Search Results (63)

Soils serve as critical carbon reservoirs, playing an essential role in climate change mitigation and agricultural sustainability. Accurate soil property determination relies on soil spectral reflectance data from Earth observation (EO), but current vegetation models often oversimplify soil conditions. This study introduces a novel approach that combines radiative transfer models (RTMs) with open-access soil spectral libraries to address this challenge. Focusing on conditions of low soil moisture content (SMC), photosynthetic vegetation (PV), and non-photosynthetic vegetation (NPV), the coupled Marmit–Leaf–Canopy (MLC) model is used to simulate early crop growth stages. The MLC model, which integrates MARMIT and PRO4SAIL2, enables the generation of mixed soil–vegetation scenarios. A simulated EO disturbed soil spectral library (DSSL) was created, significantly expanding the EU LUCAS cropland soil spectral library. A 1D convolutional neural network (1D-CNN) was trained on this database to predict Soil Organic Carbon (SOC) content. The results demonstrated relatively high SOC prediction accuracy compared to previous approaches that rely only on RTMs and/or machine learning approaches. Incorporating soil moisture content significantly improved performance over bare soil alone, yielding an R² of 0.86 and RMSE of 4.05 g/kg, compared to R² = 0.71 and RMSE = 6.01 g/kg for bare soil. Adding PV slightly reduced accuracy (R² = 0.71, RMSE = 6.31 g/kg), while the inclusion of NPV alongside moisture led to modest improvement (R² = 0.74, RMSE = 5.84 g/kg). The most comprehensive model, incorporating bare soil, SMC, PV, and NPV, achieved a balanced performance (R² = 0.76, RMSE = 5.49 g/kg), highlighting the importance of accounting for all surface components in SOC estimation. While further validation with additional scenarios and SOC prediction methods is needed, these findings demonstrate, for the first time, using radiative-transfer simulations of mixed vegetation-soil-water environments, that an EO-DSSL approach enhances machine learning-based SOC modeling from EO data, improving SOC mapping accuracy. This innovative framework could significantly improve global-scale SOC predictions, supporting the design of next-generation EO products for more accurate carbon monitoring. Full article

Soil spectroscopy offers a rapid, cost-effective alternative to traditional soil analyses for characterization and classification. Previous studies have mainly focused on predicting soil categories using single sensors, particularly visible–near-infrared (vis–NIR) or mid-infrared (MIR) spectroscopy. In this study, we evaluated the performance of vis–NIR, MIR, and their combined spectra for soil classification by partial least-squares discriminant analysis (PLSDA) and random forest (RF). Utilizing 60 typical soil profiles’ data of four soil classes from the global soil spectral library (GSSL), our results demonstrated that in PLSDA models, direct combination (optimal overall accuracy: 70.6%, kappa coefficient: 0.60) and outer product analysis (OPA) fused spectra (optimal overall accuracy: 68.1%, kappa coefficient: 0.57) outperformed vis–NIR (optimal overall accuracy: 62.2%, kappa coefficient: 0.49) but underperformed compared to MIR (optimal overall accuracy: 71.4%, kappa coefficient: 0.62). In RF models, classification accuracy using fused spectra was inferior to single spectral ranges, with MIR achieving the highest classification accuracy (optimal overall accuracy: 89.1%, kappa coefficient: 0.85). Therefore, MIR alone remains the most effective spectral range for accurate soil class discrimination. Our findings highlight the potential of MIR spectroscopy for enhancing global soil classification accuracy and efficiency, with important implications for soil resource management and agricultural planning across diverse environments. Full article

