Visible and near infrared spectroscopy in soil science

This chapter provides a review on the state of soil visible–near infrared (vis–NIR) spectroscopy. Our intention is for the review to serve as a source of up-to date information on the past and current role of vis–NIR spectroscopy in soil science. It should also provide critical discussion on issues surrounding the use of vis–NIR for soil analysis and on future directions. To this end, we describe the fundamentals of visible and infrared diffuse reflectance spectroscopy and spectroscopic multivariate calibrations. A review of the past and current role of vis–NIR spectroscopy in soil analysis is provided, focusing on important soil attributes such as soil organic matter (SOM), minerals, texture, nutrients, water, pH, and heavy metals. We then discuss the performance and generalization capacity of vis–NIR calibrations, with particular attention on sample pre-tratments, co-variations in data sets, and mathematical data preprocessing. Field analyses and strategies for the practical use of vis–NIR are considered. We conclude that the technique is useful to measure soil water and mineral composition and to derive robust calibrations for SOM and clay content. Many studies show that we also can predict properties such as pH and nutrients, although their robustness may be questioned. For future work we recommend that research should focus on: (i) moving forward with more theoretical calibrations, (ii) better understanding of the complexity of soil and the physical basis for soil reflection, and (iii) applications and the use of spectra for soil mapping and monitoring, and for making inferences about soils quality, fertility and function. To do this, research in soil spectroscopy needs to be more collaborative and strategic. The development of the Global Soil Spectral Library might be a step in the right direction

Novel approaches to modelling and monitoring of heavy metal - contaminated sites

Soil contamination is becoming more prevalent, and with increasing global population, more people are being affected. Contaminated site assessment informs management of contaminant sources, affected soil and groundwater. Inaccuracy of assessment can lead to misclassification of sites, resulting in unnecessary remediation, or failing to remediate where it is required. The research presented in this thesis sought to reduce the risk of misclassification by addressing four key aspects of assessment; sampling, detection, mapping and monitoring. The study sought to refine sample size requirements by estimating the number of samples required to determine if the mean at a site exceeded Australian contamination thresholds. A large number of samples were required, yet this may be unrealistic due to time and cost. Portable X-ray Fluorescence spectroscopy (PXRF) provides real-time analysis of soil heavy metal concentrations, enabling more samples to be collected. There is room for improvement in the accuracy of PXRF measurements, so the study explored the potential of integrating these with spectra obtained from visible-near infrared spectroscopy (vis-NIR). Integration of the two spectral methods provided a measure of precision, yet only a marginal increase in accuracy. To improve mapping methods this study obtained measurements from within the Sydney estuary catchment and integrated these, alongside freely available covariates, into linear mixed models to predict lead and zinc concentrations in soil across the catchment. The final chapter of the thesis combined linear mixed models from two time points to predict change in heavy metal concentrations over time at a remediated Sydney parkland. The models provided a detailed snapshot of heavy metal distributions and factors influencing these distributions over time. It is evident in this thesis that much can be done to improve contaminated site assessment and help ensure land is safe and secured for future generations

Razvoj normaliziranog indeksa tla za urbane studije upotrebom podataka daljinskih mjerenja

This paper presents two novel spectral soil area indices to identify bare soil area and distinguish it more accurately from the urban impervious surface area (ISA). This study designs these indices based on medium spatial resolution remote sensing data from Landsat 8 OLI dataset. Extracting bare soil or urban ISA is more challenging than extracting water bodies or vegetation in multispectral Remote Sensing (RS). Bare soil and the urban ISA area often were mixed because of their spectral similarity in multispectral sensors. This study proposes Normalized Soil Area Index 1 (NSAI1) and Normalized Soil Area Index 2 (NSAI2) using typical multispectral bands. Experiments show that these two indices have an overall accuracy of around 90%. The spectral similarity index (SDI) shows these two indices have higher separability between soil area and ISA than previous indices. The result shows that percentile thresholds can effectively classify bare soil areas from the background. The combined use of both indices measured the soil area of the study area over 71 km2. Most importantly, proposed soil indices can refine urban ISA measurement accuracy in spatiotemporal studies.Ovaj rad prikazuje dva nova spektralna indeksa tla kako bi se identificiralo golo tlo te kako bi se bolje razlikovalo od urbanih nepropusnih površina (ISA). Ti indeksi su definirani na temelju srednje prostorne rezolucije daljinskih podataka Landsat 8 OLI skupa podataka. U multispektralnim daljinskim mjerenjima (RS) prepoznavanje golog tla ili urbane ISA podloge je složenije od prepoznavanja vodenih tijela ili podloge s vegetacijom. Zbog sličnosti spektara dobivenih multispektralnim senzorima golo tlo i urbana ISA površina često se ne razlučuju. Ova studija predlaže dva normalizirana indeksa tla (NSAI1 i NSAI2) korištenjem tipičnih multispektralnih pojaseva. Eksperimenti pokazuju da ta dva indeksa imaju sveukupnu točnost od približno 90%. Indeks spektralne sličnosti (SDI) pokazuje da ta dva indeksa razlikuju golo tlo od urbane ISA podloge bolje nego dosadašnji indeksi. Rezultati pokazuju da percentilni pragovi mogu efikasno razlučiti površine s golim tlom od pozadine. Kombiniranom upotrebom oba indeksa izmjerena je površina tla veća od 71 km2. Najznačajniji rezultat je taj da predloženi indeksi tla mogu poboljšati točnost mjerenja urbanih ISA u u prostorno-vremenskim studijama

