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CLC number: S511

On-line Access: 2015-10-03

Received: 2015-04-15

Revision Accepted: 2015-09-14

Crosschecked: 2015-09-17

Cited: 6

Clicked: 1844

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Xiao-dong Song

http://orcid.org/0000-0003-4174-7162

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Journal of Zhejiang University SCIENCE B 2015 Vol.16 No.10 P.832-844

10.1631/jzus.B1500087


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


Author(s):  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

Affiliation(s):  1Institute of Remote Sensing and Information Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China; more

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

Key Words:  Phenological parameters, Intercalibration, Vegetation index, HJ-1 CCD, Landsat-8 OLI


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. Estimation of rice phenology date using integrated HJ-1 CCD and Landsat-8 OLI vegetation indices time-series images[J]. Journal of Zhejiang University Science B, 2015, 16(10): 832-844.

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author="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="Journal of Zhejiang University Science B",
volume="16",
number="10",
pages="832-844",
year="2015",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.B1500087"
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%T Estimation of rice phenology date using integrated HJ-1 CCD and Landsat-8 OLI vegetation indices time-series images
%A Jing Wang
%A Jing-feng Huang
%A Xiu-zhen Wang
%A Meng-ting Jin
%A Zhen Zhou
%A Qiao-ying Guo
%A Zhe-wen Zhao
%A Wei-jiao Huang
%A Yao Zhang
%A Xiao-dong Song
%J Journal of Zhejiang University SCIENCE B
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%D 2015
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.B1500087

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T1 - Estimation of rice phenology date using integrated HJ-1 CCD and Landsat-8 OLI vegetation indices time-series images
A1 - Jing Wang
A1 - Jing-feng Huang
A1 - Xiu-zhen Wang
A1 - Meng-ting Jin
A1 - Zhen Zhou
A1 - Qiao-ying Guo
A1 - Zhe-wen Zhao
A1 - Wei-jiao Huang
A1 - Yao Zhang
A1 - Xiao-dong Song
J0 - Journal of Zhejiang University Science B
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SP - 832
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.B1500087


Abstract: 
Accurate estimation of rice phenology is of critical importance for agricultural practices and studies. However, the accuracy of phenological parameters extracted by remote sensing data cannot be guaranteed because of the influence of climate, e.g. the monsoon season, and limited available remote sensing data. In this study, we integrate the data of HJ-1 CCD and Landsat-8 operational land imager (OLI) by using the ordinary least-squares (OLS), and construct higher temporal resolution vegetation indices (VIs) time-series data to extract the phenological parameters of single-cropped rice. Two widely used VIs, namely the normalized difference vegetation index (NDVI) and 2-band enhanced vegetation index (EVI2), were adopted to minimize the influence of environmental factors and the intrinsic difference between the two sensors. Savitzky-Golay (S-G) filters were applied to construct continuous VI profiles per pixel. The results showed that, compared with NDVI, EVI2 was more stable and comparable between the two sensors. Compared with the observed phenological data of the single-cropped rice, the integrated VI time-series had a relatively low root mean square error (RMSE), and EVI2 showed higher accuracy compared with NDVI. We also demonstrate the application of phenology extraction of the single-cropped rice in a spatial scale in the study area. While the work is of general value, it can also be extrapolated to other regions where qualified remote sensing data are the bottleneck but where complementary data are occasionally available.

基于环境减灾卫星及Landsat-8卫星的植被指数时间序列的水稻物候期提取研究

目的:鉴于中国南方地区单季稻种植区在关键生育期较难获得清晰影像的情况,利用相互校准方法并结合HJ-1 CCD和Landsat-8 OLI传感器,生成时间分辨率更高的植被指数时间序列数据,并比较不同植被指数在提取水稻物候期中的差异。
创新点:本文通过传感器相互校准获得了具有更高时间分辨率的植被指数时间序列数据,同时研究了不同植被指数在提取水稻物候期中的有效性,从而提高了水稻物候期提取的精度。
方法:利用最小二乘法对HJ-1 CCD和Landsat-8 OLI传感器提取的植被指数(EVI2和NDVI)进行相互校准,验证了不同传感器可互补使用;利用一致性分析方法,对比不同植被指数在提取单季稻物候期中的有效性;通过极值法和最大斜率法提取研究区单季稻的移栽期、抽穗期和成熟期;将利用两传感器相结合形成的新植被指数时间序列数据得到的水稻物候期提取结果,与用单一传感器得到结果进行对比,分析水稻物候期提取精度。
结论:基于环境减灾卫星及Landsat-8卫星融合后得到的植被指数时间序列数据可以有效地提高南方单季稻物候期提取的精度,EVI2的提取效果优于NDVI,极值法和最大斜率法结合提取的单季稻物候期结果与野外调查及农气站统计结果较为吻合,可以很好地应用于实际业务中。

关键词:物候提取;相互校准;植被指数;HJ-1 CCD;Landsat-8 OLI

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

Reference

[1]Anderson, J.H., Weber, K.T., Gokhale, B., et al., 2011. Intercalibration and evaluation of ResourceSat-1 and Landsat-5 NDVI. Can. J. Remote Sens., 37(2):213-219.