An attempt was made to quantify soil properties using hyperspectral remote-sensing techniques and machine-learning algorithms. In total, 100 soil samples representing various locations and soil-nutrient statuses were collected, and the samples were analyzed for soil pH, EC, soil organic carbon, available nitrogen (AN), available phosphorus (AP), and available potassium (AK) by following standard methods. Soil had a wide range of properties, i.e., pH varied from 5.62 to 8.49, EC varied from 0.08 to 1.78 dS/m, soil organic carbon varied from 0.23 to 0.94%, available nitrogen varied from 154 to 344 kg/ha, available phosphorus varied from 9.5 to 25.5 kg/ha, and available potassium varied from 131 to 747 kg/ha. The same set of soil samples were subjected to spectral reflectance measurement using SVC GER 1500 Spectroradiometer (spectral range: 350 to 1050 nm). The measured spectral signatures of various soils were organized for developing a spectral library and for deriving various spectral indices to correlate with soil properties to quantify the nutrients. The soil samples were partitioned into 60:40 ratios for training and validation, respectively. In order to select optimum bands (wavelength) from the soil spectra, we have employed metaheuristic algorithms i.e., Particle Swarm Optimization (PSO), Moth–Flame optimization (MFO), Flower Pollination Optimization (FPO), and Battle Royale Optimization (BRO) algorithm. Further partial least square regression (PLSR) was used to find the latent variable and to evaluate various algorithms for their performance in predicting soil properties. The results indicated that nutrients could be quantified from spectral reflectance measurement with fair to good accuracy through the Battle Royale Optimization technique with a $R^2$ value of 0.45, 0.32, 0.48, 0.21, 0.71, and 0.35 for pH, EC, soil organic carbon, available-N, available-P, and available-K, respectively. Full article

Emerging contaminants (ECs), encompassing pharmaceuticals, personal care products, pesticides, and industrial chemicals, represent a growing threat to ecosystems and human health due to their persistence, bioaccumulation potential, and often-unknown toxicological profiles. Addressing these challenges necessitates advanced analytical tools capable of detecting and quantifying trace levels of ECs in complex environmental matrices. This review highlights the pivotal role of mass spectrometry (MS) in monitoring ECs, emphasizing its high sensitivity, specificity, and versatility across various techniques such as Gas Chromatography-Mass Spectrometry (GC-MS), Liquid Chromatography-Mass Spectrometry (LC-MS), and High-Resolution Mass Spectrometry (HR-MS). The application of MS has facilitated the real-time detection of volatile organic compounds, the comprehensive non-targeted screening of unknown contaminants, and accurate quantification in diverse matrices including water, soil, and air. Despite its effectiveness, challenges such as matrix interferences, a lack of standardized methodologies, and limited spectral libraries persist. However, recent advancements, including hybrid MS systems and the integration of artificial intelligence (AI), are paving the way for more efficient environmental monitoring and predictive modeling of contaminant behavior. Continued innovation in MS technologies and collaborative efforts are essential to overcome existing challenges and ensure sustainable solutions for mitigating the risks associated with emerging contaminants. Full article

Precision agriculture relies on highly detailed soil maps to optimize resource use. Proximal sensing methods, such as EMI, require a certain number of soil samples and laboratory analysis to interpolate the characteristics of the soil. NIR diffuse reflectance spectroscopy offers a rapid, low-cost alternative that increases datapoints and map accuracy. This study tests and optimizes a methodology for high-detail soil mapping in a 2.5 ha hazelnut grove in Grosseto, Southern Tuscany, Italy, using both EMI sensors (GF Mini Explorer, Brno, Czech Republic) and a handheld NIR spectrometer (Neospectra Scanner, Si-Ware Systems, Menlo Park, CA, USA). In addition to two profiles selected by clustering, another 35 topsoil augerings (0–30 cm) were added. Laboratory analyses were performed on only five samples (two profiles + three samples from the augerings). Partial least square regression (PLSR) with a national spectral library, augmented by the five local samples, predicted clay, sand, organic carbon (SOC), total nitrogen (TN), and cation exchange capacity (CEC). The 37 predicted datapoints were used for spatial interpolation, using the ECa map, elevation, and DEM derivatives as covariates. Kriging with external drift (KED) was used to spatialize the results. The errors of the predictive maps were calculated using five additional validation points analyzed by conventional methods. The validation showed good accuracy of the predictive maps, particularly for SOC and TN. Full article

