CLC number: TP79
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2013-09-06
Cited: 14
Clicked: 5410
Jing-jing Shi, Jing-feng Huang, Feng Zhang. Multi-year monitoring of paddy rice planting area in Northeast China using MODIS time series data[J]. Journal of Zhejiang University Science B, 2013, 14(10): 934-946.
@article{title="Multi-year monitoring of paddy rice planting area in Northeast China using MODIS time series data",
author="Jing-jing Shi, Jing-feng Huang, Feng Zhang",
journal="Journal of Zhejiang University Science B",
volume="14",
number="10",
pages="934-946",
year="2013",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.B1200352"
}
%0 Journal Article
%T Multi-year monitoring of paddy rice planting area in Northeast China using MODIS time series data
%A Jing-jing Shi
%A Jing-feng Huang
%A Feng Zhang
%J Journal of Zhejiang University SCIENCE B
%V 14
%N 10
%P 934-946
%@ 1673-1581
%D 2013
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.B1200352
TY - JOUR
T1 - Multi-year monitoring of paddy rice planting area in Northeast China using MODIS time series data
A1 - Jing-jing Shi
A1 - Jing-feng Huang
A1 - Feng Zhang
J0 - Journal of Zhejiang University Science B
VL - 14
IS - 10
SP - 934
EP - 946
%@ 1673-1581
Y1 - 2013
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.B1200352
Abstract: The objective of this study was to investigate the tempo-spatial distribution of paddy rice in northeast China using moderate resolution imaging spectroradiometer (MODIS) data. We developed an algorithm for detection and estimation of the transplanting and flooding periods of paddy rice with a combination of enhanced vegetation index (EVI) and land surface water index with a central wavelength at 2130 nm (LSWI2130). In two intensive sites in northeast China, fine resolution satellite imagery was used to validate the performance of the algorithm at pixel and 3×3 pixel window levels, respectively. The commission and omission errors in both of the intensive sites were approximately less than 20%. Based on the algorithm, annual distribution of paddy rice in northeast China from 2001 to 2009 was mapped and analyzed. The results demonstrated that the MODIS-derived area was highly correlated with published agricultural statistical data with a coefficient of determination (R2) value of 0.847. It also revealed a sharp decline in 2003, especially in the Sanjiang Plain located in the northeast of Heilongjiang Province, due to the oversupply and price decline of rice in 2002. These results suggest that the approaches are available for accurate and reliable monitoring of rice cultivated areas and variation on a large scale.
[1]Amano, T., Zhu, Q., Wang, Y., Inoue, N., Tanaka, H., 1993. Case studies on high yields of paddy rice in Jiangsu Province, China, 1: characteristics of grain production. Jpn. J. Crop Sci., 62(2):267-274.
[2]Bachelet, D., 1995. Rice paddy inventory in a few provinces of China using AVHRR data. Geocarto Int., 10(1):23-38.
[3]Barker, J.L., Burelhach, J.W., 1992. MODIS Image Simulation from Landsat TM Imagery. ASPRS/ACSM/RT 92, American Society for Photogrammetry and Remote Sensing, Fethesda, Washington, DC, p.156-165.
[4]Ceccato, P., Gobron, N., Flasse, S., Pinty, B., Tarantola, S., 2002a. Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1. Theoretical approach. Remote Sens. Environ., 82(2-3):188-197.
[5]Ceccato, P., Flasse, S., Grégoire, J.M., 2002b. Designing a spectral index to estimate vegetation water content from remote sensing data: Part 2. Validation and applications. Remote Sens. Environ., 82(2-3):198-207.
[6]Chandrasekar, K., Sesha Sai, M.V.R., Roy, P.S., Dwevedi, R.S., 2010. Land Surface Water Index (LSWI) response to rainfall and NDVI using the MODIS vegetation index product. Int. J. Remote Sens., 31(15):3987-4005.
[7]Chen, D.Y., Huang, J.F., Jackson, T.J., 2005. Vegetation water content estimation for corn and soybeans using spectral indices derived from MODIS near- and short-wave infrared bands. Remote Sens. Environ., 98(2-3):225-236.
[8]Doraiswamy, P.C., Sinclair, T.R., Hollinger, S., Akhmedov, B., Stern, A., Prueger, J., 2005. Application of MODIS derived parameters for regional crop yield assessment. Remote Sens. Environ., 97(2):192-202.
