Munsell Soil Color Research Articles

Assessing the utility of Munsell soil color in building and evaluating spectral models for soil clay content prediction

AbstractThe present study examined how the use of soil color can help build and evaluate clay content prediction models from laboratory visible and near infrared spectroscopic data. This study was based on a regional database containing 449 soil samples collected over Karnataka state in India, which has been divided into red soils (240 samples) and black soils (209 samples) based on their Munsell soil color. Partial least squares regression models were calibrated and validated from both the regional datasets and subsets stratified as red and black soils. In addition, a random forest model was used to classify the validation soil samples into black and red classes to evaluate models’ performance. First, while the clay content predicted by the regression model built from regional data was evaluated as correct at regional scale (R2val of 0.75), this model was evaluated as more accurate over black (R2val of 0.8) than red (R2val of 0.63) soil samples. Second, the regression models built from subsets stratified per soil color provided different performances than the regression model built from the regional data, both at the regional scale and soil color scale. In conclusion, this study demonstrated that (1) predictions are highly dependent on calibration data, (2) the interpretation of prediction performances relies heavily on validation data, and (3) pedological knowledge, such as soil color, can be effectively employed as an encouraging covariate in both the construction and evaluation of regression models.

Soil Color Comparison Using Munsell Soil Color Chart and Calibrated Smartphone Camera

Soil color is a crucial property in soil fertility assessment and monitoring. However, the subjective nature of the Munsell Soil Color Chart (MSCC) can lead to uncertainty in the analysis. To address this issue, a study was conducted to develop a soil color classification model from smartphone digital imagery based on color analysis and MSCC. The study involved taking 26 soil samples from various soil types and locations in the Special Region of Yogyakarta, Indonesia. Digital images of the soil were taken through a smartphone camera and compared with observations using MSCC to compare color differences (ΔE) based on Lab values. Soil images obtained from indoor studio conditions and calibration using spydercheckr in indoor and outdoor conditions are compared with MSCC and Chromameter values. The L*a*b color space was found to be superior to RGB for predicting and detecting small differences in color. The study also found that the Munsell soil color chart (MSCC) had a lower color difference than the chromameter in all lighting conditions, indicating that the MSCC or visual assessment can better detect the main soil color or soil matrix, while chromameter readings may have errors due to soil impurities.

Evaluating mobile applications for estimating soil properties: Quality of current apps, limitations and future directions

The estimation of soil properties is an important factor in agriculture. With the advancement of smartphones and artificial intelligence, estimating soil properties such as the Munsell soil color, pH, soil organic matter and moisture is tangible, alleviating the need for laboratories, which are time-consuming and costly. While there are several smartphone applications available in different app stores that claim to provide this functionality, an evaluation of the limitations and quality is required for these apps. The objective of this study is to identify existing soil property estimation apps, devise a soil apps rating scale to evaluate the functionality along with the software quality characteristics, and provide design guidelines for improving apps of this type. A thorough search was conducted in two prominent app stores: the Google Play and Apple App store. The selected apps were then rated by four individual raters. A total of 337 apps were found in the search and from this list, 32 selected apps were reviewed using the devised app rating scale. The rating scale evaluates the apps against ten sub-scales: app metadata, aesthetics, general app features, performance and efficiency, usability, functionality, subjective quality, transparency, user comments, and the perceived impact of the app on users. The inter-rater and intra-rater reliability are calculated along with the internal consistency of the rating scale to ensure the validity of the results. The evaluation results that returned the highest-ranking mean sub-scales were performance, usability, and aesthetics. Adversely, the lowest-ranging sub-scales were functionality and general features. There was also an evident absence of the functionality to calculate any type of soil properties in all but four of the apps reviewed. Several key design considerations were identified, such as the need for automated estimation of soil properties, an improvement of general features, and the inclusion of a shared soil database.

Using Technology to Detect Erythema Across Skin Tones.

