Remote sensor application studies report, July 1, 1968 to June 30, 1969: Remote sensing reconnaissance, Mill creek area, Arbuckle Mountains, Oklahoma

1970 ◽  
Keyword(s):  
Remote Sensing ◽  
Sensor Application ◽  
Remote Sensor ◽  
Arbuckle Mountains

Development of the High Efficient Scanning Mechanism Using in Optical Space Remote Sensor

2008 ◽  
Vol 375-376 ◽  
pp. 695-699
Author(s):  
Hong Ying Yu ◽  
Xin Zhi Han ◽  
Zhao Jun Liu ◽  
Shao Fan Tang
Keyword(s):  
Remote Sensing ◽  
Infrared Spectrum ◽  
Remote Sensor ◽  
High Efficient ◽  
Mechanical Scanning ◽  
Scanning Mechanism ◽  
Scanning Systems ◽  
Wide Swath

In some remote sensing, such as meteorology and oceanography, wide swath scanning must be used. Owing to the limitation of the detector size, especially in infrared spectrum, the optical-mechanical scanning mode is necessary. Scanning efficiency is an important parameter of those scanners. The high scanning efficiency is one of the goals followed after by the designer of remote sensor. Two high efficient scanning systems are developed by authors. Those systems can have some advantages, such as simple and reliable.

THE DETECTION AND MAPPING OF OIL ON A MARSHY AREA BY A REMOTE LUMINESCENT SENSOR1

1977 ◽  
Vol 1977 (1) ◽  
pp. 197-201 ◽  
Author(s):  
Craig McFarlane ◽  
Robert D. Watson
Keyword(s):  
Remote Sensing ◽  
Damage Assessment ◽  
Oil Spills ◽  
Cost Effective ◽  
Common Reed ◽  
Deposition Pattern ◽  
Affected Area ◽  
Remote Sensor ◽  
Cost Effective Method ◽  
Contaminated Area

ABSTRACT Airborne remote sensing can be a cost-effective method for monitoring pollutants in large areas such as occur in oil spills. An opportunity to test a particular method arose when a well ruptured and for 23 days spewed a 90-meter fountain of oil into the air, dispersing the oil over a wide area. The method tested was an airborne luminescence detector with a Fraunhofer Line Discriminator (FLD) which was flown over the affected area 41 days after the well was capped to obtain a map of the deposition pattern. To calibrate the system, samples of Spartina (wire grass) and Phragmites (common reed) were collected from the contaminated area and the oil residues were eluted in cyclohexane and quantitatively analyzed in a fluorescence photometer. Good correlation was observed between the remote sensor (FLD) and the laboratory analysis. Isopleths defining the deposition pattern of oil were drawn from the remote sensing information. A discussion will be presented on the feasibility of using this instrument for similar contamination incidents for cleanup and damage assessment.

scholarly journals BM-IQE: An Image Quality Evaluator with Block-Matching for Both Real-Life Scenes and Remote Sensing Scenes

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3472
Author(s):  
Ningshan Xu ◽  
Dongao Ma ◽  
Guoqiang Ren ◽  
Yongmei Huang
Keyword(s):  
Remote Sensing ◽  
Image Quality ◽  
General Purpose ◽  
Block Matching ◽  
Natural Images ◽  
Quality Estimation ◽  
Remote Sensor ◽  
Basic Quality ◽  
Imaging Performance ◽  
Image Datasets

Like natural images, remote sensing scene images; of which the quality represents the imaging performance of the remote sensor, also suffer from the degradation caused by imaging system. However, current methods measuring the imaging performance in engineering applications require for particular image patterns and lack generality. Therefore, a more universal approach is demanded to assess the imaging performance of remote sensor without constraints of land cover. Due to the fact that existing general-purpose blind image quality assessment (BIQA) methods cannot obtain satisfying results on remote sensing scene images; in this work, we propose a BIQA model of improved performance for natural images as well as remote sensing scene images namely BM-IQE. We employ a novel block-matching strategy called Structural Similarity Block-Matching (SSIM-BM) to match and group similar image patches. In this way, the potential local information among different patches can get expressed; thus, the validity of natural scene statistics (NSS) feature modeling is enhanced. At the same time, we introduce several features to better characterize and express remote sensing images. The NSS features are extracted from each group and the feature vectors are then fitted to a multivariate Gaussian (MVG) model. This MVG model is therefore used against a reference MVG model learned from a corpus of high-quality natural images to produce a basic quality estimation of each patch (centroid of each group). The further quality estimation of each patch is obtained by weighting averaging of its similar patches’ basic quality estimations. The overall quality score of the test image is then computed through average pooling of the patch estimations. Extensive experiments demonstrate that the proposed BM-IQE method can not only outperforms other BIQA methods on remote sensing scene image datasets but also achieve competitive performance on general-purpose natural image datasets as compared to existing state-of-the-art FR/NR-IQA methods.

