Soil Reflectance Spectroscopy Research Articles

A global soil spectral calibration library and estimation service

There is growing global interest in the potential for soil reflectance spectroscopy to fill an urgent need for more data on soil properties for improved decision-making on soil security at local to global scales. This is driven by the capability of soil spectroscopy to estimate a wide range of soil properties from a rapid, inexpensive, and highly reproducible measurement using only light. However, several obstacles are preventing wider adoption of soil spectroscopy. The biggest obstacles are the large variation in the soil analytical methods and operating procedures used in different laboratories, poor reproducibility of analyses within and amongst laboratories and a lack of soil physical archives. In addition, adoption is hindered by the expense and complexity of building soil spectral libraries and calibration models. The Global Soil Spectral Calibration Library and Estimation Service is proposed to overcome these obstacles by providing a freely available estimation service based on an open, high quality and diverse spectral calibration library and the extensive soil archives of the Kellogg Soil Survey Laboratory (KSSL) of the Natural Resources Conservation Service of the United States Department of Agriculture (USDA). The initiative is supported by the Global Soil Laboratory Network (GLOSOLAN) of the Global Soil Partnership and the Soil Spectroscopy for Global Good network, which provide additional support through dissemination of standards, capacity development and research. This service is a global public good which stands to benefit soil assessments globally, but especially developing countries where soil data and resources for conventional soil analyses are most limited.

Impact of forest trees on the soil reflectance and their relation of soil oxides in Sherawa village, Erbil government- Iraq

Soil reflectance spectroscopy has become a pioneering technique for quantifying soil properties that provide data for soil classification and digital soil mapping studies. This work was conducted with the aim to analyze the spatial variability of soil spectrometric properties between surface and subsurface soil under three land use patterns namely; pine, barren land and oak in Sherawa village, northeast Iraq.
 Soil samples were collected from the three land types at two depths: surface soil at 0–15 cm and subsurface soil at 15–30 cm in duplicates totaling 6 collected soil samples. The elemental composition and mineral phases of soils were determined by X-ray fluorescent XRF and X-ray diffraction (XRD) respectively for qualitative and quantitative characterization. Magnesium oxide (MgO) was highest detected concentration in the surface soil layer reaching 34.16%, 36.75%, and 33.63%, respectively for pine, barren land and oak compared to 41.57%, 38.54%, 33.94% of the respective concentration in the subsurface soil layer samples of pine, barren and oak. Whereas, calcium and iron were the lowest percentages of pine in the surface and subsurface soil layer samples by (2.19%, 4.85%, 3.39%, and 4.55%, respectively). At the same time as, the percentages of oak in the surface and subsurface soil layer samples were (2.24%, 4%, 2.99% and 4.93%, respectively).The results indicate that the use of spectrometric analysis of soils in the field or in the laboratory can positively enhance soil characterization and typification surveys. Moreover, the influence of the silicates near 1400 nm by Al or Mg-OH and OH group were very apparent in the SWIR spectral regions of oak and pine in the subsurface soil. The results also showed the differences in soil color and reflectance between surface soil samples and subsurface surface soil for all site locations. Further studies are proceeding for more investigation at larger scale concerning analyzing the spatial variability of soil spectrometric properties with depth under different land use patterns..

Visible and Near-Infrared Reflectance Spectroscopy Analysis of a Coastal Soil Chronosequence

The soil chronosequence is a useful method for investigating pedological theories. Soil chemical, physical and mineralogical properties in chronosequences change over time and exhibit systematic and time-dependent trends, which can be used to analyze the rates and directions of pedogenic changes. The potential of soil spectroscopy as an emerging, rapid and cost-effective technique for predicting soil properties has been widely accepted and has motivated the application of spectroscopic techniques to the analysis of soil chronosequence. We present a soil chronosequence derived from 1000-year-old calcareous marine sediments and examine changes in six soil properties over this period. We evaluated the utility of a soil spectroscopic method to detect soil property changes and to predict the pedogenic properties and soil ages of the chronosequence. The results show that some soil pedogenic processes, such as soil organic matter accumulation, CaCO3 leaching and clay migration, can be identified in the millennium chronosequence. Power chronofunctions are formulated for soil organic matter (SOM) and Logarithmic chronofunctions are fitted for clay, CaCO3 and pH. These pedogenic processes are identified in the reflectance intensity and absorption features of soil spectroscopy, and pedogenic properties can be calibrated via soil reflectance spectroscopy. Profile ages can also be predicted via pseudo multi-depth spectra of soil profiles, and soil spectral curves for 0–30 cm generated the best prediction results (RPD = 1.85). We conclude that soil properties, changing due to weathering and soil formation, act as a bridge linking spectroscopy and weathering levels/pedogenic processes. The results imply that applying spectroscopy techniques to chronosequence study and mapping the degree of soil development in certain areas should be possible.

Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review

Towards the need for sustainable development, remote sensing (RS) techniques in the Visible-Near Infrared–Shortwave Infrared (VNIR–SWIR, 400–2500 nm) region could assist in a more direct, cost-effective and rapid manner to estimate important indicators for soil monitoring purposes. Soil reflectance spectroscopy has been applied in various domains apart from laboratory conditions, e.g., sensors mounted on satellites, aircrafts and Unmanned Aerial Systems. The aim of this review is to illustrate the research made for soil organic carbon estimation, with the use of RS techniques, reporting the methodology and results of each study. It also aims to provide a comprehensive introduction in soil spectroscopy for those who are less conversant with the subject. In total, 28 journal articles were selected and further analysed. It was observed that prediction accuracy reduces from Unmanned Aerial Systems (UASs) to satellite platforms, though advances in machine learning techniques could further assist in the generation of better calibration models. There are some challenges concerning atmospheric, radiometric and geometric corrections, vegetation cover, soil moisture and roughness that still need to be addressed. The advantages and disadvantages of each approach are highlighted and future considerations are also discussed at the end.

AlradSpectra: a Quantification Tool for Soil Properties Using Spectroscopic Data in R

ABSTRACT Soil reflectance spectroscopy has become an innovative method for soil property quantification supplying data for studies in soil fertility, soil classification, digital soil mapping, while reducing laboratory time and applying a clean technology. This paper describes the implementation of a Graphical User Interface (GUI) using R named AlradSpectra. It contains several tools to process spectroscopic data and generate models to predict soil properties. The GUI was developed to accomplish tasks such as perform a large range of spectral preprocessing [...]

Predicting cadmium concentration in soils using laboratory and field reflectance spectroscopy

Visible and near-infrared spectroscopy (VNIRS, 350–2500 nm) is a promising alternative to rapidly investigate soil contamination by heavy metals. To explore the possibility of predicting heavy metal concentration in soils using laboratory and field reflectance spectroscopy and examine transferability of the prediction method, 46 soil samples from a mining area, 42 soil samples from an agricultural land, and the corresponding two sets of field soil spectra were collected. Cadmium (Cd) was taken as an example in this study. The collected soil samples were air-dried, ground, sieved, and then used for laboratory spectral measurement and chemical analysis. Soil reflectance spectroscopy associated with organic matter was extracted from the VNIRS and used to predict Cd concentration based on strong sorption and retention of Cd on soil organic matter. Genetic algorithm (GA) was adopted for band selection, and the selected bands were used to calibrate the prediction model with partial least squares regression (PLSR). Compared with the prediction using entire VNIR region, the ratio of prediction to deviation (RPD) and the coefficient of determination (R2) were improved from 1.473 and 0.508 to 2.997 and 0.881 for laboratory spectra and 1.437 and 0.484 to 1.992 and 0.731 for field spectra by using spectral bands associated with organic matter in the mining area. The RPD and R2 values were improved from 1.919 and 0.707 to 3.727 and 0.923 for laboratory spectra and 1.057 and 0.036 to 1.747 and 0.646 for field spectra by the prediction method in the agricultural land. The improvement was further revealed by prediction of Cd concentration with a selected subset of soil samples from the mining area. The results suggest that predicting Cd concentration in soils with GA-PLSR using reflectance spectroscopy associated with organic matter is feasible and the prediction method could have the potential to be applied to field conditions.

Reflectance Spectroscopy (Vis-NIR) for Assessing Soil Heavy Metals Concentrations Determined by two Different Analytical Protocols, Based on ISO 11466 and ISO 14869-1

This study aimed to investigate the potency of soil reflectance spectroscopy in the visible and near infrared (Vis-NIR) spectral regions in estimating soil heavy metal pollution in the western coastal front of Thessaloniki (N. Greece) and how the protocol used for chemical analyses can affect the models’ performance. For this purpose, 49 topsoil samples were collected and the concentrations of Cd, Cr, Cu, and Pb were determined by two different analytical methods, i.e., ISO 11466 based on the technique of atomic absorbance spectrometry (AAS) and ISO 14869-1 using the technique of inductively coupled plasma-atomic emission spectrometry (ICP-AES). The spectral signatures were applied for modeling the metal concentrations by using the partial least squares regression (PLSR) method. To eliminate the “noise” of data and enhance the models’ accuracy, four spectral pre-treatment methods were used. The overall results showed that there is heavy metal pollution in the soils of specific areas in the studied region and that the use of different chemical analytical methods can affect the performance of examined prediction models. Better prediction models were created for the cases of Pb, Cu, and Cr concentrations, which were estimated by the application of ISO 14869-1, while for the case of Cd better prediction models were obtained, by the application of ISO 11466. These results may indicate that soil reflectance spectroscopy can measure the total heavy metal content in soil samples.

How accurately can soil classes be allocated based on spectrally predicted physio-chemical properties?

