Heavy Metal Soil Contamination Detection Using Combined Geochemistry and Field Spectroradiometry in the United Kingdom

Exogenously Applied Triacontanol Mitigates Cadmium Toxicity in Vigna radiata L. by Optimizing Growth, Nutritional Orchestration, and Metal Accumulation

Cadmium (Cd) is one of the foremost phytotoxic elements. Its proportion in agricultural soil is increasing critically due to anthropogenic activities. Cd stress is a major crop production threat affecting food security globally. Triacontanol (TRIA) is a phytohormone that promotes growth, development, and metabolic processes in plants. The current study explicates the mitigation of Cd toxicity in Vigna radiata L. (mung bean) seedlings through the application of TRIA by a seed priming technique under Cd stress. The role of TRIA in improving metabolic processes to promote Vigna radiata (mung bean, green gram) vegetative growth and performance under both stressed and unstressed conditions was examined during this study. To accomplish this, three doses of TRIA (10, 20, and 30 µmol L-1) were used to pretreat V. radiata seeds before they were allowed to grow for 40 days in soil contaminated with 20 mg kg-1 Cd. Cd stress lowered seed germination, morphological growth, and biomass in V. radiata plants. The maximum root and shoot lengths, fresh and dry weights of roots, and shoot and seed germination rates were recorded for TRIA2 compared with those of TRIA1 and TRIA3 under Cd stress. In Cd-stressed V. radiata plants, TRIA2 increased the content of chlorophyll a (2.1-fold) and b (3.1-fold), carotenoid (4.3-fold), total chlorophyll (3.1-fold), and gas exchange attributes, such as the photosynthetic rate (2.9-fold), stomatal conductance (6.0-fold), and transpiration rate (3.5-fold), compared with those in plants treated with only Cd. TRIA seed priming increased nutrient uptake (K1+, Na1+, Mg2+, and Zn2+), total phenolic content, total soluble protein content, and DPPH (2,2-diphenyl-1-picrylhydrazyl) activity. Additionally, TRIA2 significantly reduced the quantity of Cd in the plants (3.0-fold) and increased the metal tolerance index (6.6-fold) in plants contrasted with those in the Cd-treated plants. However, TRIA2 promoted plant growth and biomass production by lowering Cd-induced stress through modifying the plant antioxidant machinery and reducing oxidative stress. The improved yield characteristics of V. radiata seedlings treated with TRIA suggest that exogenous TRIA may be used to increase plant tolerance to Cd stress.

Mapping soil trace metal distribution using remote sensing and multivariate analysis

Trace metal soil contamination poses significant risks to human health and ecosystems, necessitating thorough investigation and management strategies. Researchers have increasingly utilized advanced techniques like remote sensing (RS), geographic information systems (GIS), geostatistical analysis, and multivariate analysis to address this issue. RS tools play a crucial role in collecting spectral data aiding in the analysis of trace metal distribution in soil. Spectroscopy offers an effective understanding of environmental contamination by analyzing trace metal distribution in soil. The spatial distribution of trace metals in soil has been a key focus of these studies, with factors influencing this distribution identified as soil type, pH levels, organic matter content, land use patterns, and concentrations of trace metals. While progress has been made, further research is needed to fully recognize the potential of integrated geospatial imaging spectroscopy and multivariate statistical analysis for assessing trace metal distribution in soils. Future directions include mapping multivariate results in GIS, identifying specific anthropogenic sources, analyzing temporal trends, and exploring alternative multivariate analysis tools. In conclusion, this review highlights the significance of integrated GIS and multivariate analysis in addressing trace metal contamination in soils, advocating for continued research to enhance assessment and management strategies.

Estimating the Heavy Metal Contents in Entisols from a Mining Area Based on Improved Spectral Indices and Catboost

