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Forest soils have large contents of carbon (C) and total nitrogen (TN), which have significant spatial variability laterally across landscapes and vertically with depth due to decomposition, erosion and leaching. Therefore, the ratio of C to TN contents (C:N), a crucial indicator of soil quality and health, is also different depending on soil horizon. These attributes can cost-effectively and rapidly be estimated using visible–near infrared–shortwave infrared (VNIR–SWIR) spectroscopy. Nevertheless, the effect of different soil layers, particularly over large scales of highly heterogeneous forest soils, on the performance of the technique has rarely been attempted. This study evaluated the potential of VNIR–SWIR spectroscopy in quantification and variability analysis of C:N in soils from different organic and mineral layers of forested sites of the Czech Republic. At each site, we collected samples from the litter (L), fragmented (F) and humus (H) organic layers, and from the A1 (depth of 2–10 cm) and A2 (depth of 10–40 cm) mineral layers providing a total of 2505 samples. Support vector machine regression (SVMR) was used to train the prediction models of the selected attributes at each individual soil layer and the merged layer (profile). We further produced the spatial distribution maps of C:N as the target attribute at each soil layer. Results showed that the prediction accuracy based on the profile spectral data was adequate for all attributes. Moreover, F was the most accurately predicted layer, regardless of the soil attribute. C:N models and maps in the organic layers performed well although in mineral layers, models were poor and maps were reliable only in areas with low and moderate C:N. On the other hand, the study indicated that reflectance spectra could efficiently predict and map organic layers of the forested sites. Although, in mineral layers, high values of C:N (≥ 50) were not detectable in the map created based on the reflectance spectra. In general, the study suggests that VNIR–SWIR spectroscopy has the feasibility of modelling and mapping C:N in soil organic horizons based on national spectral data in the forests of the Czech Republic

Soil spectral libraries (SSLs) are important big-data archives (spectra associated with soil properties) that are analyzed via machine-learning algorithms to estimate soil attributes. Since different spectral measurement protocols are applied when constructing SSLs, it is necessary to examine harmonization techniques to merge the data. In recent years, several techniques for harmonization have been proposed, among which the internal soil standard (ISS) protocol is the most largely applied and has demonstrated its capacity to rectify systematic effects during spectral measurements. Here, we postulate that a spectral transfer function (TF) can be extracted between existing (old) SSLs if a subset of samples from two (or more) different SSLs are remeasured using the ISS protocol. A machine-learning TF strategy was developed, assembling random forest (RF) spectral-based models to predict the ISS spectral condition using soil samples from two existing SSLs. These SSLs had already been measured using different protocols without any ISS treatment the Brazilian (BSSL, generated in 2019) and the European (LUCAS, generated in 2009–2012) SSLs. To verify the TF’s ability to improve the spectral assessment of soil attributes after harmonizing the different SSLs’ protocols, RF spectral-based models for estimating organic carbon (OC) in soil were developed. The results showed high spectral similarities between the ISS and the ISS–TF spectral observations, indicating that post-ISS rectification is possible. Furthermore, after merging the SSLs with the TFs, the spectral-based assessment of OC was considerably improved, from R2 = 0.61, RMSE (g/kg) = 12.46 to R2 = 0.69, RMSE (g/kg) = 11.13. Given our results, this paper enhances the importance of soil spectroscopy by contributing to analyses in remote sensing, soil surveys, and digital soil mapping.

ESTIMATING THE SOIL AGGREGATES SIZE FRACTIONS IN ARID AND SEMI-ARID ENVIRONMENTS USING PEDOTRNASFER FUNCTIONS AND SPECTRAL ANALYSIS This paper investigates the relationship of six aggregation fractions (2-1.4mm F1; 1.4-1.0 mm F2; 1.0-0.5 mm F3; 0.5-0.25 mm ; F4; 0.25-0.1mm ; F5, 0.1mm > F6) and their average (AVG) with relation to 74 soil attributes. The database used in this study is based on the heritage soil spectral library (SSL) of Israel over both semi-arid and arid regions representing seven global soil orders from the USDA list. The database is composed of chemical, physical and spectral measurements. The spectral data consisted of reflectance measurement using the IEEE standard protocol and across the VIS-NIR-SWIR spectral region as well as XRF analyses of microelements and XRD determination, wet chemistry analysis and quantitative assessment of the soil mineralogy. The correlation matrix between all attributes enables to isolate four cementation agents (CAs) of the soil micro aggregation namely: clay content, clay mineral specious (smectite), organic matter, and free iron oxides contents. Generating a Pedo Transfer Function (PTF) using these CAs revealed equations that well predict the aggregate size fraction of F1,F2,F3, and the average aggregation stage (AVG ) of all soils. A separate spectral-based analysis to evaluate directly the fraction sizes from reflectance measurements without any prior information regarding the CAs, was also generated and revealed high statistic performance with spectral assignments that belonged to the four selected CAs and their derivatives. The final conclusion was that the micro aggregation stage of soil can be assessed directly or indirectly via PTF and also by using spectral analysis and data mining approaches. Assuming that the reflectance information from hyperspectral remote sensing means such as EMIT (NASA initiative) PRISMA (ASI) and EnMAP (DLR) are available today (2022) this approach may add a great deal to the NASA mission aims at investigating the potential of soil to act as a dust source.

Over the preceding 50 years, issues of soil degradation, food insecurity, water scarcity, and loss of ecosystem services are at the center of environmental studies and global concern. These environmental and social issues have intensified the need for sustainable land management and higher-quality global-scale information on soil. The use of soil spectroscopy and hyperspectral remote sensing (HRS) has advanced the soil science discipline by providing an accurate, rapid, and inexpensive estimation of the Earth’s soil composition. In this study, we created field and lab soil spectral libraries (SSL) and predicted elementary soil properties such as water infiltration rate (WIR), soil texture content, and organic carbon (OC), essential for agricultural management. These models were applied in a case study of Campania, Italy, to the high-end HRS AVIRIS NG NASA sensor with a ground resolution of 3 meters, and 224 spectral bands along the VNIR-SWIR range (400-2500 nm). In addition, we discuss the differences between field, lab, airborne, and satellite spectra and emphasize the need for separating the models.

The collection and analysis of large amounts of information on a plant-by-plant basis contributes to the development of precision fertigation and may be achieved by combining remote-sensing technology with high-throughput phenotyping methods. Here, lettuce plants (Lactuca sativa) were grown under optimal and suboptimal nitrogen and irrigation treatments from seedlings to harvest. A Plantarray system was used to calculate and log weights, daily transpiration, and momentary transpiration rates throughout the experiment. From 15 d after planting until experiment termination, the entire array of plants was imaged hourly (from 09.00 h to 14.00 h) using a hyperspectral moving camera. Three vegetation indices were calculated from the plants’ reflectance signal: red-edge chlorophyll index (RECI), photochemical reflectance index (PRI), and water index (WI), and combined treatments, physiological measurements, and vegetation indices were compared. RECI values differed significantly between nitrogen treatments from the first day of imaging, and WI values distinguished well-irrigated from drought-treated groups before detecting significant differences in daily transpiration rate. The PRI, calculated hourly during the drought-treatment phase, changed with the momentary transpiration rate. Thus, hyperspectral imaging might be used in growing facilities to detect nitrogen or water shortages in plants before their physiological response affects yields.