Soil, a non-renewable resource, requires continuous monitoring to prevent degradation and support sustainable agriculture. Visible-near-infrared (Vis-NIR) spectroscopy is a rapid and cost-effective method for predicting soil properties. While traditional machine learning methods are commonly used for modeling Vis-NIR spectral data, large datasets may benefit more from advanced deep learning techniques. In this study, based on the large soil spectral library LUCAS, we aimed to enhance regression model performance in soil property estimation by combining Transformer and convolutional neural network (CNN) techniques to predict 11 soil properties (clay, silt, pH in CaCl₂, pH in H₂O, CEC, OC, CaCO₃, N, P, and K). The Transformer-CNN model accurately predicted most soil properties, outperforming other methods (partial least squares regression (PLSR), random forest regression (RFR), support vector machine regression (SVR), Long Short-Term Memory (LSTM), ResNet18) with a 10–24 percentage point improvement in the coefficient of determination (R²). The Transformer-CNN model excelled in predicting pH in CaCl₂, pH in H₂O, OC, CaCO₃, and N (R² = 0.94–0.96, RPD > 3) and performed well for clay, sand, CEC, P, and K (R² = 0.77–0.85, 2 < RPD < 3). This study demonstrates the potential of Transformer-CNN in enhancing soil property prediction, although future work should aim to optimize computational efficiency and explore a wider range of applications to ensure its utility in different agricultural settings. Full article

The composition of clay minerals in soils, and more particularly the presence of montmorillonite (as part of the smectite family), is a key factor in soil swell–shrinking as well as off–road vehicle mobility. Detecting these topsoil clay minerals and quantifying the montmorillonite abundance are a challenge since they are usually intimately mixed with other minerals, soil organic carbon and soil moisture content. Imaging spectroscopy coupled with unmixing methods can address these issues, but the quality of the estimation degrades the coarser the spatial resolution is due to pixel heterogeneity. With the advent of UAV-borne and proximal hyperspectral acquisitions, it is now possible to acquire images at a centimeter scale. Thus, the objective of this paper is to evaluate the accuracy and limitations of unmixing methods to retrieve montmorillonite abundance from very-high-resolution hyperspectral images (1.5 cm) acquired from a camera installed on top of a bucket truck over three different agricultural fields, in Loiret department, France. Two automatic endmember detection methods based on the assumption that materials are linearly mixed, namely the Simplex Identification via Split Augmented Lagrangian (SISAL) and the Minimum Volume Constrained Non-negative Matrix Factorization (MVC-NMF), were tested prior to unmixing. Then, two linear unmixing methods, the fully constrained least square method (FCLS) and the multiple endmember spectral mixture analysis (MESMA), and two nonlinear unmixing ones, the generalized bilinear method (GBM) and the multi-linear model (MLM), were performed on the images. In addition, several spectral preprocessings coupled with these unmixing methods were applied in order to improve the performances. Results showed that our selected automatic endmember detection methods were not suitable in this context. However, unmixing methods with endmembers taken from available spectral libraries performed successfully. The nonlinear method, MLM, without prior spectral preprocessing or with the application of the first Savitzky–Golay derivative, gave the best accuracies for montmorillonite abundance estimation using the USGS library (RMSE between 2.2–13.3% and 1.4–19.7%). Furthermore, a significant impact on the abundance estimations at this scale was in majority due to (i) the high variability of the soil composition, (ii) the soil roughness inducing large variations of the illumination conditions and multiple surface scatterings and (iii) multiple volume scatterings coming from the intimate mixture. Finally, these results offer a new opportunity for mapping expansive soils from imaging spectroscopy at very high spatial resolution. Full article

