We use remote-sensing reflectance from particulate R(rs) to determine the volume absorption coefficient a of turbid water in the 400 < lambda < 700-nm spectral region. The calculated and measured values of a(lambda) show good agreement for 0.5 < a < 10 (m(-1)). To determine R(rs) from a particulate, we needed to make corrections for remote-sensing reflectance owing to surface roughness S(rs). We determined the average spectral distribution of S(rs) from the difference in total remote-sensing reflectance measured with and without polarization. The spectral shape of S(rs) showed an excellent fit to theoretical formulas for glare based on Rayleigh and aerosol scattering from the atmosphere.
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http://dx.doi.org/10.1364/ao.37.004944 | DOI Listing |
Sci Total Environ
January 2025
Center for Geospatial Research, Department of Geography, University of Georgia, 210 Field St. Room 204, Athens, GA 30602, United States of America.
Tidal flooding can significantly impact vegetation pixel reflectance of coastal salt marshes, presenting a problem for remote sensing studies of these highly productive ecosystems. The current study aimed to spatially and temporally expand our previously developed Flooding in Landsat Across Tidal Systems (FLATS) model to detect and analyze the long-term changes in flooded marsh pixels in Landsat 5-9 imagery. As the FLATS index is only calibrated for Landsat 8, our goal was to expand the use of FLATS to a greater range of Landsat imagery and facilitate the masking of flooded pixels in long-term time series of vegetation indices.
View Article and Find Full Text PDFNowadays, spaceborne LiDAR technology, particularly ICESat-2, has become a transformative tool in marine environmental research. Unlike traditional passive optical remote sensing methods, ICESat-2 offers detailed vertical structure mapping of oceanic optical properties. Despite the potential of ICESat-2 for observing the optical vertical structure, its application in the East China Sea with complex hydrological conditions and dynamic ecosystems remains limited.
View Article and Find Full Text PDFPlant Cell Environ
January 2025
Remote Sensing Laboratory, Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, India.
The generation of spectral libraries using hyperspectral data allows for the capture of detailed spectral signatures, uncovering subtle variations in plant physiology, biochemistry, and growth stages, marking a significant advancement over traditional land cover classification methods. These spectral libraries enable improved forest classification accuracy and more precise differentiation of plant species and plant functional types (PFTs), thereby establishing hyperspectral sensing as a critical tool for PFT classification. This study aims to advance the classification and monitoring of PFTs in Shoolpaneshwar wildlife sanctuary, Gujarat, India using Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and machine learning techniques.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Institut de Recherche en Informatique de Toulouse, IRIT UMR5505 CNRS, 31400 Toulouse, France.
This review explores the applications of Convolutional Neural Networks (CNNs) in smart agriculture, highlighting recent advancements across various applications including weed detection, disease detection, crop classification, water management, and yield prediction. Based on a comprehensive analysis of more than 115 recent studies, coupled with a bibliometric study of the broader literature, this paper contextualizes the use of CNNs within Agriculture 5.0, where technological integration optimizes agricultural efficiency.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Institute of Telecommunications, AGH University of Krakow, al. Mickiewicza 30, 30-059 Krakow, Poland.
In this paper, the idea of a radar based on orthogonal frequency division multiplexing (OFDM) is applied to 5G NR Positioning Reference Signals (PRS). This study demonstrates how the estimation of the communication channel using the PRS can be applied for the identification of objects moving near the 5G NR receiver. In this context, this refers to a 5G NR base station capable of detecting a high-speed train (HST).
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