The field of soil spectroscopy involves global research in measuring soil reflectance across the VIS–NIR–SWIR region. Diverse soil spectral libraries (SSLs) have emerged, but challenges arise in merging or comparing SSLs due to variations in measurement conditions. With the rise of hyperspectral technology, the need for consolidated SSLs on a global scale has become crucial. In response, the IEEE-Standards Association Working Group P4005 was formed in 2021, uniting over 50 experts globally. This collaborative effort aims to establish a common protocol for laboratory and field spectral reflectance measurements that will enable users to develop standardized SSL. The focus currently is placed into five subgroups that focus on providing standards and protocols for measuring in VIS-NIR-SWIR, data archiving, cross calibration, and field operations, with in. The ongoing 48-month project aims to provide a protocol and a set of tools and algorithms that will enable the development of inter-compatible SSLs.

The Environmental Mapping and Analysis Program (EnMAP) is a new spaceborne German hyperspectral satellite mission, whose primary goal is to generate accurate information on the state and evolution of the Earth´s ecosystems. The core themes of EnMAP are monitoring environmental changes, ecosystem responses to human activities, and management of natural resources such as soils and minerals. EnMAP started on 1stApril 2022 and is now in operational phase since over six months, with strong expectations regarding data quality and impact on soil research. In this paper, we aim to demonstrate in a few case studies the observed current capabilities for EnMAP with regard to soil mapping based on different test sites and methodologies. Key soil properties could be derived and spatially mapped in agricultural test sites in semi-arid and temperate zones such as Soil Organic Carbon (SOC) content important for soil health and carbon sequestration, texture (clay content) important for soil fertility, and carbonate content. Additionally, we test different standard and state-of-the art methodologies, including new scenarios for time-series of hyperspectral remote sensing data for improved soil products.

Over the past 25 years, research groups worldwide have been actively studying soil reflectance across the visible, near-infrared, shortwave infrared, and thermal regions for chemometric purposes. These groups have generated numerous soil spectral libraries (SSLs) with varying coverage and using different sensors and protocols. However, merging or comparing SSLs is challenging due to factors related to instrumentation and measurement protocols. With the emergence of hyperspectral technology, there is a growing interest in utilizing SSLs for the quantitative assessment of soil properties on a global scale. P4005 "Standards and Protocols for Soil Spectroscopy" is a working group established under the IEEE-Standards Association that aims to harmonize SSLs by developing an agreed protocol. The final protocol release is expected in early 2024, enabling intercompatible, reliable, and reproducible soil spectral measurements and facilitating the development of a global SSL.

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 ( r2= 0.59 for gravimetric method; r2= 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.

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.

Mapping road conditions is an important issue for city and state authorities worldwide. Today, pavement safety is assessed by specific assemblies based on a mechanical wheel device, which is a method that is limited in its potential product and operation. In this letter, we examined the possibility of harnessing remote reflectance spectroscopy to predict asphalt's dynamic friction coefficient, thereby enabling the identification and mapping of road conditions. We used a near-infrared analysis technique to evaluate an artificial neural network prediction model designed to assess the friction coefficient solely from spectral readings. This letter describes the method for extracting such a model and presents promising results with an accuracy of R = 0.845 and high significance of P <; 0.0001 between actual and predicted friction values. This model was acquired using nine principal components and three neurons. The potential of this technology is also discussed.

The growing demand for mineral and energy resources over the last decade has placed the extractive industry under increasing pressure to monitor and reduce the environmental and societal impact throughout the life-cycle of mining operations. Despite the mounting pressure, the industry is still facing the challenge of how to define targets for, and monitor, the impact of mining. In 2010, the EU-funded EO-MINERS project (Earth Observation for Monitoring and Observing Environmental and Societal Impacts of Mineral Resources Exploration and Exploitation) was set up in an effort to help address this issue, specifically through the application of Earth Observation (EO) data. Furthermore, the aim was to help facilitate and improve interaction and dialogue between the mineral extractive industry and society in view of its sustainable development, while improving its societal acceptability. One of the primary project objectives was to develop novel yet objective EO products contributing to a constructive “trialogue” involving stakeholders such as industrialists (mining companies), regulatory bodies and the civil society. EOMINERS is scheduled to run until October 2013.

