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CLC number: S127

On-line Access: 2024-08-27

Received: 2023-10-17

Revision Accepted: 2024-05-08

Crosschecked: 2015-01-20

Cited: 5

Clicked: 7047

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Jing-feng Huang

http://orcid.org/0000-0003-4627-6021

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Journal of Zhejiang University SCIENCE B 2015 Vol.16 No.2 P.131-144

http://doi.org/10.1631/jzus.B1400150


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


Author(s):  Bao She, Jing-feng Huang, Rui-fang Guo, Hong-bin Wang, Jing Wang

Affiliation(s):  Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China; more

Corresponding email(s):   hjf@zju.edu.cn

Key Words:  Brassica napus, Freeze injury, Remote sensing, Crop monitoring, HJ-CCD


Bao She, Jing-feng Huang, Rui-fang Guo, Hong-bin Wang, Jing Wang. Assessing winter oilseed rape freeze injury based on Chinese HJ remote sensing data[J]. Journal of Zhejiang University Science B, 2015, 16(2): 131-144.

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author="Bao She, Jing-feng Huang, Rui-fang Guo, Hong-bin Wang, Jing Wang",
journal="Journal of Zhejiang University Science B",
volume="16",
number="2",
pages="131-144",
year="2015",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.B1400150"
}

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%T Assessing winter oilseed rape freeze injury based on Chinese HJ remote sensing data
%A Bao She
%A Jing-feng Huang
%A Rui-fang Guo
%A Hong-bin Wang
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%J Journal of Zhejiang University SCIENCE B
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%D 2015
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.B1400150

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A1 - Jing-feng Huang
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A1 - Jing Wang
J0 - Journal of Zhejiang University Science B
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DOI - 10.1631/jzus.B1400150


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.

基于国产环境减灾卫星遥感数据的油菜冻害评估

目的:以2011年1月发生在合肥地区的油菜冻害为案例,利用国产环境减灾卫星数据监测其灾情分布,探究自然环境条件及植被长势与灾情之间的关系。
创新点:基于遥感手段监测越冬期油菜冻害的研究鲜见报道。鉴于受灾年份的花期影像难以准确呈现油菜的实际空间分布,本文提出了一套适用于灾害年越冬时期的油菜种植区域遥感提取方法,探索了地形条件、越冬前长势、土壤湿度和最冷日期地表温度对于灾情程度的影响。
方法:以正常年份的油菜种植区域为基准,利用越冬作物在越冬前生长的特性来提取受灾年份越冬时期的油菜种植区域;利用灾后相对于灾前的归一化植被指数(NDVI)百分比变化量作为冻害监测指标来监测灾情分布;采用随机样本点抽取的灾情与各影响因素数据集,运用相关分析方法来探讨二者之间的联系,采用统计分析方法探讨灾情与坡向之间的关系,采用灰色相关分析方法考查各影响因素对于灾情的影响程度。
结论:基于国产环境减灾卫星数据可以有效地监测油菜冻害灾情,展现不同冻害等级的空间分布;在地势低洼、土壤墒情差、植株长势旺盛条件下,油菜冻害趋于严重,南坡向和西坡向生长的油菜受冻相对更为严重;各影响因素对冻害灾情的影响程度由高到低依次为:最冷日期的地表温度、土壤湿度、灾前长势、海拔高度。

关键词:油菜;冻害;遥感;作物监测;环境减灾卫星

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Bhuiyan, C., Singh, R.P., Kogan, F.N., 2006. Monitoring drought dynamics in the Aravalli region (India) using different indices based on ground and remote sensing data. Int. J. Appl. Earth Obs. Geoinf., 8(4):289-302.

[2]Breckling, B., Laue, H., Pehlke, H., 2011. Remote sensing as a data source to analyse regional implications of genetically modified plants in agriculture—oilseed rape (Brassica napus) in Northern Germany. Ecol. Indic., 11(4):942-950.

[3]Brivio, P.A., Colombo, R., Maggi, M., et al., 2002. Integration of remote sensing data and GIS for accurate mapping of flooded areas. Int. J. Remote Sens., 23(3):429-441.

[4]Calera, A., González-Piqueras, J., Melia, J., 2004. Monitoring barley and corn growth from remote sensing data at field scale. Int. J. Remote Sens., 25(1):97-109.

[5]Carroll, M.W., Glaser, J.A., Hellmich, R.L., et al., 2008. Use of spectral vegetation indices derived from airborne hyperspectral imagery for detection of European corn borer infestation in Iowa corn plots. J. Econ. Entomol., 101(5):1614-1623.

[6]Chaohu Municipal Bureau of Statistics, 2010. Chaohu Statistical Yearbook 2010. China Statistics Press, Beijing, p.264-265 (in Chinese).

[7]Currit, N., St. Clair, S.B., 2010. Assessing the impact of extreme climatic events on aspen defoliation using MODIS imagery. Geocarto Int., 25(2):133-147.

[8]Deng, J.L., 1982. Control problems of grey systems. Syst. Control Lett., 1(5):288-294.

[9]Duan, S.B., Yan, G.J., Qian, Y.G., et al., 2008. Two single-channel algorithms for retrieving land surface temperature using simulated HJ-1B data. Prog. Nat. Sci., 18(9):1001-1008 (in Chinese).