[2]Boschetti, M., Stroppiana, D., Brivio, P.A., et al., 2009. Multi-year monitoring of rice crop phenology through time series analysis of MODIS images. Int. J. Remote Sens., 30(18):4643-4662.

[3]Brown, M.E., de Beurs, K.M., 2008. Evaluation of multi-sensor semi-arid crop season parameters based on NDVI and rainfall. Remote Sens. Environ., 112(5):2261-2271.

[4]Cai, W.W., Song, J.L., Wang, J.D., et al., 2012. High spatial-and temporal-resolution NDVI produced by the assimilation of MODIS and HJ-1 data. Can. J. Remote Sens., 37(6):612-627.

[5]Chen, X.X., Vogelmann, J.E., Chander, G., et al., 2013. Cross-sensor comparisons between Landsat 5 TM and IRS-P6 AWiFS and disturbance detection using integrated Landsat and AWiFS time-series images. Int. J. Remote Sens., 34(7):2432-2453.

[6]Cong, N., Piao, S.L., Chen, A.P., et al., 2012. Spring vegetation green-up date in China inferred from SPOT NDVI data: a multiple model analysis. Agric. Forest Meteorol., 165(0):104-113.

[7]Eerens, H., Haesen, D., Rembold, F., et al., 2014. Image time series processing for agriculture monitoring. Environ. Modell. Softw., 53:154-162.

[8]FAOSTAT, 2012. Statistical Database of the Food and Agricultural Organization of the United Nations. Available from http://faostat3.fao.org/browse/Q/*/E [Accessed on Dec. 30, 2014].

[9]Gao, S., Niu, Z., Huang, N., et al., 2013. Estimating the Leaf Area Index, height and biomass of maize using HJ-1 and RADARSAT-2. Int. J. Appl. Earth Obs. Geoinf., 24:1-8.

[10]Gu, Y., Wylie, B.K., Howard, D.M., et al., 2013. NDVI saturation adjustment: a new approach for improving cropland performance estimates in the Greater Platte River Basin, USA. Ecol. Indic., 30(0):1-6.

[11]Hird, J.N., McDermid, G.J., 2009. Noise reduction of NDVI time series: an empirical comparison of selected techniques. Remote Sens. Environ., 113(1):248-258.

[12]Huete, A., Didan, K., Miura, T., et al., 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ., 83(1-2):195-213.

[13]Isobe, T., Feigelson, E.D., Akritas, M.G., et al., 1990. Linear regression in astronomy. Astrophys. J., 364(1):104-113.

[14]Ji, L., Gallo, K., 2006. An agreement coefficient for image comparison. Photogramm. Eng. Remote Sens., 72(7):823-833.

[15]Ji, L., Gallo, K., Eidenshink, J.C., et al., 2008. Agreement evaluation of AVHRR and MODIS 16-day composite NDVI data sets. Int. J. Remote Sens., 29(16):4839-4861.

[16]Jiang, Z.Y., Huete, A.R., Kim, Y., et al., 2007. 2-band enhanced vegetation index without a blue band and its application to AVHRR data. Proc. SPIE 6679, Remote Sensing and Modeling of Ecosystems for Sustainability IV, San Diego, CA, p.667905.

[17]Jiang, Z.Y., Huete, A.R., Didan, K., et al., 2008. Development of a two-band enhanced vegetation index without a blue band. Remote Sens. Environ., 112(10):3833-3845.

[18]Jönsson, P., Eklundh, L., 2004. TIMESAT—a program for analyzing time-series of satellite sensor data. Comput. Geosci., 30(8):833-845.

[19]Kim, Y., Miura, T., Jiang, Z., et al., 2010. Spectral compatibility of vegetation indices across sensors: band decomposition analysis with Hyperion data. J. Appl. Remote Sens., 4(1):043520.

[20]Lanorte, A., Lasaponara, R., Lovallo, M., et al., 2014. Fisher-Shannon information plane analysis of SPOT/ VEGETATION Normalized Difference Vegetation Index (NDVI) time series to characterize vegetation recovery after fire disturbance. Int. J. Appl. Earth Obs. Geoinf., 26:441-446.

[21]Li, P., Jiang, L.G., Feng, Z.M., 2014. Cross-comparison of vegetation indices derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) sensors. Remote Sens., 6(1):310-329.

