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Mapping paddy rice with multi-date moderate-resolution imaging spectroradiometer (MODIS) data in China

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Abstract

The objective of this study was to obtain spatial distribution maps of paddy rice fields using multi-date moderateresolution imaging spectroradiometer (MODIS) data in China. Paddy rice fields were extracted by identifying the unique characteristic of high soil moisture in the flooding and transplanting period with improved algorithms based on rice growth calendar regionalization. The characteristic could be reflected by the enhanced vegetation index (EVI) and the land surface water index (LSWI) derived from MODIS sensor data. Algorithms for single, early, and late rice identification were obtained from selected typical test sites. The algorithms could not only separate early rice and late rice planted in the same fields, but also reduce the uncertainties. The areal accuracy of the MODIS-derived results was validated by comparison with agricultural statistics, and the spatial matching was examined by ETM+ (enhanced thematic mapper plus) images in a test region. Major factors that might cause errors, such as the coarse spatial resolution and noises in the MODIS data, were discussed. Although not suitable for monitoring the inter-annual variations due to some inevitable factors, the MODIS-derived results were useful for obtaining spatial distribution maps of paddy rice on a large scale, and they might provide reference for further studies.

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References

  • CEPD (Council for Economic Planning and Development), 2006. Taiwan Statistical Data Book 2006. Council for Economic Planning and Development, Taipei.

    Google Scholar 

  • Chen, J., Jönsson, P., Tamura, M., Gu, Z., Matsushita, B., Eklundh, L., 2004. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter. Remote Sensing Environ., 91(3–4): 332–344. [doi:10.1016/j.rse.2004.03.014]

    Article  Google Scholar 

  • Cihlar, J., 1996. Identification of contaminated pixels in AVHRR composite images for studies of land biosphere. Remote Sensing Environ., 56(3):149–153. [doi:10.1016/0034-4257(95)00190-5]

    Article  Google Scholar 

  • ECCAY (Editorial Committee of China Agriculture Yearbook), 2006. China Agriculture Yearbook 2006. China Agricultural Press, Beijing (in Chinese).

    Google Scholar 

  • Fang, H., Wu, B., Liu, H., Huang, X., 1998. Using NOAA AVHRR and Landsat TM to estimate rice area year-by-year. Int. J. Remote Sensing, 19(3):521–525. [doi:10.1080/014311698216134]

    Article  Google Scholar 

  • Fensholt, R., Sandholt, I., 2003. Derivation of a shortwave infrared water stress index from MODIS near- and shortwave infrared data in a semi-arid environment. Remote Sensing Environ., 87(1):111–121. [doi:10.1016/j.rse.2003.07.002]

    Article  Google Scholar 

  • Gao, B.C., 1996. NDWI: a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing Environ., 58(3):257–266. [doi:10.1016/S0034-4257(96)00067-3]

    Article  Google Scholar 

  • Groten, S.M.E., 1993. NDVI: crop monitoring and early yield assessment of Burkina Faso. Int. J. Remote Sensing, 14(8):1495–1515. [doi:10.1080/01431169308953983]

    Article  Google Scholar 

  • Hong, S.Y., Lee, K.S., Rim, S.K., Kim, K.U., 1999. Estimation of Rice Field Area Using Two-date Landsat TM Images in Korea. Proc. IEEE Int. Geoscience and Remote Sensing Symp., 2:732–734. [doi:10.1109/IGARSS.1999.774422]

    Google Scholar 

  • Huete, A.R., Liu, H.Q., Batchily, K., van Leeuwen, W., 1997. A comparison of vegetation indices global set of TM images for EOS-MODIS. Remote Sensing Environ., 59(3):440–451. [doi:10.1016/S0034-4257(96)00112-5]

    Article  Google Scholar 

  • Huete, A.R., Didan, K., Miura, T., Rodriguez, E., Gao, X., Ferreira, L., 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing Environ., 83(1–2):195–213. [doi:10.1016/S0034-4257(02)00096-2]