In the study of the inversion of soil multi-species heavy metal element concentrations using hyperspectral techniques, the selection of feature bands is very important. However, interactions among soil elements can lead to redundancy and instability of spectral features. In this study, heavy metal elements (Pb, Zn, Mn, and As) in entisols around a mining area in Harbin, Heilongjiang Province, China, were studied. To optimise the combination of spectral indices and their weights, radar plots of characteristic-band Pearson coefficients (RCBP) were used to screen three-band spectral index combinations of Pb, Zn, Mn, and As elements, while the Catboost algorithm was used to invert the concentrations of each element. The correlations of Fe with the four heavy metals were analysed from both concentration and characteristic band perspectives, while the effect of spectral inversion was further evaluated via spatial analysis. It was found that the regression model for the inversion of the Zn elemental concentration based on the optimised spectral index combinations had the best fit, with R2 = 0.8786 for the test set, followed by Mn (R2 = 0.8576), As (R2 = 0.7916), and Pb (R2 = 0.6022). As far as the characteristic bands are concerned, the best correlations of Fe with the Pb, Zn, Mn and As elements were 0.837, 0.711, 0.542 and 0.303, respectively. The spatial distribution and correlation of the spectral inversion concentrations of the As and Mn elements with the measured concentrations were consistent, and there were some differences in the results for Zn and Pb. Therefore, hyperspectral techniques and analysis of Fe elements have potential applications in the inversion of entisols heavy metal concentrations and can improve the quality monitoring efficiency of these soils.

Application of Visible/Near-Infrared Spectroscopy and Hyperspectral Imaging with Machine Learning for High-Throughput Plant Heavy Metal Stress Phenotyping: A Review

Heavy metal pollution is becoming a prominent stress on plants. Plants contaminated with heavy metals undergo changes in external morphology and internal structure, and heavy metals can accumulate through the food chain, threatening human health. Detecting heavy metal stress on plants quickly, accurately, and nondestructively helps to achieve precise management of plant growth status and accelerate the breeding of heavy metal-resistant plant varieties. Traditional chemical reagent-based detection methods are laborious, destructive, time-consuming, and costly. The internal and external structures of plants can be altered by heavy metal contamination, which can lead to changes in plants' absorption and reflection of light. Visible/near-infrared (V/NIR) spectroscopy can obtain plant spectral information, and hyperspectral imaging (HSI) can obtain spectral and spatial information in simple, speedy, and nondestructive ways. These 2 technologies have been the most widely used high-throughput phenotyping technologies of plants. This review summarizes the application of V/NIR spectroscopy and HSI in plant heavy metal stress phenotype analysis as well as introduces the method of combining spectroscopy with machine learning approaches for high-throughput phenotyping of plant heavy metal stress, including unstressed and stressed identification, stress types identification, stress degrees identification, and heavy metal content estimation. The vegetation indexes, full-range spectra, and feature bands identified by different plant heavy metal stress phenotyping methods are reviewed. The advantages, limitations, challenges, and prospects of V/NIR spectroscopy and HSI for plant heavy metal stress phenotyping are discussed. Further studies are needed to promote the research and application of V/NIR spectroscopy and HSI for plant heavy metal stress phenotyping.

Recent progress on emerging technologies for trace elements-contaminated soil remediation

Abiotic stresses from potentially toxic elements (PTEs) have devastating impacts on health and survival of all living organisms, including humans, animals, plants, and microorganisms. Moreover, because of the rapid growing industrial activities together with the natural processes, soil contamination with PTEs has pronounced, which required an emergent intervention. In fact, several chemical and physical techniques have been employed to overcome the negative impacts of PTEs. However, these techniques have numerous drawback and their acceptance are usually poor as they are high cost, usually ineffectiveness and take longer time. In this context, bioremediation has emerged as a promising approach for reclaiming PTEs-contaminated soils through biological process using bacteria, fungus and plants solely or in combination. Here, we comprehensively reviews and critically discusses the processes by which microorganisms and hyperaccumulator plants extract, volatilize, stabilize or detoxify PTEs in soils. We also established a multi-technology repair strategy through the combination of different strategies, such as the application of biochar, compost, animal minure and stabilized digestate for stimulation of PTE remediation by hyperaccumulators plants species. The possible use of remote sensing of soil in conjunction with geographic information system (GIS) integration for improving soil bio-remediation of PTEs was discussed. By synergistically combining these innovative strategies, the present review will open very novel way for cleaning up PTEs-contaminated soils.

