Skip to main content
SpringerLink
Log in

Remote sensing monitoring of tobacco field based on phenological characteristics and time series image—A case study of Chengjiang County, Yunnan Province, China

  • Published:
Chinese Geographical Science Aims and scope Submit manuscript

Abstract

Using three-phase remote sensing images of China-Brazil Earth Resources Satellite 02B (CBERS02B) and Landsat-5 TM, tobacco field was extracted by the analysis of time series image based on the different phenological characteristics between tobacco and other crops. The spectral characteristics of tobacco and corn in luxuriant growth stage are very similar, which makes them difficult to be distinguished using a single-phase remote sensing image. Field film after tobacco seedlings transplanting can be used as significant sign to identify tobacco. Remote sensing interpretation map based on the fusion image of TM and CBERS02B’s High-Resolution (HR) camera image was used as standard reference material to evaluate the classification accuracy of Spectral Angle Mapper (SAM) and Maximum Likelihood Classifier (MLC) for time series image based on full samples test method. SAM has higher classification accuracy and stability than MLC in dealing with time series image. The accuracy and Kappa of tobacco coverage extracted by SAM are 83.4% and 0.692 respectively, which can achieve the accuracy required by tobacco coverage measurement in a large area.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Claus B, Eckehard N, Kalman S, 1994. Spectrometer for fast measurements of in vivo reflectance, absorptance, and fluorescence in the visible and near-infrared. Remote Sensing of Environment, 48(1): 18–24. DOI: 10.1016/0034-4257(94)90110-4.

    Article  Google Scholar 

  • Clevers J G P W, Van Leeuwen H J C, 1996. Combined use of optical and microwave remote sensing data for crop growth monitoring. Remote Sensing of Environment, 56(1): 42–51. DOI: 10.1016/0034-4257(95)00227-8.

    Article  Google Scholar 

  • Conese C, Maselli F, 1992. Use of error matrices to improve area estimates with maximum likelihood classification procedures. Remote Sensing of Environment, 40(2):113–124. DOI: 10.1016/0034-4257(92)90009-9.

    Article  Google Scholar 

  • Congalton R G, 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environmen, 37(1): 35–46. DOI: 10.1016/0034-4257(91)90048-B.

    Article  Google Scholar 

  • David B L, Gregory P A, 2004. Cropland distribution from temporal unmixing of modis data. Remote Sensing of Environment, 93(3): 412–422. DOI: 10.1016/j.rse.2004.08.002.

    Article  Google Scholar 

  • Delecolle R, Maas S J, Guerif M et al., 1992. Remote sensing and crop production models: present trends. ISPRS Journal of Photogrammetry & Remote Sensing, 47(4): 145–161. DOI: 10.1016/0924-2716(92)90030-D.

    Article  Google Scholar 

  • DeWit A J W, Clevers J G P W, 2004. Efficiency and accuracy of perfield classification for operational crop mapping. International Journal of Remote Sensing, 25(1): 4091–4112. DOI: 10.1080/01431160310001619580.

    Article  Google Scholar 

  • Doraiswamy P C, Sinclair T R, Hollinger S et al., 2005. Application of MODIS derived parameters for regional crop yield assessment. Remote Sensing of Environment, 97(2): 192–202. DOI: 10.1016/j.rse.2005.03.015.

    Article  Google Scholar 

  • Guerif M, Duke C L, 2000. Adjustment procedure of a crop model to the site specific characteristics of soil and crop using remote sensing data assimilation. Agriculture Ecosystems & Environment, 81(1): 57–69. DOI: 10.1016/S0167-8809(00)00168-7.

    Article  Google Scholar 

  • Han Lijian, Pan Yaozong, Jia Bin et al., 2007. Acquisition of paddy rice coverage based on multi-temporal IRS-P6 satellite AWiFS RS-data. Transactions of the Chinese Society of Agricultural Engineering, 23(5): 137–143. DOI: 1002-6819.0.2007-05-024. (in Chinese)

    Google Scholar 

  • Kruse F A, Lefkoff A B, Boardman J B et al., 1993. The spectral image processing system (SIPS)-interactive visualization and analysis of imaging spectrometer data. Remote Sensing of Environment, 44(3): l45–l63. DOI: 10.1016/0034-4257(93)900-13-N.

    Google Scholar 

  • Launay M, Guerif M, 2005. Assimilating remote sensing data into a crop model to improve predictive performance for spatial applications. Agriculture Ecosystems & Environment, 111(4):321–339. DOI: 10.1016/j.agee.2005.06.005.

    Article  Google Scholar 

  • Li Hongjun, Zheng Li, Lei Yuping et al., 2008. Estimation of water consumption and crop water productivity of winter wheat in North China Plain using remote sensing technology. Agricultural Water Management, 95(11): 1271–1278. DOI: 10.10-16/j.agwat.2008.05.003.

    Article  Google Scholar 

  • Li Xiangyang, Liu Guoshun, Yang Yongfeng et al., 2007. Relationship between hyperspectral parameters and physiological and biochemical indexes of flue-cured tobacco leaves. Agricultural Sciences in China, 6(6): 665–672. DOI: 10.1016/S16-71-2927(07)60098-4.

    Article  Google Scholar 

  • Liang S, 2001. Land-cover classification methods for multi-year AVHRR data. International Journal of Remote Sensing, 22(8): 1479–1493. DOI: 10.1080/01431160120833.