Quantitative Mapping of Soil Property Based on Laboratory and Airborne Hyperspectral Data Using Machine Learning

Soil visible and near-infrared spectroscopy provides a non-destructive, rapid and low-cost approach to quantify various soil physical and chemical properties based on their reflectance in the spectral range of 400–2500 nm. With an increasing number of large-scale soil spectral libraries established across the world and new space-borne hyperspectral sensors, there is a need to explore methods to extract informative features from reflectance spectra and produce accurate soil spectroscopic models using machine learning. Features generated from regional or large-scale soil spectral data play a key role in the quantitative spectroscopic model for soil properties. The Land Use/Land Cover Area Frame Survey (LUCAS) soil library was used to explore PLS-derived components and fractal features generated from soil spectra in this study. The gradient-boosting method performed well when coupled with extracted features on the estimation of several soil properties. Transfer learning based on convolutional neural networks (CNNs) was proposed to make the model developed from laboratory data transferable for airborne hyperspectral data. The soil clay map was successfully derived using HyMap imagery and the fine-tuned CNN model developed from LUCAS mineral soils, as deep learning has the potential to learn transferable features that generalise from the source domain to target domain. The external environmental factors like the presence of vegetation restrain the application of imaging spectroscopy. The reflectance data can be transformed into a vegetation suppressed domain with a force invariance approach, the performance of which was evaluated in an agricultural area using CASI airborne hyperspectral data. However, the relationship between vegetation and acquired spectra is complicated, and more efforts should put on removing the effects of external factors to make the model transferable from one sensor to another.:Abstract I Kurzfassung III Table of Contents V List of Figures IX List of Tables XIII List of Abbreviations XV 1 Introduction 1 1.1 Motivation 1 1.2 Soil spectra from different platforms 2 1.3 Soil property quantification using spectral data 4 1.4 Feature representation of soil spectra 5 1.5 Objectives 6 1.6 Thesis structure 7 2 Combining Partial Least Squares and the Gradient-Boosting Method for Soil Property Retrieval Using Visible Near-Infrared Shortwave Infrared Spectra 9 2.1 Abstract 10 2.2 Introduction 10 2.3 Materials and methods 13 2.3.1 The LUCAS soil spectral library 13 2.3.2 Partial least squares algorithm 15 2.3.3 Gradient-Boosted Decision Trees 15 2.3.4 Calculation of relative variable importance 16 2.3.5 Assessment 17 2.4 Results 17 2.4.1 Overview of the spectral measurement 17 2.4.2 Results of PLS regression for the estimation of soil properties 19 2.4.3 Results of PLS-GBDT for the estimation of soil properties 21 2.4.4 Relative important variables derived from PLS regression and the gradient-boosting method 24 2.5 Discussion 28 2.5.1 Dimension reduction for high-dimensional soil spectra 28 2.5.2 GBDT for quantitative soil spectroscopic modelling 29 2.6 Conclusions 30 3 Quantitative Retrieval of Organic Soil Properties from Visible Near-Infrared Shortwave Infrared Spectroscopy Using Fractal-Based Feature Extraction 31 3.1 Abstract 32 3.2 Introduction 32 3.3 Materials and Methods 35 3.3.1 The LUCAS topsoil dataset 35 3.3.2 Fractal feature extraction method 37 3.3.3 Gradient-boosting regression model 37 3.3.4 Evaluation 41 3.4 Results 42 3.4.1 Fractal features for soil spectroscopy 42 3.4.2 Effects of different step and window size on extracted fractal features 45 3.4.3 Modelling soil properties with fractal features 47 3.4.3 Comparison with PLS regression 49 3.5 Discussion 51 3.5.1 The importance of fractal dimension for soil spectra 51 3.5.2 Modelling soil properties with fractal features 52 3.6 Conclusions 53 4 Transfer Learning for Soil Spectroscopy Based on Convolutional Neural Networks and Its Application in Soil Clay Content Mapping Using Hyperspectral Imagery 55 4.1 Abstract 55 4.2 Introduction 56 4.3 Materials and Methods 59 4.3.1 Datasets 59 4.3.2 Methods 62 4.3.3 Assessment 67 4.4 Results and Discussion 67 4.4.1 Interpretation of mineral and organic soils from LUCAS dataset 67 4.4.2 1D-CNN and spectral index for LUCAS soil clay content estimation 69 4.4.3 Application of transfer learning for soil clay content mapping using the pre-trained 1D-CNN model 72 4.4.4 Comparison between spectral index and transfer learning 74 4.4.5 Large-scale soil spectral library for digital soil mapping at the local scale using hyperspectral imagery 75 4.5 Conclusions 75 5 A Case Study of Forced Invariance Approach for Soil Salinity Estimation in Vegetation-Covered Terrain Using Airborne Hyperspectral Imagery 77 5.1 Abstract 78 5.2 Introduction 78 5.3 Materials and Methods 81 5.3.1 Study area of Zhangye Oasis 81 5.3.2 Data description 82 5.3.3 Methods 83 5.3.3 Model performance assessment 85 5.4 Results and Discussion 86 5.4.1 The correlation between NDVI and soil salinity 86 5.4.2 Vegetation suppression performance using the Forced Invariance Approach 86 5.4.3 Estimation of soil properties using airborne hyperspectral data 88 5.5 Conclusions 90 6 Conclusions and Outlook 93 Bibliography 97 Acknowledgements 11