Trace metal content prediction along an AMD (acid mine drainage)-contaminated stream draining a coal mine using VNIR-SWIR spectroscopy

The current study investigated the use of VNIR-SWIR (visible/near infrared to short-wavelength infrared: 400-2500 nm) spectroscopy for predicting trace metals in overbank sediments collected in the study site. Here, we (i) derived spectral absorption feature parameters (SAFPs) from measured ground spectra for correlation with trace metal (Pb, Cd, As, and Cu) contents in overbank sediments, (ii) built univariate regression models to predict trace metal concentrations using the SAFPs, and (iii) evaluated the predictive capacities of the regression models. The derived SAFPs associated with goethite in overbank sediments were Depth433b, Asym433b, and Width433b, and those associated with kaolinite in overbank sediments were Depth1366b, Asym1366b, Width1366b, Depth2208b, Asym2208b, and Width2208b. Cadmium in the overbank sediments showed the strongest correlations with the goethite-related SAFPs, whereas Pb, As, and Cu showed strong correlations with goethite- and kaolinite-related SAFPs. The best predictive models were obtained for Cu (R2 = 0.73, SEE = 0.15) and Pb (R2 = 0.73, SEE = 0.21), while weaker models were obtained for As (R2 = 0.46, SEE = 0.31) and Cd (R2 = 0.17, SEE = 0.81). The results suggest that trace metals can be predicted indirectly using the SAFPs associated with goethite and kaolinite. This is an important benefit of VNIR-SWIR spectroscopy considering the difficulty in analyzing "trace" metal concentrations, on large scales, using conventional geochemical methods.

Estimation of Heavy Metal Content in Soil Based on Machine Learning Models

Heavy metal pollution in soil is threatening the ecological environment and human health. However, field measurement of heavy metal content in soil entails significant costs. Therefore, this study explores the estimation method of soil heavy metals based on remote sensing images and machine learning. To accurately estimate the heavy metal content, we propose a hybrid artificial intelligence model integrating least absolute shrinkage and selection operator (LASSO), genetic algorithm (GA) and error back propagation neural network (BPNN), namely the LASSO-GA-BPNN model. Meanwhile, this study compares the accuracy of the LASSO-GA-BPNN model, SVR (Support Vector Regression), RF (Random Forest) and spatial interpolation methods with Huanghua city as an example. Furthermore, the study uses the LASSO-GA-BPNN model to estimate the content of eight heavy metals (including Ni, Pb, Cr, Hg, Cd, As, Cu, and Zn) in Huanghua and visualize the results in high resolution. In addition, we calculate the Nemerow index based on the estimation results. The results denote that, the simultaneous optimization of BPNN by LASSO and GA can greatly improve the estimation accuracy and generalization ability. The LASSO-GA-BPNN model is a more accurate model for the estimate heavy metal content in soil compared to SVR, RF and spatial interpolation. Moreover, the comprehensive pollution level in Huanghua is mainly low pollution. The overall spatial distribution law of each heavy metal content is very similar, and the local spatial distribution of each heavy metal is different. The results are of great significance for soil pollution estimation.

Phytoremediation of Heavy-Metals-Contaminated Soils: A Short-Term Trial Involving Two Willow Species from Gloucester WillowBank in the UK

Phytoremediation, as a bioremediation process in which plants are used to remove contaminants from an environment, has proved to be a practical and low-cost strategy for recovering mining-affected areas. This study aims to assess the potential for use in phytoremediation of two willow species, Salix viminalis and Salix dasyclados, by testing their potential for cleaning-up a range of soils with differing heavy metal concentrations: Pb (111, 141, 192 and 249 mg /kg), Zn (778.6, 1482, 2734 and 4411 mg/kg) and Cd (3.00, 5.03, 9.14 and 16.07 mg/kg). The extracted metals were preferentially translocated to the leaves with considerably higher concentrations and relative BAFs in the case of S. viminalis. The highest recorded Zn concentration of over 0.5% was found in the leaves of S. viminalis growing in soil 4. However, under the conditions of the experiments, S. dasyclados showed greater potential for use in phytoremediation, especially if coupled with use of biomass for energy production. An assessment of the suitability of willow species in this role, with regard to wider aspects involved, such as use of resultant biomass and/or waste management, revealed good potential. Willows are fast growing, grow vigorously from coppiced stumps and have extensive root systems. Therefore, their use in bioenergy production through pyrolysis or combustion, coupled with flue gas screening, is strongly advised.

