This study was motivated by the need to improve densification of Global Horizontal Irradiance (GHI) observations, increasing the number of surface weather stations that observe it, using sensors with a sub-hour periodicity and examining the methods of spatial GHI estimation (by interpolation) with that periodicity in other locations. The aim of the present research project is to analyze the goodness of 15-minute GHI spatial estimations for five methods in the territory of Spain (three geo-statistical interpolation methods, one deterministic method and the HelioSat2 method, which is based on satellite images). The research concludes that, when the work area has adequate station density, the best method for estimating GHI every 15 min is Regression Kriging interpolation using GHI estimated from satellite images as one of the input variables. On the contrary, when station density is low, the best method is estimating GHI directly from satellite images. A comparison between the GHI observed by volunteer stations and the estimation model applied concludes that 67% of the volunteer stations analyzed present values within the margin of error (average of ±2 standard deviations).
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http://dx.doi.org/10.3390/s140406758 | DOI Listing |
Sci Total Environ
January 2025
Interdisciplinary Lab for Mathematical Ecology and Epidemiology & Department of Mathematical and Statistical Sciences, University of Alberta, Canada. Electronic address:
Prompt and accurate monitoring of cyanobacterial blooms is essential for public health management and understanding aquatic ecosystem dynamics. Remote sensing, in particular satellite observations, presents a good alternative for continuous monitoring. This study employs multispectral images from the Sentinel-2 constellation alongside ERA5-Land to enable broad-scale data acquisition.
View Article and Find Full Text PDFSci Total Environ
January 2025
Center for Spatial Technologies and Remote Sensing (CSTARS), Institute of the Environment, University of California, One Shields Avenue, Davis, CA 95616, USA. Electronic address:
Estuaries are complex ecosystems, being difficult to determine the way management actions affect them. This study quantitatively evaluated the spread of invasive submerged and floating aquatic macrophyte vegetation in Franks Tract of the Sacramento-San Joaquin Delta in response to two types of management actions, drought salinity barriers in years 2015, 2021 and 2022, and herbicide treatments in years 2004-2022. A Random Forest algorithm applied to airborne hyperspectral and satellite multispectral images generated maps of macrophyte cover in 2004-2022.
View Article and Find Full Text PDFSci Rep
January 2025
Physics Department, Science College, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia.
Semantic segmentation of high-resolution images from remote sensing is crucial across various sectors. However, due to limitations in computational resources and the complexity of network architectures, many sophisticated semantic segmentation models struggle with efficiency in real-world applications, leading to an interest in developing lightweight model like borders. These models often employ a dual-branch structure, which balances processing speed and performance effectively.
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January 2025
Key Laboratory of Road Construction Technology and Equipment of MOE, Chang'an University, Xi'an, 710064, China.
Unmanned rollers are typically equipped with satellite-based positioning systems for positional monitoring. However, satellite-based positioning systems may result in unmanned rollers driving out of the specified compaction areas during asphalt road construction, which affects the compaction quality and has potential safety hazards. Additionally, satellite-based positioning systems may encounter signal interference and cannot locate unmanned rollers.
View Article and Find Full Text PDFNat Commun
January 2025
State Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing, China.
Complex-valued neural networks process both amplitude and phase information, in contrast to conventional artificial neural networks, achieving additive capabilities in recognizing phase-sensitive data inherent in wave-related phenomena. The ever-increasing data capacity and network scale place substantial demands on underlying computing hardware. In parallel with the successes and extensive efforts made in electronics, optical neuromorphic hardware is promising to achieve ultra-high computing performances due to its inherent analog architecture and wide bandwidth.
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