A multivariate statistical and GIS approach to estimate heavy metal(loid)s in contaminated surface soils

In recent decades, there has been a growing concern over the escalating pollution of soil with heavy metal(loid)s, which poses an immediate threat to human health, food safety, and the overall soil environment. This research aimed to assess the extent of contamination, spatial distribution, sources of contamination, potential ecological risks, and health hazards associated with heavy metal(loid)s (specifically As, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, Sr, and Zn) by collecting soil samples from the surface soils in the mining region of Cerrito Blanco and Matehuala, San Luis Potosi in central Mexico. In addition to this, another study was conducted on rare trace metal(loid)s (B, Ba, Sb, Sn, and V) and other trace metals (Ca, Mg, Na, and K) in this selected region, which shows a level of contamination for those metals. The contamination levels of these heavy metal(loid)s were determined using various indices such as Igeo (geo-accumulation index), Cf (contamination factor), PLI (pollution load index), Cd (degree of contamination), mCd (modified degree of contamination), PIN (nemerow pollution index), EF (enrichment factor), and PERI (potential ecological risk index). Multivariate statistical techniques, such as principal component analysis (PCA), cluster analysis, or factor analysis, were used to identification of patterns and correlations among different heavy metal(loid)s and soil parameters. The findings indicated a significant degree of contamination in the surface soil due to heavy metal(loid)s. The integrated contamination indices and the potential ecological risk index revealed high levels of contamination and substantial ecological risks in the study areas, with particular emphasis on the need to control As in the surface soils surrounding Matehuala. Source identification of heavy metal(loid)s were performed using the APCS-MLR, PMF, and UNMIX receptor models, which detected three potential sources: mining and smelting activities, natural sources, and anthropogenic activities. The APCS-MLR model appeared to be more suitable for identifying complex contamination sources, demonstrating a better R2 coefficient and P/M (predicted/measured) ratio than the other models. Mining and smelting activities were identified as the primary factors influencing the distribution of heavy metal(loid)s in the surface soils. The most effective GIS interpolation technique was selected to analyse the spatial distribution patterns of heavy metal(loid) content, comparing five different GIS interpolation approaches such as Inverse Distance Weighting (IDW), Local Polynomial (LP), Ordinary Kriging (OK), Empirical Bayesian Kriging (EBK), and Radial Basis Functions (RBF). The results indicated regions of significant concentrations for all heavy metal(loid)s, with the northern, western, and central parts of the study area exhibiting particularly elevated levels. Ecological risk assessment based on PERI revealed considerable risk for As and moderate risk for the remaining metals. Moreover, a probabilistic evaluation of health risks indicated minimal non-carcinogenic risks (HI) for humans but significant carcinogenic risks (CR) for both adults and children. Notably, children were found to be more vulnerable to the health risks associated with exposure to these heavy metals compared to adults. Consequently, enhanced monitoring efforts are necessary to address the issue of heavy metal(loid)s contamination in the rapidly developing Matehuala regions.James Watt Scholarshi

