Spectral Characteristics Of Soil Research Articles

Use of visual-diagnostic color parameters of soils and optical reflectometry for determination of organic carbon content


 
 
 To get additional tools for the assessment of carbon sequestration, along with the visual assessment of soil coloration with the applying of A. H. Munsell’s atlas, the analysis of color and spectral characteristics of soil using portable colorimeter NixPro and reflectometer Our Sci Reflectometer was carried out in this study. Elemental analysis of soil samples using X-ray fluorescence analysis was performed and the content of organic carbon was estimated. The spectral range of reflected light, which correlates most with the content of organic soil substance, was singled out. Based on the data, received by methods of reflectometry and colorimetry, prognostic regression models were constructed. A multiple linear regression equation with a statistically authentic luminosity predictor (L*) (R2=0.61) was obtained. It allows describing the link between the content of the organic substance in the studied soils and the parameters of the color setting system CIELab, as well as the equation describing 69 % of the data link dispersion between the integrated reflection coefficient and the organic carbon content of the soil. The link between the integral reflection coefficient and the total organic substance content was found. The most correlated spectral range with the content of organic substance – 500–632 nm was singled out. Regression models, which were based exclusively on the spectral data of pre-treated H2O2 soils, increased their predictability by 8–10 %. Approaches that can complement the tools for rapid determination of the organic carbon content in the soil were presented in the work. Researchers are expanding their arsenal of technical support for estimation of color or spectral coefficients of light reflection, based on which it is possible to conduct geospatial analysis and determine the content of the organic substance in low-humus soils with a probability of 69 %.
 
 

Study on Polarized Spectral Characteristics of Soil with Different Water Content

Moisture is one of the important parameters in soil polarized spectrum. It has great significance in soil remote sensing band selection and image interpretation; it also provides the information for soil investigation and analysis on physical and chemical properties. In this paper we tested and analyzed the soil polarized spectrum with different moisture in 350 ~ 2,500 nm wavelength, to study on the relationship between soil polarized spectral data and moisture, to determine the spectral response and changes in soil moisture, and establish models between spectral data and soil moisture. We also designed a orthogonal test on the various factors that affect soil polarized spectral characteristics, in which we studied soil moisture, polarized angle, detected angle and azimuth of the various factors and their interactions. The results showed it was most significant that the soil moisture and the interaction of soil moisture and polarized angle, followed by the interaction effect of detected angle and moisture, while the polarized angle had a little impact on the soil polarized spectrum.

Remote Sensing of Fractional Green Vegetation Cover Using Spatially-Interpolated Endmembers

Fractional green vegetation cover (FVC) is a useful parameter for many environmental and climate-related applications. A common approach for estimating FVC involves the linear unmixing of two spectral endmembers in a remote sensing image; bare soil and green vegetation. The spectral properties of these two endmembers are typically determined based on field measurements, estimated using additional data sources (e.g., soil databases or land cover maps), or extracted directly from the imagery. Most FVC estimation approaches do not consider that the spectral properties of endmembers may vary across space. However, due to local differences in climate, soil type, vegetation species, etc., the spectral characteristics of soil and green vegetation may exhibit positive spatial autocorrelation. When this is the case, it may be useful to take these local variations into account for estimating FVC. In this study, spatial interpolation (Inverse Distance Weighting and Ordinary Kriging) was used to predict variations in the spectral characteristics of bare soil and green vegetation across space. When the spatially-interpolated values were used in place of scene-invariant endmember values to estimate FVC in an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image, the accuracy of FVC estimates increased, providing evidence that it may be useful to consider the effects of spatial autocorrelation for spectral mixture analysis.

Creating of digital surface model and orthophoto from ASTER satellite data and their application in land/water research