    Article  Google Scholar 

  • Liu Xulong, He Chunyang, Pan Yaozhong et al., 2006. Accuracy assessment of thematic classification based on point and cluster sample. Journal of Remote Sensing, 10(3): 366–373. DOI: 1007-4619.0.2006-03-012. (in Chinese)

    Google Scholar 

  • Ma Yuping, Wang Shili, Zhang Li et al, 2008. Monitoring winter wheat growth in North China by combining a crop model and remote sensing data. International Journal of Applied Earth Observation and Geoinformation, 10(4): 426–437. DOI: 10.1016/j.jag.2007.09.002.

    Article  Google Scholar 

  • MacDonald R B, Hall F G, 1980. Global crop forecasting. Science, 208(4445): 670–679. DOI: 10.1126/science.208.4445.670

    Article  Google Scholar 

  • Mei Xin, Cui Weihong, Zhang Xuexia et al., 2006. Monitoring of tobacco planted acreage based on multiple remote sensing sources. In: IEEE International Geoscience and Remote Sensing Symposium Proceedings. Denver, USA: 668–670. DOI: 10.1109/IGARSS.2006.175.

  • Ming C H, Merrill K R, 2002. A subpixel classifier for urban land cover mapping based on a maximum-likelihood approach and expert system rules. Photogrammetric Engineering & Remote Sensing, 68(11): 1173–1180.

    Google Scholar 

  • Moran M S, Inoue Y, Barnes E M, 1997. Opportunities and limitations for image-based remote sensing in precision crop management.Remote Sensing of Environment, 61(3): 319–346. DOI: 10.1016/S0034-4257(97)00045-X.

    Article  Google Scholar 

  • Narayanan S, Oliver W, Hartmut K L, 1999. Changes in blue-green and chlorophyll fluorescence emission and fluorescence ratios during senescence of tobacco plants. Remote Sensing of Environment, 69(3): 215–223. DOI: 10.1016/S00344257(99)-00029-2.

    Article  Google Scholar 

  • Peng Guangxiong, He Yuhua, Li Jing et al., 2007. Study on CBERS2’s CCD image cross calibration and atmospheric correction. Journal of Infrared and Millimeter Waves, 26(1): 22–27. DOI: 1001-9014.0.2007-01-004. (in Chinese)

    Google Scholar 

  • Peng Guangxiong, Li Jing, Chen Yuhao et al., 2006. High-resolution surface relative humidity computation using MODIS image in peninsular Malaysia. Chinese Geographical Science, 16(3): 260–264. DOI: 10.1007/s11769-006-0260-6.

    Article  Google Scholar 

  • Ray S S, Dadhwal V K, 2001. Estimation of crop evapotranspiration of irrigation command area using remote sensing and GIS. Agricultural Water Management, 49(3): 239–249. DOI: 10.1016/S0378-3774(00)00147-5.

    Article  Google Scholar 

  • Stehman S V, Czaplewski R L, 1998. Design and analysis for thematic map accuracy assessment: Fundamental principles. Remote Sensing of Environment, 64(3): 331–334. DOI: 10.1016/S0034-4257(98)00010-8.

    Article  Google Scholar 

  • Tso B, Mather P M, 1999. Crop discrimination using multi-temporal SAR imagery. International Journal of Remote Sensing, (12): 2443–2460. DOI: 10.1080/014311699212119.

  • Wu Binfang, Liu Haiyan, 1997. The operational methods for rice area estimation using remote sensing. Journal of Remote Sensing, 1(1): 58–63. DOI: 1007-4619.0.1997-01-007. (in Chinese)

    Google Scholar 

  • Xiao Xiangming, Boles S, Liu Jiyuan et al., 2005. Mapping paddy rice agriculture in southern China using multi-temporal MODIS images. Remote Sensing of Environment, 95(4): 480–492. DOI: 10.1016/j.rse.2004.12.009.

    Article  Google Scholar 

  • Xu Weidong, Yin Qiu, Kuang Dingbo, 2005. Comparison of different spectral match models. Journal Infrared Millimeter and Waves, 24(4): 296–231. DOI: 1001-9014.0.2005-04-012. (in Chinese)

    Google Scholar 

  • Zhang Mingwei, Zhou Qingbo, Chen Zhongxin et al., 2008. Crop discrimination in Northern China with double cropping systems using fourier analysis of time-series MODIS data. International Journal of Applied Earth Observation and Geoinformation, 10(4): 476–485. DOI: 10.1016/j.jag.2007.11.002.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing, 100101, China

    Guangxiong Peng & Weihong Cui

  2. College of Resource Environment and Tourism, Capital Normal University, Beijing, 100048, China

    Lei Deng

  3. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China

    Tao Ming

  4. The Key Laboratory of Shanghai Education Commission for Oceanic Fisheries Resources Exploitation, College of Marine Sciences, Shanghai Ocean University, Shanghai, 201306, China

    Wei Shen

Authors
  1. Guangxiong Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lei Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Weihong Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tao Ming
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wei Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lei Deng.

Additional information

Foundation item: Under the auspices of China Postdoctoral Science Foundation (No. 20080430586, 20070420018), National Natural Science Foundation of China (No. 40801161, 40801172), Sino US International Cooperation in Science and Technology (No. 2007DFA20640)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Peng, G., Deng, L., Cui, W. et al. Remote sensing monitoring of tobacco field based on phenological characteristics and time series image—A case study of Chengjiang County, Yunnan Province, China. Chin. Geogr. Sci. 19, 186–193 (2009). https://doi.org/10.1007/s11769-009-0186-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11769-009-0186-x

Keywords

Search

Navigation