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Remote Sensing of the Environment using Unmanned Aerial Systems (UAS) provides an overview of how unmanned aerial systems have revolutionized our capability to monitor river systems, soil characteristics, and related processes at unparalleled spatio-temporal resolutions. This capability has enabled enhancements in our capacity to describe water cycle and hydrological processes. The book includes guidelines, technical advice, and practical experience to support practitioners and scientists in increasing the efficiency of monitoring with the help of UAS. The book contains field survey datasets to use as practical exercises, allowing proposed techniques and methods to be applied to real world case studies.

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”.

[EN] The past decades have seen rapid advancements in space-based monitoring of essential water cycle variables, providing products related to precipitation, evapotranspiration, and soil moisture, often at tens of kilometer scales. Whilst these data effectively characterize water cycle variability at regional to global scales, they are less suitable for sustainable management of local water resources, which needs detailed information to represent the spatial heterogeneity of soil and vegetation. The following questions are critical to effectively exploit information from remotely sensed and in situ Earth observations (EOs): How to downscale the global water cycle products to the local scale using multiple sources and scales of EO data? How to explore and apply the downscaled information at the management level for a better understanding of soil-water-vegetation-energy processes? How can such fine-scale information be used to improve the management of soil and water resources? An integrative information flow (i.e., iAqueduct theoretical framework) is developed to close the gaps between satellite water cycle products and local information necessary for sustainable management of water resources. The integrated iAqueduct framework aims to address the abovementioned scientific questions by combining medium-resolution (10 m-1 km) Copernicus satellite data with high-resolution (cm) unmanned aerial system (UAS) data, in situ observations, analytical- and physical-based models, as well as big-data analytics with machine learning algorithms. This paper provides a general overview of the iAqueduct theoretical framework and introduces some preliminary results., The authors would like to thank the European Commission and Netherlands Organisation for Scientific Research (NWO) for funding, in the frame of the collaborative international consortium (iAqueduct) financed under the 2018 Joint call of the Water Works 2017 ERA-NET Cofund. This ERA-NET is an integral part of the activities developed by the Water JPI (Project number: ENWWW.2018.5); the EC and the Swedish Research Council for Sustainable Development (FORMAS, under grant 2018-02787); Contributions of B. Szabo was supported by the Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences (grant no. BO/00088/18/4).

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.

Although many Soil Spectral Libraries (SSLs) have been created globally, these libraries still have not been operationalized for end-users. To address this limitation, this study created an online Brazilian Soil Spectral Service (BraSpecS). The system was based on the Brazilian Soil Spectral Library (BSSL) with samples collected in the Visible–Near–Short-wave infrared (vis–NIR–SWIR) and Mid-infrared (MIR) ranges. The interactive platform allows users to find spectra, act as custodians of the data, and estimate several soil properties and classification. The system was tested by 500 Brazilian and 65 international users. Users accessed the platform (besbbr.com.br), uploaded their spectra, and received soil organic carbon (SOC) and clay content prediction results via email. The BraSpecS prediction provided good results for Brazilian data, but performed variably for other countries. Prediction for countries outside of Brazil using local spectra (External Country Soil Spectral Libraries, ExCSSL) mostly showed greater performance than BraSpecS. Clay R2 ranged from 0.5 (BraSpecS) to 0.8 (ExCSSL) in vis–NIR–SWIR, but BraSpecS MIR models were more accurate in most situations. The development of external models based on the fusion of local samples with BSSL formed the Global Soil Spectral Library (GSSL). The GSSL models improved soil properties prediction for different countries. Nevertheless, the proposed system needs to be continually updated with new spectra so they can be applied broadly. Accordingly, the online system is dynamic, users can contribute their data and the models will adapt to local information. Our community-driven web platform allows users to predict soil attributes without learning soil spectral modeling, which will invite end-users to utilize this powerful technique.

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.

