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 From Geometric-Optical Remote Sensing Modeling to Quantitative Remote Sensing Science—In Memory of Academician Xiaowen Li

2018 ◽  
Vol 10 (11) ◽  
pp. 1764 ◽  
Author(s):  
Qinhuo Liu ◽  
Guangjian Yan ◽  
Ziti Jiao ◽  
Qing Xiao ◽  
Jianguang Wen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Thermal Emission ◽  
Scale Effects ◽  
Remote Sensing Data ◽  
Geometric Optic ◽  
Optical Remote Sensing ◽  
Research Activities ◽  
Quantitative Remote Sensing ◽  
Vegetation Canopies ◽  
High Regard

The academician Xiaowen Li devoted much of his life to pursuing fundamental research in remote sensing. A pioneer in the geometric-optical modeling of vegetation canopies, his work is held in high regard by the international remote sensing community. He codeveloped the Li–Strahler geometric-optic model, and this paper was selected by a member of the International Society for Optical Engineering (SPIE) milestone series. As a chief scientist, Xiaowen Li led a scientific team that made outstanding advances in bidirectional reflectance distribution modeling, directional thermal emission modeling, comprehensive experiments, and the understanding of spatial and temporal scale effects in remote sensing information, and of quantitative inversions utilizing remote sensing data. In addition to his broad research activities, he was noted for his humility and his dedication in making science more accessible for the general public. Here, the life and academic contributions of Xiaowen Li to the field of quantitative remote sensing science are briefly reviewed.

scholarly journals Radiometric Calibration of UAV Remote Sensing Image with Spectral Angle Constraint

2019 ◽  
Vol 11 (11) ◽  
pp. 1291 ◽  
Author(s):  
Kaiqiu Xu ◽  
Yan Gong ◽  
Shenghui Fang ◽  
Ke Wang ◽  
Zhiheng Lin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Linear Method ◽  
Radiometric Calibration ◽  
Constraint Condition ◽  
Infrared Band ◽  
Spectral Bands ◽  
Quantitative Remote Sensing ◽  
Spectral Channels ◽  
Spectral Angle

In recent years, the acquisition of high-resolution multi-spectral images by unmanned aerial vehicles (UAV) for quantitative remote sensing research has attracted more and more attention, and radiometric calibration is the premise and key to the quantification of remote sensing information. The traditional empirical linear method independently calibrates each channel, ignoring the correlation between spectral bands. However, the correlation between spectral bands is very valuable information, which becomes more prominent as the number of spectral channels increases. Based on the empirical linear method, this paper introduces the constraint condition of spectral angle, and makes full use of the information of each band for radiometric calibration. The results show that, compared with the empirical linear method, the proposed method can effectively improve the accuracy of radiometric calibration, with the improvement range of Mean Relative Percent Error (MRPE) being more than 3% in the range of visible band and within 1% in the range of near-infrared band. Besides, the method has great advantages in agricultural remote sensing quantitative inversion.

Research of Improved Minnaert Topographic Correction Model and Application

2014 ◽  
Vol 543-547 ◽  
pp. 2780-2783
Author(s):  
Yan Zhen Wu ◽  
Zuo Cheng Wang ◽  
Fu Pin Yang ◽  
Xiao Bo Luo
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Remote Sensing Images ◽  
Topographic Correction ◽  
Correction Model ◽  
Model Based ◽  
Effective Factor ◽  
Quantitative Remote Sensing ◽  
Scs Model ◽  
Better Than

The topographic correction of remote sensing images is an important factor to improve the precision of quantitative remote sensing data processing. In the existing topographic correction models,the Minnaert model is the only model based on the assumption of non-Lambertian.the Minnaert coefficient K is an effective factor for the correction results. To improve the correction accuracy,we correct the image in accordance with the slope grading idea to select different areas from the image, then use different k values in different slope regions.In this paper, the topography correction is efficiently corrected by SCS model, Minnaert model and improved Minnaert model, based on HJCCD image and the corresponding DEM in Heihe . The results showed that the improved Minnaert model can eliminate the effect of topography better than other methods and can be widely used.

