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Remote sensing and soil spectroscopy applications are valuable techniques for soil property estimation. Soil organic matter (SOM) and calcium carbonate are important factors in soil quality, and although organic matter is well studied, calcium carbonates require more investigation. In this study, we validated the performance of laboratory soil spectroscopy for estimating the aforementioned properties with referenced in situ data. We also examined the performance of imaging spectroscopy sensors, such as the airborne HySpex and the spaceborne PRISMA. For this purpose, we applied four commonly used machine learning algorithms and six preprocessing methods for the evaluation of the best fitting algorithm.. The study took place over crop areas of Amyntaio in Northern Greece, where extensive soil sampling was conducted. This is an area with a very variable mineralogical environment (from lignite mine to mountainous area). The SOM results were very good at the laboratory scale and for both remote sensing sensors with R2 = 0.79 for HySpex and R2 = 0.76 for PRISMA. Regarding the calcium carbonate estimations, the remote sensing accuracy was R2 = 0.82 for HySpex and R2 = 0.36 for PRISMA. PRISMA was still in the commissioning phase at the time of the study, and therefore, the acquired image did not cover the whole study area. Accuracies for calcium carbonates may be lower due to the smaller sample size used for the modeling procedure. The results show the potential for using quantitative predictions of SOM and the carbonate content based on soil and imaging spectroscopy at the air and spaceborne scales and for future applications using larger datasets. [ABSTRACT FROM AUTHOR]

Remote sensing soil properties in a coherent manner is now feasible from regional to global scales. Xu et al. [[10]] showed that soil-probe contact measurements resulted in the optimum spectral quality and estimation accuracy; that sieving the soil sample into particle sizes below 1 mm and drying before spectral measurement effectively enhanced spectral quality and estimation accuracy; and that the variation in soil temperature did not have a distinct influence on spectral quality. As the change in soil organic carbon (SOC) over time is an important indicator of soil health and for CO SB 2 sb sequestration, the determination and mapping of SOC content from optical Earth observation sensors is currently an area of rigorous research. [Extracted from the article]

• Field spectral data were used to assess water-infiltration rate in sandy soils. • The created models were applied on aerial and satellite spectral imaging sensors. • The aerial sensor (HySpex) predictions were successfully validated in situ. • High spatial–spectral resolution is crucial for mapping water-infiltration rate. • The 1000–2500 nm region adds important data for assessing water-infiltration rate. The efficiency of spectral-based assessments of soil attributes using soil spectral libraries (SSLs) covering the visible–near-infrared–shortwave-infrared (VNIR–SWIR: 400–2500 nm) region has been proven in many studies. Nevertheless, as traditional SSLs are commonly developed under laboratory conditions, their application is limited for the assessment of soil surface-dependent properties such as water-infiltration rate (WIR) into the soil profile due to the sampling procedure. Currently, few studies are based on field SSLs for the prediction of physical soil properties. This study used a field-based protocol to measure soil reflectance data and WIR simultaneously in the field, and generate spectral-based decision tree models to predict WIR solely from field spectral measurements using the SoilPRO® assembly. The obtained models were applied to both airborne hyperspectral (HySpex) and satellite multispectral (Sentinel 2) data on a pixel-by-pixel basis to generate raster maps of WIR. The study areas were located in Macedonia (Greece), and were optimal for mapping WIR because the soil crust was well developed, and sites were characterized by bare soils (no vegetation coverage) with a sandy structure. Whereas the WIR map generated with the satellite data was poor due to the low spatial and spectral resolution of Sentinel 2 (20 m, 9 bands), the results obtained with the airborne hyperspectral HySpex sensor (5 m, 408 bands) were satisfactorily validated in the ground-truth stage with good prediction accuracy due to high spatial and spectral resolution. Validation accuracy of the HySpex observations using all field samples gave R2 = 0.68, whereas the predictions of the ground-truth samples that were not part of the calibration stage (field validation group) of the model gave R2 = 0.59. We concluded that these results are favourable for rapid estimation of soil surface conditions and pave the way for a wider spatial view from orbital hyperspectral remote-sensing sensors. [ABSTRACT FROM AUTHOR]

The estimation of the soil organic carbon (SOC) content plays an important role for carbon sequestration in the context of climate change, food security and soil degradation. Reflectance spectroscopy has proven to be a promising technique for SOC quantification in the laboratory and increasingly from air- and spaceborne platforms, where hyperspectral imagery provides great potential for mapping SOC on larger scales with regular updates. When applied on larger scales, soil prediction accuracy decreases due to the inhomogeneity of samples. In this paper, we examined if spectral clustering of the LUCAS EU-wide topsoil database is successful without using other covariates than the spectral database and can improve SOC model performance compared to a reference model that was calibrated on the whole database without clustering. Different clustering methodologies were tested, including a k-means clustering based on principal component analysis or based on spectral feature variables, combined with partial least squares regression (PLSR) models, and a clustering based on a local PLSR approach which builds a different multivariate model for each sample to be predicted. Furthermore, in order to allow for subsequent application to hyperspectral remote sensing data, atmospheric water wavelengths were removed from the analyses. The local PLSR approach achieved best results and was additionally applied to LUCAS spectra resampled to the upcoming hyperspectral EnMAP sensor which led to good results: R2 = 0.66, RMSEP = 5.78 g kg−1 and RPIQ = 1.93. The k-means clustering approach showed slightly better results than the reference model. Overall, our results showed similar performances for SOC prediction models compared to other approaches using PLSR with a larger spectral range and other soil parameters as covariates. This study shows that (i) it is possible to transfer the local PLSR approach onto a wavelengths reduced spectral library and to predict estimations of SOC at low-cost with reasonable accuracy based on large scale soil databases; and (ii) that the local regression approach is a valuable tool for SOC prediction models based solely on spectral data without the use of other soil covariates. Unlabelled Image • Reduced need for ground truth data by large-scale spectral clustering and modelling • Adapting existing approaches in preparation for future spaceborne SOC estimation • Comparison of clustering approaches with reference models on complete LUCAS data • Local PLSR outperforms other approaches and reference model in SOC quantification [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • 1.75 μm feature is tested for gypsum estimation with different hyperspectral sensors. • CRAD & NDGI approaches perform best to predict gypsum content at the laboratory scale. • Performance on air- and spaceborne scale mainly depend on sensor and parametrisation. Abstract High spectral resolution (hyperspectral) remote sensing has already demonstrated its capabilities for soil constituent mapping based on absorption feature parameters. This paper tests different parametrizations of the 1.75 μm gypsum feature for the determination of gypsum abundances, from the laboratory to remote sensing applications of recent as well as upcoming hyperspectral sensors. In particular, this study focuses on remote sensing imagery over the large body of the Omongwa pan located in the Namibian Kalahari. Four common absorption feature parameters are compared: band ratio through the introduction of the Normalized Differenced Gypsum Index (NDGI), the shape-based parameters Slope, and Half-Area, and the Continuum Removed Absorption Depth (CRAD). On laboratory soil samples from the pan, CRAD and NDGI approaches perform best to determine gypsum content tested in cross validated regression models with XRD mineralogical data (R² = 0.84 for NDGI and R² = 0.86 for CRAD). Subsequently the laboratory prediction functions are transferred to remote sensing imagery of spaceborne Hyperion, airborne HySpex and simulated spaceborne EnMAP sensor. Variable results were obtained depending on sensor characteristics, data quality, preprocessing and spectral parameters. Overall, the CRAD parameter in this wavelength region proved not to be robust for remote sensing applications, and the simple band ratio based parameter, the NDGI, proved robust and is recommended for future use for the determination of gypsum content in bare soils based on remote sensing hyperspectral imagery. [ABSTRACT FROM AUTHOR]