We conducted a systematic review and inventory of recent research achievements related to spaceborne and aerial Earth Observation (EO) data-driven monitoring in support of soil-related strategic goals for a three-year period (2019–2021). Scaling, resolution, data characteristics, and modelling approaches were summarized, after reviewing 46 peer-reviewed articles in international journals. Inherent limitations associated with an EO-based soil mapping approach that hinder its wider adoption were recognized and divided into four categories: (i) area covered and data to be shared; (ii) thresholds for bare soil detection; (iii) soil surface conditions; and (iv) infrastructure capabilities. Accordingly, we tried to redefine the meaning of what is expected in the next years for EO data-driven topsoil monitoring by performing a thorough analysis driven by the upcoming technological waves. The review concludes that the best practices for the advancement of an EO data-driven soil mapping include: (i) a further leverage of recent artificial intelligence techniques to achieve the desired representativeness and reliability; (ii) a continued effort to share harmonized labelled datasets; (iii) data fusion with in situ sensing systems; (iv) a continued effort to overcome the current limitations in terms of sensor resolution and processing limitations of this wealth of EO data; and (v) political and administrative issues (e.g., funding, sustainability). This paper may help to pave the way for further interdisciplinary research and multi-actor coordination activities and to generate EO-based benefits for policy and economy.

Water infiltration rate (WIR) into the soil profile was investigated through a comprehensive study harnessing spectral information of the soil surface. As soil spectroscopy provides invaluable information on soil attributes, and as WIR is a soil surface-dependent property, field spectroscopy may model WIR better than traditional laboratory spectral measurements. This is because sampling for the latter disrupts the soil-surface status. A field soil spectral library (FSSL), consisting of 114 samples with different textures from six different sites over the Mediterranean basin, combined with traditional laboratory spectral measurements, was created. Next, partial least squares regression analysis was conducted on the spectral and WIR data in different soil texture groups, showing better performance of the field spectral observations compared to traditional laboratory spectroscopy. Moreover, several quantitative spectral properties were lost due to the sampling procedure, and separating the samples according to texture gave higher accuracies. Although the visible near-infrared–shortwave infrared (VNIR–SWIR) spectral region provided better accuracy, we resampled the spectral data to the resolution of a Cubert hyperspectral sensor (VNIR). This hyperspectral sensor was then assembled on an unmanned aerial vehicle (UAV) to apply one selected spectral-based model to the UAV data and map the WIR in a semi-vegetated area within the Alento catchment, Italy. Comprehensive spectral and WIR ground-truth measurements were carried out simultaneously with the UAV–Cubert sensor flight. The results were satisfactorily validated on the ground using field samples, followed by a spatial uncertainty analysis, concluding that the UAV with hyperspectral remote sensing can be used to map soil surface-related soil properties.

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 surveys are critical for maintaining sustainable use of natural resources while minimizing harmful impacts to the ecosystem. A key soil attribute for many environmental factors, such as CO2 budget, soil fertility and sustainability, is soil organic matter (SOM), as well as its sequestration. Soil spectroscopy is a popular method to assess SOM content rapidly in both field and laboratory domains. However, 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 to 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 a 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 (

Soil contamination by potentially toxic elements (PTEs) is intensifying under increasing industrialization. Thus, the ability to efficiently delineate contaminated sites is crucial. Visible–near infrared (vis–NIR: 350–2500 nm) and X-ray fluorescence (XRF: 0.02–41.08 keV) spectroscopic techniques have attracted tremendous attention for the assessment of PTEs. Recently, the application of fused vis–NIR and XRF spectroscopy, which is based on the complementary effect of data fusion, is also increasing. Moreover, different data manipulation methods, including feature selection approaches, affect the prediction performance. This study investigated the feasibility of using single and fused vis–NIR and XRF spectra while exploring feature selection algorithms for the assessment of key soil PTEs. The soil samples were collected from one of the most heavily polluted areas of the Czech Republic and scanned using laboratory vis–NIR and XRF spectrometers. Univariate filter (UF) and genetic algorithm (GA) were used to select the bands of greater importance for the PTE prediction. Support vector machine (SVM) was then used to train the models using the full-range and feature-selected spectra of single sensors and their fusion. It was found that XRF spectra alone (primarily GA-selected) performed better than single vis–NIR and fused spectral data for predictions of PTEs. Moreover, the prediction models that were derived from the fused data set (particularly the GA-selected) enhanced the models’ accuracies as compared with the single vis–NIR spectra. In general, the results suggest that the GA-selected spectra obtained from the single XRF spectrometer (for As and Pb) and from the fusion of vis–NIR and XRF (for Pb) are promising for accurate quantitative estimation detection of the mentioned PTEs.

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.