To examine the effectiveness of the ColorMeter DSM III (ColorMeter; Cortex Technology) at grouping individuals by skin tone and measuring erythema/skin discoloration after erythema induction across skin tones. This pre/post experimental study induced erythema on a convenience sample of 61 healthy adults. Skin tone at baseline was measured using the ColorMeter, Munsell Soil Color Chart 5YR (Munsell), and Pantone SkinTone Guide (Pantone) and compared with the Eumelanin Human Skin Colour Scale (Eumelanin Scale) groupings. Erythema and melanin values on the arm immediately and after recovery time were compared with baseline values. Melanin was measured at five body regions on the face and arm. Participants were predominantly women (64% [n = 39] women, 36% [n = 22] men) and young (mean, 28.8 ± 14.3 years); 5% (n = 3) were Hispanic, 26% (n = 16) Asian, 29% (n = 18) Black, 38% (n = 23) White, and 7% (n = 4) identified with more than one race. ColorMeter lightness (L*) and melanin measures were strongly correlated with both Munsell and Pantone values. Munsell skin tone groups were not aligned with Eumelanin Scale groupings. Most participants were in the Eumelanin intermediate-low group, and this changed depending on which body location melanin value was used. The change in erythema from baseline did not differ significantly across skin tone groups at the ulnar head, but on the forearm at the delayed time point, significant differences existed between light and both medium and dark skin tone groups (P = .001; 95% CI, 0.04-0.37). The ColorMeter provides an effective objective measure of skin tone and erythema/discoloration across various skin tones and may improve on current standards for detection. The proposed Eumelanin Scale-Modified provides additional sensitivity for persons with medium skin tones.

Perceptual variance of natural soil aggregates with the Munsell soil colour charts by unexperienced observers: Case study for diverse soils

Munsell Soil Colour Charts are widely used by soil scientists as the most unbiased way for determining the colour of soil samples. However, there are only a few studies on the variance in people's assessment of the colour of soil samples on this scale and the factors that determine this variance. In this work, we analyze the colour estimates of > 100 undisturbed soil samples by 20 people. For diverse soil samples collected from Anthrosols, Arenosols, Histosols, Podzols and Retisols at the Smolenskoye Poozerye national park, the fraction of votes that support the mode differs from 0.15 to 0.63 with mean of 0.29. The agreement of people’s estimates is significantly less pronounced for hue than for value (p = 0.0001) and chroma (p = 0.002) in dark and muted, but not in light and saturated samples. On average, females vote for mode in more fraction of samples than males (p = 0.026). Moreover, there are people who vote for and against mode more frequently that it is expected by chance. Within-sample variances of estimates calculated after conversion into RGB and CIELAB systems correlate weekly with uncertainty of estimates calculated as fraction of votes that do not support the mode. Soil horizons have different variances in colour estimates. The uncertainty in colour estimation with a Munsell Soil Colour Chart is highly dependent on both sample characteristics and people. The agreement between participants in colour assessment increases substantially when neighboring Munsell chips are treated as equal.

Comparison of tristimulus spectrocolorimeter measurements and human visual assessment of South Carolina field moist Munsell soil colors

AbstractSoil color has historically been estimated visually, but inaccuracies with this method have been documented, including disagreement in evaluators, color blindness, and differences in physical color books due to weathering. As new technologies become available to soil scientists, a reliable digital method to determine soil color in the field could offer a solution to these inaccuracies. This research aimed to assess if visual estimates of soil matrix color from 111 soil horizons differed from digital measurements of Munsell soil color across three South Carolina regions. Visual estimates of soil color were conducted using Munsell color books. The XRite Capsure gave digital measurements in Munsell notation specific for soils. The absolute difference between hues was similar among assessment methods. Visual estimates of color value and chroma were statistically greater than digital measurements. However, color values and chromas were less than one chip different, suggesting no practical difference between the two assessment methods. Utilizing a digital soil color assessment tool can assist with training and validate human estimates.

Determination of Munsell Soil Colour Using Smartphones.