Soil visible and near–infrared reflectance spectroscopy is an effective tool for the rapid estimation of soil organic carbon (SOC). The development of spectroscopic technology has increased the application of spectral libraries for SOC research. However, the direct application of spectral libraries for SOC prediction remains challenging due to the high variability in soil types and soil–forming factors. This study aims to address this challenge by improving SOC prediction accuracy through spectral classification. We utilized the European Land Use and Cover Area frame Survey (LUCAS) large–scale spectral library and employed a geographically weighted principal component analysis (GWPCA) combined with a fuzzy c–means (FCM) clustering algorithm to classify the spectra. Subsequently, we used partial least squares regression (PLSR) and the Cubist model for SOC prediction. Additionally, we classified the soil data by land cover types and compared the classification prediction results with those obtained from spectral classification. The results showed that (1) the GWPCA–FCM–Cubist model yielded the best predictions, with an average accuracy of R² = 0.83 and RPIQ = 2.95, representing improvements of 10.33% and 18.00% in R² and RPIQ, respectively, compared to unclassified full sample modeling. (2) The accuracy of spectral classification modeling based on GWPCA–FCM was significantly superior to that of land cover type classification modeling. Specifically, there was a 7.64% and 14.22% improvement in R² and RPIQ, respectively, under PLSR, and a 13.36% and 29.10% improvement in R² and RPIQ, respectively, under Cubist. (3) Overall, the prediction accuracy of Cubist models was better than that of PLSR models. These findings indicate that the application of GWPCA and FCM clustering in conjunction with the Cubist modeling technique can significantly enhance the prediction accuracy of SOC from large–scale spectral libraries. Full article

In planetary science, visible (Vis) and near-infrared (NIR) reflectance spectra allow deciphering the chemical/mineralogical composition of celestial bodies’ surfaces by comparison between remotely acquired data and laboratory references. This paper presents the design of an automated test rig named Exoland Simulator equipped with two reflectance spectrometers covering the 0.38–2.2 µm range. It is designed to collect data of natural/synthetic rocks and minerals prepared in the laboratory that simulate the composition of planetary surfaces. The structure of the test rig is conceived as a Cartesian robot to automatize the acquisition. The test rig is also tested by simulating some project trajectories, and results are presented in terms of its ability to reproduce the programmed trajectories. Furthermore, preliminary spectral data are shown to demonstrate how the soil analogs’ spectra could allow an accurate remote identification of materials, enabling the creation of libraries to study the effect of multiple chemical–physical component variations on individual spectral bands. Despite the primary scope of Exoland, it can be advantageously used also for tribological purposes, to correlate the wear behavior of soils and materials with their composition by also analyzing the wear scars. Full article

This is an exercise to explore the concentration of lithium, lithium-7 isotope and the possible presence of black dirty ice on the lunar surface using spectral data obtained from the Clementine mission. The main interest in tracing the lithium and presence of dark ice on the lunar surface is closely related to future human settlement missions on the moon. We investigate the distribution of lithium and 7 Li isotope on the lunar surface by employing spectral data from the Clementine images. We utilized visible (VIS–NIR) imagery at wavelengths of 450, 750, 900, 950 and 1000 nm, along with near-infrared (NIR–SWIR) at 1100, 1250, 1500, 2000, 2600 and 2780 nm, encompassing 11 bands in total. This dataset offers a comprehensive coverage of about 80% of the lunar surface, with resolutions ranging from 100 to 500 m, spanning latitudes from 80°S to 80°N. In order to extract quantitative abundance of lithium, ground-truth sites were used to calibrate the Clementine images. Samples (specifically, 12045, 15058, 15475, 15555, 62255, 70035, 74220 and 75075) returned from Apollo missions 12, 15, 16 and 17 have been correlated to the Clementine VIS–NIR bands and five spectral ratios. The five spectral ratios calculated synthesize the main spectral features of sample spectra that were grouped by their lithium and 7 Li content using Principal Component Analysis. The ratios spectrally characterize substrates of anorthosite, silica-rich basalts, olivine-rich basalts, high-Ti mare basalts and Orange and Glasses soils. Our findings reveal a strong linear correlation between the spectral parameters and the lithium content in the eight Apollo samples. With the values of the 11 Clementine bands and the 5 spectral ratios, we performed linear regression models to estimate the concentration of lithium and 7 Li. Also, we calculated Digital Terrain Models (Altitude, Slope, Aspect, DirectInsolation and WindExposition) from LOLA-DTM to discover relations between relief and spatial distribution of the extended models of lithium and 7 Li. The analysis was conducted in a mask polygon around the Apollo 15 landing site. This analysis seeks to uncover potential 7 Li enrichment through spallation processes, influenced by varying exposure to solar wind. To explore the possibility of finding ice mixed with regolith (often referred to as `black ice’), we extended results to the entire Clementine coverage spectral indices, calculated with a library (350–2500 nm) of ice samples contaminated with various concentrations of volcanic particles. Full article