Effects of Cu and Zn contamination on chicken manure-based bioponics: Nitrogen recovery, bioaccumulation, microbial community, and health risk assessment

In bioponics, although chicken manure is an efficient substrate for vegetable production and nitrogen recovery, it is often contaminated with high Cu and Zn levels, which could potentially cause bioaccumulation in plants and pose health risks. The objectives of this study were to assess nitrogen recovery in lettuce- and pak choi-based bioponics with Cu (50-150 mg/kg) and Zn (200-600 mg/kg) supplementation, as well as their bioaccumulation in plants, root microbial community, and health risk assessment. The supplementation of Cu and Zn did not affect nitrogen concentrations and plant growth (p > 0.05) but reduced nitrogen use efficiency. Pak choi showed higher Cu and Zn bioconcentration factors than lettuce. Bacterial genera Ruminiclostridium and WD2101_soil_group in lettuce roots and Mesorhizobium in pak choi roots from Cu and Zn supplemented conditions were significantly higher (p < 0.05) than controls, suggesting microbial biomarkers in plant roots from Cu and Zn exposure bioponics depended on plant type. Health risk assessment herein revealed that consumption of bioponic vegetables with Cu and Zn contamination does not pose long-term health risks (hazard index <1) to children or adults, according to the US EPA. This study suggested that vegetable produced from chicken manure-based bioponics has low health risk in terms of Cu and Zn bioaccumulation and could be applied in commercial-scale system for nutrient recovery from organic waste to vegetable production; however, health risk from other heavy metals and xenobiotic compounds must be addressed.

Floodplain soils contamination assessment using the sequential extraction method of heavy metals from past mining activities

Floodplains are among the most precious and threatened ecosystems in the world. The study deals with floodplain soil contamination caused by 8 heavy metals (HMs) (Cd, Co, Cr, Cu, Mo, Ni, Pb, Zn) originating and transported from old mine works along the Štiavnica River in Slovakia. We determined the total HMs content and the HM fractions using BCR sequential extraction method. We selected 12 alluvial sites (AS), two contaminated sites (CS), and one reference site (RS). The sampling points were located within the riparian zones (RZ), arable lands (AL), and grasslands (GL). We confirmed soil contamination by HMs and the related ecological risk by different factors. The contamination by HMs at many AS localities was similar or even higher than at CS localities. The highest contamination factor was calculated for Cu (39.8), followed by Pb (27.4), Zn (18.2), and Cd (7.2). The HMs partitioning in the different fractions at the CS and AS localities revealed that Cd, Zn, and Pb were mainly associated with the exchangeable and reducible fractions, while Cu was mainly associated with the oxidisable fraction. The soil properties were selectively correlated with the HM fractions. Based on the ANOVA results, the effect of different ecosystem types on HM fractions was revealed.

Diagnosis of cadmium contamination in urban and suburban soils using visible-to-near-infrared spectroscopy

Previous studies have mostly focused on using visible-to-near-infrared spectral technique to quantitatively estimate soil cadmium (Cd) content, whereas little attention has been paid to identifying soil Cd contamination from a perspective of spectral classification. Here, we developed a framework to compare the potential of two spectral transformations (i.e., raw reflectance and continuum removal [CR]), three optimization strategies (i.e., full-spectrum, Boruta feature selection, and synthetic minority over-sampling technique [SMOTE]), and three classification algorithms (i.e., partial least squares discriminant analysis, random forest [RF], and support vector machine) for diagnosing soil Cd contamination. A total of 536 soil samples were collected from urban and suburban areas located in Wuhan City, China. Specifically, Boruta and SMOTE strategies were aimed at selecting the most informative predictors and obtaining balanced training datasets, respectively. Results indicated that soils contaminated by Cd induced decrease in spectral reflectance magnitude. Classification models developed after Boruta and SMOTE strategies out-performed to those from full-spectrum. A diagnose model combining CR preprocessing, SMOTE strategy, and RF algorithm achieved the highest validation accuracy for soil Cd (Kappa = 0.74). This study provides a theoretical reference for rapid identification of and monitoring of soil Cd contamination in urban and suburban areas.

Pollution characteristics and human health risk assessments of toxic metals and particle pollutants via soil and air using geoinformation in urbanized city of Pakistan

