Literature DB >> 25644468

Assessing winter oilseed rape freeze injury based on Chinese HJ remote sensing data.

Bao She1, Jing-feng Huang, Rui-fang Guo, Hong-bin Wang, Jing Wang.   

Abstract

The winter oilseed rape (Brassica napus L.) accounts for about 90% of the total acreage of oilseed rape in China. However, it suffers the risk of freeze injury during the winter. In this study, we used Chinese HJ-1A/1B CCD sensors, which have a revisit frequency of 2 d as well as 30 m spatial resolution, to monitor the freeze injury of oilseed rape. Mahalanobis distance-derived growing regions in a normal year were taken as the benchmark, and a mask method was applied to obtain the growing regions in the 2010-2011 growing season. The normalized difference vegetation index (NDVI) was chosen as the indicator of the degree of damage. The amount of crop damage was determined from the difference in the NDVI before and after the freeze. There was spatial variability in the amount of crop damage, so we examined three factors that may affect the degree of freeze injury: terrain, soil moisture, and crop growth before the freeze. The results showed that all these factors were significantly correlated with freeze injury degree (P<0.01, two-tailed). The damage was generally more serious in low-lying and drought-prone areas; in addition, oilseed rape planted on south- and west-oriented facing slopes and those with luxuriant growth status tended to be more susceptible to freeze injury. Furthermore, land surface temperature (LST) of the coldest day, soil moisture, pre-freeze growth and altitude were in descending order of importance in determining the degree of damage. The findings proposed in this paper would be helpful in understanding the occurrence and severity distribution of oilseed rape freeze injury under certain natural or vegetation conditions, and thus help in mitigation of this kind of meteorological disaster in southern China.

Entities:  

Keywords:  Brassica napus; Crop monitoring; Freeze injury; HJ-CCD; Remote sensing

Mesh:

Year:  2015        PMID: 25644468      PMCID: PMC4322424          DOI: 10.1631/jzus.B1400150

Source DB:  PubMed          Journal:  J Zhejiang Univ Sci B        ISSN: 1673-1581            Impact factor:   3.066


  1 in total

1.  Use of spectral vegetation indices derived from airborne hyperspectral imagery for detection of European corn borer infestation in Iowa corn plots.

Authors:  Matthew W Carroll; John A Glaser; Richard L Hellmich; Thomas E Hunt; Thomas W Sappington; Dennis Calvin; Ken Copenhaver; John Fridgen
Journal:  J Econ Entomol       Date:  2008-10       Impact factor: 2.381

  1 in total
  2 in total

1.  Estimation of rice phenology date using integrated HJ-1 CCD and Landsat-8 OLI vegetation indices time-series images.

Authors:  Jing Wang; Jing-feng Huang; Xiu-zhen Wang; Meng-ting Jin; Zhen Zhou; Qiao-ying Guo; Zhe-wen Zhao; Wei-jiao Huang; Yao Zhang; Xiao-dong Song
Journal:  J Zhejiang Univ Sci B       Date:  2015-10       Impact factor: 3.066

2.  Automatic freezing-tolerant rapeseed material recognition using UAV images and deep learning.

Authors:  Lili Li; Jiangwei Qiao; Jian Yao; Jie Li; Li Li
Journal:  Plant Methods       Date:  2022-01-13       Impact factor: 4.993

  2 in total

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