3U CubeSat-Based Hyperspectral Remote Sensing by Offner Imaging Hyperspectrometer with Radially-Fastened Primary Elements

Sensors, Journal Year: 2024, Volume and Issue: 24(9), P. 2885 - 2885

Published: April 30, 2024

This paper presents findings from a spaceborne Earth observation experiment utilizing novel, ultra-compact hyperspectral imaging camera aboard 3U CubeSat. Leveraging the Offner optical scheme, camera’s hyperspectrometer captures images of terrestrial regions with 200 m spatial resolution and 12 nanometer spectral across 400 to 1000 wavelength range, covering 150 channels in visible near-infrared spectrums. The is specifically designed for deployment on CubeSat nanosatellite platform, featuring robust all-metal cylindrical body hyperspectrometer, coaxial arrangement elements ensures optimal compactness vibration stability. performance was rigorously evaluated through numerical simulations prior construction. Analysis data acquired over year-long orbital operation demonstrates CubeSat’s ability produce various vegetation indices, including normalized difference index (NDVI). A comparative study European Space Agency’s Sentinel-2 L2A shows strong agreement at critical points, confirming suitability Notably, ISOI can generate unique beyond reach L2A, underscoring its potential advancing remote sensing applications.

Language: Английский

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Inversion of Water Quality Parameters from UAV Hyperspectral Data Based on Intelligent Algorithm Optimized Backpropagation Neural Networks of a Small Rural River

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(1), P. 119 - 119

Published: Jan. 2, 2025

The continuous and effective monitoring of the water quality small rural rivers is crucial for sustainable development. In this work, machine learning models were established to predict a typical river based on quantity measured data UAV hyperspectral images. Firstly, spectral preprocessed using fractional order derivation (FOD), standard normal variate (SNV), normalization (Norm) enhance response characteristics parameters. Second, method combining Pearson’s correlation coefficient variance inflation factor (PCC–VIF) was utilized decrease dimensionality features improve input data. Again, screened features, back-propagation neural network (BPNN) model optimized mixture genetic algorithm (GA) particle swarm optimization (PSO) as means estimating parameter concentrations. To intuitively evaluate performance hybrid algorithm, its prediction accuracy compared with that conventional algorithms (Random Forest, CatBoost, XGBoost, BPNN, GA–BPNN PSO–BPNN). results show GA–PSO–BPNN turbidity (TUB), ammonia nitrogen (NH3-N), total (TN), phosphorus (TP) exhibited optimal coefficients determination (R2) 0.770, 0.804, 0.754, 0.808, respectively. Meanwhile, also demonstrated good robustness generalization ability from different periods. addition, we used visualize parameters in study area. This work provides new approach refined rivers.

Language: Английский

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Crop Yield Prediction and Price Forecasting Using Machine Learning

Lecture notes in networks and systems, Journal Year: 2025, Volume and Issue: unknown, P. 357 - 381

Published: Jan. 1, 2025

Language: Английский

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The Integration of IoT (Internet of Things) Sensors and Location-Based Services for Water Quality Monitoring: A Systematic Literature Review

Sensors, Journal Year: 2025, Volume and Issue: 25(6), P. 1918 - 1918

Published: March 19, 2025

The increasing demand for clean and reliable water resources, coupled with the growing threat of pollution, has made real-time quality (WQ) monitoring assessment a critical priority in many urban areas. Urban environments encounter substantial challenges maintaining WQ, driven by factors such as rapid population growth, industrial expansion, impacts climate change. Effective WQ is essential safeguarding public health, promoting environmental sustainability, ensuring adherence to regulatory standards. advancement Internet Things (IoT) sensor technologies smartphone applications presents an opportunity develop integrated platforms assessment. Advances IoT provide transformative solution monitoring, revolutionizing way we assess manage our resources. Moreover, recent developments Location-Based Services (LBSs) Global Navigation Satellite Systems (GNSSs) have significantly enhanced accessibility accuracy location information. With proliferation GNSS services, GPS, GLONASS, Galileo, BeiDou, users now access diverse range data that are more precise than ever before. These advancements it easier integrate information into various applications, from planning disaster management transportation. availability multi-GNSS support allows improved satellite coverage reduces potential signal loss or densely built environments. To harness this enable seamless integration LBSs sustainable systematic literature review was conducted determine past trends future opportunities. This research aimed limitations traditional systems while fostering understanding positioning capabilities development. highlights both using offering insights current state technology its There pressing need integrated, system cost-effective accessible. Such should leverage networks continuous immediate feedback, spatially dynamic insights, empowering stakeholders address issues collaboratively efficiently.

Language: Английский

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Water Quality Prediction Method Coupling Mechanism Model and Machine Learning for Water Diversion Projects with a Lack of Data

Water Resources Management, Journal Year: 2025, Volume and Issue: unknown

Published: March 24, 2025

Language: Английский

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Application of deep learning models with spectral data augmentation and Denoising for predicting total phosphorus concentration in water pollution

Journal of the Taiwan Institute of Chemical Engineers, Journal Year: 2024, Volume and Issue: 167, P. 105852 - 105852

Published: Dec. 3, 2024

Language: Английский

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Water Quality Parameters Modeling of Thamirabarani River

Published: July 4, 2024

Language: Английский

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Prediction of Teaching Quality of Open Online Courses based on Weighted Markov Chain

Published: June 14, 2024

Language: Английский

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