[22]Li, S.H., Xiao, J.T., Ni, P., et al., 2014. Monitoring paddy rice phenology using time series MODIS data over Jiangxi Province, China. Int. J. Agric. Biol. Eng., 7(6):28-36.

[23]Mielke, P.W.J., Berry, K.J., 2001. Permutation Methods: a Distance Function Approach. Springer, New York, p.155-238.

[24]Pan, Z., Huang, J., Zhou, Q., et al., 2015. Mapping crop phenology using NDVI time-series derived from HJ-1 A/B data. Int. J. Appl. Earth Obs. Geoinf., 34(0):188-197.

[25]Panda, S.S., Ames, D.P., Panigrahi, S., 2010. Application of vegetation indices for agricultural crop yield prediction using neural network techniques. Remote Sens., 2(3):673-696.

[26]Peng, D.L., Huete, A.R., Huang, J.F., et al., 2011. Detection and estimation of mixed paddy rice cropping patterns with MODIS data. Int. J. Appl. Earth Obs. Geoinf., 13(1):13-23.

[27]Pyongsop, R.I., Ma, Z.B., Qi, Q.W., et al., 2010. Cloud and shadow removal from Landsat TM data. J. Remote Sens., 14(3):534-545.

[28]Rawlings, J.O., Pantula, S.G., Dickey, D.A., 1990. Applied regression analysis: a research tool. J. Oper. Res. Soc., 41(8):782-783.

[29]Rouse, J.W., Haas, R.H., Schell, J.A., et al., 1974. Monitoring the vernal advancement of retrogradation of natural vegetation. Technical Report No. E74-10113, Texas AM University, College Station, TX, USA.

[30]Sakamoto, T., Yokozawa, M., Toritani, H., et al., 2005. A crop phenology detection method using time-series MODIS data. Remote Sens. Environ., 96(3-4):366-374.

[31]She, B., Huang, J.F., Guo, R.F., et al., 2015. Assessing winter oilseed rape freeze injury based on Chinese HJ remote sensing data. J. Zhejiang Univ.-Sci. B (Biomed. & Biotechnol.), 16(2):131-144.

[32]Shi, J.J., Huang, J.F., Zhang, F., 2013. Multi-year monitoring of paddy rice planting area in Northeast China using MODIS time series data. J. Zhejiang Univ.-Sci. B (Biomed. & Biotechnol.), 14(10):934-946.

[33]Sprent, P., Dolby, G.R., 1980. The geometric mean functional-relationship. Biometrics, 36(3):547-550.

[34]Steven, M.D., Malthus, T.J., Baret, F., et al., 2003. Intercalibration of vegetation indices from different sensor systems. Remote Sens. Environ., 88(4):412-422.

[35]Teillet, P.M., Ren, X.M., 2008. Spectral band difference effects on vegetation indices derived from multiple satellite sensor data. Can. J. Remote Sens., 34(3):159-173.

[36]Valsami, G., Macheras, P., 1995. The geometric mean functional relationship approach to linear regression in pharmaceutical studies: application to the estimation of binding parameters. Pharm. Pharmacol. Comm., 1(12):551-554.

[37]Wang, J., Huang, J., Zhang, K., et al., 2015. Rice fields mapping in fragmented area using multi-temporal HJ-1A/B CCD images. Remote Sens., 7(4):3467-3488.

[38]Wu, W.B., Yang, P., Tang, H.J., et al., 2010. Characterizing spatial patterns of phenology in cropland of China based on remotely sensed data. Agric. Sci. China, 9(1):101-112.

[39]Xie, Y.C., Sha, Z.Y., Yu, M., 2008. Remote sensing imagery in vegetation mapping: a review. J. Plant Ecol., 1(1):9-23.

[40]Xin, J., Yu, Z., van Leeuwen, L., et al., 2002. Mapping crop key phenological stages in the north China plain using NOAA time series images. Int. J. Appl. Earth Obs. Geoinf., 4(2):109-117.

[41]You, X.Z., Meng, J.H., Zhang, M., et al., 2013. Remote sensing based detection of crop phenology for agricultural zones in China using a new threshold method. Remote Sens., 5(7):3190-3211.

[42]Yu, F.F., Price, K.P., Ellis, J., et al., 2003. Response of seasonal vegetation development to climatic variations in eastern central Asia. Remote Sens. Environ., 87(1):42-54.

[43]Zhang, L.W., Huang, J.F., Guo, R.F., et al., 2013. Spatio-temporal reconstruction of air temperature maps and their application to estimate rice growing season heat accumulation using multi-temporal MODIS data. J. Zhejiang Univ.-Sci. B (Biomed. & Biotechnol.), 14(2):144-161.

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