Prof. Lingli Tang Profile

Prof. Lingli Tang

at Aerospace Information Research Institute CAS

SPIE Involvement:

Author

Publications (2)

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 | 12 September 2021 Presentation + Paper

Estimation of leaf area index at the late growth stage of crops using unmanned aerial vehicle hyperspectral images

Weiping Kong, Wenjiang Huang, Lingling Ma, Binbin Chen, Chuanrong Li, Lingli Tang

Proceedings Volume 11856, 118560X (2021) https://doi.org/10.1117/12.2611121

KEYWORDS: Vegetation, Unmanned aerial vehicles, Reflectivity, Hyperspectral imaging, Remote sensing, Data modeling, Data analysis, Sensors, Spectral resolution

Read Abstract +

Estimation of leaf area index (LAI) is of vital importance to improve the prediction accuracy of crops quality and yield. However, it is more difficult to precisely assess LAI at the late growth stages of crops due to the influences of leaf senescence and soil background. Unmanned aerial vehicles (UAVs), with hyperspectral sensors onboard, can acquire high spatial and spectral resolution images and provide detailed information of fields, and consequently, are widely used for monitoring the biophysical parameters of crops in precision agriculture. The aim of this study was to evaluate the potential of UAV-based hyperspectral data in LAI estimation for sunflower and maize at the milk-filling stage, with machine learning regression algorithms (MLRA) for data analyses. Three algorithms including linear regression (LR), partial least square regression (PLSR) and kernel ridge regression (KRR) were used with the individual vegetation index (VI), VI-combination and spectral reflectance of full wavelengths as input variables. Results indicate that from the perspective of accuracy of estimation models, the PLSR based on VI-combination derived from hyperspectral images outperformed the LR based on individual VI and KRR based on VI-combination or spectral reflectance, which was proven to be the most suitable for the LAI estimation for both maize and sunflower at late growth stage, with 68% and 64% of the variation in LAI were explained, respectively. From the perspective of VIs tested, the modified triangular vegetation index (MTVI1) and improved soil-adjusted vegetation index (MSAVI) were found to be the best LAI estimators for maize and sunflower. Meanwhile, the contributions of the two VIs were also superior over other VIs tested in developing estimation models based on the PLSR method.

Proceedings Article | 22 August 2020 Presentation + Paper

Evaluation of an automated channel-selection method for application on the retrieval of different gas profiles from ultra-spectral thermal infrared data

Weiyuan Yao, Beibei Zhang, Ning Wang, Lingling Ma, Chuanrong Li, Lingli Tang

Proceedings Volume 11504, 115040D (2020) https://doi.org/10.1117/12.2568240

KEYWORDS: Spectroscopy, Spectral resolution, Infrared spectroscopy

Read Abstract +

A novel automated channel-selection method based on the gas sensitivity and weighting function characteristics has been applied on simulated ultra-spectral thermal infrared data for CO profile retrieval in our previous work. The method consists of two steps: 1) channels with abundant gas information and insensitivity to other gases are selected as the initial channel group, 2) the optimal channel group is then obtained by optimizing the distribution of the weighting function. The retrieval results show that the method can reduce the redundancy of channels and improve the retrieval accuracy and efficiency of CO profiles. In this paper, the proposed method is assessed by applying on the retrieval of O₃ and CH₄ profiles from the ultra-spectral data and then a set of channels are selected for each gas and atmospheric situation. By comparing to the Optimal Sensitivity Profile (OSP) method, which suggests good performance in the literature, it shows that the selected channels by the proposed method in all the sets are less correlated and some channels with special information but relatively low sensitivity are screened. The root mean square errors (RMSEs) of the most retrieved gas profiles by the novel method are smaller than these by the OSP one. The results indicate that the automated channel-selection method can facilitate the retrieval accuracy for different gas profiles from ultra-spectral data and may have application in the ultra-spectral feature selection and data compression.

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