Hyperspectral Imaging

Article

Satellite Imagery to Map Topsoil Organic Carbon Content over Cultivated Areas: An Overview

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There is a need to update soil maps and monitor soil organic carbon (SOC) in the upper horizons or plough layer for enabling decision support and land management, while complying with several policies, especially those favoring soil carbon storage. This review paper is dedicated to the satellite-based spectral approaches for SOC assessment that have been achieved from several satellite sensors, study scales and geographical contexts in the past decade. Most approaches relying on pure spectral models have been carried out since 2019 and have dealt with temperate croplands in Europe, China and North America at the scale of small regions, of some hundreds of km2: dry combustion and wet oxidation were the analytical determination methods used for 50% and 35% of the satellite-derived SOC studies, for which measured topsoil SOC contents mainly referred to mineral soils, typically cambisols and luvisols and to a lesser extent, regosols, leptosols, stagnosols and chernozems, with annual cropping systems with a SOC value of ~15 g·kg−1 and a range of 30 g·kg−1 in median. Most satellite-derived SOC spectral prediction models used limited preprocessing and were based on bare soil pixel retrieval after Normalized Difference Vegetation Index (NDVI) thresholding. About one third of these models used partial least squares regression (PLSR), while another third used random forest (RF), and the remaining included machine learning methods such as support vector machine (SVM). We did not find any studies either on deep learning methods or on all-performance evaluations and uncertainty analysis of spatial model predictions. Nevertheless, the literature examined here identifies satellite-based spectral information, especially derived under bare soil conditions, as an interesting approach that deserves further investigations. Future research includes considering the simultaneous analysis of imagery acquired at several dates i.e., temporal mosaicking, testing the influence of possible disturbing factors and mitigating their effects fusing mixed models incorporating non-spectral ancillary information.

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Hyperspectral Imaging (HSI) supports the efficient management of urban resources and infrastructure. Traditional methods of monitoring and analysing urban environments often fall short in providing detailed, accurate, and timely data. HSI overcomes these limitations by offering a comprehensive view of urban landscapes, which can significantly enhance decision-making processes in areas such as environmental monitoring, infrastructure maintenance, and public safety.

Hyperspectral imaging is an advanced remote sensing technology that captures and processes information across a wide spectrum of light beyond what the human eye can perceive. Unlike standard imaging systems that capture images in three colour bands (Red, Green, and Blue), HSI systems divide the spectrum into hundreds of narrow spectral bands. This detailed spectral data enables the identification of materials, detection of chemical compositions, and assessment of various physical properties with unparalleled precision. For instance, HSI can detect pollutants in the air, identify different materials used in buildings, and monitor vegetation health in urban parks.

Implementing HSI in urban settings involves mounting hyperspectral sensors on satellites, drones, or ground-based platforms. These sensors collect data by scanning the urban area and recording the spectral signature of each pixel in the image. Advanced algorithms then process this data to create detailed maps and reports that can be used by city planners, environmental agencies, and infrastructure managers. This technology not only improves the accuracy of urban analysis but also speeds up the data collection process, allowing for real-time monitoring and rapid response to emerging issues.

As urban areas continue to expand and face increasing pressures from climate change, pollution, and population growth, the need for precise and efficient management tools becomes essential. HSI provides a level of detail and accuracy that can lead to more sustainable urban development. For example, it can help cities reduce their carbon footprint by identifying areas where energy efficiency can be improved , or renewable energy sources can be optimally deployed.

In disaster response, HSI provides rapid and accurate damage assessments, identifies structurally compromised buildings, and locates trapped individuals, improving emergency response efficiency. For infrastructure maintenance, HSI detects early signs of wear and tear in buildings, bridges, and roads, allowing for timely interventions that prevent costly repairs and enhance safety. Additionally, HSI supports urban planning by providing detailed data for designing sustainable and resilient cities, integrating green spaces, and optimising land use.

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