Abstract Bare soil surfaces vary spatially and temporally as a result of – among other - meteorological conditions and land management practices. Literature shows that most of the existing soil surface corrections are dependent on laboratory measurements. These measurements are time consuming and therefore unsuitable for large amounts of airborne or satellite spectroscopy data. Soil moisture and soil surface roughness present main challenges for in-field and remote sensing application of soil spectroscopy and soil moisture has substantial influence on surface reflectance. Here, we present a straightforward method for soil moisture reduction applicable to large amounts of airborne or satellite spectroscopy data. Multi-temporal airborne imaging spectrometer data was collected with the Airborne Prism EXperiment (APEX) in September 2013, May 2014 and May 2015 on the same pilot site. A new soil moisture index was developed to quantify the soil moisture of the airborne imaging spectroscopy data (R2 of 0.59). The index appeared to be closely related to the existing NINSOL index. A, laboratory based, independent soil moisture spectral dataset was used to quantify the correction factor per wavelength based on the wavelength dependent relative differences. After minimizing the effect of soil moisture variability, the airborne spectrometer data better approximate the reference dry-soil spectra (up to 21.3%). Then, the corrected data were tested for soil properties prediction (soil organic matter (SOM), sand silt and clay). The soil moisture corrected spectra performed marginally better for SOM (RMSE from 3.13 to 3.10) and silt (RMSE from 5.74 to 5.72), although not for clay (RMSE from 9.14 to 9.33) and sand (RMSE from 5.98 to 6.31). The methodology shows that field spectra can be corrected to better match laboratory conditions, focusing on soil moisture as main varying factor. Next step should include the various types of soil surface variation and their interactions. A better understanding of these interactions is needed and can be achieved by field-scale experiments and by modelling the different processes. Highlights • There is a need for minimisation of the effect of soil surface variations in large-scale satellite data. • Development of new soil moisture index, applicable to both field and airborne spectroscopy data. • First development of a correction factor for soil moisture variability for airborne spectroscopy data. • Improved airborne imaging spectrometer data with compensated soil moisture. [ABSTRACT FROM AUTHOR]

The most commonly used approach to estimate soil variables from remote-sensing data entails time-consuming and expensive data collection including chemical and physical laboratory analysis. Large spectral libraries could be exploited to decrease the effort of soil variable estimation and obtain more widely applicable models. We investigated the feasibility of a new approach, referred to as bottom-up, to provide soil organic carbon (SOC) maps of bare cropland fields over a large area without recourse to chemical analyses, employing both the pan-European topsoil database from the Land Use/Cover Area frame statistical Survey (LUCAS) and Airborne Prism Experiment (APEX) hyperspectral airborne data. This approach was tested in two areas having different soil characteristics: the loam belt in Belgium, and the Gutland-Oesling region in Luxembourg. Partial least square regression (PLSR) models were used in each study area to estimate SOC content, using both bottom-up and traditional approaches. The PLSR model's accuracy was tested on an independent validation dataset. Both approaches provide SOC maps having a satisfactory level of accuracy (RMSE = 1.5-4.9 g⋅kg-1; ratio of performance to deviation (RPD) = 1.4-1.7) and the inter-comparison did not show differences in terms of RMSE and RPD either in the loam belt or in Luxembourg. Thus, the bottom-up approach based on APEX data provided high-resolution SOC maps over two large areas showing the within- and between-field SOC variability. [ABSTRACT FROM AUTHOR]

This study combines spaceborne multitemporal and hyperspectral data to analyze the spatial distribution of surface evaporite minerals and changes in a semi-arid depositional environment associated with episodic flooding events, the Omongwa salt pan (Kalahari, Namibia). The dynamic of the surface crust is evaluated by a change-detection approach using the Iterative-reweighted Multivariate Alteration Detection (IR-MAD) based on the Landsat archive imagery from 1984 to 2015. The results show that the salt pan is a highly dynamic and heterogeneous landform. A change gradient is observed from very stable pan border to a highly dynamic central pan. On the basis of hyperspectral EO-1 Hyperion images, the current distribution of surface evaporite minerals is characterized using Spectral Mixture Analysis (SMA). Assessment of field and image endmembers revealed that the pan surface can be categorized into three major crust types based on diagnostic absorption features and mineralogical ground truth data. The mineralogical crust types are related to different zones of surface change as well as pan morphology that influences brine flow during the pan inundation and desiccation cycles. These combined information are used to spatially map depositional environments where the more dynamic halite crust concentrates in lower areas although stable gypsum and calcite/sepiolite crusts appear in higher elevated areas. [ABSTRACT FROM AUTHOR]

With the upcoming availability of the next generation of high quality orbiting hyperspectral sensors, a major step toward improved regional soil mapping and monitoring and delivery of quantitative soil maps is expected. This study focuses on the determination of the prediction accuracy of spectral models for the mapping of common soil properties based on upcoming EnMAP (Environmental Mapping and Analysis Program) satellite data using semi-operational soil models. Iron oxide (Fed), clay, and soil organic carbon (SOC) content are predicted in test areas in Spain and Luxembourg based on a semi-automatic Partial-Least-Square (PLS) regression approach using airborne hyperspectral, simulated EnMAP, and soil chemical datasets. A variance contribution analysis, accounting for errors in the dependent variables, is used alongside classical error measurements. Results show that EnMAP allows predicting iron oxide, clay, and SOC with an R2 between 0.53 and 0.67 compared to Hyperspectral Mapper (HyMap)/Airborne Hyperspectral System (AHS) imagery with an R2 between 0.64 and 0.74. Although a slight decrease in soil prediction accuracy is observed at the spaceborne scale compared to the airborne scale, the decrease in accuracy is still reasonable. Furthermore, spatial distribution is coherent between the HyMap/AHS mapping and simulated EnMAP mapping as shown with a spatial structure analysis with a systematically lower semivariance at the EnMAP scale. [ABSTRACT FROM AUTHOR]

Monitoring soil surface dynamics in semi-arid agricultural landscapes becomes increasingly important due to the vulnerability of these ecosystems to desertification processes. Observations using remote sensing via the traditionally used visible-near infrared (VNIR) and shortwave infrared (SWIR) wavelength regions can be limited due to the special characteristics of such soils (e.g. rich in quartz, poor in clay minerals, coarse textured, and grain coatings). In this laboratory-based work we demonstrate the capabilities of the thermal infrared between 8 and 14 μm (longwave infrared) to detect and quantify small ranges of the soil properties sand-, clay, and soil organic carbon (SOC) content, as they appear in the semi-arid agricultural landscapes of the Mullewa region in Western Australia. All of the three soil properties could be predicted using the longwave infrared (LWIR) spectra with higher accuracy and precision than from the VNIR-SWIR wavelength region. The study revealed the complex relationships between the soil properties and the VNIR-SWIR soil spectra, which were caused by the spectral influence of the soils' grain coatings (based on iron and clay minerals). These difficulties could be handled more appropriately by the prediction models based on the LWIR soil spectra. Our results indicate that in order to quantitatively monitor farming areas for such erosion-related soil properties; remote sensing using the LWIR wavelength region would produce better estimates than using the wavelength ranges in the VNIR-SWIR. [ABSTRACT FROM AUTHOR]

Hyperspectral images were acquired along the Front Range Urban Corridor in Colorado to determine the feasibility of identification and mapping of expansive clay soils with two, high-SNR imaging spectrometers. Swelling soils are a major geologic hazard, and cause extensive damage world-wide every year. The cost of postconstruction mitigation and standard engineering soil tests for creation of regional maps are immense. Smectite is the clay mineral group that has the greatest swelling potential and is responsible for most of the severe swelling soil damage observed in Colorado. Data sets were acquired from 1997 to 1999 with the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and the Hyperspectral Mapper (HyMap). Using a matched filtering algorithm, maps of exposed clay material were produced, despite a strong vegetation cover. Among those exposures, spectral discrimination and identification of variable clay mineralogy such as smectite, smectite/illite, and kaolinite, in decreasing order of swelling potential hazard, was possible. The comparison of the results from the two sensors showed that higher spatial resolution provided purer image endmembers in more heterogeneous sites, but did not exhibit more endmembers and did not identify new natural outcrops that a lower spatial resolution data set would miss in a homogeneous terrain. However, an increase in the signal-to-noise ratio (SNR) of the instrument by pixel summation made possible the identification of low reflectance exposures. This work demonstrates that, using recent instruments and well-established methodologies, imaging spectrometry can be of practical help for the detection and mapping of expansive clays. [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]