The Italian Space Agency (ASI)'s PRecursore IperSpettrale della Missione Applicativa (PRISMA) hyperspectral remote sensing (HSR) satellite was launched on 22 March 2019. This spectral imaging sensor, with 238 bands across the 400-2500 nm range and 30-m spatial resolution, can advance remote-sensing studies for vast applications worldwide. The present study evaluates the capability of the sensor's L1 (Top-of the- atmosphere TOA radiance) and L2D (reflectance) products for mapping minerals and geology in Makhtesh Ramon (MR), a national park in southern Israel that covers approximately 200 km2. The exceptional geological features in this area, with hardly any vegetation and mostly clear skies year-round, make it an ideal site for remote-sensing studies in general and HSR studies in particular. The quality of the PRISMA sensor's technical and thematic performance was assessed by examining its radiometry and spectral products against a highly calibrated airborne HSR sensor (AisaFENIX 1K; 420 spectral bands, range 375-2500 nm, 1.5-m spatial resolution). Airborne and field data acquisition captured the entire MR area, followed by comprehensive fieldwork with a portable ASD FieldSpec spectrometer (400-2500 nm spectral range). The PRISMA sensor's radiance performance was evaluated by examining the top of the atmosphere radiance product against simulated radiance using the MODTRAN® radiative transfer code. The simulated radiance used in-situ ground reflectance measurements in several locations across MR. The L1 and L2D products presented fair results with some outliers for PRISMA's L2D SWIR 2 long-wavelength region. The L2D product was further studied to map mineral occurrences and demonstrated promising results compared to MR's well-known geology and mineral distribution, and it was highly correlated to the high-spatial-resolution spectral mapping generated by the AisaFENIX 1K airborne sensor. Based on these results, we successfully mapped different types of minerals, such as iron oxides in the VNIR, gypsum in SWIR 1, and clay in SWIR 2. The PRISMA data's reflectance interference across the longer wavelengths of SWIR 2 did not permit fine mapping for carbonates, probably because of the L2D's poor performance in this spectral range. The quantitative performance of PRISMA's mineral mapping was judged relative to the quantitative products of the AisaFENIX 1K sensor for these same minerals, revealing about 80% accuracy for PRISMA's products.

Mineral nutrition is essential for optimal plant growth. Phosphorus (P) is a relatively small component of leaf dry weight, with a concentration in plant foliage of less than 1%. Despite its low co...

International audience; Spectroscopic measurements of soil samples are reliable because they are highly repeatable and reproducible. They characterise the samples' mineral-organic composition. Estimates of concentrations of soil constituents are inevitably less precise than estimates obtained conventionally by chemical analysis. But the cost of each spectroscopic estimate is at most one-tenth of the cost of a chemical determination. Spectroscopy is cost-effective when we need many data, despite the costs and errors of calibration. Soil spectroscopists understand the risks of over-fitting models to highly dimensional multivariate spectra and have command of the mathematical and statistical methods to avoid them. Machine learning has fast become an algorithmic alternative to statistical analysis for estimating concentrations of soil constituents from reflectance spectra. As with any modelling, we need judicious implementation of machine learning as it also carries the risk of over-fitting predictions to irrelevant elements of the spectra. To use the methods confidently, we need to validate the outcomes with appropriately sampled, independent data sets. Not all machine learning should be considered 'black boxes'. Their interpretability depends on the algorithm, and some are highly interpretable and explainable. Some are difficult to interpret because of complex transformations or their huge and complicated network of parameters. But there is rapidly advancing research on explainable machine learning, and these methods are finding applications in soil science and spectroscopy. In many parts of the world, soil and environmental scientists recognise the merits of soil spectroscopy. They are building spectral libraries on which they can draw to localise the modelling and derive soil information for new projects within their domains. We hope our article gives readers a more balanced and optimistic perspective of soil spectroscopy and its future. Highlights Spectroscopy is reliable because it is a highly repeatable and reproducible analytical technique. Spectra are calibrated to estimate concentrations of soil properties with known error. Spectroscopy is cost-effective for estimating soil properties. Machine learning is becoming ever more powerful for extracting accurate information from spectra, and methods for interpreting the models exist. Large libraries of soil spectra provide information that can be used locally to aid estimates from new samples.

Soil moisture (SM) is an essential element in the hydrological cycle influencing land-atmosphere interactions and rainfall-runoff processes. Quantification of the spatial and temporal behaviour of SM at field scale is vital for understanding water availability in agriculture, ecosystems research, river basin hydrology and water resources management. Uncrewed Arial Systems (UAS) offer an extraordinary opportunity to bridge the existing gap between point-scale field observations and satellite remote sensing providing high spatial details at relatively low costs. Moreover, UAS data can help the construction of downscaling models which can link the land surface features and SM to identify the importance level of each predictor. To optimize the usage of data from UAS surveys for generating high-resolution SM at field scale, a comparative study of various SM retrieval or downscaling methods can be beneficial.In this study, four methods, which include the apparent thermal inertia method, Kubelka–Munk method (KM), simplified temperature-vegetation triangle method, and random forest model (RF), were compared by theory background, data requirements, operation procedures and SM estimation results. The above-mentioned models have been tested using UAS data and point measurements collected on the Monteforte Cilento site (MFC2) in the Alento river basin (Campania, Italy) which is an 8 hectares cropland area (covered by walnuts, cherry, and olive trees). A number of long-term studies on the vadose zone have been conducted across a range of spatial scales. The thermal inertia model is built upon the dependence of the thermal diffusion on SM, which were inferred from diachronic thermal infrared data. The Kubelka–Munk Model is a spectral model to retrieve surface SM using optical data. The simplified temperature–vegetation triangle model, was used to map surface SM based on simultaneous information of the vegetation coverage and surface temperature. In addition, we also introduce an SM downscaling method using the RF model and SENTINEL-1 CSAR 1km SM product.The study is concluded with the inter-comparison of methods. The results from KM have the highest resolution which is the same as the input multispectral data. The results of RF and KM provides information only for bare soil pixels according to the principle of the model. Results show good performances for all methods, but the simplified triangle and thermal inertia model provides better performances in terms of correlation coefficient and RMSE measured with respect to in-situ measurements. In addition, it is worthy to say that the RF downscaling method reveals the features controlling the spatial distributions of SM at a different scale.This research is a part of EU COST-Action “HARMONIOUS” and waterJPI project “iAqueduct”.

To ensure the sustainability of the soil ecosystem, which is the basis for food production, efficient large-scale baseline predictions and trend assessments of key soil properties are necessary. In that regard, visible, near-infrared, and shortwave infrared (VNIR–SWIR) spectroscopy can provide an alternative for the expensive wet chemistry. In this paper, we examined the application of the Multiple-Kernel Learning (MKL) approach to soil spectroscopy by integrating the information from heterogeneous features. In particular, the proposed three-level MKL framework acts in the following way: at the first level, it uses multiple kernels at each spectral feature (wavelength) to maximize the information of each band. At the second level, it performs implicit feature selection at the spectral source level, enabling it to provide interpretable results. Finally, at the third level of integration it combines the complementary information contained within a pool of spectral sources, each derived from its own set of pre-processing techniques. Additionally, at this stage, the proposed approach is also capable of fusing heterogeneous sources of information, such as auxiliary predictors, which can assist the spectral predictions. The experimental analysis was conducted using the pan-European LUCAS (Land Use/Cover Area frame statistical Survey) topsoil database, with a goal to predict from the VNIR–SWIR spectra the concentration of soil organic carbon (SOC), a key indicator for agricultural productivity and environmental resilience. The particle size distribution which describes the soil texture was selected as the set of auxiliary predictors. The proposed MKL framework was compared with other state-of-the-art approaches, and the results indicated that it attains the best performance in terms of accuracy, whilst at the same time producing interpretable results.

The Brazil nut effect (BNE) is a physical phenomenon by which large granular particles (i.e., archaeological artifacts) in a bed of small disturbed particles (i.e., soil), rise to the top surfaces. This paper examines the physical forces acting on archaeological artifacts—scattered on the surface and buried underground—to identify the major elements of site formation processes (SFPs). Combining theoretical advances in archaeology, pedology, granular physics and spectroscopy, we conducted accelerated laboratory tests on seven typical Israeli soils to form a SFP model. We suggest that the SFPs are the result of two opposing and continuous processes: soil coverage of the site started soon after human activity has ceased, and a force(s) that tends to lift buried artifacts up to exposed surfaces, acting in accordance with Brazil nut effect (BNE). The post-burial forces pressuring artifact movement upward are affected by the artifacts' density and size, soil characteristics and the local environment. As a result, some archaeological artifacts reach exposed surfaces, some are lifted to higher soil deposits but remain buried, and the rest remain in their original burial context.

