Spectral Reflectance Of Soils Research Articles

Hyperspectral Imaging of Soil and Crop: A Review

The remote sensing is one of the precision technologies, can be used to monitor and assess the target area or object such as soil, crop, and water. Hyperspectral imaging (HSI), also known as imaging spectrometry or hyperspectral remote sensing, is a combined technique of spectroscopy and imaging system for sensing spectral information of an area or object. It involves capturing images of an object using multiple distinct optical bands that cover a wide range of the electromagnetic spectrum (350-2500 nm). The hyperspectral bands are continuous, narrow, and contagious and contain hundreds and thousands of numbers. Hyperspectral remote sensing is particularly valuable for gathering precise and up-to-date information necessary for agricultural planning and precision farming. HSI technology is the employment of hyperspectral sensors aids in analyzing soil physical (bulk density, texture, water content), chemical (pH, EC, SOC, and macro and micro nutrients), biological (SOM) properties and helps to categorize different crop varieties, identify pests and diseases, and assess crop yield and water stress in plants. The spectral reflectance of soil is affected by its properties such as mineral composition (Fe oxides), organic matter, soil moisture, and texture. For example, the spectral reflectance will be more if soil has less organic matter. The chemical bonds of soil molecules interact with the electromagnetic spectrum, and produce distinct pattern of reflectance. But the data collected from hyperspectral imaging are required big storage due to its large amount of data and finding the most appropriate hyperspectral image classification algorithm is a challenging task. So, these problems should be solved in future and national soil spectral library is needed for calibration of models which helps for efficient use of hyperspectral imaging technology.

Geospatial modeling and mapping of soil organic carbon and texture from spectroradiometric data in Nile basin

Rapid soil properties monitoring and modeling are crucial for sustainable soil management interventions. The analysis of soil properties in the laboratory using standard procedures is time-consuming and requires the use of chemicals. The research aimed to develop a regression model based on the visible-near-infrared-short-wave infrared (Vis-NIR-SWIR, 0.35–2.5 μm) spectral reflectance of soils in Aba Gerima (425 ha), a highland of the Ethiopian Nile Basin. Soil samples were obtained at a 0–20 cm depth from 101 plots in the study area and analyzed for soil organic carbon (SOC) and texture using standard laboratory procedures. Soil reflectance was measured using a spectroradiometer on the air-dried, ground, and sieved soils. ArcGIS software 10.5 was used to conduct the semi-variogram and geostatistical methods—Ordinary Kriging (OK) and Least Distance Weighting (IDW). Strong degrees of spatial dependence are depicted through semi-variogram models. This outcome illustrates a strong spatial dependence and continuity between nearby soil samples. The prediction accuracy of the model based on the values of coefficient of determination (R2) and root mean square error (RMSE) was explained. For clay content, the R2 and RMSE obtained in the Vis-NIR-SWIR were excellent (0.91) and 2.93. An R2 of 0.85 with an RMSE of 1.96 was obtained in predicting sand. The SOC model established indicated an R2 of 0.94 and an RMSEC of 0.01. The generated maps showed that the soil characteristics' spatial variability was adequate to forecast the values of soil fertility indicators in the research area and nearby regions. The soil maps may assist farmers and policymakers in identifying soil nutrient status, improving soil management strategies, rising crop productivity, and guaranteeing ecological integrity. Moreover, further study is required to corroborate our findings and improve the prediction performance of Vis-NIR-SWIR spectroscopy to help better land management interventions.

Use of soil spectral reflectance to estimate texture and fertility affected by land management practices in Ethiopian tropical highland.

As classical soil analysis is time-consuming and expensive, there is a growing demand for visible, near-infrared, and short-wave infrared (Vis-NIR-SWIR, wavelength 350-2500 nm) spectroscopy to predict soil properties. The objectives of this study were to investigate the effects of soil bunds on key soil properties and to develop regression models based on the Vis-NIR-SWIR spectral reflectance of soils in Aba Gerima, Ethiopia. Soil samples were collected from the 0-30 cm soil layer in 48 experimental teff (Eragrostis tef) plots and analysed for soil texture, pH, organic carbon (OC), total nitrogen (TN), available phosphorus (av. P), and potassium (av. K). We measured reflectance from air-dried, ground, and sieved soils with a FieldSpec 4 Spectroradiometer. We used regression models to identify and predict soil properties, as assessed by the coefficient of determination (R2), root mean square error (RMSE), bias, and ratio of performance to deviation (RPD). The results showed high variability (CV ≥ 35%) and substantial variation (P < 0.05 to P < 0.001) in soil texture, OC, and av. P in the catchment. Soil reflectance was lower from bunded plots. The pre-processing techniques, including multiplicative scatter correction, median filter, and Gaussian filter for OC, clay, and sand, respectively were used to transform the soil reflectance. Statistical results were: R2 = 0.71, RPD = 8.13 and bias = 0.12 for OC; R2 = 0.93, RPD = 2.21, bias = 0.94 for clay; and R2 = 0.85 with RPD = 7.54 and bias = 0.0.31 for sand with validation dataset. However, care is essential before applying the models to other regions. In conclusion, the findings of this study suggest spectroradiometry can supplement classical soil analysis. However, more research is needed to increase the prediction performance of Vis-NIR-SWIR reflectance spectroscopy to advance soil management interventions.