[9]Fang, H.L., Liang, S.L., 2005. A hybrid inversion method for mapping leaf area index from MODIS data: experiments and application to broadleaf and needleleaf canopies. Remote Sens. Environ., 94(3):405-424.
[10]Gao, B.C., 1996. NDWI—a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ., 58(3):257-266.
[11]Gumma, M.K., Nelson, A., Thenkabail, P.S., Singh, A.N., 2011. Mapping rice areas of south Asia using MODIS multitemporal data. J. Appl. Remote Sens., 5(1):53547.
[12]Hall, D.K., Riggs, G.A., Salomonson, V.V., DiGirolamo, N.E., Bayr, K.J., 2002. MODIS snow-cover products. Remote Sens. Environ., 83(1-2):181-194.
[13]Huete, A.R., Liu, H.Q., Batchily, K., van Leeuwen, W., 1997. A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sens. Environ., 59(3):440-451.
[14]Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X., Ferreira, L.G., 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ., 83(1-2):195-213.
[15]Khush, G.S., 2005. What it will take to feed 5.0 billion rice consumers in 2030. Plant Mol. Biol., 59(1):1-6.
[16]Kim, H., Huete, A.R., Nagler, P.N., Glenn, E., Emmerich, W., Scott, R.L., 2004. Monitoring Riparian and Semi-arid Upland Vegetation Using Vegetation and Water Indices from the MODIS Satellite Sensor. Research Insights in Semiarid Ecosystems (RISE), University of Arizona, Tucson.
[17]Liu, X.R., Zheng, J., Li, X.H., 2010. Rice purchase and sale market of northeast situation analyze in 2010/2011. China Grain Econ., (12):36-39 (in Chinese).
[18]Liu, Y.S., Wang, D.W., Gao, J., Deng, W., 2005. Land use/cover changes, the environment and water resources in Northeast China. Environ. Manage., 36(5):691-701.
[19]Lunetta, R.S., Knight, J.F., Ediriwickrema, J., Lyon, J.G., Worthy, L.D., 2006. Land-cover change detection using multi-temporal MODIS NDVI data. Remote Sens. Environ., 105(2):142-154.
[20]Luo, L., Wang, Z.M., Song, K.S., Zhang, B., Liu, D.W., Ren, C.Y., Zhang, S.M., 2009. Research on the correlation between NDVI and climatic factors of different vegetations in the Northeast China. Acta Bot. Bor. Occid. Sin., 29(4):800-808 (in Chinese).
[21]Ma, C.L., 2008. The study on estimation of vegetation NPP of Xiaerxili nature protection area based on RS. Remote Sens. Technol. Appl., 23(3):323-327 (in Chinese).
[22]Macdonald, R.B., Hall, F.G., 1980. Global crop forecasting. Science, 208(4445):670-679.
[23]Malingreau, J.P., 1986. Global vegetation dynamics: satellite-observations over Asia. Int. J. Remote Sens., 7(9):1121-1146.
[24]Neue, H.U., 1993. Methane emission from rice fields. Bioscience, 43(7):466-474.
[25]Okamoto, K., Fukuhara, M., 1996. Estimation of paddy field area using the area ratio of categories in each mixel of Landsat TM. Int. J. Remote Sens., 17(9):1735-1749.
[26]Peng, D.L., Huete, A.R., Huang, J.F., Wang, F.M., Sun, H.S., 2011. Detection and estimation of mixed paddy rice cropping patterns with MODIS data. Int. J. Appl. Earth Obs. Geoinf., 13(1):13-23.
[27]Quarmby, N.A., Milnes, M., Hindle, T.L., Silleos, N., 1993. The use of multi-temporal NDVI measurements from AVHRR data for crop yield estimation and prediction. Int. J. Remote Sens., 14(2):199-210.
[28]Rosenzweig, C., Strzepek, K.M., Major, D.C., Iglesias, A., Yates, D.N., McCluskey, A., Hillel, D., 2004. Water resources for agriculture in a changing climate: international case studies. Global Environ. Chang., 14(4):345-360.
[29]Roy, D.P., Borak, J.S., Devadiga, S., Wolfe, R.E., Zheng, M., Descloitres, J., 2002. The MODIS land product quality assessment approach. Remote Sens. Environ., 83(1-2):62-76.