Soils are an essential factor contributing to the agricultural production of rainfed crops such as barley and triticale cereals. Changing environmental conditions and inadequate land management are endangering soil quality and productivity and, in turn, crop quality and productivity are affected. Advances in hyperspectral remote sensing are of great use for the spatial characterization and monitoring of the soil degradation status, as well as its impact on crop growth and agricultural productivity. In this study, hyperspectral airborne data covering the visible, near-infrared, short-wave infrared, and thermal infrared (VNIR–SWIR–TIR, 0.4–12 µm) were acquired in a Mediterranean agricultural area of central Spain and used to analyze the spatial differences in vegetation vitality and grain yield in relation to the soil degradation status. Specifically, leaf area index (LAI), crop water stress index (CWSI), and the biomass of the crop yield are derived from the remote sensing data and discussed regarding their spatial differences and relationship to a classification of erosion and accumulation stages (SEAS) based on previous remote sensing analyses during bare soil conditions. LAI and harvested crop biomass yield could be well estimated by PLS regression based on the hyperspectral and in situ reference data (R2 of 0.83, r of 0.91, and an RMSE of 0.2 m2 m−2 for LAI and an R2 of 0.85, r of 0.92, and an RMSE of 0.48 t ha−1 for grain yield). In addition, the soil erosion and accumulation stages (SEAS) were successfully predicted based on the canopy spectral signal of vegetated crop fields using a random forest machine learning approach. Overall accuracy was achieved above 71% by combining the VNIR–SWIR–TIR canopy reflectance and emissivity of the growing season with topographic information after reducing the redundancy in the spectral dataset. The results show that the estimated crop traits are spatially related to the soil’s degradation status, with shallow and highly eroded soils, as well as sandy accumulation zones being associated with areas of low LAI, crop yield, and high crop water stress. Overall, the results of this study illustrate the enormous potential of imaging spectroscopy for a combined analysis of the plant-soil system in the frame of land and soil degradation monitoring.

The hyperspectral (HSR) sensors Earth Surface Mineral Dust Source Investigation (EMIT) of the National Aeronautics and Space Administration (NASA) and Environmental Mapping and Analysis Program (EnMAP) of the German Aerospace Center (DLR) were recently launched. These state-of-the-art sensors have joined the already operational HSR sensors DESIS (DLR), PRISMA (Italian Space Agency), and HISUI (developed by the Japanese Ministry of Economy, Trade, and Industry METI and Japan Aerospace Exploration Agency JAXA). The launching of more HSR sensors is being planned for the near future (e.g., SBG of NASA, and CHIME of the European Space Agency), and the challenge of monitoring and maintaining their calibration accuracy is becoming more relevant. We proposed two test sites: Amiaz Plain (AP) and Makhtesh Ramon (MR) for spectral, radiometric, and geometric calibration/validation (CAL/VAL). The sites are situated in the arid environment of southern Israel and are in the same overpass coverage. Both test sites have already demonstrated favorable results in assessing an HSR sensor’s performance and were chosen to participate in the EMIT and EnMAP validation stage. We first evaluated the feasibility of using AP and MR as CAL/VAL test sites with extensive datasets and sensors, such as the multispectral sensor Landsat (Landsat5 TM and Landsat8 OLI), the airborne HSR sensor AisaFENIX 1K, and the spaceborne HSR sensors DESIS and PRISMA. Field measurements were taken over time. The suggested methodology integrates reflectance and radiometric CAL/VAL test sites into one operational protocol. The method can highlight degradation in the spectral domain early on, help maintain quantitative applications, adjust the sensor’s radiometric calibration during its mission lifetime, and minimize uncertainties of calibration parameters. A PRISMA sensor case study demonstrates the complete operational protocol, i.e., performance evaluation, quality assessment, and cross-calibration between HSR sensors. These CAL/VAL sites are ready to serve as operational sites for other HSR sensors.

For decades, the Příbram district in the Czech Republic has been affected by industrial and mining activities. These activities are important sources of heavy metal pollutants that are detrimental to soil quality. A recent study examined visible–near-infrared (VNIR), shortwave-infrared (SWIR) and X-ray fluorescence (XRF) spectroscopy to model soil lead (Pb) content in a selected area located in the Příbram district. Following that study, and using the same chemical and geographical data, we examined the spatial distribution of Pb content in the soil, with a combination of different traditional spatial analyses (Moran’s I, hotspot analysis, and Kriging) that were significantly validated. One of the novel points of this work is the use of the Getis–Ord hotspot analysis before the execution of a Kriging interpolation model to better emphasize clustering patterns. The results indicated that Pb was a spatially dependent soil property and through extensive in situ sampling, it was possible to generate a very accurate Kriging interpolation model. The high-Pb hotspots coincided with topographic obstacles that were modeled using topographic profiles extracted from the open-source Google Earth platform, indicating that Pb content does not always exhibit a direct relationship with topographic height as a result of runoff, due to the contribution of topographic steps. This observation provides a new perspective on the relationship between Pb content and topographic patterns.

Visible, near-infrared and shortwave-infrared (VNIR-SWIR) spectroscopy is an efficient approach for predicting soil properties because it reduces the time and cost of analyses. However, its advantages are hampered by the presence of soil moisture, which masks the major spectral absorptions of the soil and distorts the overall spectral shape. Hence, developing a procedure that skips the drying process for soil properties assessment directly from wet soil samples could save invaluable time. The goal of this study was twofold: proposing two approaches, partial least squares (PLS) and nearest neighbor spectral correction (NNSC), for dry spectral prediction and utilizing those spectra to demonstrate the ability to predict soil clay content. For these purposes, we measured 830 samples taken from eight common soil types in Israel that were sampled at 66 different locations. The dry spectrum accuracy was measured using the spectral angle mapper (SAM) and the average sum of deviations squared (ASDS), which resulted in low prediction errors of less than 8% and 14%, respectively. Later, our hypothesis was tested using the predicted dry soil spectra to predict the clay content, which resulted in R2 of 0.69 and 0.58 in the PLS and NNSC methods, respectively. Finally, our results were compared to those obtained by external parameter orthogonalization (EPO) and direct standardization (DS). This study demonstrates the ability to evaluate the dry spectral fingerprint of a wet soil sample, which can be utilized in various pedological aspects such as soil monitoring, soil classification, and soil properties assessment.

In this paper, the use of a novel evolutionary fuzzy rule-based system (FRBS) for the prediction of Soil Organic Carbon from visible, near-infrared, and short-wave infrared (VNIR/SWIR) spectra and the textural information as additional predictor is examined. Compared to other techniques, the proposed model generates a compact set of rules with a high interpretation degree, mapping local input to local output regions. This is achieved through an evolutionary learning procedure which is applied to establish linguistic rules and assist in the interpretation of the association between spectra and the target property. The rule base may be also decomposed into texture-specific sets of rules, allowing a more detailed analysis on a per textural class basis. These intrinsic properties enable the development of spectral prototype signatures and sparse feature utilization histograms at different levels of aggregation, i.e. per textural class and/or output region. The proposed model is applied to the LUCAS topsoil database comprised of roughly 18,000 mineral samples across 23 European Union member-states. We first demonstrate the enhanced interpretation capabilities of our fuzzy approach, which can assist in the extraction of fruitful knowledge governing the association between soil properties and VNIR/SWIR spectra. The model is then compared with other contemporary approaches, namely PLS, SVM, and Cubist. The results indicate that our approach produced compact and interpretable results with fair prediction accuracies (equivalent with the best approach).