[10]Feng, M.C., Yang, W.D., Cao, L.L., et al., 2009. Monitoring winter wheat freeze injury using multi-temporal MODIS data. Agric. Sci. China, 8(9):1053-1062.

[11]Gu, L.H., Hanson, P.J., Post, W.M., et al., 2008. The 2007 eastern US spring freeze: increased cold damage in a warming world? BioScience, 58(3):253-262.

[12]Hefei Municipal Bureau of Statistics, 2012. Hefei Statistical Yearbook 2012. China Statistics Press, Beijing, p.195 (in Chinese).

[13]Huete, A.R., Liu, H.Q., Batchily, K., et al., 1997. A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sens. Environ., 59(3):440-451.

[14]Jiménez-Muñoz, J.C., Sobrino, J.A., 2003. A generalized single-channel method for retrieving land surface temperature from remote sensing data. J. Geophys. Res., 108(D22):4688.

[15]Kaufman, Y.J., Gao, B.C., 1992. Remote sensing of water vapor in the near IR from EOS/MODIS. IEEE Trans. Geosci. Remote Sens., 30(5):871-884.

[16]King, D.J., Olthof, I., Pellikka, P.K.E., et al., 2005. Modeling and mapping damage to forests from an ice storm using remote sensing and environmental data. Nat. Hazards, 35(3):321-342.

[17]Liang, E.Y., Shao, X.M., He, J.C., 2005. Relationships between tree growth and NDVI of grassland in the semi-arid grassland of north China. Int. J. Remote Sens., 26(13):2901-2908.

[18]Luedeling, E., Hale, A., Zhang, M.H., et al., 2009. Remote sensing of spider mite damage in California peach orchards. Int. J. Appl. Earth Obs. Geoinf., 11(4):244-255.

[19]McFeeters, S.K., 1996. The use of the normalized difference water index (NDWI) in the delineation of open water features. Int. J. Remote Sens., 17(7):1425-1432.

[20]Oksanen, E., Freiwald, V., Prozherina, N., et al., 2005. Photosynthesis of birch (Betula pendula) is sensitive to springtime frost and ozone. Can. J. For. Res., 35(3):703-712.

[21]Olthof, I., King, D.J., Lautenschlager, R.A., 2004. Mapping deciduous forest ice storm damage using Landsat and environmental data. Remote Sens. Environ., 89(4):484-496.

[22]Qin, Z.H., Li, W.J., Xu, B., et al., 2004. The estimation of land surface emissivity for Landsat TM6. Remote Sens. Land Resourc., (3):28-32, 36, 41 (in Chinese).

[23]Ren, J.Q., Chen, Z.X., Zhou, Q.B., et al., 2008. Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China. Int. J. Appl. Earth Obs. Geoinf., 10(4):403-413.

[24]Rural Social and Economic Investigation Division, National Bureau of Statistics, 2012. China Rural Statistical Yearbook 2012. China Statistics Press, Beijing, p.142, 154 (in Chinese).

[25]Sandholt, I., Rasmussen, K., Andersen, J., 2002. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sens. Environ., 79(2-3):213-224.

[26]Sanyal, J., Lu, X.X., 2005. Remote sensing and GIS-based flood vulnerability assessment of human settlements: a case study of Gangetic West Bengal, India. Hydrol. Process., 19(18):3699-3716.

[27]Silleos, N., Perakis, K., Petsanis, G., 2002. Assessment of crop damage using space remote sensing and GIS. Int. J. Remote Sens., 23(3):417-427.

[28]Su, Z.B., Yacob, A., Wen, J., et al., 2003. Assessing relative soil moisture with remote sensing data: theory, experimental validation, and application to drought monitoring over the North China Plain. Phys. Chem. Earth., 28(1-3):89-101.

[29]Tan, Z.K., Ding, M.H., Wang, L.H., et al., 2009. Monitoring freeze injury and evaluating losing to sugar-cane using RS and GPS. In: Li, D.L., Zhao, C.J. (Eds.), Computer and Computing Technologies in Agriculture II, Volume 1. Springer, p.307-316.

[30]Townsend, P.A., Walsh, S.J., 1998. Modeling floodplain inundation using an integrated GIS with radar and optical remote sensing. Geomorphology, 21(3-4):295-312.

[31]Vicente-Serrano, S.M., 2007. Evaluating the impact of drought using remote sensing in a Mediterranean, semi-arid region. Nat. Hazards, 40(1):173-208.

[32]Wan, Z., Wang, P., Li, X., 2004. Using MODIS land surface temperature and normalized difference vegetation index products for monitoring drought in the southern Great Plains, USA. Int. J. Remote Sens., 25(1):61-72.

[33]Wang, H.F., Gu, X.H., Wang, J.H., et al., 2012. Monitoring winter wheat freeze injury based on multi-temporal data. Intell. Autom. Soft Comput., 18(8):1035-1042.

[34]Wang, J., Rich, P.M., Price, K.P., et al., 2004. Relations between NDVI and tree productivity in the central Great Plains. Int. J. Remote Sens., 25(16):3127-3138.

[35]Wardlow, B.D., Egbert, S.L., Kastens, J.H., 2007. Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains. Remote Sens. Environ., 108(3):290-310.

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