Wehrhahn and Sommer [[9]] provide an example of high-resolution UAV-based SOC mapping in a number of cropland fields in hummocky terrain where tillage erosion creates high spatially-distributed SOC dynamics. Soil organic matter (SOM) is essential for preserving a healthy soil that provides good soil structure and high fertility and water -holding capacity. First, the local PLSR uses the European LUCAS soil spectral database to quantify the SOC content for soil samples from the study site, and second, a remote sensing model is calibrated based on the local PLSR SOC results and the corresponding image spectra. [Extracted from the article]

Soil degradation is a major threat for European soils and therefore, the European Commission recommends intensifying research on soil monitoring to capture changes over time and space. Imaging spectroscopy is a promising technique to create spatially accurate topsoil maps based on hyperspectral remote sensing data. We tested the application of a local partial least squares regression (PLSR) to airborne HySpex and simulated satellite EnMAP (Environmental Mapping and Analysis Program) data acquired in north-eastern Germany to quantify the soil organic carbon (SOC) content. The approach consists of two steps: (i) the local PLSR uses the European LUCAS (land use/cover area frame statistical survey) Soil database to quantify the SOC content for soil samples from the study site in order to avoid the need for wet chemistry analyses, and subsequently (ii) a remote sensing model is calibrated based on the local PLSR SOC results and the corresponding image spectra. This two-step approach is compared to a traditional PLSR approach using measured SOC contents from local samples. The prediction accuracy is high for the laboratory model in the first step with R2 = 0.86 and RPD = 2.77. The HySpex airborne prediction accuracy of the traditional approach is high and slightly superior to the two-step approach (traditional: R2 = 0.78, RPD = 2.19; two-step: R2 = 0.67, RPD = 1.79). Applying the two-step approach to simulated EnMAP imagery leads to a lower but still reasonable prediction accuracy (traditional: R2 = 0.77, RPD = 2.15; two-step: R2 = 0.48, RPD = 1.41). The two-step models of both sensors were applied to all bare soils of the respective images to produce SOC maps. This local PLSR approach, based on large scale soil spectral libraries, demonstrates an alternative to SOC measurements from wet chemistry of local soil samples. It could allow for repeated inexpensive SOC mapping based on satellite remote sensing data as long as spectral measurements of a few local samples are available for model calibration. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Salt pans are highly dynamic environments that are difficult to study by in situ methods because of their harsh climatic conditions and large spatial areas. Remote sensing can help to elucidate their environmental dynamics and provide important constraints regarding their sedimentological, mineralogical, and hydrological evolution. This study utilizes spaceborne multitemporal multispectral optical data combined with spectral endmembers to document spatial distribution of surface crust types over time on the Omongwa pan located in the Namibian Kalahari. For this purpose, 49 surface samples were collected for spectral and mineralogical characterization during three field campaigns (2014–2016) reflecting different seasons and surface conditions of the salt pan. An approach was developed to allow the spatiotemporal analysis of the salt pan crust dynamics in a dense time-series consisting of 77 Landsat 8 cloud-free scenes between 2014 and 2017, covering at least three major wet–dry cycles. The established spectral analysis technique Sequential Maximum Angle Convex Cone (SMACC) extraction method was used to derive image endmembers from the Landsat time-series stack. Evaluation of the extracted endmember set revealed that the multispectral data allowed the differentiation of four endmembers associated with mineralogical mixtures of the crust's composition in dry conditions and three endmembers associated with flooded or muddy pan conditions. The dry crust endmember spectra have been identified in relation to visible, near infrared, and short-wave infrared (VNIR–SWIR) spectroscopy and X-ray diffraction (XRD) analyses of the collected surface samples. According these results, the spectral endmembers are interpreted as efflorescent halite crust, mixed halite–gypsum crust, mixed calcite quartz sepiolite crust, and gypsum crust. For each Landsat scene the spatial distribution of these crust types was mapped with the Spectral Angle Mapper (SAM) method and significant spatiotemporal dynamics of the major surface crust types were observed. Further, the surface crust dynamics were analyzed in comparison with the pan's moisture regime and other climatic parameters. The results show that the crust dynamics are mainly driven by flooding events in the wet season, but are also influenced by temperature and aeolian activity in the dry season. The approach utilized in this study combines the advantages of multitemporal satellite data for temporal event characterization with advantages from hyperspectral methods for the image and ground data analyses that allow improved mineralogical differentiation and characterization. [ABSTRACT FROM AUTHOR]

Extensive efforts are currently being devoted to the establishment of soil spectral libraries on regional, national, continent-wide and global domains. In particular, a new development goes in the direction of global harmonized soil spectral databases that shall be acquired following common standards and procedures so that they can be merged with other soil spectral libraries. The choice of standards is important, especially with respect to their behaviors when laboratory conditions, such as humidity, change. In this study, we test the application and robustness of the Internal Soil Standard (ISS) spectral re-alignment procedure on an extended number of soil samples acquired at different laboratories. In particular, a focus is placed on the comparison between two standards Lucky Bay (LB) and Wylie Bay (WB), for their performance with different humidity laboratory conditions. LB and WB are almost pure quartz sands from Australia. For this, 71 soil samples from Israel with different mineralogical background and variable soil organic matter contents are scanned at two laboratories. The scanning took place with different spectral measurement protocols and extreme diverse conditions in terms of laboratory humidity and moist. All scans are completed by the repeated scans of the two Australian white sands through all scan batches to harmonize the spectral measurements. Our results show that the ISS minimizes the visual spectral variation, aligns the minor and extreme systematic changes of the protocols, and makes them more stable. Furthermore, the LB and WB standards do not exhibit equal performance regarding to relatively dry and humid conditions. The WB standard provides more stable and satisfactory results in humid condition. However, the high performance of the LB sample in spectral correction is still observed, particularly in laboratories with lower moist. Accordingly, these analyses suggest that in the ISS re-alignment procedure, the WB sample is more reliable to be used in humid laboratory condition. • Testing the ISS method for spectral harmonization of a large number of soil spectra acquired in different laboratories • Testing the effects of different setups and extreme different laboratory humidity conditions • Comparing the performances of two standards LB and WB in the ISS re-alignment procedure • WB is recommended to be used in laboratories with high moist condition [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) loss is one of the main causes of soil degradation in croplands. Thus, spatial and temporal monitoring of SOC is extremely important, both from the environmental and economic perspective. In this regard, the high temporal, spatial, and spectral resolution of the Sentinel-2 data can be exploited for monitoring SOC contents in the topsoil of croplands. In this study, we aim to test the effect of the threshold for a spectral index linked to soil moisture and crop residues on the performance of SOC prediction models using the Multi-Spectral Instrument (MSI) Sentinel-2 and the European Land Use/cover Area frame Statistical survey (LUCAS) topsoil database. The LUCAS spectral data resampled according to MSI/Sentinel-2 bands, which were used to build SOC prediction models combining pairs of the bands. The SOC models were applied to a Sentinel-2 image acquired in North-Eastern Germany after removing the pixels characterized by clouds and green vegetation. Then, we tested different thresholds of the Normalized Burn Ratio 2 (NBR2) index in order to mask moist soil pixels and those with dry vegetation and crop residues. The model accuracy was tested on an independent validation database and the best ratio of performance to deviation (RPD) was obtained using the average between bands B6 and B5 (Red-Edge Carbon Index: RE-CI) (RPD: 4.4) and between B4 and B5 (Red-Red-Edge Carbon Index: RRE-CI) (RPD: 2.9) for a very low NBR2 threshold (0.05). Employing a higher NBR2 tolerance (higher NBR2 values), the mapped area increases to the detriment of the validation accuracy. The proposed approach allowed us to accurately map SOC over a large area exploiting the LUCAS spectral library and, thus, avoid a new ad hoc field campaign. Moreover, the threshold for selecting the bare soil pixels can be tuned, according to the goal of the survey. The quality of the SOC map for each tolerance level can be judged based on the figures of merit of the model. [ABSTRACT FROM AUTHOR]