Soil colour is one of the most important factors in agriculture for monitoring soil health and determining its properties. For this purpose, Munsell soil colour charts are widely used by archaeologists, scientists, and farmers. The process of determining soil colour from the chart is subjective and error-prone. In this study, we used popular smartphones to capture soil colours from images in the Munsell Soil Colour Book (MSCB) to determine the colour digitally. These captured soil colours are then compared with the true colour determined using a commonly used sensor (Nix Pro-2). We have observed that there are colour reading discrepancies between smartphone and Nix Pro-provided readings. To address this issue, we investigated different colour models and finally introduced a colour-intensity relationship between the images captured by Nix Pro and smartphones by exploring different distance functions. Thus, the aim of this study is to determine the Munsell soil colour accurately from the MSCB by adjusting the pixel intensity of the smartphone-captured images. Without any adjustment when the accuracy of individual Munsell soil colour determination is only 9% for the top 5 predictions, the accuracy of the proposed method is 74%, which is significant.

Sediments/rocks color and Munsell Soil Color chart (a color tool for sedimentologists)

Sediments/rocks color and Munsell Soil Color chart (a color tool for sedimentologists)

Munsell Soil Colour Classification Using Smartphones through a Neuro-Based Multiclass Solution

Colour is a property widely used in many fields to extract information in several ways. In soil science, colour provides information regarding the chemical and physical characteristics of soil, such as genesis, composition, and fertility, amongst others. Thus, accurate estimation of soil colour is essential for many disciplines. To achieve this, experts traditionally rely on comparing Munsell colour charts with soil samples, which is a laborious process. In this study, we proposed using artificial neural networks to catalogue soil colour with a two-step classification. Firstly, the hue variable is estimated, and then the remaining two coordinates, value and chroma. Our experiments were conducted using three different, common cameras (one digital camera and two mobile phones). The results of our tests showed a 20% improvement in classification accuracy using the lowest-quality camera and an average accuracy of over 90%.

Morphologic, Radiographic, and Stereomicroscopic Evaluation of Teeth Subjected to High Temperatures: An In vitro Forensic Study

Abstract Background To evaluate morphologic, radiographic, and stereomicroscopic changes of teeth when subjected to specific high temperatures in both direct heat and gradual heat and to compare the changes among two heat groups and also among different types of teeth. Materials and Methods An in vitro study was conducted on 90 healthy extracted human teeth, in which the teeth were divided into two groups. Group 1 (45) was subjected to direct heat and Group 2 (45) was subjected to gradual heat. Group 1 and Group 2 were further subdivided into three subgroups (A) anteriors, (B) premolars, and (C) molars each consisting of 15 teeth. Teeth were subjected to temperatures of 200°C, 400°C, 600°C, 800°C, and 1000°C in a muffle electric furnace. The color changes were noted using the Revised Munsell soil color chart and the radiographic and stereomicroscopic changes were evaluated. The results obtained were subjected to statistical analysis. Results Teeth in direct heat showed more destructive changes at lower temperatures, i.e., 400°C and 600°C when compared to gradual heat where changes are observed at 600°C and 800°C. Among different types of teeth, anteriors and premolars showed more changes when compared to molars. Interpretation and Conclusion It can be concluded that dental evidence may provide clues to solve the mystery in fire investigations as dental structures are the last to be destroyed under extreme conditions, whether temperature, acid, or putrefaction.

Evaluating Variability of Physico-chemical Properties of Soils from Urban Park of Tasik Permaisuri Cheras for Forensic Purpose

Forensic soil analysis is commonly used to establish a link between soil collected from a suspect (known samples) and soil collected from a crime scene (questioned sample) (1). Taman Tasik Permaisuri is located about 7km from Kuala Lumpur, one of the capital cities of Malaysia. Being a densely populated residential place, the rate of crime reported is also high. Soil is one of the most common types of evidence encountered in various types of crimes (2). The understanding of the variability of soil of a place is of fundamental importance to assess reliable evidence on forensic investigations (3). Therefore, this work aimed to evaluate the physico-chemical variability of soils collected from the urban park of Taman Tasik Permaisuri. A total of 20 soil samples were collected from several proximate sites around the park. The pH of all the samples dissolved in distilled water were measured using a pH meter. Meanwhile, colour of the samples in dried and wet conditions were recorded based on the Munsell Soil Color Chart. The variability of the physico-chemical properties was first explored using levelplot and then assessed via one-way ANOVA test). Fig. 1 illustrates the variability of the 20 soil samples by pH and soil color observed under dried condition using levelplot. In overall, the soils were mainly slight acidic or neutral. The soils exhibited rather low variability in pH value despite the one-way ANOVA coupled with LSD test (Fig. 2) denoted that the samples can be divided into ten subgroups. In conclusion, the physico-chemical properties of soils collected from the urban park of Taman Tasik Permaisuri were rather homogenous with mild variability.