In modeling chemical attributes using hyperspectral data, nonlinear relationships between the predictor and the response are frequent. The common nonlinear modeling techniques improve prediction accuracy but suffer from low interpretability of the models. In this paper, we demonstrate a new multivariate modeling method, denoted as spectral assignment-oriented partial least squares (SAO-PLS), which is designed to provide a nonlinear modeling solution with strong interpretability products. The need for this approach is apparent when a given sample population consists of different spectral features for different levels of the response. Accordingly, the suggested SAO-PLS algorithm segments the data in an optimal location on the response distribution by maximizing the difference in spectral assignments between two clusters. SAO-PLS is applied here to two test cases with different characteristics: 1) an established data set containing airborne hyperspectral data of asphaltic roads, merged with in situ measured dynamic friction values captured using a standardized method and 2) a soil spectral library, spectrally measured with an analytical spectral device spectrometer, to which organic carbon measurements were applied. Our results demonstrate the superiority of SAO-PLS over partial least-squares regression for both model accuracy and interpretability, providing a deeper understanding of the underlying processes.

The current study was driven by the need to derive improved soil information whereby the strengths of memory-based learning and soil spectroscopy are exploited towards addressing regional challenges and supporting sustainable development across the Balkan, North Africa, and Middle East regions. In this study we focused on a novel derivation of the Spectrum Based Learner (SBL) algorithm by i) taking into account both the geographical proximity and the spectral similarity in the computation of the distance between samples, and ii) using an optimal pair of spectral pre-treatments with the first used for the determination of neighbours while the latter for the estimation of the target property. The proposed approach was tested on a recently developed standardized Soil Spectral Library (SSL) across the VIS-NIR-SWIR spectral region range (350–2500 nm) which comprises 1760 soil samples from 9 different countries and is considered the largest and most diverse soil database infrastructure in the region compatible with other SSLs. Here, we found that our approach presents a great potential for predicting soil texture contents (Clay, Sand, and Silt), organic carbon (OC), Calcium Carbonate (CaCO3), pH, and Electrical Conductivity (EC). These results outperform the predictive performance of other state of the art global and local approaches that have been applied to similar large and complex soil datasets. The use of geographical coordinates in the computation of the sample similarities enhanced the predictions of soil properties, since it allowed the generation of local subsets that present similar soil compositions. In addition, we conclude that the various spectral sources as derived by a set of predefined spectral pre-processing techniques contain complementary information which should be combined instead of relying solely on the best spectral pre-processing technique. This approach could be effectively utilized to enhance the predictions of soil properties in large and complex SSLs, since it decreased the Root Mean Square Errors (RMSEs) by a relative mean of 6.47% (average value across the properties – decrease ranging from 2.90% to 9.09%) for the various soil properties, compared to other global and local algorithms. We conclude that national SSLs that were measured under a standardization process could further contribute to the global initiative to address challenges and support a data-centric approach for informed decision making with regards to environmental and agricultural issues.

One of the most important characteristics of any quantitative method is its detection limit, which is the lowest detectable amount of analyte in a sample in a given method. The detection limit helps to know to which extent a method is applicable and valid. The detection of petroleum hydrocarbon contamination in bare soils is important, because of its vast distribution and its negative effects on humans and the environment. Hyperspectral remote sensing is an acceptable, cost-effective and spatially comprehensive tool, which has detection possibilities of petroleum hydrocarbon. However, previous studies that engaged with this topic did not directly consider the detection limit of the method and no detection limit has been systematically explored. In this study, an outdoor experiment was executed in which three types of soil were contaminated with 14 levels of pure crude oil and measured in outdoor conditions with a hyperspectral camera (950–2500 nm) at three distances from the camera (4, 8 and 12 m). For each soil type, and for each distance, the detection limit was systematically calculated. The results show that the detection limit is a dynamic range affected by the spatial domain and the soil type. Moreover, a significant (p

Soil spectroscopy has proven efficient for providing quantitative properties of soil chromophores and soil classification. However, building generic/global models might bring to light some problems which stem from the false premise that each chromophore affects all soil types equally. The objective of this paper is to offer a new cluster-based approach for the assessment of soil properties and compensating issues that might rise from using heterogeneous soil dataset. For that purpose, 1480 soil samples from varying climate zones in Israel were used. All samples were air-dried, ground, and sieved to 2 mm, then CaCO3, organic matter (OM), clay and sand contents together with the spectral signature were measured according to laboratory protocols. The spectral analysis was performed using the Spectral Angle Mapper (SAM) algorithm, spectral gradient and k-means clusters to split the dataset into distinct clusters. Subsequently, cluster-based models were performed and compared with generic models. Later, we focused on the OM parameter and applied the OM detection limit approach for a more robust and accurate assessment. Our results show that soil property prediction improved significantly when using the cluster-based models compared to the generic models and therefore should be considered when dealing with large and heterogeneous databases.

Soil moisture (SM) is an essential element in the hydrological cycle influencing land-atmosphere interactions and rainfall-runoff processes. High-resolution mapping of SM at field scale is vital for understanding spatial and temporal behavior of water availability in agriculture. Unmanned Arial Systems (UAS) offer an extraordinary opportunity to bridge the existing gap between point-scale field observations and satellite remote sensing providing high spatial details at relatively low costs. Moreover, this data can help the construction of downscaling models to generate high-resolution SM maps. For instance, random Forest (RF) regression model can link the land surface features and SM to identify the importance level of each predictor.The RF regression model has been tested using a combination of satellite imageries, UAS data and point measurements collected on the experimental area Monteforte Cilento site (MFC2) in the Alento river basin (Campania, Italy) which is an 8 hectares cropland area (covered by walnuts, cherry, and olive trees). This area has been selected given the number of long-term studies on the vadose zone that have been conducted across a range of spatial scales.The coarse resolution data cover from Jan 2015 to Dec 2019 and include SENTINEL-1 CSAR 1km SM product, 1km Land surface temperature and NDVI products from MODIS and 30m thermal band (brightness temperature), red and green band data (atmospherically corrected surface reflectance) from LANDSAT-8, and SRTM DEM from NASA. High-resolution land-surface features data from UAS-mounted optical, thermal, multispectral, and hyperspectral sensors were used to generate high-resolution SM and related soil attributes.It is to note that the available satellite-based soil moisture data has a coarse resolution of 1km while the UAS-based land surface features of the extremely high resolution of 16cm. We deployed a two-step downscaling approach to address the smooth effect of spatial averaging of soil moisture, which depends on different elements at small and large scale. Specifically, different combinations of predictors were adopted for different scales of gridded soil moisture data. For example, in the downscaling procedure from 1km resolution to 30m resolution, precipitation, land-surface temperature (LST), vegetation indices (VIs), and elevation were used while LST, VIs, slope, and topographic index were selected for the downscaling from 30m to 16cm resolution. Indeed, features controlling the spatial distributions of soil moisture at different scale reflect the characteristics of the physical process: i) the surface elevation and rainfall patterns control the first downscaling model; ii) the topographic convergence and local slope become more relevant to reach a more detailed resolution. In conclusion, the study highlighted that RF regression model is able to interpret fairly well the spatial patterns of soil moisture at the scale of 30m starting from a resolution of 1km, while it is highlighted that the second downscaling step (up to few centimeters) is much more complex and requires further studies.This research is a part of EU COST-Action “HARMONIOUS: Harmonization of UAS techniques for agricultural and natural ecosystems monitoring”.Keywords: soil moisture, downscaling, Unmanned Aerial Systems, random forest, HARMONIOUS