    Article  Google Scholar 

  • Jönsson, P., Eklundh, L., 2004. TIMESAT: a program for analyzing time-series of satellite sensor data. Comput. & Geosci., 30(8):833–845. [doi:10.1016/j.cageo.2004.05.006]

    Article  Google Scholar 

  • Li, C., Qiu, J., Frolking, S., Xiao, X., Salas, W., Moore, B., Boles, S., Huang, Y., Sass, R., 2002. Reduced methane emissions from large-scale changes in water management of China’s rice paddies during 1980–2000. Geophys. Res. Lett., 29(20):1972. [doi:10.1029/2002GL015370]

    Article  Google Scholar 

  • Li, Y., Liao, Q., Li, X., Liao, S., Chi, G., Peng, S., 2003. Towards an operational system for regional-scale rice yield estimation using a time-series of Radarsat ScanSAR images. Int. J. Remote Sensing, 24(21):4207–4220. [doi:10.1080/0143116031000095970]

    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 

  • McCloy, K.R., Smith, F.R., Robinson, M.R., 1987. Monitoring rice areas using Landsat MSS data. Int. J. Remote Sensing, 8(5):741–749. [doi:10.1080/01431168708948685]

    Article  Google Scholar 

  • Nageswara Rao, P.P., Rao, V.R., 1987. Rice crop identification and area estimation using remotely sensed data from Indian cropping patterns. Int. J. Remote Sensing, 8(4):639–650. [doi:10.1080/01431168708948670]

    Article  MathSciNet  Google Scholar 

  • Neue, H., Boonjawat, J., 1998. Methane Emissions from Rice Fields. In: Galloway, J., Melillo, J. (Eds.), Asian Change in the Context of Global Climate Change. IGBP Book Series. Cambridge University Press, Cambridge, UK, p.187–209.

    Google Scholar 

  • Okamoto, K., Kawashima, H., 1999. Estimation of riceplanted area in the tropical zone using a combination of optical and microwave satellite sensor data. Int. J. Remote Sensing, 20(5):1045–1048. [doi:10.1080/014311699213091]

    Article  Google Scholar 

  • Ribbes, F., le Toan, T., 1999. Rice field mapping and monitoring with RADARSAT data. Int. J. Remote Sensing, 20(4):745–765. [doi:10.1080/014311699213172]

    Article  Google Scholar 

  • Roy, D.P., Borak, J.S., Devadiga, S., Zheng, M., Descloitres, J., 2002. The MODIS land product quality assessment approach. Remote Sensing Environ., 83(1–2):62–76. [doi:10.1016/S0034-4257(02)00087-1]

    Article  Google Scholar 

  • Sakamoto, T., Yokozawa, M., Toritani, H., Shibayama, M., Ishitsuka, N., Ohno, H., 2005. A crop phenology detection method using time-series MODIS data. Remote Sensing Environ., 96(3—4):366–374. [doi:10.1016/j.rse.2005.03.008]

    Article  Google Scholar 

  • Savitzky, A., Golay, M.J.E., 1964. Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem., 36(8):1627–1639. [doi:10.1021/ac60214a047]

    Article  Google Scholar 

  • Sellers, P.J., Tucker, C.J., Collatz, G.J., Los, S.O., Justice, C.O., Dazlich, D.A., Randall, D.A., 1994. A global 1° by 1° NDVI data set for climate studies: Part II. The generation of global fields of terrestrial biophysical parameters from the NDVI. Int. J. Remote Sensing, 15(17):3519–3545. [doi:10.1080/01431169408954343]

    Article  Google Scholar 

  • Shao, Y., Fan, X., Liu, H., 2001. Rice monitoring and production estimation using multitemporal RADARSAT. Remote Sensing Environ., 76(3):310–325. [doi:10.1016/S0034-4257(00)00212-1]

    Article  Google Scholar 

  • Tennakoon, S.B., Murty, V.V.N., Eiumnoh, A., 1992. Estimation of cropped area and grain yield of rice using remote sensing data. Int. J. Remote Sensing, 13(3):427–439. [doi:10.1080/01431169208904047]