Soil water content and photosynthetically active radiation influences soil color assessment

Determining Munsell soil color in the field is subject to environmental conditions, including soil water content (SWC) and light intensity. New digital spectral technology designed for determining soil color may offer accurate assessments regardless of environmental conditions. This study was conducted to determine the impacts of SWC and light on visual observations and digital measurements of soil color value and chroma, the two color components most important for soil use interpretations for siting and designing onsite wastewater systems. Munsell color was measured using the XRite Capsure™ and visually using Munsell Color Books on 111 soil horizons from three South Carolina regions. While, in general, higher color values and lower chromas were determined from oven dried peds in comparison to field moist peds, the influence of SWC was region specific. Of most importance were higher color chromas documented from field moist peds compared to oven dried peds in the Coastal region. In this region, gley chromas were determined in unsaturated soils emphasizing the importance of carefully evaluating other landscape features when interpreting the soil for onsite wastewater treatment. While SWC affected the agreement between digital measurements and visual observations, in most regions the difference was less than one color chip and of no practical significance. Varying photosynthetically active radiation (PAR) did not affect digital measurements and had minimal influence on visual observations. Digital measurements of soil color value and chroma may offer validation of soil color under varying lighting conditions and could be a promising tool for training new soil evaluators in color assessment.

Using Nix color sensor and Munsell soil color variables to classify contrasting soil types and predict soil organic carbon in Eastern India

Optimal soil management depends on rapid and frequent monitoring of key soil properties, which are conventionally measured in the laboratory using laborious wet-chemistry protocols. The Nix color sensor has recently exhibited promise for predicting several soil properties using soil color. This study evaluated the relationship between the Munsell Soil Color Chart (MSCC) color values of dry and ground surface soil samples to those reported by the Nix color sensor with (NixSTD) and without MSCC standardization (NixNON-STD) to classify 371 samples collected from three contrasting soil types, collected from three agro-climatic zones (coastal saline zone, red and laterite zone, and Gangetic alluvial zone) and to predict soil organic carbon (OC) using different multivariate data mining algorithms. Comparing the CIEL*a*b* color values reported by the MSCC and the NixSTD, an acceptable mean color difference (ΔE*ab) value of 5.20 was obtained, indicating the potential accuracy of the Nix sensor. Principal component analysis efficiently clustered the soil types using the RGB variables extracted from the MSCC color chips in tandem with the NixSTD/NixNON-STD data. Both classification tree and linear support vector machine algorithms perfectly classified all three contrasting soil types using NixNON-STD data alone. Besides, the combination of the MSCC and the NixNON-STD datasets produced the best OC prediction (R2 = 0.66) via random forest (RF) algorithm and indicated the potential of Nix in digital soil morphometrics. In most of the RF models, redness (a*), yellowness (b*), and yellow (Y) variables appeared influential, presumably because of their negative correlation with OC in red and laterite soils. More research is warranted to measure the impacts of variable soil moisture and other confounding soil morphological features on the soil classification and OC prediction performance to extend the approach for classifying soil types and predicting OC in-situ.

Discolouration and strength reduction of Bambusa vulgaris after the weathering process

Bambusa vulgaris Schrad was studied for its colour and strength before and after weathering process. Bamboo splits of 300 mm (length) x 20 mm (width) x original thickness were conditioned in a conditioning room at 20±2°C and 65±2% relative humidity before and after weathering for 3, 6, and 9 weeks. A Minolta colour reader and a Munsell soil colour chart were used to assess the colour changes (ΔE) and whitish value (W) of bamboo splits before and after the weathering process. Using Universal Testing Machine, the strength of weathered bamboo splits was tested. Results show the colour of bamboo splits changed dramatically (8% to 21%) over time, whereas the whitish value (W) of the bamboo splits reduced 8% to 31% after 3 to 9 weeks of exposure. Bamboo density was reduced by 3% to 17%. Modulus of Elasticity and Modulus of Rupture, in terms of strength qualities, were greatly reduced (20 MPa to 24 MPa) and (12 MPa to 16 MPa), respectively. After being exposed to the weather for 3, 6, and 9 weeks, the colour of Bambusa vulgaris changed substantially, and the density and strength of bamboo decreased.

