This study evaluates the potential of EnMAP spaceborne imaging spectroscopy for mapping and monitoring topsoil properties in two diverse Mediterranean agricultural regions. The research employs state of the art linear and nonlinear machine learning techniques, spectral transformations, and pre-processing methods to analyze EnMAP data quality in comparison to in-situ measurements. The findings demonstrate promising accuracy levels in estimating key soil properties, highlighting the potential of EnMAP imagery for global soil property mapping and monitoring applications.

Ice-free areas in Antarctica contain fragile ecosystems and are hotspots of biodiversity due to a concentration of flora and fauna. As these areas are often isolated and with difficult access, using hyperspectral satellite-borne data and remote sensing techniques presents great advantages for characterizing and monitoring their extension and surface cover characteristics. The objective of this work was to map key soil properties related to the Adélie penguin colony and associated flora and fauna of Seabee Hook at Cape Hallett using EnMAP data. Initial mapping results of extractable phosphorous (P), total organic carbon (TOC) and total nitrogen (TN) were obtained, and the distribution could be related to the intense activities of the penguin colony that comes to breed in the study area.

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.

The Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) is a new ESA Earth Observation mission which consists in developing a hyperspectral satellite to support EU policies on the management of natural resources, ultimately helping to address the global issue of food security. One of the mission activities is associated to the development of the CHIME-E2E (End-to-End) Performance Simulator that shall be used to evaluate the sensor design and future processing modules provided by the partners by simulating future CHIME images and thematic products. In the frame of this activity, the CHIME Mission Advisory Group (MAG) has identified a collection of five core high priority products (HPP) that includes the retrieval of canopy nitrogen, leaf nitrogen content, leaf mass/area, soil organic carbon content (SOC) and kaolinite abundance. In this paper, we present the first results of applying the L2B prototype processing to hyperspectral airborne and spatial imagery used to simulate realistic CHIME data, to derive soil and mineral maps. The obtained results demonstrate the potential of the next generation of Copernicus missions with high spectral resolution and wide swath imaging satellite for geoscience research and applications.

This study introduces the development of Spatially Upscaled Soil Spectral Libraries (SUSSL) approach to assess spectral disturbances caused by variations in surface conditions in remote sensing-based soil property prediction. The SUSSL incorporates realistic cropland reflectance scenarios using spectral modelling and aggregation techniques. By convoluting the spectral database to multispectral and hyperspectral satellite sensors, the sensitivity of spectral indices in retrieving undisturbed surface reflectance is evaluated. Preliminary findings indicate that the spectral disturbance effects significantly impact the accuracy of soil organic carbon (SOC) estimations, resulting in a noticeable loss compared to bare soil spectra. However, strict filtering criteria using spectral indices exhibit promise in enhancing SOC modelling performance, particularly for multispectral sensors. Hyperspectral sensors demonstrate higher baseline accuracies even in disturbed soil cases. This research highlights the importance of accounting for surface condition variations for reliable soil property mapping. Future work involves leveraging machine learning techniques on SUSSL data to improve prediction accuracy and spatial coverage of soil properties using Earth Observation data.

Soils are an essential factor contributing to agricultural production of rainfed crops such as barley and triticale cereals. Inadequate land management is endangering soil quality and productivity, and in turn crop quality and productivity are affected. In this paper, hyperspectral VNIR-SWIR airborne data (0.4-2.5 µm) is used to analyze spatial differences in vegetation vitality related to soil degradation status in a Mediterranean agricultural area of central Spain. Specifically, biophysical indices such as the leaf area index (LAI) are spatially derived from the remote sensing data and discussed regarding the land degradation status. The results of this study illustrated the potential of hyperspectral remote sensing in the context of crop and land resource monitoring.

This research focuses on a semi-arid, agricultural area in Central Spain near Madrid, in which airborne hyperspectral images have been obtained. Small-scale soil erosion features are exposed at the surface as a consequence of human induced soil erosion derived mainly from tillage practice. Such features are associated with different soil horizons and rock outcrops with contrasted physical and chemical characteristics. Results show that the identification and mapping of different soil surface horizons linked to soil erosion and depositional stages can be achieved over selected test sites based on the spectroscopy data at high spatial resolution. Linked with field validation data and geomorphological analyses, the spatial mapping of the soil erosion and depositional stages is consistent with the soil erosion models implemented for the region. Preliminary multiscale analyses at 3 m, 6 m, and 30 m show the effect of increasing spatial mixing in the field-of-view of the sensor due to the variability at small scale of the different soil horizons representing the surface topsoil.

Traditional handwriting recognition algorithms rely heavily on small lexicons and clean word images. Unfortunately, emergency medical documents do not satisfy either of these conditions. This is a significant road-block that is hampering efforts to rapidly convert valuable offline healthcare handwriting data into digital content that can be efficiently mined for information. This paper describes a strategy whereby given an image representing a noisy handwritten word from a medical document, and a large lexicon consisting of English, medical and pharmacological words, symbols, abbreviations and acronyms, significantly reduces the size of the lexicon while keeping the unknown desired entry within the lexicon. The approach combines geometric interpretations of the word image along with contextual inference of concepts to reduce lexicons for word recognition. The data extracted can then be efficiently and securely disseminated for epidemiological and outbreak detection/analysis. Experimental results on NY State PCR forms are reported.

Emergency health care facilities lack automated medical form recognition systems necessary for efficient epidemiological and health surveillance analysis. The task is to extract handwritten text from the New York State (NYS) Pre-Hospital Care Report (PCR) and determine its ASCII translation. Our approach hybridizes image processing and semantic lexicon pruning to compensate for the otherwise enormous lexicon size. In this paper we expand on our IEEE CBMS 2001 paper, which provided a conceptual overview, by probing into our recognizer design and performance measurements.

The lack of use of high technology in the healthcare delivery system is especially apparent in the emergency medical information systems (EMIS) area. For example, in New York State, all patients who enter the emergency medical service (EMS) are tracked through their pre-hospital care to the emergency room using a pre-hospital care report (PCR). Our goal is to automate the collection of data from the PCR and enable efficient maintenance and dissemination of information. The task involves the automatic extraction and transliteration of the handwritten text in the response boxes provided on the form. The objective is to produce, for each form image, an ASCII transcription of the handwritten contents of the response boxes. These responses could be then used to populate a database. The database itself would then emerge as a valuable resource for enabling data mining and knowledge discovery for the entire medical community.