Designing a sampling strategy for soil property mapping from remote sensing imagery entails making decisions about sampling pattern and number of samples. A consistent number of ancillary data strongly related to the target variable allows applying a sampling strategy that optimally covers the feature space. This study aims at evaluating the capability of multispectral (Sentinel-2) and hyperspectral (EnMAP) satellite data to select the sampling locations in order to collect a calibration dataset for multivariate statistical modelling of the Soil Organic Carbon (SOC) content in the topsoil of croplands. We tested different sampling strategies based on the feature space, where the ancillary data are the spectral bands of the Sentinel-2 and of simulated EnMAP satellite data acquired in Demmin (north-east Germany). Some selection algorithms require setting the number of samples in advance (random, Kennard-Stones and conditioned Latin Hypercube algorithms) where others automatically provide the ideal number of samples (Puchwein, SELECT and Puchwein+SELECT algorithm). The SOC content and the spectra extracted at the sampling locations were used to build random forest (RF) models. We evaluated the accuracy of the RF estimation models on an independent dataset. The lowest Sentinel-2 normalized root mean square error (nRMSE) for the validation set was obtained using Puchwein (nRMSE: 8.7%), and Kennard-Stones (9.2%) algorithms. The most efficient sampling strategies, expressed as the ratio between accuracy and number of samples per hectare, were obtained using Puchwein with EnMAP and Puchwein+SELECT algorithm with Sentinel-2 data. Hence, Sentinel-2 and EnMAP data can be exploited to build a reliable calibration dataset for SOC mapping. For EnMAP, the different selection algorithms provided very similar results. On the other hand, using Puchwein and Kennard-Stones algorithms, Sentinel-2 provided a more accurate estimation than the EnMAP. The calibration datasets provided by EnMAP data provided lower SOC variability and lower prediction accuracy compared to Sentinel-2. This was probably due to EnMAP coarser spatial resolution (30 m) less adequate for linkage to the sampling performed at 10 m scale. [ABSTRACT FROM AUTHOR]

The Department 93 of the State Authority for Geology, Raw Materials and Mining (LGRB) Baden-Württemberg has the task of carrying out the systematic soil survey in Baden-Württemberg, Germany's third-largest state in southwest Germany. The results are used, for example, in the implementation of soil protection concerns in planning (regional planning, urban land-use planning and large-scale projects such as long-distance traffic routes and power lines), in water management (water framework regulations, discharge modelling), in agriculture (cross-compliance erosion, disadvantaged areas) and in nature conservation (biotope network). In current work processes, the information source satellite-based earth observation has only been used sporadically. Nowadays free high quality multispectral imagery is available from space such as from the twin Sentinel-2 (S2) sensors providing wide coverage, minimum five-day global revisit-time at 10 to 20 m spatial scale, and improved spectral characteristics than previous multispectral satellites. The capabilities of the S2 sensors for soil assessment (soil organic carbon, soil texture) were recently demonstrated in local areas using ground databases for calibration (Gholizadeh et al., Castaldi et al., 2018). In this study, we evaluate the potential of satellite data of the Copernicus Mission for the assessment of area-wide, high-resolution, near-surface soil parameters (e.g. organic carbon content, clay content, soil moisture, stone content, soil roughness) for the Baden-Württemberg region in southeastern Germany. In particular, this work is based on the collaborative project BopaBW supported by the Copernicus-Services program in Germany and lead by the LGRB, Freiburg. BopaBW aims at the development of a data processing concept for the derivation of additional soil information for soil maps from Copernicus satellite imagery. The aim is to elaborate the relationships of near-surface soil parameter and their spectral characteristics by fusing the multispectral S2 data with analyzed soil samples of arable and vine-growing areas in Baden-Wuerttemberg and hyperspectral field and laboratory spectrometer measurements. For calibration and validation, extensive ground truth data from soil surveys including chemical and physical soil properties and hyperspectral field measurements are available.One of the most important cornerstones in the use of Copernicus earth observation data is the operational and long-term availability of physically homogeneous, high-quality data. The requirements on the products for the integration into the official LGRB tasks have to be defined with regard to temporal and spatial availability, quality standards as well as processing levels for the creation of the data processing concept. The technical implementation of the overall concept is carried out during the project in an iterative process. [ABSTRACT FROM AUTHOR]

Imaging spectroscopy (IS) has largely demonstrated its capabilities for the quantitative derivation of several top-soil properties remotely. Upcoming spaceborne hyperspectral sensors that will be put in space in 2019-2020 (PRISMA, EnMAP, HISUI) and in the following years (e.g. SHALOM, the NASA mission SBG/HypsIRI, the ESA candidate mission CHIME) will open the way for the global mapping and monitoring of soils. Nonetheless, the potential of IS for soil applications at large scale has not been yet fully exploited. In particular, an avenue of development is toward global harmonized soil databases such as global soil spectral libraries. These databases are crucial since they can allow the development of robust calibrations for the multivariate prediction of soil properties applicable from regional to global scale. To date there are many spectral libraries that are being developed at the country scale (e.g. France, Israel, Brazil, China, Australia, Denmark), at the continent scale (EU-wide LUCAS soil database), or at the global scale (World Soil Spectral Library of Viscarra-Rossel et al). One of the main issues in the development of such spectral libraries is about the use of standards and protocols and production of harmonized data bases so that (i) data are controlled and reproducible, and (ii) current databases acquired at different locations with different protocols can be merged. In this study, we tested the application and robustness of the Internal Soil Standard (ISS) method for the spectral alignment and correction of spectral measurements on an extended number of soil samples acquired at different laboratories. Furthermore, we tested how the ISS commonly used sand standards, Lucky Bay (LB) and Wylie Bay (WB) samples, almost pure quartz sands from Australia, perform in different humidity laboratory conditions. Currently, most users of the ISS method use the LB sample. Then 71 soil samples from Israel with different mineralogical background and variable soil organic matter contents were scanned at Tel-Aviv (TAU, Israel) and Potsdam (GFZ, Germany) laboratories. The scanning took place with different spectral measurement protocols and extreme diverse conditions in terms of laboratory humidity and moist. All scans were completed by the repeated scans of the two Australian white sands through all scan batches to harmonize the spectral measurements. The results showed that the ISS minimized the visual spectral variation, aligned the minor and extreme systematic changes of the protocols, and made them more stable. Also, it showed that the LB and WB did not exhibit equal performance regarding to relatively dry and humid conditions. The WB standard provided more stable and satisfactory results in humid condition. However, the high performance of the LB sample in spectral correction was still obvious, particularly in laboratories with lower moist. Accordingly, these analyses suggested that during the ISS procedure, attention to the laboratory relative moist conditions must be taken, and in humid laboratory conditions, the WB sample is more reliable to be used than the LB sample. [ABSTRACT FROM AUTHOR]