Study on the Hyperspectral Retrieval and Ecological Risk Assessment of Soil Cr, Ni, Zn Heavy Metals in Tailings Area.

The large-scale rapid monitoring of heavy metal pollution has become a hot topic due to increasing contamination of Tailings soil by heavy metal. In order to explore the possibility of using soil spectrum to estimate the content of heavy metals in soil and realize the rapid monitoring of soil heavy metals in the Yangshanchong tailings area in Tongling, China. The spectral reflectance of soil and the content of heavy metals (Cr, Ni, Zn) in soil were determined. The optimal bands of Cr, Ni and Zn elements in soil appeared at 467nm, 467nm and 468nm respectively, and the maximum correlation coefficients were - 0.716, - 0.685 and - 0.630. The inversion model of element Cr constructed under the Reciprocal Transformation Second Derivative has a better effect, and its determination coefficient R2 is 0.613; It is better to construct the model of elements Ni and Zn in the form of Reciprocal Transformation First Derivative, and their determination coefficients R2 are 0.724 and 0.603, respectively. The results of the single factor index method showed that the pollution degree of heavy metal elements in the soil in the study area is Ni > Zn > Cr; the Nemerow comprehensive pollution index method showed that the three elements in the study area were polluted to varying degrees, and the comprehensive pollution index was in order Ni > Zn > Cr; Comprehensive potential ecological hazard index evaluation, the pollution degree and ecological risk of the study area were low.

Deep Learning Application for Predicting Soil Organic Matter Content by VIS-NIR Spectroscopy

Deep learning is characterized by its strong ability of data feature extraction. This method can provide unique advantages when applying it to visible and near-infrared spectroscopy for predicting soil organic matter (SOM) content in those cases where the SOM content is negatively correlated with the spectral reflectance of soil. This study relied on the SOM content data of 248 red soil samples and their spectral reflectance data of 400–2450 nm in Fengxin County, Jiangxi Province (China) to meet three objectives. First, a multilayer perceptron and two convolutional neural networks (LeNet5 and DenseNet10) were used to predict the SOM content based on spectral variation and variable selection, and the outcomes were compared with that from the traditional back-propagation neural network (BPN). Second, the four methods were applied to full-spectrum modeling to test the difference to selected feature variables. Finally, the potential of direct modeling was evaluated using spectral reflectance data without any spectral variation. The results of prediction accuracy showed that deep learning performed better at predicting the SOM content than did the traditional BPN. Based on full-spectrum data, deep learning was able to obtain more feature information, thus achieving better and more stable results (i.e., similar average accuracy and far lower standard deviation) than those obtained through variable selection. DenseNet achieved the best prediction result, with a coefficient of determination (R2) = 0.892 ± 0.004 and a ratio of performance to deviation (RPD) = 3.053 ± 0.056 in validation. Based on DenseNet, the application of spectral reflectance data (without spectral variation) produced robust results for application-level purposes (validation R2 = 0.853 ± 0.007 and validation RPD = 2.639 ± 0.056). In conclusion, deep learning provides an effective approach to predict the SOM content by visible and near-infrared spectroscopy and DenseNet is a promising method for reducing the amount of data preprocessing.

Hyperspectral Estimation Model of Forest Soil Organic Matter in Northwest Yunnan Province, China

