Developing a broad spectrum atmospheric aerosol characterization for remote sensing platforms over desert regions

2014 ◽  
Author(s):  
Shadrian B. Strong ◽  
Andrea M. Brown
Keyword(s):  
Remote Sensing ◽  
Broad Spectrum ◽  
Atmospheric Aerosol ◽  
Sensing Platforms ◽  
Desert Regions ◽  
Aerosol Characterization

scholarly journals New Orthophoto Generation Strategies from UAV and Ground Remote Sensing Platforms for High-Throughput Phenotyping

2021 ◽  
Vol 13 (5) ◽  
pp. 860
Author(s):  
Yi-Chun Lin ◽  
Tian Zhou ◽  
Taojun Wang ◽  
Melba Crawford ◽  
Ayman Habib
Keyword(s):  
Remote Sensing ◽  
Control Strategies ◽  
Target Function ◽  
Canopy Cover ◽  
Surface Model ◽  
Flowering Date ◽  
Sensing Platforms ◽  
Geolocation Accuracy ◽  
New Strategies

Remote sensing platforms have become an effective data acquisition tool for digital agriculture. Imaging sensors onboard unmanned aerial vehicles (UAVs) and tractors are providing unprecedented high-geometric-resolution data for several crop phenotyping activities (e.g., canopy cover estimation, plant localization, and flowering date identification). Among potential products, orthophotos play an important role in agricultural management. Traditional orthophoto generation strategies suffer from several artifacts (e.g., double mapping, excessive pixilation, and seamline distortions). The above problems are more pronounced when dealing with mid- to late-season imagery, which is often used for establishing flowering date (e.g., tassel and panicle detection for maize and sorghum crops, respectively). In response to these challenges, this paper introduces new strategies for generating orthophotos that are conducive to the straightforward detection of tassels and panicles. The orthophoto generation strategies are valid for both frame and push-broom imaging systems. The target function of these strategies is striking a balance between the improved visual appearance of tassels/panicles and their geolocation accuracy. The new strategies are based on generating a smooth digital surface model (DSM) that maintains the geolocation quality along the plant rows while reducing double mapping and pixilation artifacts. Moreover, seamline control strategies are applied to avoid having seamline distortions at locations where the tassels and panicles are expected. The quality of generated orthophotos is evaluated through visual inspection as well as quantitative assessment of the degree of similarity between the generated orthophotos and original images. Several experimental results from both UAV and ground platforms show that the proposed strategies do improve the visual quality of derived orthophotos while maintaining the geolocation accuracy at tassel/panicle locations.

Remote sensing of atmospheric aerosol over China

2001 ◽  
Author(s):  
Jietai Mao ◽  
Junhua Zhang ◽  
Chengcai Li ◽  
Meihua Wang
Keyword(s):  
Remote Sensing ◽  
Atmospheric Aerosol

Accurate spectrally modulating polarimeters for atmospheric aerosol characterization

2015 ◽  
Author(s):  
Jeroen H. H. Rietjens ◽  
Martijn Smit ◽  
Gerard van Harten ◽  
Antonio Di Noia ◽  
Otto P. Hasekamp ◽  
...  
Keyword(s):  
Atmospheric Aerosol ◽  
Aerosol Characterization

scholarly journals Microscopic Observations of Core‐Shell Particle Structure and Implications for Atmospheric Aerosol Remote Sensing

2018 ◽  
Vol 123 (24) ◽  
Author(s):  
Florin Unga ◽  
Marie Choël ◽  
Yevgeny Derimian ◽  
Karine Deboudt ◽  
Oleg Dubovik ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Atmospheric Aerosol ◽  
Core Shell ◽  
Particle Structure ◽  
Shell Particle ◽  
Aerosol Remote Sensing

Satellite and Aircraft Remote Sensing Platforms

2021 ◽  
pp. 1053-1068
Author(s):  
Manfred Wendisch ◽  
André Ehrlich ◽  
Peter Pilewskie
Keyword(s):  
Remote Sensing ◽  
Sensing Platforms

scholarly journals Ionospheric Polarization Techniques for Robust NVIS Remote Sensing Platforms