Using Nix Color Sensor and Munsell Soil Color Variables to Classify Contrasting Soil Types and Predict Soil Organic Carbon in Eastern India

Using Nix Color Sensor and Munsell Soil Color Variables to Classify Contrasting Soil Types and Predict Soil Organic Carbon in Eastern India

Measuring Soil Colour to Estimate Soil Organic Carbon Using a Large-Scale Citizen Science-Based Approach

Rapid, low-cost methods for large-scale assessments of soil organic carbon (SOC) are essential for climate change mitigation. Our work explores the potential for citizen scientists to gather soil colour data as a cost-effective proxy of SOC instead of conventional lab analyses. The research took place during a 2-year period using topsoil data gathered by citizen scientists and scientists from urban parks in the UK and France. We evaluated the accuracy and consistency of colour identification by comparing “observed” Munsell soil colour estimates to “measured” colour derived from reflectance spectroscopy, and calibrated colour observations to ensure data robustness. Statistical relationships between carbon content obtained by loss on ignition (LOI) and (i) observed and (ii) measured soil colour were derived for SOC prediction using three colour components: hue, lightness, and chroma. Results demonstrate that although the spectrophotometer offers higher precision, there was a correlation between observed and measured colour for both scientists (R2 = 0.42; R2 = 0.26) and citizen scientists (R2 = 0.39; R2 = 0.19) for lightness and chroma, respectively. Foremost, a slightly stronger relationship was found for predicted SOC using the spectrophotometer (R2 = 0.69), and citizen scientists produced comparable results (R2 = 0.58), highlighting the potential of a large-scale citizen-based approach for SOC monitoring.

Assessing the soil color by traditional method and a smartphone: a comparison

Based on the hypothesis that there is a high agreement between pedologists and a smartphone application in the assessment of soil color; the objective was to compare the perceptions of pedologists and an application in obtaining the color of an Argissolo [Lixisol] (A, E and B horizons). Ten aggregates of each horizon were collected. In a single day, under the same lighting conditions, three pedologists described the color components (hue, value, and chroma) of each aggregate (dry and moist soil) using the Munsell soil color chart. Each one of the ten aggregates, from each horizon, was photographed (dry and moist soil sequence) using the camera of a Motorola Moto G4 Plus smartphone. The distance of the camera to the aggregates was 25 ± 5 cm. Also, each aggregate was placed on a white sheet of A4 size paper (background). The application used was Soil Analysis Pro. The percentage of agreement between pedologists and application was obtained concerning hue, value, and chroma. The data were subjected to analysis of variance, in a completely randomized design, with ten replicates. Action Stat® software was used for statistical analysis. It was concluded that the agreement between pedologists and the smartphone application was medium for hue and chroma and low for value. For the dry soil condition, there is a high agreement between pedologists and the smartphone application, especially in the perception of hue and chroma. Thus, the smartphone application has the potential to be used in routine descriptions of soil color.

Analysis of soil color variables and their relationships between two field-based methods and its potential application for wetland soils