Remote Sensing of Biophysical Parameters

Vegetation plays an essential role in the study of the environment through plant respiration and photosynthesis. Therefore, the assessment of the current vegetation status is critical to modeling terrestrial ecosystems and energy cycles. Canopy structure (LAI, fCover, plant height, biomass, leaf angle distribution) and biochemical parameters (leaf pigmentation and water content) have been employed to assess vegetation status and its dynamics at scales ranging from kilometric to decametric spatial resolutions thanks to methods based on remote sensing (RS) data.Optical RS retrieval methods are based on the radiative transfer processes of sunlight in vegetation, determining the amount of radiation that is measured by passive sensors in the visible and infrared channels. The increased availability of active RS (radar and LiDAR) data has fostered their use in many applications for the analysis of land surface properties and processes, thanks to their insensitivity to weather conditions and the ability to exploit rich structural and texture information. Optical and radar data fusion and multi-sensor integration approaches are pressing topics, which could fully exploit the information conveyed by both the optical and microwave parts of the electromagnetic spectrum.This Special Issue reprint reviews the state of the art in biophysical parameters retrieval and its usage in a wide variety of applications (e.g., ecology, carbon cycle, agriculture, forestry and food security)

Identify the opportunities provided by developments in earth observation and remote sensing for national scale monitoring of soil quality

Defra wish to establish to what extent national-scale soil monitoring (both state and change) of a series of soil indicators might be undertaken by the application of remote sensing methods. Current soil monitoring activities rely on the field-based collection and laboratory analysis of soil samples from across the landscape according to different sampling designs. The use of remote sensing offers the potential to encompass a larger proportion of the landscape, but the signal detected by the remote sensor has to be converted into a meaningful soil measurement which may have considerable uncertainty associated with it. The eleven soil indicators which were considered in this report are pH, organic carbon, bulk density, phosphorus (Olsen P), nitrogen (total N), magnesium (extractable), potassium (extractable), copper (aqua regia extractable), cadmium (aqua regia extractable), zinc (aqua regia extractable) and nickel (aqua regia extractable). However, we also comment on the potential use of remote sensing for monitoring of soil depth and (in particular) peat depth, plus soil erosion and compaction. In assessing the potential of remote sensing methods for soil monitoring of state and change, we addressed the following questions: 1. When will these be ready for use and what level of further development is required? 2. Could remote sensing of any of these indicators replace and/or complement traditional field based national scale soil monitoring? 3. Can meaningful measures of change be derived? 4. How could remote soil monitoring of individual indicators be incorporated into national scale soil monitoring schemes? To address these questions, we undertook a comprehensive literature and internet search and also wrote to a range of international experts in remote sensing. It is important to note that the monitoring of the status of soil indicators, and the monitoring of their change, are two quite different challenges; they are different variables and their variability is likely to differ. There are particular challenges to the application of remote sensing of soil in northern temperate regions (such as England and Wales), including the presence of year-round vegetation cover which means that soil spectral reflectance cannot be captured by airborne or satellite observations, and long-periods of cloud cover which limits the application of satellite-based spectroscopy. We summarise the potential for each of the indicators, grouped where appropriate. Unless otherwise stated, the remote sensing methods would need to be combined with ground-based sampling and analysis to make a contribution to detection of state or change in soil indicators. Soil metals (copper (Cu), cadmium (Cd), zinc (Zn), nickel (Ni)): there is no technical basis for applying current remote sensing approaches to monitor either state or change of these indicators and there are no published studies which have shown how this might be achieved. Soil nutrients: the most promising remote sensing technique to improve estimates of the status of extractable potassium (K) is the collection and application of airborne radiometric survey (detection of gamma radiation by low-flying aircraft) but this should be investigated further. This is unlikely to assist in monitoring change. Based on published literature, it may be possible to enhance mapping the state of extractable magnesium (Mg), but not to monitor change, using hyperspectral (satellite or airborne) remote sensing in cultivated areas. This needs to be investigated further. There are no current remote sensing methods for detecting state or change of Olsen (extractable) phosphorus (P). Organic carbon and total nitrogen: Based on published literature, it may be possible to enhance mapping the state of organic carbon and total nitrogen (but not to monitor change), using hyperspectral (satellite or airborne) remote sensing in cultivated areas only. In applying this approach the satellite data are applied using a statistical model which is trained using ground-based sampling and analysis of soil