In this work, we develop a software suite for studies of atmosphere–underlying SNOW-spaceborne optical receiver light TRANsmission calculations (SNOWTRAN) with applications for the solution of forward and inverse radiative transfer problems in polar regions. Assuming that the aerosol load is extremely low, the proposed theory does not require the numerical procedures for the solution of the radiative transfer equation and is based on analytical equations for the spectral nadir reflectance and simple approximations for the local optical properties of atmosphere and snow. The developed model is validated using EnMAP and PRISMA spaceborne imaging spectroscopy data close to the Concordia research station in Antarctica. A new, fast technique for the determination of the snow grain size and assessment of the snowpack vertical inhomogeneity is then proposed and further demonstrated on EnMAP imagery over the Aviator Glacier and in the vicinity of the Concordia research station in Antarctica. The results revealed a large increase in precipitable water vapor at the Concordia research station in February 2023 that was linked to a warming event and a four times larger grain size at Aviator Glacier compared with Dome C. [ABSTRACT FROM AUTHOR]

EnMAP (Environmental Mapping and Analysis Program) is a high-resolution imaging spectroscopy remote sensing mission that was successfully launched on April 1st, 2022. Equipped with a prism-based dual-spectrometer, EnMAP performs observations in the spectral range between 418.2 nm and 2445.5 nm with 224 bands and a high radiometric and spectral accuracy and stability. EnMAP products, with a ground instantaneous field-of-view of 30 m × 30 m at a swath width of 30 km , allow for the qualitative and quantitative analysis of surface variables from frequently and consistently acquired observations on a global scale. This article presents the EnMAP mission and details the activities and results of the Launch and Early Orbit and Commissioning Phases until November 1st, 2022. The mission capabilities and expected performances for the operational Routine Phase are provided for existing and future EnMAP users. • EnMAP launched on April 1, 2022, and entered routine operations on November 2, 2022. • Satellite checked out and cycle of acquisition planning to product delivery confirmed. • In-flight spectral, radiometric and geometric calibrations performed and analyzed. • Fully-automatic image processing chain for three user product levels released. • Quality assessment and product validation of imaging spectroscopy data conducted. [ABSTRACT FROM AUTHOR]

Abstract The short revisit time of the Sentinel-2 (S2) constellation entails a large availability of remote sensing data, but S2 data have been rarely used to predict soil organic carbon (SOC) content. Thus, this study aims at comparing the capability of multispectral S2 and airborne hyperspectral remote sensing data for SOC prediction, and at the same time, we investigated the importance of spectral and spatial resolution through the signal-to-noise ratio (SNR), the variable importance in the prediction (VIP) models and the spatial variability of the SOC maps at field and regional scales. We tested the capability of the S2 data to predict SOC in croplands with quite different soil types and parent materials in Germany, Luxembourg and Belgium, using multivariate statistics and local ground calibration with soil samples. We split the calibration dataset into sub-regions according to soil maps and built a multivariate regression model within each sub-region. The prediction accuracy obtained by S2 data is generally slightly lower than that retrieved by airborne hyperspectral data. The ratio of performance to deviation (RPD) is higher than 2 in Luxembourg (2.6) and German (2.2) site, while it is 1.1 in the Belgian area. After the spectral resampling of the airborne data according to S2 band, the prediction accuracy did not change for four out of five of the sub-regions. The variable importance values obtained by S2 data showed the same trend as the airborne VIP values, while the importance of SWIR bands decreased using airborne data resampled according the S2 bands. These differences of VIP values can be explained by the loss of spectral resolution as compared to APEX data and the strong difference in terms of SNR between the SWIR region and other spectral regions. The investigation on the spatial variability of the SOC maps derived by S2 data has shown that the spatial resolution of S2 is adequate to describe SOC variability both within field and at regional scale. [ABSTRACT FROM AUTHOR]

The paper presents the first retrievals of clean snow properties using spaceborne hyperspectral observations via the Environmental Mapping and Analysis Program (EnMAP). The location close to the Concordia station at the Dome C Plateau (Antarctica) was selected. At this location, the atmospheric effects (except molecular light scattering and absorption) are weak, and the simplified atmospheric correction scheme could be applied. The ice grain size, snow specific surface area, and snow spectral and broadband albedos were retrieved using single-view EnMAP measurements. In addition, we propose a technique to retrieve trace gas concentrations (e.g., water vapor and ozone) from EnMAP observations over the snow surfaces. A close correspondence of satellite and ground-measured parameters was found. [ABSTRACT FROM AUTHOR]

The commonly used optical technique for estimating organic matter in soils is based on a chemometric approach. Many studies have examined the correlation between spectral response and organic matter content, but none have investigated uncertainties based on unequal spectral responses in various soil types or spectral detection limit. The aim of this study was to systematically examine the spectral responses of five different soils with increasing amounts of organic matter to evaluate detection limits in point (ASD) and image (HySpex) domains. In addition, we evaluated the contribution of clay content and soils' initial organic matter on the assessed detection limit for each sensor. Large spectral variations were found between soils with the same organic matter content and between the two sensors when calculating the detection limit. Thus, applying a generic prediction model of organic matter using all soil types results in a rough estimation due to the spectrum affiliation with a certain class and not due to correct organic matter analysis. The importance of this study lies in showing that the use of spectroscopy for spectral-based organic matter detection should be practiced with caution, by highlighting the variant and inconsistent effects of organic matter in soils, and discussing the problematic assessment of organic matter when using a chemometric approach. [ABSTRACT FROM AUTHOR]

Arctic tundra ecosystems exhibit small-scale variations in species composition, micro-topography as well as significant spatial and temporal variations in moisture. These attributes result in similar spectral characteristics between distinct vegetation communities. In this study we examine spectral variability at three phenological phases of leaf-out, maximum canopy, and senescence of ground-based spectroscopy, as well as a simulated Environmental Mapping and Analysis Program (EnMAP) and simulated Sentinel-2 reflectance spectra, from five dominant low-Arctic tundra vegetation communities in the Toolik Lake Research Area, Alaska, in order to inform spectral differentiation and subsequent vegetation classification at both the ground and satellite scale. We used the InStability Index (ISI), a ratio of between endmember and within endmember variability, to determine the most discriminative phenophase and wavelength regions for identification of each vegetation community. Our results show that the senescent phase was the most discriminative phenophase for the identification of the majority of communities when using both ground-based and simulated EnMAP reflectance spectra. Maximum canopy was the most discriminative phenophase for the majority of simulated Sentinel-2 reflectance data. As with previous ground-based spectral characterization of Alaskan low-Arctic tundra, the blue, red, and red-edge parts of the spectrum were most discriminative for all three reflectance datasets. Differences in vegetation colour driven by pigment dynamics appear to be the optimal areas of the spectrum for differentiation using high spectral resolution field spectroscopy and simulated hyperspectral EnMAP and multispectral Sentinel-2 reflectance spectra. The phenological aspect of this study highlights the potential exploitation of more extreme colour differences in vegetation observed during senescence when hyperspectral data is available. The results provide insight into both the community and seasonal dynamics of spectral variability to better understand and interpret currently used broadband vegetation indices and also for improved spectral unmixing of hyperspectral aerial and satellite data which is useful for a wide range of applications from fine-scale monitoring of shifting vegetation composition to the identification of vegetation vigor. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract: Microtopography and roughness are highly dynamic properties of the soil surface and important factors governing surface runoff and erosion processes. While various remote sensing technologies were successfully applied for topography measurements at different spatial scales, there is a lack of field studies that collected systematically microtopography data over long observation periods. In this paper an approach to measure and quantify surface roughness in the field based on laser scanning technologies is presented. Between June 2004 and November 2005 97 in-situ measurements were conducted in a test site with two different sandy substrates in vegetation-free conditions. Two-dimensional high-resolution (1 mm) datasets where generated for eight micro erosion plots of 0.25 to 2.9 m2 in size. Dynamics and pattern formation were quantified for surface roughness and surface height changes. Roughness patterns at different scales were analyzed by local roughness indices using sliding windows of 3 to 55 mm in size. Results show strong spatial and temporal dynamics in surface roughness as well as substrate-specific variations. Temporal roughness variations could be detected and were linked to precipitation patterns. The methods presented in this paper are considered suitable to generate high-resolution datasets on spatiotemporal and multi-scale microtopography patterns and to advance the understanding of surface processes at small scales in natural environments. [Copyright &y& Elsevier]

