Gyro sensor calibration of ISRO’s remote sensing satellites

2022 ◽  
Author(s):  
Pooja Prasad ◽  
Bijoy Kumar Dai ◽  
B. N. Ramakrishna
Keyword(s):  
Remote Sensing ◽  
Sensor Calibration ◽  
Gyro Sensor

scholarly journals AN ANALYSIS OF REMOTE SENSING SENSOR ISSUES AND SENSOR CROSS CALIBRATION

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Sheron Henry Christy
Keyword(s):  
Remote Sensing ◽  
Natural Resources ◽  
Disaster Management ◽  
Satellite Images ◽  
Good Alternative ◽  
Point Of View ◽  
Sensor Calibration ◽  
Alternative Technology ◽  
Irs P6 ◽  
Cross Calibration

Remote sensing is a very good alternative technology for managing natural resources as compared to conventional technologies. This paper highlights the various challenges in UAS sensors. Comparison of IRS-P6 and Land Sat sensors is described from accuracy point of view by covering same areas by both the sensors which gives the performance features of both the sensors. Inter sensor calibration is depicted to realize its importance in applications like precision farming, disaster management, etc. requiring multiple dated satellite images.

scholarly journals Multispectral Sensor Calibration and Characterization for sUAS Remote Sensing

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4453 ◽  
Author(s):  
Mamaghani ◽  
Salvaggio
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Laboratory Method ◽  
Sensor Calibration ◽  
Radiometric Calibration ◽  
End User ◽  
Sensing Applications ◽  
A Value ◽  
Remote Sensing Applications

This paper focuses on the calibration of multispectral sensors typically used for remote sensing. These systems are often provided with "factory" radiometric calibration and vignette correction parameters. These parameters, which are assumed to be accurate when the sensor is new, may change as the camera is utilized in real-world conditions. As a result, regular calibration and characterization of any sensor should be conducted. An end-user laboratory method for computing both the vignette correction and radiometric calibration function is discussed in this paper. As an exemplar, this method for radiance computation is compared to the method provided by MicaSense for their RedEdge series of sensors. The proposed method and the method provided by MicaSense for radiance computation are applied to a variety of images captured in the laboratory using a traceable source. In addition, a complete error propagation is conducted to quantify the error produced when images are converted from digital counts to radiance. The proposed methodology was shown to produce lower errors in radiance imagery. The average percent error in radiance was −10.98%, −0.43%, 3.59%, 32.81% and −17.08% using the MicaSense provided method and their "factory" parameters, while the proposed method produced errors of 3.44%, 2.93%, 2.93%, 3.70% and 0.72% for the blue, green, red, near infrared and red edge bands, respectively. To further quantify the error in terms commonly used in remote sensing applications, the error in radiance was propagated to a reflectance error and additionally used to compute errors in two widely used parameters for assessing vegetation health, NDVI and NDRE. For the NDVI example, the ground reference was computed to be 0.899 ± 0.006, while the provided MicaSense method produced a value of 0.876 ± 0.005 and the proposed method produced a value of 0.897 ± 0.007. For NDRE, the ground reference was 0.455 ± 0.028, MicaSense method produced 0.239 ± 0.026 and the proposed method produced 0.435 ± 0.038.

scholarly journals Observations and Recommendations for the Calibration of Landsat 8 OLI and Sentinel 2 MSI for Improved Data Interoperability

2018 ◽  
Vol 10 (9) ◽  
pp. 1340 ◽  
Author(s):  
Dennis Helder ◽  
Brian Markham ◽  
Ron Morfitt ◽  
Jim Storey ◽  
Julia Barsi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Sensor Calibration ◽  
Landsat 8 ◽  
Data Sets ◽  
Optical Remote Sensing ◽  
Data Interoperability ◽  
Satellite Systems ◽  
Combining Data ◽  
The Impact ◽  
Sentinel 2