Soil organic matter (SOM) is an important index to evaluate soil fertility and soil quality, while playing an important role in the terrestrial carbon cycle. The technology of hyperspectral remote sensing is an important method to estimate SOM content efficiently and accurately. This study researched the best hyperspectral estimation model for SOM content in Shangri-La forest soil. The spectral reflectance of soils with sizes of 2 mm, 1 mm, 0.50 mm, and 0.25 mm were measured indoors. After smoothing and de-noising, the reciprocal reflectance (RR), logarithmic reflectance (LR), first-derivative reflectance (FR), reciprocal first-derivative reflectance (RFR), logarithmic first-derivative reflectance (LFR), and mathematical transformations of the original spectral reflectance (REF) were carried out to analyze the relevance of spectral reflectance and SOM content and extract the characteristic bands. Finally the simple linear regression (SLR), multiple stepwise linear regression (SMLR), and partial least squares regression (PLSR) models for SOM content estimation were established. The results showed that: (1) With the decrease of soil particle size, the spectral reflectance increased. The smaller the soil particle sizes, the more obvious was the increase in spectral reflectance. (2) The sensitive bands of SOM were mainly in the 580–690 nm range (correlation coefficient (R) &gt; 0.6, p-value (p) &lt; 0.01), and the spectral information of SOM could be significantly enhanced by first-order differential transformation. (3) Comparing the three models, PLSR had better estimation ability than SMLR and SLR. The precision of the 0.25 mm soil particle size and the LFR index in the PLSR estimation model of SOM content was the best (coefficient of determination of validation (Rv2) = 0.91, root mean square error of validation (RMSEv) = 13.41, the ratio of percent deviation (RPD) = 3.33). The results provide a basis for monitoring SOM content rapidly in the forests of Northwest Yunnan, and provide a reference for forest SOM estimation in other areas.

Innovative Intelligent Methodology for the Classification of Soil Salinization Degree Using a Fractional-Order Master-Slave Chaotic System

Soil salinization has become a highly significant eco-system issue that is encountered all over the world. Serious soil salinization leads to soil deterioration and has a negative impact on sustainable development of the eco-system and agriculture. However, the spectral reflectance of soils with high overlap and indecipherability makes it difficult to classify the soil salinization degree quickly and accurately. In this paper, an innovative, intelligent methodology using a fractional-order chaotic system to classify the soil salinization degree is proposed. To select a suitable order for the fractional-order chaotic system, the integer-order and noninteger order master-slave Lorenz chaotic systems were used to observe variations in the phase plane distributions. Movement traces of the chaotic system show that severely saline soil will exhibit more active changes, and its distribution status of the Lorenz chaotic system will be more scattered. After analyzing the characteristics of phase plane distributions, a preferred 0.9 fractional-order chaotic system is selected to obtain good analytical characteristics. Finally, extenics theory is used to verify the accuracy of salinization status classified by the coordinate values of the chaotic attractors, and an extenic matter element model is established to analyze the salinization degree. From the results, it was found that 100% analysis accuracy in the judgment of salinization level could be achieved under noninteger order status, and this judgment method is also suitable for soils in different human activity areas. This method has now become a benchmark for testing soil salinization with a chaotic system and is an innovative method that can be used to test the soil salinization degree quickly and accurately.

Allocate soil individuals to soil classes with topsoil spectral characteristics and decision trees

Spectral reflectance of soil is a function of physical and chemical characteristics and its internal structure. Spectral reflectance provides a novel approach for soil allocation. This paper presents a nondestructive, rapid, and low-cost soil allocation method using topsoil spectral characteristics to allocate soil at the level of soil great group within Genetic Soil Classification of China. We measured the spectral reflectance in the visible and near-infrared regions (400–2500 nm) of 148 soil samples from 4 soil classes in the Songnen Plain of northeast China. We extracted the spectral characteristic parameters with clear physiochemical meanings for the topsoil samples, and compared these to the principle component, first spectral derivative and Continuum Removal of soil reflectance. Models were built using the K-means Clustering (K-mean), Multi-layer Perceptron Neural Network (MLPNN), Support Vector Machine (SVM), and Decision Tree (DT) methods. The DTs allocation model based on topsoil spectral characteristic parameters had the highest allocation accuracy. Only the allocation accuracy of Cambisols was <85%, because the spectral curve of Cambisols topsoil was similar to its adjacent soil due to soil erosion. This new method could simplify digital soil mapping, because topsoil spectra are easier to obtain than multilayer soil spectral data.

Diagnosis of gypsiferous soil and determination of gypsum content using their spectral properties

The aims of this study was to electromagnetic spectrum (E.M.S) in determining the spectral signature of the soil according to their gypsum content at different moisture levels, to recasting of the gypsum using the best wavelength and to creation mathematic models for forecasting. The study was carried out at laboratories of college of agriculture, Anbar University. Soil with 42% gypsum content was brought from Salah Al-Den province and washed by distilled water to remove different amount of gypsum and finally to get soils with 3, 8, 17, 28 and 36% gypsum contents. Soils were packed in polyvinyl chloride tubes (PVC) with 7.5 cm diameter and 30 cm height with eight replicates for each gypsum level. Soils were moisten by capillary to maintain homogeneity of soil moisture along the column. Spectral reflectance of soil was measured by spectroradiometer at moisture level of 24%. The results showed an absorption band at 1750 nm wavelength was appeared. The depth of this band was increased as increasing of gypsum content in soil. There is highly significant correlation between gypsum content and spectral reflectance at each of infrared bands (B6) and (S.B). The correlation coefficients at the band B6 were 0.978, 0.979 and 0.972, respectively. Linear, Quadratic and cubic models were represent this relationship. Gypsum was correlated linear, quadratic and cubic by a positive regression relationship with B6. Linear, quadratic and cubic models were the best and more efficient to forecasting gypsum in soil with determination coefficient were 0.99, 0.99 and 0.99 respectively.

