![]() ![]() In assessment of the application of sensors for inland water monitoring the needs for macrophyte mapping are often not included ( Mouw et al., 2015). Malthus, in Bio-optical Modeling and Remote Sensing of Inland Waters, 2017 9.7.6 Assessment of Remote Sensing PlatformsĪlong with the challenges of surface effects and water column depth and turbidity, aquatic macrophyte monitoring using remote sensing platforms also includes issues associated with spatial, spectral, and radiometric resolutions. Originally designed in order to evaluate the speed and direction of wind across ocean surfaces, the potential of these sensors for the monitoring of continental surfaces on a regional or global scale has been demonstrated. Thus, while current SAR are characterized by a spatial resolution of about 1 m, with a strong radiometric variation linked to the speckle effect, satellite scatterometers have a spatial resolution accurate to about 10 km, with an precision on the estimation of σ 0 within a tenth of a decibel. These promote spatial resolution at the cost of radiometric resolution. Scatterometers are therefore radar sensors complementary to synthetic aperture radars (SAR). However, as we will aim to demonstrate, this high radiometric resolution is obtained at the cost of spatial resolution. Scatterometers are sidelooking radar sensors designed to precisely evaluate the radar backscatter coefficient σ 0 of the surfaces being observed. Eric Mougin, in Land Surface Remote Sensing in Continental Hydrology, 2016 Abstract: This chapter is intended as a resource to be aware of challenges and the future potential of hyperspectral RS to current and prospective users of high spectral resolution data to extract meaningful information for their research and applications. The promise of upcoming missions with higher spatial and spectral resolution sensors in orbit in the near future will increase the utility of hyperspectral data in several research domains and will likely increase the number of users of HSI for soils, forestry, agriculture, urban, and cryosphere research. This chapter provides a perspective on the evolution of hyperspectral RS methods and applications along with challenges and barriers faced during research and innovation activities. In the chapters of this book, the state of the art has been presented, outlining the advantages of hyperspectral imaging (HSI) systems over multispectral data, and key future challenges and research directions with HSI have been illustrated. ![]() This trend has led to increasing complexity of data types ranging from low to high spatial and spectral resolutions and data dimensionality. Remote sensing (RS) technology has rapidly advanced in terms of radiometric, spatial, and spectral resolution. Bimal Bhattacharya, in Hyperspectral Remote Sensing, 2020 Abstract ![]()
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