2020 ◽  
Vol 10 (11) ◽  
pp. 3730 ◽  
Author(s):  
Josep M. Maso ◽  
Jordi Male ◽  
Joaquim Porte ◽  
Joan L. Pijoan ◽  
David Badia
Keyword(s):  
Remote Sensing ◽  
Power Transmission ◽  
Low Cost ◽  
Density Ratio ◽  
Satellite Communications ◽  
Selection Combining ◽  
Noise Spectral Density ◽  
Diversity Techniques ◽  
Sensing Platform ◽  
Sensing Platforms

Every year more interest is focused on high frequencies (HF) communications for remote sensing platforms due to their capacity to establish links of more than 250 km without a line of sight and due to them being a low-cost alternative to satellite communications. In this article, we study the ionospheric ordinary and extraordinary waves to improve the applications of near vertical incidence skywave (NVIS) on a single input multiple output (SIMO) configuration. To obtain the results, we established a link of 95 km to test the diversity combining of ordinary and extraordinary waves by using selection combining (SC) and equal-gain combining (EGC) on a remote sensing platform. The testbench is based on digital modulation transmissions with power transmission between 3 and 100 W. The results show us the main energy per bit to noise spectral density ratio (Eb/N0) and the bit error rate (BER) differences between ordinary and extraordinary waves, SC, and EGC. To conclude, diversity techniques show us a decrease of the power transmission need, allowing for the use of compact antennas and increasing battery autonomy. Furthermore, we present three different improvement options for NVIS SIMO remote sensing platforms depending on the requirements of bitrate, power consumption, and efficiency of communication.

scholarly journals Survey of Deep-Learning Approaches for Remote Sensing Observation Enhancement

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3929 ◽  
Author(s):  
Grigorios Tsagkatakis ◽  
Anastasia Aidini ◽  
Konstantina Fotiadou ◽  
Michalis Giannopoulos ◽  
Anastasia Pentari ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Large Body ◽  
Super Resolution ◽  
Imaging Systems ◽  
Learning Approaches ◽  
Language Understanding ◽  
Learning Tasks ◽  
Trade Offs ◽  
Sensing Platforms

Deep Learning, and Deep Neural Networks in particular, have established themselves as the new norm in signal and data processing, achieving state-of-the-art performance in image, audio, and natural language understanding. In remote sensing, a large body of research has been devoted to the application of deep learning for typical supervised learning tasks such as classification. Less yet equally important effort has also been allocated to addressing the challenges associated with the enhancement of low-quality observations from remote sensing platforms. Addressing such channels is of paramount importance, both in itself, since high-altitude imaging, environmental conditions, and imaging systems trade-offs lead to low-quality observation, as well as to facilitate subsequent analysis, such as classification and detection. In this paper, we provide a comprehensive review of deep-learning methods for the enhancement of remote sensing observations, focusing on critical tasks including single and multi-band super-resolution, denoising, restoration, pan-sharpening, and fusion, among others. In addition to the detailed analysis and comparison of recently presented approaches, different research avenues which could be explored in the future are also discussed.

Atmospheric aerosol–cloud-stability relationship as observed with optical and radio remote sensing techniques

1998 ◽  
Vol 49 (1) ◽  
pp. 65-76 ◽  
Author(s):  
P.C.S. Devara ◽  
G. Pandithurai ◽  
P.E. Raj ◽  
R.S. Maheskumar ◽  
K.K. Dani
Keyword(s):  
Remote Sensing ◽  
Atmospheric Aerosol ◽  
Aerosol Cloud ◽  
Remote Sensing Techniques

scholarly journals Subarctic atmospheric aerosol composition: 1. Ambient aerosol characterization

2009 ◽  
Vol 114 (D13) ◽  
Author(s):  
Beth Friedman ◽  
Hanna Herich ◽  
Lukas Kammermann ◽  
Deborah S. Gross ◽  
Almut Arneth ◽  
...  
Keyword(s):  
Atmospheric Aerosol ◽  
Aerosol Composition ◽  
Ambient Aerosol ◽  
Aerosol Characterization

scholarly journals Atmospheric aerosol characterization with the Dutch–Chinese FAST formation flying mission

2010 ◽  
Vol 66 (7-8) ◽  
pp. 1044-1051 ◽  
Author(s):  
Eberhard Gill ◽  
Daan Maessen ◽  
Erik Laan ◽  
Stefan Kraft ◽  
Gang-tie Zheng
Keyword(s):  
Atmospheric Aerosol ◽  
Formation Flying ◽  
Aerosol Characterization
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