Toxic metals and particle pollutants in urbanized cities have significantly increased over the past few decades mainly due to rapid urbanization and unplanned infrastructure. This research aimed at estimating the concentration of toxic metals and particle pollutants and the associated risks to public health across different land-use settings including commercial area (CA), urban area (UA), residential area (RA), and industrial area (IA). A total of 47 samples for both soil and air were collected from different land-use settings of Faisalabad city in Pakistan. Mean concentrations of toxic metals such as Mn, Zn, Pb, Ni, Cr, Co, and Cd in all land-use settings were 92.68, 4.06, 1.34, 0.16, 0.07, 0.03, and 0.02 mg kg^-1, respectively. Mean values of PM₁₀, PM_2.5, and Mn in all land-use settings were found 5.14, 1.34, and 1.9 times higher than the World Health Organization (WHO) guidelines. Mn was found as the most hazardous metal in terms of pollution load index (PLI) and contamination factor (CF) in the studied area. Health risk analysis for particle pollutants using air quality index (AQI) and geoinformation was found in the range between good to very critical for all the land-use settings. The hazard quotient (HQ) and hazard index (HI) were higher for children in comparison to adults, suggesting that children may be susceptible to potentially higher health risks. However, the cancer risk (CR) value for Pb ingestion (1.21 × 10^-6) in children was lower than the permissible limit (1 × 10^-4 to 1 × 10^-6). Nonetheless, for Cr inhalation, CR value (1.09 × 10^-8) was close to tolerable limits. Our findings can be of valuable assistance toward advancing our understanding of soil and air pollutions concerning public health in different land-use settings of the urbanized cities of Pakistan.

Applications of Nanomaterials for Heavy Metal Removal from Water and Soil: A Review

Heavy metals are toxic and non-biodegradable environmental contaminants that seriously threaten human health. The remediation of heavy metal-contaminated water and soil is an urgent issue from both environmental and biological points of view. Recently, nanomaterials with excellent adsorption capacities, great chemical reactivity, active atomicity, and environmentally friendly performance have attracted widespread interest as potential adsorbents for heavy metal removal. This review first introduces the application of nanomaterials for removing heavy metal ions from the environment. Then, the environmental factors affecting the adsorption of nanomaterials, their toxicity, and environmental risks are discussed. Finally, the challenges and opportunities of applying nanomaterials in environmental remediation are discussed, which can provide perspectives for future in-depth studies and applications.

Phytoremediation of toxic metals present in soil and water environment: a critical review

Heavy metals are one of the most hazardous inorganic contaminants of both water and soil environment composition. Normally, heavy metals are non-biodegradable in nature because of their long persistence in the environment. Trace amounts of heavy metal contamination may pose severe health problems in human beings after prolonged consumption. Many instrumental techniques such as atomic absorption spectrophotometry, inductively coupled plasma-mass spectrometry, X-ray fluorescence, neutron activation analysis, etc. have been developed to determine their concentration in water as well as in the soil up to ppm, ppb, or ppt levels. Recent advances in these techniques along with their respective advantages and limitations are being discussed in the present paper. Moreover, some possible remedial phytoremediation approaches (phytostimulation, phytoextraction, phyotovolatilization, rhizofiltration, phytostabilization) have been presented for the removal of the heavy metal contamination from the water and soil environments.

Estimation of heavy metals using deep neural network with visible and infrared spectroscopy of soil

Heavy metal contamination in soil disturbs the chemical, biological, and physical soil conditions and adversely affects the health of living organisms. Visible and near-infrared spectroscopy (VNIRS) shows a potential feasibility for estimating heavy metal elements in soil. Moreover, deep learning models have been shown to successfully deal with complex multi-dimensional and multivariate nonlinear data. Thus, this study implemented a deep learning method on reflectance spectra of soil samples to estimate heavy metal concentrations. A convolutional neural network (CNN) was adopted to estimate arsenic (As), copper (Cu), and lead (Pb) concentrations using measured soil reflectance. In addition, a convolutional autoencoder was utilized as a joint method with the CNN for dimensionality reduction of the reflectance spectra. Furthermore, artificial neural network (ANN) and random forest regression (RFR) models were built for heavy metal estimation. Principal component analysis was utilized for dimensionality reduction of the ANN and RFR models. Among these models, the CNN model with convolutional autoencoder showed the highest accuracies for As, Cu, and Pb estimates, having R² values of 0.86, 0.74, and 0.82, respectively. The convolutional autoencoder disentangled the relevant features of multiple heavy metal elements and delivered robust features to the CNN model, thereby generating relatively accurate estimates.

Utilizing Hyperspectral Remote Sensing for Soil Gradation

Soil gradation is an important characteristic for soil mechanics. Traditionally soil gradation is performed by sieve analysis using a sample from the field. In this research, we are interested in the application of hyperspectral remote sensing to characterize soil gradation. The specific objective of this work is to explore the application of hyperspectral remote sensing to be used as an alternative to traditional soil gradation estimation. The advantage of such an approach is that it would provide the soil gradation without having to obtain a field sample. This work will examine five different soil types from the Keweenaw Research Center within a laboratory-controlled environment for testing. Our study demonstrates a correlation between hyperspectral data, the percent gravel and sand composition of the soil. Using this correlation, one can predict the percent gravel and sand within a soil and, in turn, calculate the remaining percent of fine particles. This information can be vital to help identify the soil type, soil strength, permeability/hydraulic conductivity, and other properties that are correlated to the gradation of the soil.