A deeper understanding of the agricultural sector is needed to provide the informed and transparent framework required to meet increasing resource demands and pressures, without compromising sustainability. In this regard, an integrated management of the ecosystems is critical to address the priorities laid out by global policies and, achieve land degradation neutrality and resource efficient regions. Soils are an essential component of the ecosystem, they function as an important carbon storage, and provide the basis of agricultural activity. For the sustainable management of soil resources, and to prevent land degradation the regular assessments of spatially referenced soil conditions is essential. Critical soil properties, such as texture and organic and inorganic carbon content, provides farmers with the information to detect soil vulnerable to soil erosion and land degradation in its early stages in order to locally intervene and to assess soil fertility. Hyperspectral remote sensing been proven to be an effective method for the quantitative prediction of topsoil properties. However, remote sensing observations of the traditionally used visible-near infrared (VNIR) and shortwave infrared (SWIR) wavelength regions (0.4-2.5 µm) can be limited for the estimation of coarse texture soils due to the lack of distinct spectral characteristics of these properties in the VNIR-SWIR (e.g., sand content, quartz and feldspar mineralogy). Spectral information from the longwave infrared region (LWIR, 8-12 μm) has the potential to improve the determination of these properties, due to the presence of fundamental vibration modes of silicate and carbonate minerals, as well carbon-hydrogen bonds in this spectral range.The main objective of this study is to evaluate the increased analytical potential of combined VNIR-SWIR and LWIR hyperspectral remote sensing for the estimation of soil properties with the focus on soil organic matter, texture and mineralogical composition. In the frame of EnMAP GFZ/FU airborne campaign in Northern Greece in September 2019, an airborne survey with the HySpex VNIR-SWIR and Hyper-Cam LWIR cameras mounted on a Cessna airplane. A simultaneous ground sampling campaign took place at the agricultural landscape of the Amyntaio region including fields spectroscopy for calibration and validation porpoise, as well as soil sampling of bare soil fields. Fields in the study area have highly variable topsoil composition ranging from silicate to carbonate rich mineralogy, loamy to clay texture and to organic carbon rich fields around a lignite mine in the south-east of the area. Different statistical and machine learning methods such as Partial Least Squares (PLS) and Random Forest (RF) regression are applied to derive soil properties and the variable importance of the spectral dataset is discussed. A further goal of this study is the simulation and validation of the soil products with recent relevant satellite sensors (e.g., EnMAP, PRISMA, ECOSTRESS), as well as upcoming next generation of hyperspectral optical and thermal multispectral satellite missions (ESA CHIME and LSTM, NASA/JPL SBG) to evaluate their potential for quantitative soil properties mapping.

Soil surveys are critical for maintaining sustainable use of natural resources while minimizing harmful impacts to the ecosystem. A key soil attribute for many environmental parameters, such as CO2 budget, soil fertility and sustainability, is soil organic matter (SOM), and its sequestration. Soil spectroscopy is a popular method to assess SOM content rapidly in both field and laboratory domains. However, the SOM source composition differs from soil to soil and the use of spectral-based models for quantifying SOM may present limited accuracy when applying a generic approach for SOM assessment. We therefore examined the extent to which the generic approach can assess SOM contents of different origin using spectral-based models. We created an artificial big dataset composed of pure dune sand as a SOM-free background which was artificially mixed with increasing amounts of different organic matter (OM) sources obtained from commercial compost of different origins. Dune sand has high albedo and yields optimal conditions for SOM detection. This study combined two methods: partial least squares regression for the prediction of SOM content from reflectance values across the 400–2500 nm region, and soil spectral detection limit (SSDL) to judge the prediction accuracy. Spectral-based models to assess SOM content were evaluated with each OM source as well as with a merged dataset that contained all of the generated samples (generic approach). The latter was concluded to have limitations for assessing low amounts of SOM (

There was an error in the original publication [1] in ‘4.1. The Web Service Advantages and Limitations’, on page 18, in a sentence regarding Soil-Spec4GG. The sentence was incorrectly typed/inserted. The construction of the paragraph positioned the Soil-Spec in the wrong place, and gave a misinterpretation. The authors strongly state that Soil-Spec4GG is a confidently reliable project. Our idea in the texts was to emphasize the importance of this project, but our mistyping created the opposite. We humbly apologize and ratify that it was not an intentional error. We hope that this effort maintains the respect of the scientific community. A correction has been made to 4.1. The Web Service Advantages and Limitations. Replaced “…other ongoing global spectral community efforts (e.g., Soil-Spec4GG) are more vertical with researchers subsuming people’s spectral data without a data sharing policy that fully acknowledges and credits the user’s labor and costs of field data collection”. with “Other global spectral communities are also making similar efforts as our work which will increase the spectroscopy efforts (e.g., Soil-Spec4GG). On the other side, there are vertical groups with researchers subsuming people’s spectral data without a data sharing policy that fully acknowledges and credits the user’s labor and costs of field data collection”. Supplementary Materials, Table S1, indicated in the Materials and Methods. Consider the following footnotes to this material: The open access databases mentioned: Lucas and ICRAF are available in http://esdac.jrc.ec.europa.eu/content/lucas-2009-topsoil-data (accessed on 21 February 2022) and https://doi.org/10.34725/DVN/MFHA9C (accessed on 21 February 2022), respectively. The authors apologize for any inconvenience caused and state that the scientific conclusions are unaffected. The original publication has also been updated.

The Earth Surface Mineral Dust Source Investigation, EMIT, is planned to operate from the International Space Station starting no earlier than the fall of 2021. EMIT will use visible to short wavelength infrared imaging spectroscopy to determine the mineral composition of the arid land dust source regions of the Earth to advance our knowledge of the radiative forcing effect of these aerosols. Mineral dust emitted into the atmosphere under high wind conditions is an element of the Earth system with many impacts to the Earth’s energy balance, atmosphere, surface, and oceans. The Earth’s mineral dust cycle with source, transport, and deposition phases are studied with advanced Earth System Models. Because the chemical composition, optical and surface properties of soil particles vary strongly with the mineral composition of the source, these models require knowledge of surface soil mineral dust source composition to accurately understand dust impacts on the Earth system now and in the future. At present, compositional knowledge of the Earth’s mineral dust source regions from existing data sets is uncertain as a result of limited measurements. EMIT will use spectroscopically-derived surface mineral composition to update the prescribed boundary conditions for state-of-the-art Earth System Models. The EMIT-initialized models will be used to investigate the impact of direct radiative forcing in the Earth system that depends strongly on the composition of the mineral dust aerosols emitted into the atmosphere. These new measurements and related products will be used to address the EMIT science objectives and made available to the science community for additional investigations. An overview of the EMIT science, development, and mission is presented in this paper.

In the frame of the science preparation activities for the upcoming German hyperspectral satellite mission EnMAP, an airborne survey took place in September 2019 with hyperspectral VNIR-SWIR-LWIR data using the HySpex sensor and the newly acquired Hyper-Cam LWIR camera from the GeoResearch Center Potsdam (GFZ) mounted on the airborne platform Cessna-T207A from the Free University Berlin (FUB). Although logistically complex conditions with several teams distributed in different locations, all the sites in central and northern Greece could be successfully acquired under clear sky conditions, and all data could be demilitarized providing 45 flight stripes covering a total area of 300 km2.This abstract is focusing on the Amyntaio soil site in northern Greece, an agricultural area of variable soil composition from carbonate rich to clay/silt content to organic carbon rich fields around the lignite mine south of the area, over which 11 flight stripes could be acquired. The science goals of the Amyntaio soil campaign were: (a) Simulation of hyperspectral satellite imagery and demonstration of the potential of upcoming spaceborne hyperspectral sensors (EnMAP, CHIME) for global soil mapping and monitoring; (b) Large test and validation for existing soil algorithms such as the HYSOMA / ENSOMAP software tools for the prediction of top-soil quantitative surface properties; (c) Data validation and comparison of soil products with recent relevant satellite sensors (e.g. S2, PRISMA, ECOSTRESS); (d) Enlargement of global soil spectral libraries with harmonised standards and testbed for their use as calibration-validation data for soil spectral models.Simultaneous to the airborne survey, an intensive ground-based campaign took place in the area focusing on the acquisition of soil data, VNIR-SWIR and LWIR in-situ data with field spectroradiometers (PSR+, ASD FieldSpec3, MEMS, Handheld FTIR), fractional vegetation cover with RGB and UAV RGB data, soil moisture, infiltrometer and spectral data in undisturbed soil crust with the SoilPRO device, and Cal-Val data acquisition at the same time than the overflight (Temperature-loggers, ASD VNIR-SWIR, handheld FTIR) over bare soils and black/white thermal targets.We present the project objectives, selected field, airborne, satellite data, with preliminary analyses that show the high data quality and the potential of multi hyperspectral airborne campaigns as a support for basic science developments and satellite mission preparations. The results represent how more sensor flexibility can bridge the gap from in-situ to satellite scale. Further airborne flights and carefully designed in situ campaigns will allow testing and iterative improvement of new observational modalities for soil monitoring based on the integrated information from satellite platforms with the one provided by in-situ systems on the ground and air.