Mineralogy and spectral reflectance of soils and tailings from historical gold mines, Nova Scotia

Gold was mined in 64 districts in southern Nova Scotia between 1861 and the early 1940s, followed by limited, intermittent production up to the present. There is extensive dispersion of arsenic- and mercury-bearing mine tailings in the receiving environment downstream from many of these sites. Elevated mercury concentrations, highest near old stamp mill foundations, occur because of the mercury amalgamation process used to extract gold until the 1940s. Arsenic, on the other hand, occurs naturally in arsenopyrite, which is associated with the gold-bearing quartz veins and host rocks. Tailings are composed of fine sand- to silt-sized quartz, feldspar, illite and chlorite, and represent the primary rock-forming minerals in the metasedimentary host rocks of the Cambro-Ordovician Meguma Supergroup. Carbonate and sulphide minerals occur in minor to trace amounts, along with secondary minerals such as scorodite (FeAsO 4 ·2H 2 O). The extent of tailings dispersal can be mapped through hyperspectral remote sensing methods, as these major mineral components provide an identifiable spectral signature through visible, near infrared and short-wave infrared regions. This paper examines the mineralogy of soils, tills and tailings in the Upper and Lower Seal Harbour gold districts of Nova Scotia. Ground-truthing of space-borne hyperspectral data demonstrates the potential for remote mapping of the spatial extent of these historical mine wastes.

Effects of granulometric and mineralogical composition on spectral reflectance of soils in a Sahelian area

During the Hapex-Sahel international investigation, portable radiometers (Barringer and Cimel) were used to study, in the optical domain, the relationship between soil mineralogical and granulometric compositions and radiometric indices: redness index, colour index and texture index. The results show that the granulometric distribution and the mineralogical composition have both important influence on these indices. In this Sahelian area, for soils having more than 1% of hematite, the redness index presents a high correlation with hematite content. For soils with less than 1% of hematite, the redness index depends on the size of the particles and their distribution at the surface. For eolian sandy and ferricrust soils, the colour and texture indices present good correlations, respectively, with iron oxide and kaolinite content. No such correlation is observed for ferruginous soils because of the complex interactions existing between iron oxides, kaolinite and the size of particles.

各種の裸地土壌におけるアルベドと分光反射率の土壌水分依存性

各種の裸地土壌におけるアルベドと分光反射率の土壌水分依存性

Bi-directional reflectance factor of 14 soil classes from Brazil

Abstract The spectral reflectance of soils is required for effective use of remote sensing products. The absence of studies concerned with spectral reflectance of the soils from the tropical region in the 400 to 2500 nm spectral range is the main motivation of this research. The objective of this study was to present spectral reflectance data from different tropical soil types. This spectral characterization was done through measurements of the bi-directional reflectance factor of 111 selected soil samples, grouped in 14 tropical soil classes, taken from 53 sites (Sao Paulo State, Brazil). The measurements were made with a spectroradiometer operating in the 400 to 2500 nm region of the electromagnetic spectrum. Each soil sample is associated to a set of physical and chemical analyses data, with part of these published in descriptive reports of soil surveys.

Spectral reflectance characteristics of soils and its correlation with soil properties and surface conditions

In-situ spectral reflectance of soils was measured at various test sites of India in four spectral bands within the visible and near-infrared wavelength comparable to Landsat Multispectral Scanner (MSS). Reflectance behaviour of soils under different field conditions was analysed and the spectral reflectance curves for different soil types were obtained. Soil samples pertaining to each test site were analysed for mechanical composition, physioco-chemical properties to identify their relationship with soil reflectance. These spectral reflectance curves were further examined as to their usefulness in discriminating various soil types. Five distinct soil types namely, Black cotton soils (Typic Pellusterts), Marine Soils (Typic Halaquepts), Lateritic Soils (Plinthic Tropohumults), Alluvial Soils (Typic Ustochrepts), Coastal Sandy Soils (Typic Psammaquents), were discriminated on the basis of significant relationships between the spectral reflectance data and soil properties.