    Article  Google Scholar 

  • Turner, M.D., Congalton, R.G., 1998. Classification of multi-temporal SPOT-XS satellite data for mapping rice fields on a West African flood plain. Int. J. Remote Sensing, 19(1):21–41. [doi:10.1080/014311698216404]

    Article  Google Scholar 

  • van der Gon, H.D., 2000. Changes in CH4 emission from rice fields from 1960s to 1990s: 1. Impacts of modern rice technology. Glob. Biogeochem. Cycles, 14(1):61–72. [doi:10.1029/1999GB900096]

    Article  Google Scholar 

  • Viovy, N., Arino, O., Belward, A., 1992. The best index slope extraction (BISE): a method for reducing noise in NDVI time-series. Int. J. Remote Sensing, 13(8):1585–1590. [doi:10.1080/01431169208904212]

    Article  Google Scholar 

  • Xiao, X., Boles, S., Frolking, S., Salas, W., Moore, B., Li, C., He, L., Zhao, R., 2002a. Landscape-scale characterization of cropland in China using Vegetation and Landsat TM images. Int. J. Remote Sensing, 23(18):3579–3594. [doi:10.1080/01431160110106069]

    Article  Google Scholar 

  • Xiao, X., Boles, S., Frolking, S., Salas, W., Moore, B., Li, C., He, L., Zhao, R., 2002b. Observation of flooding and rice transplanting of paddy rice fields at the site to landscape scales in China using Vegetation sensor data. Int. J. Remote Sensing, 23(15):3009–3022. [doi:10.1080/01431160110107734]

    Article  Google Scholar 

  • Xiao, X., Boles, S., Liu, J., Zhuang, D., Frolking, S., Li, C., Salas, W., Moore, B., 2005. Mapping paddy rice agriculture in southern China using multi-temporal MODIS images. Remote Sensing Environ., 95(4):480–492. [doi:10.1016/j.rse.2004.12.009]

    Article  Google Scholar 

  • Xiao, X., Boles, S., Frolking, S., Li, C., Babu, J.Y., Salas, W., Moore, B., 2006. Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images. Remote Sensing Environ., 100(1):95–113. [doi:10.1016/j.rse.2005.10.004]

    Article  Google Scholar 

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Authors and Affiliations

  1. Institute of Agricultural Remote Sensing and Information Application, Zhejiang University, Hangzhou, 310029, China

    Hua-sheng Sun, Jing-feng Huang & Dai-liang Peng

  2. Key Lab of Agricultural Remote Sensing and Information System, Hangzhou, 310029, China

    Hua-sheng Sun & Dai-liang Peng

  3. MOE Key Lab of Environmental Remediation and Ecological Health, Zhejiang University, Hangzhou, 310029, China

    Jing-feng Huang

  4. Department of Soil, Water, and Environmental Science, University of Arizona, Tucson, AZ, 85721, USA

    Alfredo R. Huete

  5. Resources and Environment Department, Xinjiang Agricultural University, Urumchi, 830052, China

    Feng Zhang

  6. College of Surveying and Mapping, Xuzhou Normal University, Xuzhou, 221116, China

    Hua-sheng Sun

Authors
  1. Hua-sheng Sun
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  2. Jing-feng Huang
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  3. Alfredo R. Huete
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  4. Dai-liang Peng
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  5. Feng Zhang
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Correspondence to Jing-feng Huang.

Additional information

Project supported by the National High-Tech Research and Development Program (863) of China (No. 2006AA120101), the National Natural Science Foundation of China (No. 40871158/D0106), and the Key Technologies Research and Development Program of China (No. 2006BAD10A01)

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Sun, Hs., Huang, Jf., Huete, A.R. et al. Mapping paddy rice with multi-date moderate-resolution imaging spectroradiometer (MODIS) data in China. J. Zhejiang Univ. Sci. A 10, 1509–1522 (2009). https://doi.org/10.1631/jzus.A0820536

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  • DOI: https://doi.org/10.1631/jzus.A0820536

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