Soil class maps are useful representations of the landscape distribution of holistic soil functions. However these are often only available as generalized classes at small cartographic scales. One reason is that allocating a soil profile to a class in most current soil classification system requires laboratory determination of many diagnostic soil properties. The advantage of reflectance spectroscopy along with the development of spectral libraries can provide a relatively low-cost solution to this problem. Reflectance spectroscopy has demonstrated its ability to rapidly predict soil physio-chemical properties; however prediction accuracy varies among soil properties. When properties predicted with different accuracies are used to substitute for traditional laboratory determinations in allocating a soil profile to a class, the resulting reliability of the allocation is questionable. The objective of this research is to explore whether the soil properties predicted by reflectance spectroscopy can be used to correctly allocate soil profiles into soil taxa at different hierarchical levels. Two hundred and six soil profiles were allocated to eight Orders, 12 Suborders, 23 Groups and 49 Subgroups according to Chinese Soil Taxonomy, with the help of ten soil properties predicted by spectra using ten-fold cross-validated PLSR modelling. The overall allocation accuracy at Order, Suborder, Group and Subgroup level was 98.5%, 98.5%, 87.7% and 76.0% respectively. These results show that soil reflectance spectroscopy can assist in allocation of profiles. When predicted soil properties with varying accuracy are used for soil allocation, propagation of prediction errors and model uncertainties must be considered. We propose the use of multiple indicators (RPD, confidence intervals, comparison of RMSE and threshold requirements) to evaluate the allocation results.

Estimating soil zinc concentrations using reflectance spectroscopy

Soil contamination by heavy metals has been an increasingly severe threat to nature environment and human health. Efficiently investigation of contamination status is essential to soil protection and remediation. Visible and near-infrared reflectance spectroscopy (VNIRS) has been regarded as an alternative for monitoring soil contamination by heavy metals. Generally, the entire VNIR spectral bands are employed to estimate heavy metal concentration, which lacks interpretability and requires much calculation. In this study, 74 soil samples were collected from Hunan Province, China and their reflectance spectra were used to estimate zinc (Zn) concentration in soil. Organic matter and clay minerals have strong adsorption for Zn in soil. Spectral bands associated with organic matter and clay minerals were used for estimation with genetic algorithm based partial least square regression (GA-PLSR). The entire VNIR spectral bands, the bands associated with organic matter and the bands associated with clay minerals were incorporated as comparisons. Root mean square error of prediction, residual prediction deviation, and coefficient of determination (R2) for the model developed using combined bands of organic matter and clay minerals were 329.65mgkg−1, 1.96 and 0.73, which is better than 341.88mgkg−1, 1.89 and 0.71 for the entire VNIR spectral bands, 492.65mgkg−1, 1.31 and 0.40 for the organic matter, and 430.26mgkg−1, 1.50 and 0.54 for the clay minerals. Additionally, in consideration of atmospheric water vapor absorption in field spectra measurement, combined bands of organic matter and absorption around 2200nm were used for estimation and achieved high prediction accuracy with R2 reached 0.640. The results indicate huge potential of soil reflectance spectroscopy in estimating Zn concentrations in soil.

Improving spatial estimation of soil organic matter in a subtropical hilly area using covariate derived from vis-NIR spectroscopy

Spatial variability of soil properties is complex in the hilly areas of subtropical China that are heavily influenced by human activities. Large datasets are needed for achieving accurate spatial estimates of soil properties in these areas, and it can be difficult due to the budget and time restrictions. Soil diffuse reflectance contains the integrated information of soil properties, thus can provide useful auxiliary variables to improve the spatial estimation of soil properties. This study aimed to improve the spatial estimation of soil organic matter (OM) in a typical subtropical hilly area of China, with principal component (PC) extracted from soil visible and near-infrared (vis-NIR) reflectance spectroscopy used as co-variable. Spatial estimation was performed using ordinary kriging (OK) and co-kriging (CK). A set of 125 samples collected from soil surface layer was used. To simulate the under-sampling situation of target variable, a subset was constructed by randomly sampling 50% of the total samples. The results showed that compared to OK, CK provided the decreased root mean square error (RMSE) by about 45% in cross validations and improved estimation map with much lower estimation variances. This study indicates that for the soil properties and functional attributes that are well correlated with soil reflectance and are sparsely sampled, combined use of soil reflectance spectroscopy technique and multivariate geostatistical method will provide a powerful solution for accurate spatial estimation of soil OM.

The Spectral Reflectance Properties of Soil Structural Crusts in the 1.2‐ to 2.5‐μm Spectral Region

A controlled investigation of the spectral signature of soil's structural crust in the 1.2‐ to 2.5‐μm spectral region was conducted to investigate the feasibility of accounting for the crust status solely from the spectral information. This research was conducted because there is no valid method for in situ assessment of soil‐crust characteristics in agricultural fields while and after the crust is formed. Through the use of a laboratory rainfall simulator and a sensitive spectrometer, we showed that significant spectral differences occurred between the crust and the bulk soil for three selected soils from Israel. The study was divided into two parts: Part 1, in which qualitative observations of the three soils were conducted under one level of rainstorm energy, and Part 2, where a selected soil was further investigated under varying levels of rain energy. The spectral differences obtained for the crusted soil are related to the texture and mineralogy of the soil's surface. It was concluded that the relationship between structural crust and soil reflectance spectroscopy can be used as a tool for estimating soil properties governed by the physical crust formation, such as infiltration rate, soil runoff, and erosion. It was further suggested that this methodology be tested within a remote sensing domain, using field, air, or spaceborne hyperspectral sensors.