Commonly Used Correction Model Comparison, Improvement and Precision Analysis of Radiation Remote Sensing Image

2014 ◽  
Vol 998-999 ◽  
pp. 1013-1017
Author(s):  
Xiao Li Liu
Keyword(s):  
Remote Sensing ◽  
Model Comparison ◽  
Remote Sensing Data ◽  
Remote Sensing Image ◽  
Surface Radiation ◽  
Precision Analysis ◽  
Mountain Area ◽  
Correction Model ◽  
Radiation Correction ◽  
Quantitative Remote Sensing

Remote sensing image radiation correction is a key technique for quantitative remote sensing data processing is essential, especially in the surface undulating mountains, surface radiation affected by topography, the radiation correction of remote sensing inversion error can reduce the surface information, so as to maximize the accuracy of remote sensing investigation of mountain area. In this paper, the ETM image in mountain area of Western Beijing as an example, application of cosine correction model and Sandmeier correction model of image are topographic radiation correction, and then proposed an improved Sandmeier correction model, and carries on the precision analysis from the visual effect and quantitative parameters. Experiments show that, the improved Sandmeier correction model eliminates the influence of topography, greatly improving the accuracy of remote sensing image Topographic Radiation correction.

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.

Removing Cloud of Grey Remote Sensing Image Based on Network Communication

2014 ◽  
Vol 513-517 ◽  
pp. 3165-3169
Author(s):  
Min Min Yue
Keyword(s):  
Remote Sensing ◽  
Rapid Development ◽  
Remote Sensing Data ◽  
Remote Sensing Image ◽  
Cloud Formation ◽  
Network Communication ◽  
Fusion Model ◽  
Modern Computer ◽  
Model Combining ◽  
Set Up

Remote sensing technology has rapid development in the past half one century, it is widely used in various fields and society. But the clouds have affected the quality of remote sensing data, how to effectively use the modern computer science and technology to remove the cloud is a hot issue in the field. From the theory of cloud formation in the remote sensing image, we analyze the formation mechanism, and based on this we do two layers decomposition and reconstruct the structure according to wavelet transform in network communication, and establish the image degradation model. Combining Fourier transformation, we set up the removing cloud fusion model of remote sensing image. Through the simulation experiment, the effect is significant. To a certain extent, it provides technical support for theory study and practice operation.

scholarly journals Quantitative Remote Sensing of Metallic Elements for the Qishitan Gold Polymetallic Mining Area, NW China

2021 ◽  
Vol 13 (13) ◽  
pp. 2519
Author(s):  
Gong Cheng ◽  
Huikun Huang ◽  
Huan Li ◽  
Xiaoqing Deng ◽  
Rehan Khan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Kriging Interpolation ◽  
Geochemical Data ◽  
Surface Element ◽  
Metallic Elements ◽  
Geochemical Exploration ◽  
Sensing Data ◽  
Quantitative Remote Sensing ◽  
Ore Prospecting

The recent development in remote sensing imagery and the use of remote sensing detection feature spectrum information together with the geochemical data is very useful for the surface element quantitative remote sensing inversion study. This aim of this article is to select appropriate methods that would make it possible to have rapid economic prospecting. The Qishitan gold polymetallic deposit in the Xinjiang Uygur Autonomous Region, Northwest China has been selected for this study. This paper establishes inversion maps based on the contents of metallic elements by integrating geochemical exploration data with ASTER and WorldView-2 remote sensing data. Inversion modelling maps for As, Cu, Hg, Mo, Pb, and Zn are consistent with the corresponding geochemical anomaly maps, which provide a reference for metallic ore prospecting in the study area. ASTER spectrum covers short-wave infrared and has better accuracy than WorldView-2 data for the inversion of some elements (e.g., Au, Hg, Pb, and As). However, the high spatial resolution of WorldView-2 drives the final content inversion map to be more precise and to better localize the anomaly centers of the inversion results. After scale conversion by re-sampling and kriging interpolation, the modeled and predicted accuracy of the models with square interpolation is much closer compare with the ground resolution of the used remote sensing data. This means our results are much satisfactory as compared to other interpolation methods. This study proves that quantitative remote sensing has great potential in ore prospecting and can be applied to replace traditional geochemical exploration to some extent.

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