While the Munsell Soil Color Chart (MSCC) is the most frequently used, well-established field method for reading soil color, the Nix Color Sensor (NCS) is an inexpensive, app-based alternative that can complement or potentially substitute for the MSCC. Soils were collected and their colors were measured from four forested sites across Northern Virginia within the Chesapeake Bay Watershed using both the MSCC and NCS. Three MSCC variables and 15 NCS variables were collected in the field; a methodology was established to use these “measured” (M) variables to derive 9 NCS calculated (C) variables. A stepwise correlation identified NCS variables most suitable for relating the NCS to each of the MSCC attributes: hue (H), value (V), and chroma (CM). Ultimately, H, V, and CM were deemed to be best represented by HRGB calculated from the RGB color space (ρ = 0.56), L from the CIE-Lab color space (ρ = 0.73), and ẑ = Z/(X + Y + Z) from the XYZ color space (ρ = −0.80), respectively (p < 0.001). The corresponding explanatory powers of final NCS variables (i.e., HRGB, L, and ẑ) for H, V, and CM were 26%, 54%, and 62%, respectively (p <0.01). Significant differences in ẑ between soils identified as hydric and nonhydric, but lack of nonoverlapping ranges, indicate a potential for the NCS to complement the MSCC in assessing wetland soil color in an accessible and reproducible manner, including hydric soil identifications for wetland delineation practices. Further study with more data over various types of soils is necessary to establish stronger relationships between the NCS and MSCC. Nonetheless, the method of characterizing soil color variables from the two field methods presented in the study can serve as a template for future studies or environmental education programs desiring to use the NCS as a complement to the MSCC.

Soil Organic Carbon Distribution in a Humid Tropical Plain of Cameroon: Interrelationships with Soil Properties

Soil organic carbon (SOC) determination is very important in the assessment of agronomic potential of a soil. The objective of this study was to determine SOC contents and stock distribution with depth in relation to selected soil properties. Five types of soils, namely, Mollic Endoaquents, Oxyaquic Paleudalfs, Oxyaquic Udifluvents, and Mollic Udifluvents from a humid tropical plain and Typic Eutrudepts from an adjacent foot slope, were studied. The soils have all developed from fluvial sediments. Morphological and physicochemical characteristics of the soils were obtained using standard methods. Soil texture varied across the different sites and within soil profiles with textural classes of genetic horizons ranging from sandy loam to heavy clay. The soils are generally young soils under development as indicated by their high silt/clay ratios which ranged between 0.23 and 2.45. All the soils were generally acidic with pH-H2O values ranging from 4.5 to 6.2. Exchangeable H+ and Al3+ ranged from 0.5 to 2.3 and 0.2 to 3.3 cmolckg−1, respectively. SOC contents are generally higher in surface horizons and decrease with depth. In general, SOC correlated significantly with bulk density (BD) (r = −0.648, p &lt; 0.01 ), water holding capacity (r = 0.589, p &lt; 0.01 ), exchangeable Al3+ (r = 0.707, p &lt; 0.01 ), and exchangeable H+ (r = 0.456, p &lt; 0.05 ). The correlation between SOC and exchangeable Al3+ was strongest in the Mollic Endoaquents (r = 0.931, p &lt; 0.01 ). SOC contents correlated significantly with Munsell soil color attributes, explaining between 40 and 57% of SOC variation. Total SOC stocks at a depth of 100 cm varied between 260.1 and 363.5 t·ha−1, and the variation in SOC stocks across a profile appears to be controlled by genetic horizon depth, while land use type influences SOC stock variations across genetic surface horizons.

Is It Better to Be Objectively Wrong or Subjectively Right?

ABSTRACTFor many years, archaeologists have relied on Munsell Soil Color Charts (MSCC) as tools for standardizing the recording of soil and sediment colors in the field and artifacts such as pottery in the lab. Users have identified multiple potential sources of discrepancy in results, such as differences in inter-operator perception, light source, or moisture content of samples. In recent years, researchers have developed inexpensive digital methods for color identification, but these typically cannot be done in real time. Now, a field-ready digital color-matching instrument is marketed to archaeologists as a replacement for MSCC, but the accuracy and overall suitability of this device for archaeological research has not been demonstrated. Through three separate field and laboratory trials, we found systematic mismatches in the results obtained via device, including variable accuracy against standardized MSCC chips, which should represent ideal samples. At the same time, the instrument was consistent in its readings. This leads us to question whether using the “subjective” human eye or the “objective” digital eye is preferable for data recording of color. We discuss how project goals and limitations should be considered when deciding which color-recording method to employ in field and laboratory settings, and we identify optimal procedures.