Combining data from multiple sensors into a single seamless time series, also known as data interoperability, has the potential for unlocking new understanding of how the Earth functions as a system. However, our ability to produce these advanced data sets is hampered by the differences in design and function of the various optical remote-sensing satellite systems. A key factor is the impact that calibration of these instruments has on data interoperability. To address this issue, a workshop with a panel of experts was convened in conjunction with the Pecora 20 conference to focus on data interoperability between Landsat and the Sentinel 2 sensors. Four major areas of recommendation were the outcome of the workshop. The first was to improve communications between satellite agencies and the remote-sensing community. The second was to adopt a collections-based approach to processing the data. As expected, a third recommendation was to improve calibration methodologies in several specific areas. Lastly, and the most ambitious of the four, was to develop a comprehensive process for validating surface reflectance products produced from the data sets. Collectively, these recommendations have significant potential for improving satellite sensor calibration in a focused manner that can directly catalyze efforts to develop data that are closer to being seamlessly interoperable.

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 Optical Passive Sensor Calibration for Satellite Remote Sensing and the Legacy of NOAA and NIST Cooperation

2014 ◽  
Vol 119 ◽  
pp. 235 ◽  
Author(s):  
Raju Datla ◽  
Michael Weinreb ◽  
Joseph Rice ◽  
B. Carol Johnson ◽  
Eric Shirley ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Sensor Calibration ◽  
Passive Sensor

scholarly journals Analyzing Performances of Different Atmospheric Correction Techniques for Landsat 8: Application for Coastal Remote Sensing

2019 ◽  
Vol 11 (4) ◽  
pp. 469 ◽  
Author(s):  
Christopher Ilori ◽  
Nima Pahlevan ◽  
Anders Knudby
Keyword(s):  
Remote Sensing ◽  
Atmospheric Correction ◽  
Sensor Calibration ◽  
Landsat 8 ◽  
Wide Field ◽  
Surface Reflectance ◽  
Atmospheric Conditions ◽  
Climate Data ◽  
In Situ Data ◽  
Level 2

Ocean colour (OC) remote sensing is important for monitoring marine ecosystems. However, inverting the OC signal from the top-of-atmosphere (TOA) radiance measured by satellite sensors remains a challenge as the retrieval accuracy is highly dependent on the performance of the atmospheric correction as well as sensor calibration. In this study, the performances of four atmospheric correction (AC) algorithms, the Atmospheric and Radiometric Correction of Satellite Imagery (ARCSI), Atmospheric Correction for OLI ‘lite’ (ACOLITE), Landsat 8 Surface Reflectance (LSR) Climate Data Record (Landsat CDR), herein referred to as LaSRC (Landsat 8 Surface Reflectance Code), and the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) Data Analysis System (SeaDAS), implemented for Landsat 8 Operational Land Imager (OLI) data, were evaluated. The OLI-derived remote sensing reflectance (Rrs) products (also known as Level-2 products) were tested against near-simultaneous in-situ data acquired from the OC component of the Aerosol Robotic Network (AERONET-OC). Analyses of the match-ups revealed that generic atmospheric correction methods (i.e., ARCSI and LaSRC), which perform reasonably well over land, provide inaccurate Level-2 products over coastal waters, in particular, in the blue bands. Between water-specific AC methods (i.e., SeaDAS and ACOLITE), SeaDAS was found to perform better over complex waters with root-mean-square error (RMSE) varying from 0.0013 to 0.0005 sr−1 for the 443 and 655 nm channels, respectively. An assessment of the effects of dominant environmental variables revealed AC retrieval errors were influenced by the solar zenith angle and wind speed for ACOLITE and SeaDAS in the 443 and 482 nm channels. Recognizing that the AERONET-OC sites are not representative of inland waters, extensive research and analyses are required to further evaluate the performance of various AC methods for high-resolution imagers like Landsat 8 and Sentinel-2 under a broad range of aquatic/atmospheric conditions.

Sign in / Sign up

Export Citation Format

Share Document