An Approach for Route Optimization in Applications of Precision Agriculture Using UAVs

This research paper focuses on providing an algorithm by which (Unmanned Aerial Vehicles) UAVs can be used to provide optimal routes for agricultural applications such as, fertilizers and pesticide spray, in crop fields. To utilize a minimum amount of inputs and complete the task without a revisit, one needs to employ optimized routes and optimal points of delivering the inputs required in precision agriculture (PA). First, stressed regions are identified using VegNet (Vegetative Network) software. Then, methods are applied for obtaining optimal routes and points for the spraying of inputs with an autonomous UAV for PA. This paper reports a unique and innovative technique to calculate the optimum location of spray points required for a particular stressed region. In this technique, the stressed regions are divided into many circular divisions with its center being a spray point of the stressed region. These circular divisions would ensure a more effective dispersion of the spray. Then an optimal path is found out which connects all the stressed regions and their spray points. The paper also describes the use of methods and algorithms including travelling salesman problem (TSP)-based route planning and a Voronoi diagram which allows applying precision agriculture techniques.

On-board spectral calibration algorithm for an airborne hyperspectral imager and elimination of the effect of the atmospheric underlying surface

As an image-spectrum merging technology, the hyperspectral sensor has become an important part in remote sensing. The spectral calibration results of the hyperspectral sensor measured in the laboratory should be refined when applied to the on-board imaging spectrometer due to variations between the laboratory and actual flight environments. The larger the spectrum offset of the spectral response function (SRF), the worse the retrieval accuracy of the reflective characteristics of the targets. Therefore, an on-board spectral calibration algorithm based on standard diffuse boards with a gradient of reflectivity is proposed in this paper. By constructing the differential function of radiation transfer coefficients, the center wavelength offsets of SRF can be solved. In addition, a back-propagation neural network has been established to eliminate the effect of the atmospheric underlying surface and improve the accuracy of on-board spectral calibration. The three-sigma confidence interval of the on-board spectral precision is $ \pm 0.23\,\,{\rm nm}$±0.23nm (uncertainty $ \lt {0.05}$<0.05 spectral pixels). The algorithm can be applied to a general hyperspectral sensor.

Estimation of Arsenic Content in Soil Based on Laboratory and Field Reflectance Spectroscopy

In this study, in order to solve the difficulty of the inversion of soil arsenic (As) content using laboratory and field reflectance spectroscopy, we examined the transferability of the prediction method. Sixty-three soil samples from the Daye city area of the Jianghan Plain region of China were taken and studied in this research. The characteristic wavelengths of soil As content were then extracted from the full bands based on iteratively retaining informative variables (IRIV) coupled with Spearman’s rank correlation analysis (SCA). Firstly, the IRIV algorithm was used to roughly select the original spectral data. Gaussian filtering (GF), first derivative (FD) filtering, and gaussian filtering again (GFA) pretreatments were then used to improve the correlation between the spectra and soil As content. A subset with absolute correlation values greater than 0.6 was then retained as the optimal subset after each pretreatment. Finally, partial least squares regression (PLSR), Bayesian ridge regression (BRR), ridge regression (RR), kernel ridge regression (KRR), support vector machine regression (SVMR), eXtreme gradient boosting (XGBoost) regression, and random forest regression (RFR) models were used to estimate the soil As values using the different characteristic variables. The results showed that, compared with the traditional method based on IRIV, using the characteristic bands selected by the IRIV-SCA method can effectively improve the prediction accuracy of the models. For the laboratory spectra experiment stage, the six most representative characteristic bands were selected. The performance of IRIV-SCA-SVMR was found to be the best, with the coefficient of determination (R²), root-mean-square error (RMSE), and mean absolute error (MAE) in the validation set being 0.97, 0.22, and 0.11, respectively. For the field spectra experiment stage, the 12 most representative characteristic bands were selected. The performance of IRIV-SCA-XGBoost was found to be the best, with the R², RMSE, and MAE in the validation set being 0.83, 0.35, and 0.29, respectively. The accuracy and stability of the inversion of soil As content are significantly improved by the use of the proposed method, and the method could be used to provide accurate data for decision support for the treatment and recovery of As pollution over a large area.