[30]Sakamoto, T., Yokozawa, M., Toritani, H., Shibayama, M., Ishitsuka, N., Ohno, H., 2005. A crop phenology detection method using time-series MODIS data. Remote Sens. Environ., 96(3-4):366-374.
[31]Sari, D.K., Ismullah, I.H., Sulasdi, W.N., Harto, A.B., 2010. Detecting rice phenology in paddy fields with complex cropping pattern using time series MODIS data. ITB J. Sci., 42A(2):91-106.
[32]Shao, Y., Fan, X.T., Liu, H., Xiao, J.H., Ross, S., Brisco, B., Brown, R., Staples, G., 2001. Rice monitoring and production estimation using multitemporal RADARSAT. Remote Sens. Environ., 76(3):310-325.
[33]Sun, H.S., Huang, J.F., Huete, A.R., Peng, D.L., Zhang, F., 2009. Mapping paddy rice with multi-date moderate-resolution imaging spectroradiometer (MODIS) data in China. J. Zhejiang Univ.-Sci. A, 10(10):1509-1522.
[34]Vermote, E.F., Vermeulen, A., 1999. Atmospheric Correction Algorithm: Spectral Reflectances (MOD09), MODIS Algorithm Technical Background Document, Version 4.0. Department of Geography, University of Maryland.
[35]Vermote, E.F., El Saleous, N., Justice, C.O., Kaufman, Y.J., Privette, J.L., Remer, L., Roger, J.C., Tanré, D., 1997. Atmospheric correction of visible to middle-infrared EOS-MODIS data over land surfaces: background, operational algorithm and validation. J. Geophys. Res., 102(D14):17131-17141.
[36]Wada, Y., Ohira, W., 2004. Reconstructing Cloud Free SPOT/Vegetation Using Harmonic Analysis with Local Maximum Fitting. 25th Asian Conference on Remote Sensing. Tailand, p.1663-1667.
[37]Xiao, X.M., Boles, S., Frolking, S., Salas, W., Moore III, B., Li, C., He, L., Zhao, R., 2002. 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 Sens., 23(15):3009-3022.
[38]Xiao, X.M., Boles, S., Liu, J.Y., Zhuang, D.F., Frolking, S., Li, C.S., Salas, W., Moore, B., 2005. Mapping paddy rice agriculture in southern China using multi-temporal MODIS images. Remote Sens. Environ., 95(4):480-492.
[39]Xiao, X.M., Boles, S., Frolking, S., Li, C.S., Babu, J.Y., Salas, W., Moore III, B., 2006. Mapping paddy rice agriculture in south and southeast Asia using multi-temporal MODIS images. Remote Sens. Environ., 100(1):95-113.
[40]Zhan, X., Defries, R., Townshend, J.R.G., Dimiceli, C., Hansen, M., Huang, C., Sohlberg, R., 2000. The 250 m global land cover change product from the moderate resolution imaging spectroradiometer of NASA’s earth observing system. Int. J. Remote Sens., 21(6-7):1433-1460.
[41]Zhang, L.Z., Li, M., Wu, Z.F., Liu, Y.J., 2011. Vegetation cover change and its mechanism in Northeast China based on SPOT/NDVI data. J. Arid Land Resourc. Environ., 25(1):171-175 (in Chinese).
[42]Zhang, X.Y., Friedl, M.A., Schaaf, C.B., Strahler, A.H., Hodges, J.C.F., Gao, F., Reed, B.C., Huete, A., 2003. Monitoring vegetation phenology using MODIS. Remote Sens. Environ., 84(3):471-475.
[43]Zhang, Y., Wang, C.Z., Wu, J.P., Qi, J.G., Salas, W.A., 2009. Mapping paddy rice with multitemporal ALOS/PALSAR imagery in southeast China. Int. J. Remote Sens., 30(23):6301-6315.
[44]Zhang, Y., Wang, Y.Y., Su, S.L., Li, C.S., 2011. Quantifying methane emissions from rice paddies in Northeast China by integrating remote sensing mapping with a biogeochemical model. Biogeosciences, 8(5):1225-1235.
Open peer comments: Debate/Discuss/Question/Opinion
<1>