Symptoms of root stress are hard to detect using non-invasive tools. This study reveals proof of concept for vegetation indices’ ability, usually used to sense canopy status, to detect root stress, and performance status. Pepper plants were grown under controlled greenhouse conditions under different potassium and salinity treatments. The plants’ spectral reflectance was measured on the last day of the experiment when more than half of the plants were already naturally infected by root disease. Vegetation indices were calculated for testing the capability to distinguish between healthy and root-damaged plants using spectral measurements. While no visible symptoms were observed in the leaves, the vegetation indices and red-edge position showed clear differences between the healthy and the root-infected plants. These results were achieved after a growth period of 32 days, indicating the ability to monitor root damage at an early growing stage using leaf spectral reflectance.

Potassium is a macro element in plants that is typically supplied to crops in excess throughout the season to avoid a deficit leading to reduced crop yield. Transpiration rate is a momentary physiological attribute that is indicative of soil water content, the plant’s water requirements, and abiotic stress factors. In this study, two systems were combined to create a hyperspectral–physiological plant database for classification of potassium treatments (low, medium, and high) and estimation of momentary transpiration rate from hyperspectral images. PlantArray 3.0 was used to control fertigation, log ambient conditions, and calculate transpiration rates. In addition, a semi-automated platform carrying a hyperspectral camera was triggered every hour to capture images of a large array of pepper plants. The combined attributes and spectral information on an hourly basis were used to classify plants into their given potassium treatments (average accuracy = 80%) and to estimate transpiration rate (RMSE = 0.025 g/min, R2 = 0.75) using the advanced ensemble learning algorithm XGBoost (extreme gradient boosting algorithm). Although potassium has no direct spectral absorption features, the classification results demonstrated the ability to label plants according to potassium treatments based on a remotely measured hyperspectral signal. The ability to estimate transpiration rates for different potassium applications using spectral information can aid in irrigation management and crop yield optimization. These combined results are important for decision-making during the growing season, and particularly at the early stages when potassium levels can still be corrected to prevent yield loss.

Thermal inertia has been applied to map soil water content exploiting remote sensing data in the short and long wave regions of the electromagnetic spectrum. Over the last years, optical and thermal cameras were sufficiently miniaturized to be loaded onboard of unmanned aerial systems (UASs), which provide unprecedented potentials to derive hyperspatial resolution thermal inertia for soil water content mapping. In this study, we apply a simplification of thermal inertia, the apparent thermal inertia (ATI), over pixels where underlying thermal inertia hypotheses are fulfilled (unshaded bare soil). Then, a kriging algorithm is used to spatialize the ATI to get a soil water content map. The proposed method was applied to an experimental area of the Alento River catchment, in southern Italy. Daytime radiometric optical multispectral and day and nighttime radiometric thermal images were acquired via a UAS, while $in \,\,situ$ soil water content was measured through the thermo-gravimetric and time domain reflectometry (TDR) methods. The determination coefficient between ATI and soil water content measured over unshaded bare soil was 0.67 for the gravimetric method and 0.73 for the TDR. After interpolation, the correlation slightly decreased due to the introduction of measurements on vegetated or shadowed positions ( $r^{2} = 0.59$ for gravimetric method; $r^{2} = 0.65$ for TDR). The proposed method shows promising results to map the soil water content even over vegetated or shadowed areas by exploiting hyperspatial resolution data and geostatistical analysis.

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.