Quantification of the spatial and temporal behavior of soil moisture is vital for understanding water availability in agriculture, ecosystems research, river basin hydrology and water resources management. Unmanned Aerial Systems (UAS) offer a great potential in monitoring this parameter at sub-meter level and at relatively low cost. The standardization of operational procedures for soil moisture monitoring with UAS can be beneficial to understanding and quantify the quality of retrieved soil moisture (e.g., from different platforms and sensors).In this study, soil moisture retrieved from UAS using different retrieval algorithms was compared to collocated ground measurements. The thermal inertia model builds upon the dependence of the thermal diffusion on soil moisture. The soil thermal inertia is quantified by processing visible and near-infrared (VIS-NIR) and thermal infrared (TIR) images, acquired at two different times of a day. The temperature–vegetation trapezoidal model is also used to map soil moisture over vegetated pixels. This trapezoidal model depicts the soil moisture dependence of the surface energy balance. The comparison of the two algorithms helps define a preliminary standard procedure for retrieving soil moisture with UAS.As a case study, a typical cropland area with olive orchard, cherry and walnut trees in the region of Monteforte Cilento (Italy, Salerno) is used, where optical and thermal images and in situ data were simultaneously acquired. In the Alento observatory, long-term studies on vadose zone hydrology have been conducting across a range of spatial scales. Our findings provide an important contribution towards improving our knowledge on evaluating the ability of UAS to map soil moisture, in support of sustainable natural resources management and climate change studies.This research is a part of EU COST-Action “HARMONIOUS: Harmonization of UAS techniques for agricultural and natural ecosystems monitoring”.Keywords: soil moisture, Unmanned Aerial Systems, thermal inertia, HARMONIOUS

Earth Observation (EO) has an immense potential as an enabling tool for mapping the spatial variation of the topsoil layer. Additionally, machine learning based algorithms deployed on cloud computing infrastructures have a great potential to revolutionize the processing of EO data. This paper aims to present a multi-dimensional Sentinel-based Soil Monitoring Scheme (S2MoS) based on open-access Copernicus Sentinel data and the Google Earth Engine platform to map soil properties. Building on key results from existing data mining approaches to extract bare soil reflectance values the current study presents i) preliminary insights on the synergistic use of open access SAR and optical images obtained from Sentinel-1 and Sentinel-2 sensors; and ii) evaluate the efficiency of machine learning algorithms to predict soil attributes based on multi-temporal analysis. In that regard, this study evaluated, based on Sentinel images extending over a 3 years period (2017-2019), the performance of two state of the art machine learning approaches, namely random forest and neural networks. Spatial thresholds values of 0.25 and 0.075 for Normalized Difference Vegetation Index and Normalized Burn Ratio 2 indices respectively were applied to mask bare soil pixels. In this study, we used 5000 soil data belonging to cropland land use from the European LUCAS topsoil database. We calibrated the models based on 4000 soil samples and then validated this approach with the rest 1000 samples predict soil clay content. A higher prediction performance (R2=0.53) was achieved by the inclusion of both types (SAR and optical) of observations using the neural network model, demonstrating an improvement of about 5% in overall accuracy compared to the R2 using the multi-year median optical composite.

Soil mineralogy holds important information on the soil origin and development. Most common minerals in soils—quartz, clay minerals and carbonates—present fundamental spectral features in the longwave infrared (LWIR) region (8.0–12 μm range), whereas quartz is featureless in the optical region (0.4–2.5 μm range). A procedure for determining the soil surface mineralogy from hyperspectral LWIR data was used to assess the interaction with desert dust particles that accumulate on the soil surface during dust storms. Ground- and field-based hyperspectral LWIR images of different types of Israeli soils, before and after dispersion of desert dust-like material on the surface, were acquired with the Telops Hyper-Cam sensor, to calculate the surface emissivity spectra of soils, representing the surface mineralogy. Identifying mineral-related emissivity features and calculating their relative intensities, using two created indices―SQCMI (Soil Quartz Clay Mineral Index) and SCI (Soil Carbonate Index)―enabled determining the content of quartz, clay minerals, and carbonates in the soil in a semi-quantitative manner—from more to less abundant, and identifying changes in their abundance resulting from the dispersion of dust on the surface. The dust affected the mineral-related spectral features of the soil surface, depending on the mineral composition of the dust compared to soil surface mineralogy, and its amount. The ability to detect minor mineralogical changes on the soil surface using high spectral resolution LWIR data was demonstrated.

Soil is an essential component in the environment and is vital for food security. It provides ecosystem services, filters water, supplies nutrients to plants, provides us with food, stores carbon, regulates greenhouse gases emissions and it affects our climate. Traditional soil survey methodologies are complicated, expensive, and time-consuming. Visible and infrared spectroscopy can effectively characterize soil properties. Spectral measurements are rapid, precise and inexpensive. The spectra contain information about soil properties, which comprises minerals, organic compounds, and water. Today, several Soil Spectral Libraries (SSLs) are being created worldwide because these datasets have a notable potential to be used as training datasets for machine learning methods that will benefit precision agriculture activity for better management of food production. Nonetheless, as SSL's are created under laboratory conditions it is not clear if it can be used to infer field conditions in situ and/or from the sky. Thus, study the relationship between RS, field spectroscopy and the laboratory measurements of soil is very important. Accordingly, this study postulates that traditional SSLs don't simulate the real spectral signatures in the field that both, satellite and airborne sensors measure as well, because they are affected by factors that are not an integral part of the soil, such as: moisture, litter, human and animal activity, plow, grass, dung, waste, etc… However, under laboratory conditions, these factors are usually removed for the preparation of SSLs. Thus, given the several SSLs available, it is necessary to evaluate the protocols that were used in these SSLs. The objective of this study is to evaluate the gap between field and laboratory spectral measurements through the analysis of the performance of spectral based models. This procedure combined two soil spectral libraries that contain 114 samples that were measured in the laboratory as well as in the field. The nature of the dataset is varied, because these samples were collected from six different fields in three countries of the Mediterranean basin: Israel, Greece and Italy. Moreover, 63 samples are mainly sandy and 51 are mainly clayey. In order to obtain optimal spectral measurements in the field, we used a new optical apparatus that simulates the sun's radiation. Next, we generated PLSR models to estimate one of the most important hydrological parameters namely “infiltration rate” that control the runoff stage, soil erosion and water storage in the soil profile. This property is strongly affected by the surface characteristics. Finally, the field based spectral model was adapted to an UAV hyperspectral sensor in order to estimate the infiltration rate from the sky. The results were successfully validated in field, and we concluded that for the estimation of the infiltration rate, SSLs must be created using surface reflectance in field because laboratory protocols can be detrimental for the performance of the dataset in question.