Important environmental parameters in arctic periglacial landscapes (i.e. permafrost temperature, active-layer depth, soil moisture, precipitation, vegetation cover) will very likely change in a warming climate. The thawing of permafrost, especially, might cause massive landscape changes due to thermokarst and an enhanced release of greenhouse gasses from the large amounts of carbon stored in frozen deposits, resulting in positive climate-warming feedback. For the identification, mapping, and quantification of such changes on various scales up to the entire circum-Arctic, remote sensing and spatial data analysis are essential tools. In this study an extensive field-work dataset including spectral surface properties, vegetation, soils, and geomorphology was acquired in the largest Arctic delta formed by a single river, the Siberian Lena River Delta. A portable field spectrometer (ASD FieldSpec Pro FR®) was used for spectral surveys of terrain surfaces, and optical satellite data (Landsat Enhanced Thematic Mapper (ETM+), CHRIS-Proba) were used for the characterization, manual mapping, and automatic classification of typical periglacial land-cover units in the Lena Delta. Qualitative data from soils, vegetation, soil moisture, and relief units were correlated with the field-spectral data and catalogued for a wide variety of surface types. The wide range of micro- and meso-scale variations of periglacial surface features in the delta results in distinctive spectral characteristics for different land-cover units. The three main delta terraces could also be spectrally separated and characterized. The present dataset provides a basis for further spectral data acquisitions in the Lena Delta and for comparisons with periglacial surfaces from other regions. [Copyright &y& Elsevier]

• Spectral and terrain features for mapping cropland soils in Brazil. • Detailed soil spatial distribution and SOC stock was estimated in croplands. • Predicted maps revealed a high variability of topsoil among the cropped areas. • Earth Observation data is a valuable source for mapping and monitoring cropland soils. Despite the natural spatial variability, cropland soils are subject to many interventions that can lead to alterations of soil functioning. As the cropland expansion took place in Brazil the last decades, leading to significant land-use change and environmental impacts, detailed information about soils is fundamental for sustainable development. Thus, considering the lack of spatially explicit information about cropland soils in Brazil, we aimed at performing high-resolution mapping of key topsoil attributes using spectral and terrain features extracted from Earth observation data (EOD). With the resulting information, we also aimed at performing a general examination of the main agricultural regions and estimate the total organic carbon stocks on croplands soils. For this, we obtained environmental predictors from the historical collection of Landsat data and the digital elevation model from Shuttle Radar Topographic Mission at the cloud-based platform of Google Earth Engine. The environmental predictors (30 m spatial resolution) with georeferenced soil samples (n = 5097) were used for predicting the topsoil content (0–20 cm) of clay, sand, silt, cation exchange capacity, pH, soil organic carbon (SOC) and SOC stock. Prediction models of clay, sand, SOC content, and SOC stocks had the best performance metrics, achieving a R2 ranging from 0.44 to 0.74 and ratio of performance to the interquartile range higher than 1.5. The predicted maps revealed the variability of topsoil among the cropped areas, indicating that the agricultural expansion took place on sandy soils. The SOC stock map provided consistent estimates compared to previous datasets but revealed additional information at the local and regional scales. Thus, this study supports the proposition that EOD is a valuable source for extracting environmental features for mapping and monitoring cropland soils at finer resolutions, assisting the evaluation of soil spatial distribution and the historical agriculture expansion over large geographical areas. [ABSTRACT FROM AUTHOR]

• Soil spectral information is sensitive to scanning conditions. • The derivative spectra from the FOSS sensor produced the best SOC prediction model. • The derivative corrected merged spectra significantly improved the model accuracy. • Derivative spectra plus ISS perform well in development of large spectral libraries. Visible–near infrared–shortwave infrared (VNIR–SWIR) spectroscopy is being increasingly used for soil organic carbon (SOC) assessment. Common practice consists of scanning soil samples using a single spectrometer. Considerations have rarely been documented of the effects of using multiple instruments and scanning conditions on SOC model calibration that occur when merging soil spectral libraries (SSLs), particularly in soils with low SOC concentration and using both field spectroradiometers and laboratory fixed spectrometers. To address this gap, we scanned 143 low-SOC-content soil samples using three spectrometers (ASD FieldSpec 3, ASD FieldSpec 4, and FOSS XDS) and four setup features - FOSS, contact probe (CP), dark-box (DB), and open laboratory (LAB) - at three laboratories. The application of an internal soil standard (ISS) to align one laboratory spectrum with another for spectral correction and spectral merging of various SSLs was examined. SOC models were developed using i) data from each single spectrometer – single laboratory separately and ii) merged data from multiple spectrometers – different laboratories, applying the 1st derivatives of spectra and random forest (RF) regression. The results indicate that the spectral shape and wavelength position of key features obtained from all spectrometers and setups did not show any noticeable differences, though spectra based on FOSS setup, particularly on low-SOC samples, demonstrated greater range in absolute derivative values regardless of ISS application. The derivative ISS-corrected spectra showed less variation among different spectrometers compared to their uncorrected raw reflectance spectra. All single spectrometer models predicted SOC reasonably well. However, the spectra acquired by the FOSS setup predicted SOC more accurately (R2 = 0.77, RPIQ = 3.30, RMSE = 0.22 %, and SD = 0.04) than the spectra acquired by the other setups. The models derived from merged uncorrected raw reflectance spectra yielded poor results (R2 = 0.48, RPIQ = 2.33, RMSE = 0.33 %, and SD = 0.10); nevertheless, assessment of SOC using the 1st derivative ISS-corrected merged SSLs considerably improved the prediction accuracy (R2 = 0.70, RPIQ = 3.10, RMSE = 0.25 %, and SD = 0.06). Hence, the derivative spectra coupled with the ISS correction improved the accuracy of SOC prediction models obtained from the merged soil spectra collected in different environments using different instruments. We therefore recommend application of the ISS spectral alignment method linked to the 1st derivative approach to enhance the compilation of SSLs at the regional and global scales for SOC assessment. [ABSTRACT FROM AUTHOR]

Abstract Soil color is a key indicator of soil properties and conditions, exerting influence on both agronomic and environmental variables. Conventional methods for soil color determination have come under scrutiny due to their limited accuracy and reliability. In response to these concerns, we developed an innovative system that leverages 35 years of satellite imagery in conjunction with in-situ soil spectral measurements. This approach enables the creation of a global soil color map with a fine spatial resolution of 30 m x 30 m. The system initially identifies bare earth areas worldwide using reflectance bands acquired from Landsat 4 through Landsat 8 between 1985 and 2020. Soil color was quantified using the CIE-XYZ coordinates, utilizing 8005 soil spectral measurements within the visible range (380–780 nm) as ground truth data. We established transfer functions to convert Landsat reflectance bands to standardized XYZ color coordinates. These transfer functions were subsequently applied to images of bare surfaces, covering approximately 38.5% of the Earth's surface. We validated the resulting global soil color map using statistical indices derived from an independent set of ground-truth spectral data, demonstrating a high degree of agreement. By creating the world's first global soil color map, we have set a baseline for future spatial and temporal monitoring of soil conditions, thus enhancing our understanding and management of our planet's vital soil resources. • Global bare surface image (BaSCI) was created from a 35-years Landsat collection. • BaSCI covers 38.5% of Earth's surface and 82.2% of the world's agricultural land. • BaSCI and the global spectral library had moderate to high correlations. • Integrated proximal and remote sensing data leveraged topsoil color mapping. • National and continental soil assessments could benefit from BaSCI and color maps. [ABSTRACT FROM AUTHOR]