Soil analysis using near-infrared spectroscopy has shown great potential to be an alternative to traditional laboratory analysis, and there is continuously increasing interest in building large-scale soil spectral libraries (SSLs). However, due to issues such as high non-linearity in soil spectral data and complexity in soil spatial variation, the establishment of robust prediction models for soil spectral libraries remains a challenge. This study aimed to investigate the performance of deep learning algorithms, including long short-term memory (LSTM) and LSTM–convolutional neural networks (LSTM–CNN) integrated models, to predict the soil organic matter (SOM) of a provincial-scale SSL, and compare it to the normally used local weighted regression (LWR) model. The Hebei soil spectral library (HSSL) contains 425 topsoil samples (0–20 cm), of which every 3 soil samples were collected from dry land, irrigated land, and paddy fields, respectively, in different counties of Hebei Province, China. The results show that the accuracy of the validation dataset rank as follows: LSTM–CNN (R²_p = 0.96, RMSE_p = 1.66 g/kg) > LSTM (R²_p = 0.83, RMSE_p = 3.42 g/kg) > LWR (R²_p = 0.82, RMSE_p = 3.79 g/kg). The LSTM–CNN model performed the best, mainly due to its comprehensive ability to effectively extract spatial and temporal features. Meanwhile, the LSTM model achieved higher accuracy than the LWR model, owing to its built-in memory unit and its advantage of faster feature band extraction. Thus, it was suggested to use deep learning algorithms for SOM predictions in SSLs. However, their performance on larger-scale SSLs such as continental/global SSLs still needs to be further investigated. Full article

Mineral mapping from satellite images provides valuable insights into subsurface mineral alteration for geothermal exploration. In previous studies, eight fundamental algorithms were used for mineral mapping utilizing USGS spectra, a collection of reflectance spectra containing samples of minerals, rocks, and soils created by the USGS. We used an ASD FieldSpec 4 Hi-RES NG portable spectrometer to collect spectra for analyzing ASTER images of the Coso Geothermal Field. Then, we established the ground-truth information and the spectral library by analyzing 97 samples. Samples collected from the field were analyzed using the CSIRO TSG (The Spectral Geologist of the Commonwealth Scientific and Industrial Research Organization). Based on the mineralogy study, multiple high-purity spectra of geothermal alteration minerals were selected from collected data, including alunite, chalcedony, hematite, kaolinite, and opal. Eight mineral spectral target detection algorithms were applied to the preprocessed satellite data with a proposed local spectral library. We measured the highest overall accuracy of 87% for alunite, 95% for opal, 83% for chalcedony, 60% for hematite, and 96% for kaolinite out of these eight algorithms. Three, four, five, and eight algorithms were fused to extract mineral alteration with the obtained target detection results. The results prove that the fusion of algorithms gives better results than using individual ones. In conclusion, this paper discusses the significance of evaluating different mapping algorithms. It proposes a robust fusion approach to extract mineral maps as an indicator for geothermal exploration. Full article