Hyperspectral data acquired for different seasons provide the means to derive relevant plant biophysical properties during the growing season over agricultural areas, as well as determine soil properties, when the soils are exposed, e.g. during fallow or after harvesting. This combined information can give a detailed insight on the effect of soil degradation on vegetation growth and finally crop yield. In the Mediterranean region, land use practices for crop cultivation have a long history exploiting soils as a natural resource. The soils are an essential factor contributing to agricultural production of rainfed crops such as cereals, olive groves and vineyards. Inadequate land management is endangering soil quality and productivity, and in turn crop quality and productivity are affected. Therefore, the main objective of this work is to map crop stress related to soil degradation and land management practices within a Mediterranean environment focusing on hyperspectral data within the visible, near-infrared, and short-wave infrared as well as thermal infrared (0.4-12 µm) and test the transferability of the methods used to future hyperspectral space-borne sensors such as PRISMA, EnMAP, SHALOM, CHIME and SBG.In this framework, CASI and AHS hyperspectral imagery have been obtained during the growing season within the Camarena agricultural area located in central Spain. The area is characterized by a Mediterranean climate, a gently undulating relief, evolved soils and traditional rainfed agriculture area. In this environment a combination of tillage erosion as a result of plowing practices, as well as water erosion, has led to the exposure of different soil horizons at the surface with contrasting soil properties. These surface properties have been previously characterized as erosion stages of the same cultivated area in a fallow state. Simultaneous to the airborne acquisitions, intensive field campaigns took place for the characterization of soil and crop variability. This included field spectroradiometry measurements of the different surface covers and vegetation parameters such as Leaf Area Index (LAI), leaf chlorophyll content, plant biomass and grain yield in locations with variable soil erosion and deposition stages from low to very high eroded soils. First results based on random forest modeling between the soil erosion stage mapping and the AHS/CASI remote sensing imagery of the growing season indicate a strong link between the soil conditions and the spectral properties of the crops. Furthermore, biophysical parameters derived from the imagery in the green season such as Leaf Area Index and Leaf Water Content correlated also well with the soil erosion stages. For selected test sites it could be shown that low crop yields are associated with 1) highly eroded areas, where exposure of the calcite rich bedrock can cause deficiency in nutrient uptake and 2) very sandy accumulation areas that are depleted in nutrients and have low potential for water retention. Whereas highest crop yields are associated with clay and iron rich, moderately to low eroded soils. This study integrates optical VNIR-SWIR-TIR spectral domain and present preliminary results that emphasize the strong influence of soil quality on crop stress and production.

Reflectance of light across the visible, near-infrared and shortwave infrared (VIS-NIR-SWIR, 0.4–2.5 µm) spectral region is very useful for investigating mineralogical, physical and chemical properties of soils, which can reduce the need for traditional wet chemistry analyses. As many collections of multispectral satellite data are available for environmental studies, a large extent with medium resolution mapping could be benefited from the spectral measurements made from remote sensors. In this paper, we explored the use of bare soil composites generated from the large historical collections of Landsat images for mapping cropland topsoil attributes across the European extent. For this task, we used the Geospatial Soil Sensing System (GEOS3) for generating two bare soil composites of 30 m resolution (named synthetic soil images, SYSI), which were employed to represent the median topsoil reflectance of bare fields. The first (framed SYSI) was made with multitemporal images (2006–2012) framed to the survey time of the Land-Use/Land-Cover Area Frame Survey (LUCAS) soil dataset (2009), seeking to be more compatible to the soil condition upon the sampling campaign. The second (full SYSI) was generated from the full collection of Landsat images (1982–2018), which although displaced to the field survey, yields a higher proportion of bare areas for soil mapping. For evaluating the two SYSIs, we used the laboratory spectral data as a reference of topsoil reflectance to calculate the Spearman correlation coefficient. Furthermore, both SYSIs employed machine learning for calibrating prediction models of clay, sand, soil organic carbon (SOC), calcium carbonates (CaCO3), cation exchange capacity (CEC), and pH determined in water, using the gradient boosting regression algorithm. The original LUCAS laboratory spectra and a version of the data resampled to the Landsat multispectral bands were also used as reference of prediction performance using VIS-NIR-SWIR multispectral data. Our results suggest that generating a bare soil composite displaced to the survey time of soil observations did not improve the quality of topsoil reflectance, and consequently, the prediction performance of soil attributes. Despite the lower spectral resolution and the variability of soils in Europe, a SYSI calculated from the full collection of Landsat images can be employed for topsoil prediction of clay and CaCO3 contents with a moderate performance (testing R2, root mean square error (RMSE) and ratio of performance to interquartile range (RPIQ) of 0.44, 9.59, 1.77, and 0.36, 13.99, 1.54, respectively). Thus, this study shows that although there exist some constraints due to the spatial and temporal variation of soil exposures and among the Landsat sensors, it is possible to use bare soil composites for mapping key soil attributes of croplands across the European extent.

Spectral-based models extracted from laboratory reflectance in the 400–2500 nm spectral range to predict soil attributes may not be applicable to soil spectra acquired in the field. This is because laboratory sampling procedures disturb the natural soil surface's status. We investigated this issue by using the soil surface-dependent property of water-infiltration rate (WIR). We created a dataset with 114 samples collected from six fields with varying textures located in three different Mediterranean countries (Israel, Greece, Italy). Using the field and laboratory spectral datasets, we demonstrated that WIR is better predicted by field vs. laboratory measurements (R2 = 0.92 and 0.56, respectively). We also developed a transfer function (TF) to predict the field spectral measurements from the laboratory spectra. Use of the TF-processed dataset considerably improved the WIR prediction using laboratory information (from R2 = 0.56 to 0.76). It was concluded that soil surface reflectance values can be estimated based on laboratory spectra using a TF. The generated TF enables exploiting soil spectral libraries for remote-sensing views and for assessing surface-related soil properties.

This paper presents measurements of the azimuth and zenith angle-dependence of the targets that are used for supervised vicarious calibration (SVC) methodology of hyperspectral sensors. Surface radiance measure at-sensor from airborne instruments, such as the AisaDUAL and HyMap sensors, show an apparent noise and shift that limit the information content and even impair the collected data (after removing the atmospheric attenuation). The drift from actual radiance is contributed by various sources; e.g., atmospheric gases and aerosols, and sensor stability. Yet one of the major contributors to it during nadir scanning is the directional effect of target reflectance at variable illumination geometry, also known as Bidirectional Reflectance Distribution Function (BRDF). The SVC method, which is based on ground dark nets of varying densities, was able to correct for the above factors and recalibrate the sensor's radiance during data acquisition. The main assumption of the SVC method is that the net targets are a Lambertian surface. In this paper, the BRDF measurements of the SVC net targets were carried out using a goniometer and controlled laboratory conditions. These measurements revealed anisotropic properties of the nets targets, which were further introduced to the radiometric calibration process are significant, especially with the high density nets (> 50% black net cover). The paper presents a look-up-table of the BRDF correction coefficient factors for each net target (identified and separated by its density and mixing level once placed over a bright background) at various zenith (from − 10° to − 30°) and azimuth (from 0° to 360° at intervals of 10°) angles. The coefficients were calculated for the near infrared spectral region (at 981 nm), and should be applied to the full-reflected spectral range across visible, near and shortwave-infrared wavelengths (400–2500 nm). The acquired results in this empirical study showed that the SVC calibration nets targets are characterized by incoherent BRDF effect. The darker and denser calibration targets absorb the down-welling radiance, and thus, reduce the amount of intermediate multiple scattering, normally accrued in all other (less dense) examined targets. The impact of target density on the BRDF correction coefficient and its dependence with illumination zenith and azimuth angles is studied and presented. The correction coefficient factors obtained in the laboratory were implemented in two real airborne hyperspectral campaigns of two different sensors: AisaDUAL and HyMAP over the same calibration site. The proposed BRDF SVC net corrections enables fine-tuning of the vicarious calibration procedure, while increasing the quality of the radiometric data. Thus, it is recommended to use the BRDF coefficients obtained in this paper for any airborne hyperspectral sensor that uses the SVC method in order to achieve better calibration accuracy.