• Large differences in relative absorbances were found across twenty instruments. • All instruments delivered good predictions when calibrated internally. • Predictive performance varied when a single existing library was used for calibration. • Standard Normal Variate helps reduce dissimilarities across instruments. • However, spectral standardization may be necessary in contrasting situations. Diffuse reflectance spectroscopy has been extensively employed to deliver timely and cost-effective predictions of a number of soil properties. However, although several soil spectral laboratories have been established worldwide, the distinct characteristics of instruments and operations still hamper further integration and interoperability across mid-infrared (MIR) soil spectral libraries. In this study, we conducted a large-scale ring trial experiment to understand the lab-to-lab variability of multiple MIR instruments. By developing a systematic evaluation of different mathematical treatments with modeling algorithms, including regular preprocessing and spectral standardization, we quantified and evaluated instruments' dissimilarity and how this impacts internal and shared model performance. We found that all instruments delivered good predictions when calibrated internally using the same instruments' characteristics and standard operating procedures by solely relying on regular spectral preprocessing that accounts for light scattering and multiplicative/additive effects, e.g., using standard normal variate (SNV). When performing model transfer from a large public library (the USDA NSSC-KSSL MIR library) to secondary instruments, good performance was also achieved by regular preprocessing (e.g., SNV) if both instruments shared the same manufacturer. However, significant differences between the KSSL MIR library and contrasting ring trial instruments responses were evident and confirmed by a semi-unsupervised spectral clustering. For heavily contrasting setups, spectral standardization was necessary before transferring prediction models. Non-linear model types like Cubist and memory-based learning delivered more precise estimates because they seemed to be less sensitive to spectral variations than global partial least square regression. In summary, the results from this study can assist new laboratories in building spectroscopy capacity utilizing existing MIR spectral libraries and support the recent global efforts to make soil spectroscopy universally accessible with centralized or shared operating procedures. [ABSTRACT FROM AUTHOR]

The sampling strategy for mapping soil properties from remote sensing imagery entails making decisions about sampling pattern, size and location. The availability of a consistent number of ancillary data strongly related to the target variable allows applying sampling strategies that optimally cover the feature space. This study aims at evaluating the capability of multispectral (Sentinel-2) and hyperspectral (EnMAP) satellite data to select the sampling locations in order to collect a calibration dataset as basis for multivariate statistical modelling of the Soil Organic Carbon (SOC) content. Remote sensing spectra can be exploited first to set the sampling strategy and then as independent variables for the prediction models of the target variables. We tested different sampling strategies based on the feature space, where the ancillary data are the spectral bands of the Sentinel-2 and of simulated EnMAP satellite data acquired in Demmin (north-Est Germany). Some selection algorithms require to set the number of samples in advance (random, Kennard-Stones and conditioned Latin Hypercube algorithm) and others automatically provide the ideal number of sampling units (Puchwein, SELECT and Puchwein+SELECT algorithm). The SOC content and the spectra extracted at the sampling locations were used to build random forest (RF) models. The accuracy of the RF estimation models was evaluated on an independent dataset. The highest Sentinel-2 ratio of performance to deviation (RPD) for the validation set was obtained using Puchwein (RPD: 2.5), and Kennard-Stones (RPD: 2.4) algorithm. A strong positive correlation was detected between the standard deviation of the calibration dataset and the validation accuracy. The efficiency of the sampling strategies, as ratio between accuracy and number of samples per hectare, is highest using Puchwein with EnMAP and Puchwein+SELECT algorithm with Sentinel-2 data. The achieved results demonstrated that Sentinel-2 and EnMAP data can be exploited to build a reliable calibration dataset for SOC mapping; moreover the efficiency of the sampling strategy selection can be improved using algorithms that provide the number of sampling units. For EnMAP, the different selection algorithms provided very similar results. On the other hand, using Puchwein and Kennard-Stones algorithms, Sentinel-2 provided a more accurate estimation than the EnMAP. The calibration datasets provided by EnMAP data provided in this case lower SOC variability and lower prediction accuracy than compared to Sentinel-2. This was probably due to EnMAP coarser spatial resolution (30 m) less adequate for linkage to the sampling performed at 10 m scale. [ABSTRACT FROM AUTHOR]

The short revisit time of the Sentinel-2 (S2) constellation entails a large availability of remote sensing data, but S2 data have been rarely used to predict soil organic carbon (SOC) content. Thus, this study aims at comparing the capability of multispectral S2 and airborne hyperspectral remote sensing data for SOC prediction, and at the same time, we investigated the importance of spectral and spatial resolution through the signal-to-noise ratio (SNR), the variable importance in the prediction (VIP) models and the spatial variability of the SOC maps at field and regional scales. We tested the capability of the S2 data to predict SOC in croplands with quite different soil types and parent materials in Germany, Luxembourg and Belgium, using multivariate statistics and local ground calibration with soil samples. We split the calibration dataset into sub-regions according to soil maps and built a multivariate regression model within each sub-region. The prediction accuracy obtained by S2 data is generally slightly lower than that retrieved by airborne hyperspectral data. The ratio of performance to deviation (RPD) is higher than 2 in Luxembourg (2.6) and German (2.2) site, while it is 1.1 in the Belgian area. After the spectral resampling of the airborne data according to S2 band, the prediction accuracy did not change for four out of five of the sub-regions. The variable importance values obtained by S2 data showed the same trend as the airborne VIP values, while the importance of SWIR bands decreased using airborne data resampled according the S2 bands. These differences of VIP values can be explained by the loss of spectral resolution as compared to APEX data and the strong difference in terms of SNR between the SWIR region and other spectral regions. The investigation on the spatial variability of the SOC maps derived by S2 data has shown that the spatial resolution of S2 is adequate to describe SOC variability both within field and at regional scale. [ABSTRACT FROM AUTHOR]

Abstract Tools that enhance the management of the soil resource are vital for sustainability and continued productivity. The assessment and monitoring of surface-soil condition provide key information required to effectively manage this resource. This research used a simulated Environmental Mapping and Analysis Program (EnMAP) hyperspectral image cube of an agricultural region in semi-arid Mediterranean Spain to classify soil erosion states. Multiple Endmember Spectral Mixture Analysis (MESMA) was used to derive within-pixel fractions of eroded and accumulated soil states. Soil states (eroded, intermediate, accumulated) were classified with an overall accuracy of 70.3% using the unmixing fraction maps and terrain derivatives. The results generated in this research provided an outlook for the applicability of the future EnMAP sensor for soil erosion investigations in semi-arid Mediterranean environments. Highlights • Mapping soil erosion and condition continue to be challenging. • Future hyperspectral satellites should assist in measuring soil erosion status. • Satellite simulation is essential to assist in future data product development and testing. • Simulated data demonstrated that the future EnMAP satellite can map erosion status with an overall accuracy of 70.3%. [ABSTRACT FROM AUTHOR]

The Web Service Advantages and Limitations. i B Replaced b "...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". B with b "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". [Extracted from the article]

