Dr. Gen Wang Profile

Dr. Gen Wang

SPIE Involvement:

Author

Publications (3)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 13 December 2024 Paper

Research on the construction method of sample set in CO2 retrieval based on statistical analysis method

Jiang Yun, Huaduan Feng, Pan Yue, Wang Gen

Proceedings Volume 13504, 1350406 (2024) https://doi.org/10.1117/12.3045868

KEYWORDS: Carbon dioxide, Statistical analysis, Reflectivity, Remote sensing, Atmospheric sensing

Read Abstract +

China's carbon reduction and emission reduction under the ‘double carbon’ target requires high-precision atmospheric CO₂ inversion data. High-precision atmospheric CO2 satellite observation is the basis for accurate positioning of emission sources. Aiming at the problem that the inversion algorithm is difficult to converge in the existing inversion methods, a statistical method integrating multi-source information is explored to obtain accurate prior profiles. It provides relatively accurate initial values for subsequent high-precision CO₂ inversion algorithms in order to improve the accuracy of CO₂ inversion. The statistical inversion method establishes a regression relationship model between satellite spectrum and atmospheric CO₂ concentration, and the inversion accuracy is affected by the representativeness of the sample and the regression analysis method. According to the difference of regional characteristics, the CO₂ profile samples are counted, and the sensitivity of environmental parameters is analyzed to construct a representative sample set. It is verified that the profile obtained by inversion is very close to the real profile, which verifies the feasibility of the method.

Proceedings Article | 8 November 2024 Paper

Prediction method of wheat yield in typical crop area of Hefei based on neural network

Yun Jiang, Feng Chen, Yue Pan, Gen Wang, Bo Song

Proceedings Volume 13416, 134161P (2024) https://doi.org/10.1117/12.3049641

KEYWORDS: Neural networks, Remote sensing, Data acquisition, Convolutional neural networks, Satellites

Read Abstract +

China is a country with a large population. Ensuring food security is related to China 's national economy and people's livelihood and social stability. Wheat is the most widely planted, the largest area and the most productive food crop in the world. The timely estimation of wheat yield has a significant impact on crop production, food prices and food security. Wheat yield is one of the important indicators to evaluate agricultural productivity. In view of the difficulty of manual estimation of wheat yield, it is proposed to apply convolutional neural network to wheat yield estimation, so as to provide reference for agricultural productivity estimation and guide agricultural production management decision-making. In this paper, Anhui Shuyu Ecological Farm and Changfeng Lixin Family Farm were selected as the research objects, and the wheat distribution map of the farm was obtained by using the convolutional neural network. It is estimated that the annual output of the farm in 2021 will be 317065kg and 790210kg, respectively, and the statistical data of 333750kg and 858920kg provided by Anhui Shuyu Ecological Agriculture Co., Ltd. and Changfeng Lixin Family Farm. The error is 4.9 % and 7.9 %, respectively. which verifies the effectiveness of the estimation method.

Proceedings Article | 24 May 2023 Paper

CO2 retrieval method based on GaoFen-5 satellite data

Yun Jiang, Xianhua Wang, Hanhan Ye, Hailiang Shi, Yue Pan, Gen Wang

Proceedings Volume 12706, 127060N (2023) https://doi.org/10.1117/12.2682540

KEYWORDS: Clouds, Carbon dioxide, Vegetation, Fluorescence, Chlorophyll, Satellites, MODIS, Scattering, Carbon monoxide, Tunable filters

Read Abstract +

Cloud pollution problem in remote sensing data，Atmospheric scattering ,the presence and changes of chlorophyll fluorescence in vegetation, etc., all interfere with the inversion of CO₂, affecting the accuracy of CO₂ inversion.In this study, the Greenhouse Gases Monitoring Instrument (GMI) data collected from GaoFen-5 (GF-5) satellite was applied to the cloud detection study in O₂ A band. The detection results were compared with the cloud judgment product of the moderate resolution imaging spectroradiometer (MODIS), and the proposed algorithm can filterout 90% of the clear sky data. Based on this, a more in-depth study of atmospheric CO₂ retrieval was carried out. The CO₂ retrieval results were compared with those data collected from the Total Carbon Column Observing Network (TCCON) and Greenhouse Gases Observing Satellite, and the results showed that the average accuracy of CO₂ retrieval results was better than 1%. In addition, the correlation coefficient between the results of CO₂ retrieval method and the data collected from TCCON was 0.85. Due to the effect of vegetation chlorophyll fluorescence, the CO₂ retrieval results based on the GMI data were higher than TCCON collected data. After the corrections to reduce the effect of vegetation chlorophyll fluorescence, the correlation coefficient of the CO₂ retrieval results between GMI and TCCON

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years