A new assembly for measuring surface reflectance in the field was developed and built to fill the gap between laboratory and field spectral measurements of the soil surface. This device, named SoilPRO (Soil field PRObe), can be connected to any field spectrometer fiber tip and used to measure representative and undisturbed surfaces of different soil types, low grass and artificial materials. Here, the SoilPRO is presented and compared to reflectance-measurement methods that are commonly used in the laboratory (dark box configuration and the ASD® contact probe) and the field (bare fiber handheld by pistol grip and ASD contact probe). The SoilPRO's performance was evaluated by conducting continuous tests in the laboratory and outdoors with soil samples, and in the field with five different surface types as test targets. The SoilPRO's reflectance products in the field were very similar to those of commonly used methods under optimal conditions, while eliminating most of their associated errors. The device provides a new approach to spectral data acquisition in the field that enables measuring undisturbed soil surfaces while maintaining constant and stable environmental and operational conditions. The SoilPRO measures the reflectance of a representative target's footprint across the spectrometer's full range of sensitivity. The measurement is not dependent on the sun's radiation or atmospheric variations, or on operator stability or measurement geometry, and it does not disturb the surface being measured. Reflectance is an inherent property, and the assembly provides superb reflectance accuracy for the surface area in question.

Visible, near-infrared and shortwave-infrared (VNIR–SWIR) spectroscopy has proven to be an efficient, rapid and low-cost method for soil spectral analysis that can improve on the results obtained from today's traditional methods of conducting soil surveys. Nonetheless, this tool is used mostly in the laboratory and at surface level. The main objective of this paper is to develop a new optical method for characterizing soil profiles, towards improving the efficiency and accuracy of the traditional soil survey. We used airborne hyperspectral data from the AisaFENIX sensor for surface classification and ASD spectral measurements of soil samples for subsurface analysis. A total of 643 soil samples were extracted from 48 cores, each core representing a soil profile. All samples were air-dried, crushed and sieved, and then analyzed by ASD spectrometer under laboratory conditions. Clay content was also measured to provide additional information. The 3D spectral data were analyzed using SAM algorithm, spectral gradient (m), k-means clustering and gley horizon parameter (G) to classify soils and distinguish between soil horizons in each core. The results suggest that these parameters can provide satisfactory results both from laboratory measurements and hyperspectral remote sensing data (R2 = 0.81 for clay content and R2 = 0.78 for gleying conditions) in order to distinguish between the soil horizons using 3D spectral information. Moreover, the method is satisfactory for obtaining soil types from 3D spectral sensing as well as evaluating the catena development and other spatial soil distributions.

The at-sensor radiance of a pixel in a longwave infrared (LWIR) image represents the surface temperature, the surface emissivity, which is similar during the day and at night, and the atmospheric contribution, which is expressed differently during the day and at night. Based on this, an automated procedure, which locates pixels for which each absorption feature in their radiance during the day appears as an emission feature at night, indicative of atmospheric contribution, was developed. The average day or night spectrum of these indicative pixels was applied as a gain factor spectrum to the entire day or night image, respectively, reducing the atmospheric contribution and emphasizing the surface spectral features, represented by emissivity, of each pixel in the image. The procedure was examined on LWIR hyperspectral data cubes acquired over two different areas, and enabled effective reduction of the atmospheric features in both area data sets.

The surface organic horizons in forest soils have been affected by air and soil pollutants, including potentially toxic elements (PTEs). Monitoring of PTEs requires a large number of samples and adequate analysis. Visible–near infrared (vis–NIR: 350–2500 nm) spectroscopy provides an alternative method to conventional laboratory measurements, which are time-consuming and expensive. However, vis–NIR spectroscopy relies on an empirical calibration of the target attribute to the spectra. This study examined the capability of vis–NIR spectra coupled with machine learning (ML) techniques (partial least squares regression (PLSR), support vector machine regression (SVMR), and random forest (RF)) and a deep learning (DL) approach called fully connected neural network (FNN) to assess selected PTEs (Cr, Cu, Pb, Zn, and Al) in forest organic horizons. The dataset consists of 2160 samples from 1080 sites in the forests over all the Czech Republic. At each site, we collected two samples from the fragmented (F) and humus (H) organic layers. The content of all PTEs was higher in horizon H compared to F horizon. Our results indicate that the reflectance of samples tended to decrease with increased PTEs concentration. Cr was the most accurately predicted element, regardless of the algorithm used. SVMR provided the best results for assessing the H horizon (R2 = 0.88 and RMSE = 3.01 mg/kg for Cr). FNN produced the best predictions of Cr in the combined F + H layers (R2 = 0.89 and RMSE = 2.95 mg/kg) possibly due to the larger number of samples. In the F horizon, the PTEs were not predicted adequately. The study shows that PTEs in forest soils of the Czech Republic can be accurately estimated with vis–NIR spectra and ML approaches. Results hint in availability of a large sample size, FNN provides better results.

A method for estimating the level of noise of a hyperspectral data cube is proposed. The method includes noise reduction using the Minimum Noise Fraction (MNF) transform and mapping the spectral dissimilarity between the pixels in the resulting noise-reduced radiance image, as well as in the original noisy radiance image, using the Spectral Angle Mapper (SAM) algorithm. Comparing the two maps, on a pixel by pixel basis, gives a value indicating the addition of noise to the spectrum of each pixel. An average value for the entire image is calculated, defined as the Image Noise Indicator (INI). In practice, this value indicates the quality of the data. Combining the INI value with the level of radiance enables estimating the Image Noise Level (INL). The method was applied and examined on a noisy synthetic image and then implemented for over 20 acquired images from different hyperspectral sensors and their noise level was estimated. Further examination showed that the INI value is independent of the heterogeneity of the hyperspectral data cube. The INI value, indicating the noise level, might increase as a geo-reference procedure is applied to hyperspectral data cube. The INI–INL can also be used as a quality indicator (QI) in the ongoing effort to objectively certify hyperspectral remote sensing images for practical usages.

Mapping road conditions is an important issue for city and state authorities worldwide. Today, pavement safety is assessed by specific assemblies based on a mechanical wheel device, which is a method that is limited in its potential product and operation. In this letter, we examined the possibility of harnessing remote reflectance spectroscopy to predict asphalt's dynamic friction coefficient, thereby enabling the identification and mapping of road conditions. We used a near-infrared analysis technique to evaluate an artificial neural network prediction model designed to assess the friction coefficient solely from spectral readings. This letter describes the method for extracting such a model and presents promising results with an accuracy of ${R} = 0.845$ and high significance of $P between actual and predicted friction values. This model was acquired using nine principal components and three neurons. The potential of this technology is also discussed.

The internal soil standard ISS concept in which a soil standard sample, exhibiting stable spectral performance, is used to normalize and align all other soil spectral measurements – was further examined herein. Different spectrometers Spectral Evolution and ASD Spectral Pro were used to measure a set of soil samples with the soil standards sample as a reference. Two sand dune samples served as the ISS to align measurements made under different conditions and the results were compared. It was shown that the ISS method was able to correct the spectral information from one spectrometer to another; however, the differences in the results obtained when using the two different soil standards are discussed. The main conclusion of this paper is that the soil spectral user community should adopt the ISS method for the benefit of all, and the sooner the better. This will allow much more effective exploitation of all data sets acquired on a daily basis by the growing soil spectral community that still lacks standardization procedures.