Mapping soil organic carbon (SOC) stock can serve as a resilience indicator for climate change. As part of the carbon dioxide (CO₂) sink, soil has recently become an integral part of the global carbon agenda to mitigate climate change. We used hyperspectral remote sensing to model the SOC stock in the Sele River plain located in the Campania region in southern Italy. To this end, a soil spectral library (SSL) for the Campania region was combined with an aerial hyperspectral image acquired with the AVIRIS–NG sensor mounted on a Twin Otter aircraft at an altitude of 1433 m. The products of this study were four raster layers with a high spatial resolution (1 m), representing the SOC stocks and three other related soil attributes: SOC content, clay content, and bulk density (BD). We found that the clay minerals’ spectral absorption at 2200 nm has a significant impact on predicting the examined soil attributes. The predictions were performed by using AVIRIS–NG sensor data over a selected plot and generating a quantitative map which was validated with in situ observations showing high accuracies in the ground-truth stage (OC stocks [RPIQ = 2.19, R² = 0.72, RMSE = 0.07]; OC content [RPIQ = 2.27, R² = 0.80, RMSE = 1.78]; clay content [RPIQ = 1.6 R² = 0.89, RMSE = 25.42]; bulk density [RPIQ = 1.97, R² = 0.84, RMSE = 0.08]). The results demonstrated the potential of combining SSLs with remote sensing data of high spectral/spatial resolution to estimate soil attributes, including SOC stocks. Full article

This work presents a methodology for the hydrological characterization of natural and urban landscapes, focusing on accurate estimations of infiltration capacity and runoff characteristics. By combining existing methods from the literature, we created a systemic process that integrates satellite-based vegetation maps, topography, and soil permeability data. This process generates a detailed vegetation classification and slope-corrected composite curve number (CN_cα) map using information at the subpixel level, which is crucial for estimating excess runoff during intense precipitation events. The algorithm designed with this methodology is automated and utilizes freely accessible multispectral imagery. Leveraging the vegetation–impervious–soil (V-I-S) model, it is assumed that land cover comprises V-I-S components at each pixel. Automated Music and spectral Separability-based Endmember Selection is employed on a generic spectral library to obtain the most relevant V-I-S endmember spectra for a particular image, which is then employed in multiple endmember spectral mixture analysis to obtain V-I-S fraction maps. The derived fractions are utilized in combination with the Normalized Difference Vegetation Index and the Modified Normalized Difference Water Index to adapt the CN_cα map to different seasons and climatic conditions. The methodology was applied to Esch-sur-Alzette, Luxembourg, over a four-year period to validate the methodology and quantify the increase in the impervious surface area in the commune and the relationship with the runoff dynamics. This approach provides valuable insights into infiltration and runoff dynamics across diverse temporal and geographic ranges. Full article

Recently, considerable efforts have been devoted to the estimation of soil properties using optical payloads mounted on drones or satellites. Nevertheless, many studies focus on diverse pretreatments and modeling techniques, while there continues to be a conspicuous absence of research examining the impact of parameters related to optical remote sensing payloads on predictive performance. The main aim of this study is to evaluate how the spectral resolution and signal-to-noise ratio (SNR) of spectrometers affect the precision of predictions for soil organic matter (SOM) content. For this purpose, the initial soil spectral library was partitioned into to two simulated soil spectral libraries, each of which were individually adjusted with respect to the spectral resolutions and SNR levels. To verify the consistency and generality of our results, we employed four multiple regression models to develop multivariate calibration models. Subsequently, in order to determine the minimum spectral resolution and SNR level without significantly affecting the prediction accuracy, we conducted ANOVA tests on the RMSE and ${\mathrm{R}}^2$ obtained from the independent validation dataset. Our results revealed that (i) the factors significantly affecting SOM prediction performance, in descending order of magnitude, were the SNR levels > spectral resolutions > estimation models, (ii) no substantial difference existed in predictive performance when the spectral resolution fell within 100 nm, and (iii) when the SNR levels exceeded 15%, altering them did not notably affect the SOM predictive performance. This study is expected to provide valuable insights for the design of future optical remote sensing payloads aimed at monitoring large-scale SOM dynamics. Full article