Remote Sensing of Tropospheric Temperature and Water Vapor by Integrated Observing Systems

1997 ◽  
Vol 78 (9) ◽  
pp. 1991-2006 ◽  
Author(s):  
Edgeworth R. Westwater
Keyword(s):  
Remote Sensing ◽  
Water Vapor ◽  
Precipitable Water ◽  
Sensor Data ◽  
Tropospheric Temperature ◽  
Positioning Systems ◽  
Remote Sensors ◽  
Remote Sensor ◽  
Virtual Temperature

In the last decade, substantial advances have been made in the remote sensing of tropospheric temperature and water vapor. Techniques include measurement of virtual temperature by Radio Acoustic Sounding Systems (RASS), the combination of RASS with satellite soundings, the measurement of precipitable water vapor by Global Positioning Systems, the measurement of water vapor profiles by Raman and differential absorption lidar, and the measurement of both temperature and water vapor profiles by Fourier Transform Infrared Radiometers. However, none of the techniques, by itself, is capable of satisfying most meteorological and climate needs. Thus, determination of profiles from combinations of data and sensors is the only practical way of satisfying these needs. In this paper, some of the techniques used for combining remote sensor data are outlined, some of the current sensors are described, and then examples of data derived from these combinations are presented. The role of the radiosonde in remote sensor evaluation, retrievals, and calibration is discussed. Finally, some of the new possibilities for combined remote sensors are presented.

Inversion of Aerosol Optical Depth Based on MODIS Remote Sensor

2015 ◽  
Vol 738-739 ◽  
pp. 209-212
Author(s):  
Cheng Fan Li ◽  
Yang Yang Dai ◽  
Fei Liu ◽  
Jun Juan Zhao
Keyword(s):  
Remote Sensing ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Solar Spectrum ◽  
Weather Condition ◽  
Pollution Monitoring ◽  
Sensor Data ◽  
Remote Sensor ◽  
Haze Pollution ◽  
Weather Situation

The remote sensing technology can accurate inverse the aerosol optical depth so as to demonstrate the haze distribution. Taking the moderate resolution imaging spectroradiometer (MODIS) remote sensor data as the data source, the aerosol optical depth of Shanghai area on December 6, 2013 is inversed from the use of second simulation of satellite signal in the solar spectrum (6S) and NASA V5.2 algorithm, and then the formation has already been analyzed from the three aspects of human activities, weather situation and foreign pollutants. The results show that the inversion aerosol optical depth from MODIS remote sensing image gradually decreased mainly from northwest to southeast, and the foreign pollutants plays the leading role in this haze pollution incidents in a certain weather condition. It can provide the references for haze pollution monitoring and early warning using remote sensor data.

scholarly journals Remote sensing detection enhancement

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Tian J. Ma
Keyword(s):  
Remote Sensing ◽  
Moving Objects ◽  
Signal To Noise Ratio ◽  
Detection Threshold ◽  
False Alarms ◽  
Camera Motion ◽  
Low Contrast ◽  
Low Snr ◽  
Number Of False Alarms ◽  
Remote Sensor

AbstractBig Data in the area of Remote Sensing has been growing rapidly. Remote sensors are used in surveillance, security, traffic, environmental monitoring, and autonomous sensing. Real-time detection of small moving targets using a remote sensor is an ongoing, challenging problem. Since the object is located far away from the sensor, the object often appears too small. The object’s signal-to-noise-ratio (SNR) is often very low. Occurrences such as camera motion, moving backgrounds (e.g., rustling leaves), low contrast and resolution of foreground objects makes it difficult to segment out the targeted moving objects of interest. Due to the limited appearance of the target, it is tough to obtain the target’s characteristics such as its shape and texture. Without these characteristics, filtering out false detections can be a difficult task. Detecting these targets, would often require the detector to operate under a low detection threshold. However, lowering the detection threshold could lead to an increase of false alarms. In this paper, the author will introduce a new method that improves the probability to detect low SNR objects, while decreasing the number of false alarms as compared to using the traditional baseline detection technique.

scholarly journals Calibration and Data Quality Assurance Technical Advancements for Quantitative Remote Sensing in the DRAGON 4 Project

2021 ◽  
Vol 13 (24) ◽  
pp. 4996
Author(s):  
Lingling Ma ◽  
Yongguang Zhao ◽  
Chuanrong Li ◽  
Philippe Goryl ◽  
Cheng Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Local Governments ◽  
Control Strategies ◽  
Remote Sensing Data ◽  
Sensor Calibration ◽  
Radiometric Calibration ◽  
Sensor Performance ◽  
Quantitative Remote Sensing ◽  
Remote Sensor ◽  
Set Up