Analyzing Temporal and Spatial Characteristics of Crop Parameters Using Sentinel-1 Backscatter Data

The knowledge about heterogeneity on agricultural fields is essential for a sustainable and effective field management. This study investigates the performance of Synthetic Aperture Radar (SAR) data of the Sentinel-1 satellites to detect variability between and within agricultural fields in two test sites in Germany. For this purpose, the temporal profiles of the SAR backscatter in VH and VV polarization as well as their ratio VH/VV of multiple wheat and barley fields are illustrated and interpreted considering differences between acquisition settings, years, crop types and fields. Within-field variability is examined by comparing the SAR backscatter with several crop parameters measured at multiple points in 2017 and 2018. Structural changes, particularly before and after heading, as well as moisture and crop cover differences are expressed in the backscatter development. Furthermore, the crop parameters wet and dry biomass, absolute and relative vegetation water content, leaf area index (LAI) and plant height are related to SAR backscatter parameters using linear and exponential as well as multiple regression. The regression performance is evaluated using the coefficient of determination (R 2 ) and the root mean square error (RMSE) and is strongly dependent on the phenological growth stage. Wheat shows R 2 values around 0.7 for VV backscatter and multiple regression and most crop parameters before heading. Single fields even reach R 2 values above 0.9 for VV backscatter and for multiple regression related to plant height with RMSE values around 10 cm. The formulation of clear rules remains challenging, as there are multiple influencing factors and uncertainties and a lack of conformity.

Impact of Fractional Calculus on Correlation Coefficient between Available Potassium and Spectrum Data in Ground Hyperspectral and Landsat 8 Image

As the level of potassium can interfere with the normal circulation process of biosphere materials, the available potassium is an important index to measure the ability of soil to supply potassium to crops. There are rarely studies on the inversion of available potassium content using ground hyperspectral remote sensing and Landsat 8 multispectral satellite data. Pretreatment of saline soil field hyperspectral data based on fractional differential has rarely been reported, and the corresponding relationship between spectrum and available potassium content has not yet been reported. Because traditional integer-order differential preprocessing methods ignore important spectral information at fractional-order, it is easy to reduce the accuracy of inversion model. This paper explores spectral preprocessing effect based on Grünwald–Letnikov fractional differential (order interval is 0.2) between zero-order and second-order. Field spectra of saline soil were collected in Fukang City of Xinjiang. The maximum absolute of correlation coefficient between ground hyperspectral reflectance and available potassium content for five mathematical transformations appears in the fractional-order. We also studied the tendency of correlation coefficient under different fractional-order based on seven bands corresponding to the Landsat 8 image. We found that fractional derivative can significantly improve the correlation, and the maximum absolute of correlation coefficient under five spectral transformations is in Band 2, which is 0.715766 for the band at 467 nm. This study deeply mined the potential information of spectra and made up for the gap of fractional differential for field hyperspectral data, providing a new perspective for field hyperspectral technology to monitor the content of soil available potassium.

Preflight Spectral Calibration of Airborne Shortwave Infrared Hyperspectral Imager with Water Vapor Absorption Characteristics

Due to the strong absorption of water vapor at wavelengths of 1350–1420 nm and 1820–1940 nm, under normal atmospheric conditions, the actual digital number (DN) response curve of a hyperspectral imager deviates from the Gaussian shape, which leads to a decrease in the calibration accuracy of an instrument’s spectral response functions (SRF). The higher the calibration uncertainty of SRF, the worse the retrieval accuracy of the spectral characteristics of the targets. In this paper, an improved spectral calibration method based on a monochromator and the spectral absorptive characteristics of water vapor in the laboratory is presented. The water vapor spectral calibration method (WVSCM) uses the difference function to calculate the intrinsic DN response functions of the spectral channels located in the absorptive wavelength range of water vapor and corrects the wavelength offset of the monochromator via the least-square procedure to achieve spectral calibration throughout the full spectral responsive range of the hyper-spectrometer. The absolute spectral calibration uncertainty is ±0.125 nm. We validated the effectiveness of the WVSCM with two tunable semiconductor lasers, and the spectral wavelength positions calibrated by lasers and the WVSCM showed a good degree of consistency.