Forest ecosystems are among the main parts of the biosphere; however, they have been endangered from the significant elevation and harmful effects of air and soil pollutants, including potentially toxic elements (PTEs). The concentration of PTEs in forest soils varies not only laterally but also vertically with depth. Forest surface organic horizons are of particular interest in forest ecosystem monitoring due to their role as stable adsorbents of the deposited atmospheric substances. Therefore, the main purpose of this study was to conduct rapid examinations of forest soils PTEs (Cr, Cu, Pb, Zn, and Al), testing the capability of VIS--NIR spectroscopy coupled with machine learning (ML) techniques (partial least square regression (PLSR), support vector machine regression (SVMR), and random forest (RF)) and fully connected neural network (FNN), a deep learning (DL) approach, in forest organic horizons. One-thousand-and-eighty forested sites across the Czech Republic at two soil layers, defining the fragmented (F) and humus (H) organic horizons, were investigated (total 2160 samples). PTEs as well as total Fe and SOC, as auxiliary data, were conventionally and spectrally determined and modelled in the combined organic horizons (F + H) and in each individual horizon using the ML and DL algorithms. Results indicated that the concentration of all PTEs was higher in the horizon H compared to the F horizon. Although the spectral reflectance of samples tended to decrease with increased PTEs concentration. Strongly significant positive correlations between all PTEs and total Fe in all horizons were obtained, which were higher in the H and F + H horizons than the F horizon. The highest correlations of PTEs with the spectra were at 460--590~nm, which is mostly linked to the presence of Fe-oxide. These results show the importance of Fe for spectral prediction of PTEs. Cr and Al were the most accurately predicted elements, regardless of the applied learning technique. SVMR provided the best results in assessing the H horizon (e.g., R\(^2\) = 0.88 and root mean square error (RMSE) = 3.01~mg/kg, and R\(^2\) = 0.82 and RMSE = 1682.25~mg/kg for Cr and Al, respectively); however, FNN predicted the combined F + H horizons the best (R\(^2\) = 0.89 and RMSE = 2.95~mg/kg, and R\(^2\) = 0.86 and RMSE = 1593.64~mg/kg for Cr and Al, respectively) due to the larger number of samples. In the F horizon, almost no parameters were predicted adequately. This study shows that given the availability of larger sample sizes, FNN can be a more promising technique compared to ML methods for assessment of Cr and Al concentration based on national spectral data in the forests of the Czech Republic.

A novel hyperspectral-physiological system that monitors plants dynamic response to abiotic alterations was developed. The system is a sensor-to-plant platform which can determine the optimal time of day during which physiological traits can be successfully identified via spectral means. The directly measured traits include momentary and daily transpiration rates throughout the daytime and daily and periodical plant weight loss and gain. The system monitored and evaluated pepper plants response to varying levels of potassium fertilization. Significant momentary transpiration rates differences were found between the treatments during 07:00–10:00 and 14:00–17:00. The simultaneous frequently measured high-resolution spectral data provided the means to correlate the two measured data sets. Significant correlation coefficients between the spectra and momentary transpiration rates resulted with a selection of three bands (ρ523, ρ697 and ρ818nm) that were used to capture transpiration rate differences using a normalized difference formula during the morning, noon and the afternoon. These differences also indicated that the best results are not always obtained when spectral (remote or proximal) measurements are typically preformed around noon (when solar illumination is the highest). Valuable information can be obtained when the spectral measurements are timed according to the plants’ dynamic physiological status throughout the day, which may vary among plant species and should be considered when planning remote sensing data acquisition.

Earth observation (EO) has an immense potential as being an enabling tool for mapping spatial characteristics of the topsoil layer. Recently, deep learning based algorithms and cloud computing infrastructure have become available with a great potential to revolutionize the processing of EO data. This paper aims to present a novel EO-based soil monitoring approach leveraging open-access Copernicus Sentinel data and Google Earth Engine platform. Building on key results from existing data mining approaches to extract bare soil reflectance values the current study delivers valuable insights on the synergistic use of open access optical and radar images. The proposed framework is driven by the need to eliminate the influence of ambient factors and evaluate the efficiency of a convolutional neural network (CNN) to effectively combine the complimentary information contained in the pool of both optical and radar spectral information and those form auxiliary geographical coordinates mainly for soil. We developed and calibrated our multi-input CNN model based on soil samples (calibration = 80% and validation 20%) of the LUCAS database and then applied this approach to predict soil clay content. A promising prediction performance (R2 = 0.60, ratio of performance to the interquartile range (RPIQ) = 2.02, n = 6136) was achieved by the inclusion of both types (synthetic aperture radar (SAR) and laboratory visible near infrared–short wave infrared (VNIR-SWIR) multispectral) of observations using the CNN model, demonstrating an improvement of more than 5.5% in RMSE using the multi-year median optical composite and current state-of-the-art non linear machine learning methods such as random forest (RF; R2 = 0.55, RPIQ = 1.91, n = 6136) and artificial neural network (ANN; R2 = 0.44, RPIQ = 1.71, n = 6136). Moreover, we examined post-hoc techniques to interpret the CNN model and thus acquire an understanding of the relationships between spectral information and the soil target identified by the model. Looking to the future, the proposed approach can be adopted on the forthcoming hyperspectral orbital sensors to expand the current capabilities of the EO component by estimating more soil attributes with higher predictive performance.