Satellite data has become a commonly used information source. Landscapes components such as water, inorganic substances, vegetation, and the atmosphere may be distinguished making use of their spectral characteristics. The above mentioned components may be further divided. For example, inorganic substances may be subdivided into soil, minerals, build up areas etc. The spectral characteristics of soils are determined by moisture, humus contents, mineral composition, surface structure, and the stage of eroding processes. The development in remote sensing tends either to the data acquisition in more spectral bands or the improvement of the resolution of remote sensing data. The terra satellite ranks among new generation satellites; its orbital parameters are similar to the parameters of the Landsat system. ASTER (Advanced Spaceborn Thermal Emission and Reflection Radiometer) is one of the onboard instruments on Terra satellite and captures data in 14 spectral bands. The VNIR (Visible Near Infrared) subsystem provides 15 m spatial resolution data. Two of the VNIR subsystem telescopes enable stereoscopic data evaluation. A stereo-pair consists of 3N (nadir) and 3B (backward) images. A couple of 3N and 3B images can be used for the creation of a digital surface model (DSM) and orthophoto. This article describes the creation of DSM and orthophoto of an area located in the north-west part of the Czech Republic. Images of the area were made in years 2002 and 2005. In this work, level 1B images were used, i.e. images with radiometric and geometric corrections already applied. The model was created through the use of 21 control points selected in each scene. The standard error of co-ordinates of the control points is up to 15 m, the elevation standard error is approx. 30 m. The accuracy of the final DSM and orthophoto was tested on a set of 13 check points. The position standard error in DSM and orthophoto is approx. 15 m, i.e. just about the size of one pixel of the original data. The elevation standard error of the checkpoints is up to 40 m. The output can be used as a basis for small-scale maps. Using one scene acquired by ASTER instruments, a DSM and orthophoto covering an area of 60 × 60 km can be created. Keywords: remote sensing; ASTER; digital surface model; orthophoto

Field-derived spectral characteristics to classify conventional and conservation agricultural practices

Field-derived hyperspectral reflectance of soil and wheat were collected during a wheat-growing season (February, March and April) using a portable spectrophotometer in an experimental field receiving conventional and conservation agricultural practices. These practices included two types of tillage, conventional and conservation, and two types of nutrient sources, inorganic and a combination of inorganic–organic fertilizers. This research aims at discriminating conventional and conservation practices and identifying the most applicable bands for discrimination. Spectral characteristics of soil and wheat indicated that soil spectra were useful to discriminate tillage practices, while wheat spectra were useful to discriminate nutrient sources. Cluster and principal component analysis (PCA) revealed that the best time to collect soil and wheat spectra for tillage and nutrient sources differentiation was at the beginning of the season. According to the best time for spectra collection, wavelengths more suitable for discriminating tillage practices were in the VIS and NIR ranges, and in the NIR range for nutrient sources. Furthermore, spectra and band-wise normalization were applied indistinctly to the data sets. It was understood that soil or wheat spectra could not be independently used to discriminate treatments (interactions between tillage and nutrient sources), unless the spectra are normalized and a method such as PCA used.

Spectral analysis of soils from mafic/ultramafic rocks of Cerro Mantiqueira, south-west of Rio Grande do Sul, Brazil

In remote sensing, information about an object of interest is most frequently derived by analysing the spectral characteristics of the electromagnetic energy emanating from it. In this context, the spectral characteristics of soils and rocks can provide useful information about their chemical and mineralogical composition. In this study, an investigation is conducted in order to examine the relationship between the spectral data as obtained by a spectroradiometer and the chemical and mineralogical composition of soils originated from mafic/ultramafic rocks. In order to help in understanding this relationship, the Karhunen–Loève (KL) transformation, also known as principal components (PC) transformation, was applied to the data to determine the relationship between spectral reflectance patterns and the soil's chemical and mineralogical composition. The first three principal components (PC1, PC2 and PC3), i.e. the components associated with the three largest eigenvalues, were analysed. The experiments have shown that a clear interpretation can be assigned to each of the three components. The first component (PC1) can be related to the sample albedo. The second component (PC2) conveys information with respect to the slope of the sample's spectral reflectance curve, from the visible to the infrared portions of the spectrum. The third component (PC3) relates to the contents of iron-bearing and calcium/magnesium-bearing minerals in the soil sample. The approach proposed in this study may help to better understand the relationship between the sample's spectral pattern and its chemical and mineralogical composition.

Cropland Measurement Using Thematic Mapper Data and Radiometric Model

To halt erosion and desertification, it is necessary to quantify resources that are affected. Necessary information includes inventory of croplands and desert areas as they change over time. Several studies indicate the value of remote sensor data as input to inventories. In this study, the radiometric modeling of spectral characteristics of soil and vegetation provides the theoretical basis for the remote sensing approach. Use of Landsat Thematic Mapper images allows measurement of croplands in Saudi Arabia, demonstrating the capability of the approach. The inventory techniques and remote sensing approach presented are potentially useful in developing countries.