This letter presents results of the angular variation of millimeter-wave-radiation backscattering coefficient for various levels of soil moisture content. The research sets the basis for a method, which enables assessment of the soil-moisture content in the upper part of the shallow root zone. Further development of the method should provide a microprofile measurement of soil moisture up to the root zone depth. The method is based on the emerging technology of millimeter waves, providing improved spatial resolution of the subsurface concurrent with surface mapping. The development and the use of the method described herein will make it easier to analyze and understand processes governing the soil-water interface, such as soil crusting, infiltration, runoff, and soil erosion.

Change detection of imaging spectroscopy data is widely used in many applications. Among them, environmental monitoring is of great importance. In this paper, we introduce a new automated method, termed spectral overlapping threshold (SOT), to derive a threshold to distinguish between “change” and “no change” areas. The method exploits the overlapping regions in multi-strip mosaic images, which are regarded as “no change” areas because they are acquired only a few minutes apart. The method consists of two steps. First, similarity measures are applied to the overlapping areas. Then, the histogram of the similarity values are computed and the thresholds for each land use land cover (LULC) category are determined. The method is independent of the underlying SM used to detect changes, and is demonstrated here for the spectral angle measure (SAM), spectral information divergence (SID), Euclidean distance (ED) and spectral correlation measure (SCM). This process is demonstrated for a mosaic of HyMap sensor data acquired in 2009 and 2010 over Sokolov mining area, Czech Republic.

In a Mediterranean semiarid area in Central Spain, with dominant rainfed agriculture, hyperspectral airborne data, supported by field spectroscopy have been obtained, allowing a spectral identification of bare soil with the corresponding erosion stages. The definition of soil erosion stages is based on a spectral characterization supported by morphological, physical and chemical features of contrasted soil surfaces as a result of soil loss. Different soil erosion stages were defined within two bare-soil sites representing the soil variability of the area, and where such stages are spatially represented. The validation of selected image derived endmebers was a key step to carry out a partial unmixing for determining soil erosion stages. A preliminary spatial distribution of advanced and intermediate erosion stages was obtained for the most representative soil types.

Earth Observing-1 Hyperion data were found to be relatively noisy and to contain significant cross-track spectral curvature nonlinearity disturbances, known as thesmile/frowneffect. A method for the correction of spectral curvature effects (smile) in Hyperion images, termedtrend line smile correction(TLSC), is presented. The method is based on the assumption that there is a partial correlation between data spectral nonuniformity, due to the smile and eigenvalues gradient that mostly appears in the first minimum noise fraction (MNF) image (MNF-1). However, MNF-1 consists of both spatial and spectral information. Therefore, it is hypothesized that adaptation applied to MNF-1, according to exclusively spectrally derived parameters (e.g., atmospheric absorption features) can account specifically for the smile effect in the data. A set of normalization factors, calculated from the spectral derivative at the right-hand side of the O2absorption feature (760 nm), MNF-1 and the moderate-resolution atmospheric transmittance radiative transfer model, are used to scale the initial MNF-1. The image is corrected after the inverse conversion of the MNF to radiance space. The methodology was tested on four different Hyperion scenes and consistently outperformed other tested methods by up to nine times. As a result, thematic mapping, using the TLSC-corrected reflectance data cube, was shown to be consistent with the geology maps of the study area.

Spectroscopy has existed for more than three centuries now. Nonetheless, significant scientific advances have been achieved. We discuss the history of spectroscopy in relation to emerging technologies and applications. Advanced focal plane arrays, optical design, and intelligent on-board logic are prime prospective technologies. Scalable approaches in pre-processing of imaging spectrometer data will receive additional focus. Finally, we focus on new applications monitoring transitional ecological zones, where human impact and disturbance have highest impact as well as in monitoring changes in our natural resources and environment. We conclude that imaging spectroscopy enables mapping of biophysical and biochemical variables of the Earth's surface and atmospheric composition with unprecedented accuracy.