Robust calibration and validation (Cal and Val) should guarantee the accuracy of the retrieved information, make the remote sensing data consistent and traceable, and maintain the sensor performance during the operational phase. The DRAGON program has set up many remote sensing research topics on various application domains. In order to promote the effectiveness of data modeling and interpretation, it is necessary to solve various challenges in Cal and Val for quantitative RS applications. This project in the DRAGON 4 program aims to promote the cooperation of the Cal and Val experts from European and Chinese institutes in Cal and Val activities, and several achievements have been obtained in the advanced on-orbit optical sensor calibration, as well as microwave remote sensor calibration and product generation. The outcomes of the project have benefited the related remote sensing modeling and product retrieval, and promoted the radiometric calibration network (RadCalNet) as an international operational network for calibration, intercalibration, and validation. Moreover, this project provided local governments with a more accurate OMI NO2 data in China, which were used to study the air quality control during APEC period, Parade period and G20 period. This will be of ongoing be value for monitoring atmospheric environmental quality and formulating pollution control strategies.

scholarly journals Resolution of an important discrepancy between remote and in-situ measurements of tropospheric BrO during Antarctic enhancements

2012 ◽  
Vol 5 (4) ◽  
pp. 5419-5448 ◽  
Author(s):  
H. K. Roscoe ◽  
N. Brough ◽  
A. E. Jones ◽  
F. Wittrock ◽  
A. Richter ◽  
...  
Keyword(s):  
Remote Sensing ◽  
The Arctic ◽  
Optical Absorption Spectroscopy ◽  
Free Troposphere ◽  
Aircraft Measurements ◽  
Near Surface ◽  
Remote Sensor ◽  
Ionisation Mass Spectrometry ◽  
Surface Technique

Abstract. Tropospheric BrO was measured by a ground-based remote-sensing spectrometer at Halley in Antarctica, and BrO was measured by remote-sensing spectrometers in space using similar spectral regions and Differential Optical Absorption Spectroscopy (DOAS) analyses. Near-surface BrO was simultaneously measured at Halley by Chemical Ionisation Mass Spectrometry (CIMS), and in an earlier year near-surface BrO was measured at Halley over a long path by a DOAS spectrometer. During enhancement episodes, total amounts of tropospheric BrO from the ground-based remote-sensor were similar to those from space, but if we assume that the BrO was confined to the boundary layer they were very much larger than values measured by either near-surface technique. This large apparent discrepancy can be resolved if substantial amounts of BrO were in the free troposphere during most enhancement episodes. Amounts observed by the ground-based remote sensor at different elevation angles, and their formal inversions to vertical profiles, also show that much of the BrO was often in the free troposphere. This is consistent with the ~5 day lifetime of Bry, from the enhanced BrO observed during some Antarctic blizzards, and from aircraft measurements of BrO well above the surface in the Arctic.

scholarly journals Upper tropospheric CH<sub>4</sub> and N<sub>2</sub>O retrievals from MetOp/IASI within the project MUSICA

2017 ◽  
Author(s):  
Omaira E. García ◽  
Eliezer Sepúlveda ◽  
Matthias Schneider ◽  
Andreas Wiegele ◽  
Christian Borger ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Mixing Ratios ◽  
Remote Sensor ◽  
Individual Observation ◽  
Atmospheric Sounding ◽  
Different Seasons ◽  
Ch4 And N2o ◽  
The Individual ◽  
Better Than

Abstract. This paper presents upper tropospheric methane (CH4) and nitrous oxide (N2O) concentrations retrieved from thermal infrared spectra as observed by the remote sensor IASI (Infrared Atmospheric Sounding Interferometer) on-board the EUMETSAT/MetOp meteorological satellites. The CH4 and N2O mixing ratios are retrieved as side products of the MetOp/IASI retrieval developed for the European Research Council project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water). The MUSICA/IASI CH4 and N2O retrieval strategy is described in detail as well as their characterisation in terms of the vertical resolution and expected errors. Theoretically, we document that MUSICA/IASI products can capture the upper tropospheric CH4 and N2O variability (at ≈ 300–350 hPa) with a precision better than 2 %. We compare the remote sensing data to coincident high precision aircraft vertical profiles taken within the HIAPER Pole-to-Pole Observations (HIPPO) project and empirically estimate a precision of 2.1 % (38.2 ppbv) for each individual IASI CH4 observation. The precision is improved to 1.7 % (32.1 ppbv) for IASI data that have been averaged within 2° × 2° boxes. For N2O the empirically estimated precision is 2.7 % (8.7 ppbv) for each individual observation and 2.1 % (6.9 ppbv) for the 2° × 2° averages. The empirical study works with data from the missions HIPPO1 and HIPPO5, which cover latitudes between 67º S and 80º N during typical winter and summer conditions in both hemispheres, thus being reasonably representative for global observation during different seasons. In addition, we present a product that combines the CH4 and N2O retrieval estimates. The combination is made a-posteriori and we theoretically and empirically show that the combined product has a much better precision than the individual CH4 and N2O products. For the combined product the theoretical precision is 0.8 % and the comparison with HIPPO data gives an empirical precision estimate of 1.5 % (26.3 ppbv) when considering all individual IASI observations and of 1.2 % (21.8 ppbv) for the 2° ×2° averages. In the case that the horizontal, vertical and temporal variation of N2O can be robustly modeled, we can easily reconstruct CH4 from the combined product and generate high quality IASI CH4 data.

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