◽  
Soil Texture ◽  
Cation Exchange Capacity ◽  
Soil Property ◽  
Exchange Capacity ◽  
Strong Correlations ◽  
Soil Maps ◽  
Hypothetical Data ◽  
Digital Soil Maps

Soil texture is an independent and innate soil property and other dynamic soil properties such as electrical conductivity (EC), organic carbon (OC) content and cation exchange capacity (CEC) are mostly dependent on it. An attempt was made to develop a model for numerically simulating soil texture and also to construct relationships of the developed model with other soil properties. Hypothetical data of particle size distribution and our data were used to justify and validate the newly defined indices. Scatter diagrams showed good association between the indices and hypothetical data of soil separates. Moreover, similar trends were observed between the line charts of USDA soil textural class codes and the indices. Strong correlations (r = 0.78–0.96) were found between the indices and soil separates (sand, silt and clay) for our data. However, the indices demonstrated moderate correlations (r = –0.34 to –0.55) with EC and OC of the soils. Strong nonlinear relationships were found between CEC and the three indices (R2 = 0.699, R2 = 0.732 and R2 = 0.672 (all P < 0.001). Furthermore, the variability of EC, OC and CEC within a single USDA textural class and customised textural index groups were described using the developed model. The developed indices showed excellent fitness for simulation of soil texture and demonstrated an extended applicability in terms of their relationships with soil properties related to soil texture, which will help in constructing digital soil maps.

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