Mr. Bo Li Profile

Bo Li

at Northeast Normal Univ

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 | 10 October 2018 Presentation + Paper

Rain use efficiency changes and its effects on land surface phenology in the Songnen Plain, Northeast China

Fang Huang, Ping Wang, Shuai Chang, Bo Li

Proceedings Volume 10783, 107830E (2018) https://doi.org/10.1117/12.2325086

KEYWORDS: Vegetation, Ecosystems, Data acquisition, Data centers, Climate change, Carbon, Satellites

Read Abstract +

Rain-use efficiency (RUE) acts as a typical indicator of ecosystem function. Land surface phenology (LSP) assesses the vegetation activity during the growing season at the ecosystem level. The Songnen Plain (SNP) is located in semi-humid to semi-arid transition ecological fragile zone in Northeast China. RUE in growing season (May-September) was calculated using time series GIMMS NDVI3g images and precipitation data for the period of 1983-2012. The phenology metrics including the start (SOS) and end (EOS) dates of growing season for each year was extracted. Spatial trends of RUE and LSP were examined by applying a linear regression model with time. The correlation analysis was used to analyze the effects of RUE on LSP. The results showed that RUE increased slightly with an undulating trend. Spatially, the highest positive slopes indicating increased trend of RUE were observed in northern and eastern forest. The advanced in SOS was mainly distributed in northern forest areas. 12.2% of the landscape experienced highly increase trend in EOS with a rate of 0.38 days per year. The length of growing season (LOS) was prolonged in 14.2% of the total land. EOS dates in the southern salinized grassland and cropland were mainly negatively correlated with RUE. The results of the significance test show that 2.95% of the pixels were significantly and positively correlated with RUE, indicating that an increase in the RUE would delay the EOS. Increasing RUE promoted the extension of the LOS, particularly in the forest areas.

SPIE Journal Paper | 24 September 2018

Developing an integrated index based on phenological metrics for evaluating cadmium stress in rice using Sentinel-2 data

Ning Sun, Ping Wang, Fang Huang, Bo Li

JARS, Vol. 12, Issue 03, 036018, (September 2018) https://doi.org/10.1117/12.10.1117/1.JRS.12.036018

KEYWORDS: Cadmium, Metals, Remote sensing, Soil contamination, Vegetation, Soil science, Photosynthesis, Contamination, Pollution, Sun

Read Abstract +

Effectively assessing cadmium (Cd) contamination in crops is crucial for the sustainable development of an agricultural ecosystem and for environmental security. We developed an integrated stress index (SI) based on two phenological metrics to effectively evaluate Cd stress in rice crops. The selected four experimental areas are located in Zhuzhou City, Hunan Province, China. Six Sentinel-2 images were acquired in 2017, and heavy metal concentrations in soil were measured. The change rate of CIre (CRCIre) and the time-integrated CIre (TICIre) were obtained from daily red-edge chlorophyll index (CIre) time-series using Sentinel-2 data. The CRCIre and TICIre were used to characterize the photosynthetic rate and biomass, respectively. SI was calculated by Fisher discriminant analysis based on CRCIre and TICIre from two experimental areas, and it was verified using another two experimental areas. The results were the following: (i) when SI ≥ 0, rice was under mild stress and when SI < 0, rice was under severe stress. (ii) The SI effectively evaluated Cd stress levels with an overall discriminatory accuracy of 86.02%. This research provides a potential new method to evaluate Cd stress in rice by remote sensing through phenology.

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