Imaging spectroscopy, also known as hyperspectral remote sensing, is based on the characterization of Earth surface materials and processes through spectrally-resolved measurements of the light interacting with matter. The potential of imaging spectroscopy for Earth remote sensing has been demonstrated since the 1980s. However, most of the developments and applications in imaging spectroscopy have largely relied on airborne spectrometers, as the amount and quality of space-based imaging spectroscopy data remain relatively low to date. The upcoming Environmental Mapping and Analysis Program (EnMAP) German imaging spectroscopy mission is intended to fill this gap. An overview of the main characteristics and current status of the mission is provided in this contribution. The core payload of EnMAP consists of a dual-spectrometer instrument measuring in the optical spectral range between 420 and 2450 nm with a spectral sampling distance varying between 5 and 12 nm and a reference signal-to-noise ratio of 400:1 in the visible and near-infrared and 180:1 in the shortwave-infrared parts of the spectrum. EnMAP images will cover a 30 km-wide area in the across-track direction with a ground sampling distance of 30 m. An across-track tilted observation capability will enable a target revisit time of up to four days at the Equator and better at high latitudes. EnMAP will contribute to the development and exploitation of spaceborne imaging spectroscopy applications by making high-quality data freely available to scientific users worldwide. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• VNIR-SWIR spectra were used to estimate PTEs (Cr, Cu, Ni and Zn) in Technosols. • Combining different soil preparation and spectral pre-processing steps. • Increasing soil preparation improves PLSR and RFR prediction accuracy. • Laboratory hyperspectral imagery provides good quality data for highly heterogeneous soil samples. Reflectance spectroscopy in the visible-infrared and shortwave infrared (450–2500 nm) wavelength region is a rapid, cost-effective and non-destructive method that can be used to monitor heavy metal (PTE, potential toxic elements) contaminated areas. Due to the PTE pollution that has accumulated in the course of wastewater treatment, the existence of Technosols presents an environmental problem, a potential source for PTE uptake by vegetation, or even the release of PTEs into groundwater. In this study, multivariate procedures using Partial Least Squares Regression (PLSR) and Random Forest Regression (RFR) are applied to quantify relationships between soil heavy metal concentration (Cr, Cu, Ni, Zn) and reflectance data of highly contaminated Technosols from a former sewage farm near Berlin, Germany. Laboratory measurements of 110 soil samples in four different preparation steps were acquired with HySpex hyperspectral cameras. The impact of the different preparation steps, namely "oven-dried", "sieved", "ground", "LOI", was evaluated for its potential to enhance the method performance or to reduce the time-consuming soil sample preparation. Furthermore, different spectral pre-processing methods were evaluated regarding improvements of spectral modelling performance and their ability to minimise noise and multiple scattering effects. Considering the optimal coefficient of determination (R2), PLSR shows an improving performance and accuracy with increasing preparation steps such as ground or LOI for all metals of interest (R2_Cr: 0.52–0.78; R2_Cu: 0.36–0.73; R2_Ni: 0.19–0.42 and R2_Zn: 0.41–0.74). RFR shows a weaker estimation performance for all metals, even when using higher sample preparation levels (R2_Cr: 0.36–0.62; R2_Cu: 0.17–0.72; R2_Ni: 0.20–0.35 and R2_Zn: 0.26–0.67). The results show that an application of methods such as PLSR for the prediction of PTE concentration in Technosols is still a challenge but provides more robust estimations than the user-friendly RFR method. Additionally, this study shows that PTE estimation performance in heterogeneous soil samples can be improved by increased laboratory soil preparation steps and further spectral pre-processing steps. [ABSTRACT FROM AUTHOR]

In this study we tested the feasibility of the thermal infrared (TIR) wavelength region (within the atmospheric window between 8 and 11.5 μm) together with the traditional solar reflective wavelengths for quantifying soil properties for coarse-textured soils from the Australian wheat belt region. These soils have very narrow ranges of texture and organic carbon contents. Soil surface spectral signatures were acquired in the laboratory, using a directional emissivity spectrometer (μFTIR) in the TIR, as well as a bidirectional reflectance spectrometer (ASD FieldSpec) for the solar reflective wavelengths (0.4-2.5 μm). Soil properties were predicted using multivariate analysis techniques (partial least square regression). The spectra were resampled to operational imaging spectroscopy sensor characteristics (HyMAP and TASI-600). To assess the relevance of specific wavelength regions in the prediction, the drivers of the PLS models were interpreted with respect to the spectral characteristics of the soils' chemical and physical composition. The study revealed the potential of the TIR (for clay: R2 = 0.93, RMSEP = 0.66% and for sand: R2 = 0.93, RMSEP = 0.82%) and its combination with the solar reflective region (for organic carbon: R2 = 0.95, RMSEP = 0.04%) for retrieving soil properties in typical soils of semi-arid regions. The models' drivers confirmed the opto-physical base of most of the soils' constituents (clay minerals, silicates, iron oxides), and emphasizes the TIR's advantage for soils with compositions dominated by quartz and kaolinite. [ABSTRACT FROM AUTHOR]

Abstract: The tributary rivers Amu Darya and Syr Darya contribute major amounts of water to the hydrological budget of the endorheic Aral Sea. Processes controlling the flow of water into rivers in the headwater systems in Tien Shan (Kyrgyzstan) and Pamir (Tajikistan) are therefore most relevant. Lake water mineralization is strongly dependent on river discharge and has been inferred from spectrometrically determined gypsum and other salt contents. Comparison of high-resolution mineralization data with tree ring data, other proxies for tracing temperature and snow cover in NW China, and accumulation rates in the Guliya Ice Core indicate that mineralization over the past ~2000yrs in the Aral Sea reflects snow cover variability and glacier extent in Tien Shan and Pamir (at the NW and W edges of the Tibetan Plateau). Snow cover in W Central Asia is preferentially a winter expression controlled by temperature patterns that impact the moisture-loading capacity over N Europe and NW Asia (Clark et al., 1999). We observed that the runoff, resulting from warmer winter temperatures in W Central Asia and resulting in a reduction of snow cover, decreased between AD 100–300, AD 1150–1250, AD 1380–1450, AD 1580–1680 and during several low frequency events after AD 1800. Furthermore, we observed a negative relationship between the amount of mineralization in the Aral Sea and SW summer monsoon intensity starting with the Little Ice Age. Based on these observations, we conclude that the lake level changes during the past ~2000yrs were mostly climatically controlled. Around AD 200, AD 1400 and during the late 20th century AD, human activities (namely irrigation) may also have synergistically influenced discharge dynamics in the lower river courses. [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]

A systematic review and inventory of recent research relating to optical remote sensing of Arctic vegetation was conducted, and thematic and geographical trends were summarized. Research was broadly categorized into four major themes of (1) time series, including NDVI trends and shrub expansion; (2) disturbance and recovery, including tundra fires, winter warming, herbivory, permafrost disturbance, and anthropogenic change; (3) vegetation properties, including biomass, primary productivity, seasonality, phenology, and pigments; and (4) classification and mapping. Remaining challenges associated with remote sensing of Arctic vegetation were divided into three categories and discussed. The first are issues related to environmental controls including disturbance, hydrology, plant functional types, phenology and the tundra-taiga ecotone, and understanding their influence on interpretation and validation of derived remote sensing trends. The second are issues of upscaling and extrapolation related to sensor physics and the comparability of data from multiple spatial, spectral, and temporal resolutions. The final category identifies more philosophical challenges surrounding the future of data accessibility, big data analysis, sharing and funding policies among major data providers such as national space agencies and private companies, as well as user groups in the public and private sectors. The review concludes that the best practices for the advancement of optical remote sensing of Arctic vegetation include (1) a continued effort to share and improve in situ -validated datasets using camera networks and small Unmanned Aerial Vehicles, (2) data fusion with non-optical data, (3) sensor continuity, consistency, and comparability, and (4) free availability and increased sharing of data. These efforts are necessary to generate high quality, temporally dense datasets for identifying trends in Arctic tundra vegetation. • Optical RS of Arctic vegetation benefits from legacy and emerging technologies. • NDVI time-series dominate recent optical remote sensing in the Arctic. • Derivation of Arctic tundra vegetation properties lacks in-situ validation. • Better characterization of environmental controls on Arctic RS trends is needed. • Sensor continuity and comparability are necessary for temporally dense datasets. [ABSTRACT FROM AUTHOR]