Mapping paddy rice distribution using multi-temporal Landsat imagery in the Sanjiang Plain, northeast China

Abstract

Information of paddy rice distribution is essential for food production and methane emission calculation. Phenology-based algorithms have been utilized in the mapping of paddy rice fields by identifying the unique flooding and seedling transplanting phases using multi-temporal moderate resolution (500 m to 1 km) images. In this study, we developed simple algorithms to identify paddy rice at a fine resolution at the regional scale using multi-temporal Landsat imagery. Sixteen Landsat images from 2010–2012 were used to generate the 30 m paddy rice map in the Sanjiang Plain, northeast China—one of the major paddy rice cultivation regions in China. Three vegetation indices, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Land Surface Water Index (LSWI), were used to identify rice fields during the flooding/transplanting and ripening phases. The user and producer accuracies of paddy rice on the resultant Landsat-based paddy rice map were 90% and 94%, respectively. The Landsat-based paddy rice map was an improvement over the paddy rice layer on the National Land Cover Dataset, which was generated through visual interpretation and digitalization on the fine-resolution images. The agricultural census data substantially underreported paddy rice area, raising serious concern about its use for studies on food security.

Keywords

phenology / flooding / transplanting / ripening / land use

References

[1] Belder P, Bouman B A M, Cabangon R, Lu G, Quilang E J P, Li Y H, Spiertz J H J, Tuong T P (2004). Effect of water-saving irrigation on rice yield and water use in typical lowland conditions in Asia. Agric Water Manage, 65(3): 193–210

[2] Biradar C M, Xiao X M (2011). Quantifying the area and spatial distribution of double- and triple-cropping croplands in India with multi-temporal MODIS imagery in 2005. Int J Remote Sens, 32(2): 367–386

[3] Cohen W B, Yang Z G, Kennedy R (2010). Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync- Tools for calibration and validation. Remote Sens Environ, 114(12): 2911–2924

[4] Congalton R G (1991). A review of asessing the accuracy of classifications of remotely sensed data. Remote Sens Environ, 37(1): 35–46

[5] Döll P (2002). Impact of climate change and variability on irrigation requirements: a global perspective. Clim Change, 54(3): 269–293

[6] Dong J W, Xiao X M, Chen B Q, Torbick N, Jin C, Zhang G L, Biradar C (2013). Mapping deciduous rubber plantations through integration of PALSAR and multi-temporal Landsat imagery. Remote Sens Environ, 134: 392–402

[7] Hansen M C, Potapov P V, Moore R, Hancher M, Turubanova S A, Tyukavina A, Thau D, Stehman S V, Goetz S J, Loveland T R, Kommareddy A, Egorov A, Chini L, Justice C O, Townshend J R G (2013). High-resolution global maps of 21st-century forest cover change. Science, 342(6160): 850–853

[8] Huang C Q, Coward S N, Masek J G, Thomas N, Zhu Z L, Vogelmann J E (2010a). An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks. Remote Sens Environ, 114(1): 183–198

[9] Huang N, Wang Z M, Liu D W, Niu Z (2010b). Selecting sites for converting farmlands to wetlands in the Sanjiang Plain, Northeast China, based on remote sensing and GIS. Environ Manage, 46(5): 790–800

[10] 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

[11] Huete A R, Liu H Q, Batchily K, vanLeeuwen W (1997). A comparison of vegetation indices global set of TM images for EOS-MODIS. Remote Sens Environ, 59(3): 440–451

[12] Kennedy R E, Yang Z G, Cohen W B (2010). Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr- Temporal segmentation algorithms. Remote Sens Environ, 114(12): 2897–2910

[13] Kuenzer C, Knauer K (2013). Remote sensing of rice crop areas. Int J Remote Sens, 34(6): 2101–2139

[14] Laba M, Smith S D, Degloria S D (1997). Landsat-based land cover mapping in the lower Yuna River watershed in the Dominican Republic. Int J Remote Sens, 18(14): 3011–3025

[15] Li C S, Mosier A, Wassmann R, Cai Z C, Zheng X H, Huang Y, Tsuruta H, Boonjawat J, Lantin R (2004). Modeling greenhouse gas emissions from rice-based production systems: sensitivity and upscaling. Global Biogeochem Cycles, 18(1): GB1043

[16] Li P, Feng Z M, Jiang L G, Liu Y J, Xiao X M (2012). Changes in rice cropping systems in the Poyang Lake Region, China during 2004‒2010. J Geogr Sci, 22(4): 653–668

[17] Liu J, Liu M, Tian H, Zhuang D, Zhang Z, Zhang W, Tang X, Deng X (2005). Spatial and temporal patterns of China's cropland during 1990−2000: an analysis based on Landsat TM data. Remote Sens Environ, 98(4): 442–456

[18] Masek J G, Huang C Q, Wolfe R, Cohen W, Hall F, Kutler J, Nelson P (2008). North American forest disturbance mapped from a decadal Landsat record. Remote Sens Environ, 112(6): 2914–2926

[19] McCloy K R, Smith F R, Robinson M R (1987). Monitoring rice areas using LANDSAT MSS data. Int J Remote Sens, 8(5): 741–749

[20] Müller H, Rufin P, Griffiths P, Barros Siqueira A J, Hostert P (2015). Mining dense Landsat time series for separating cropland and pasture in a heterogeneous Brazilian savanna landscape. Remote Sens Environ, 156: 490–499

[21] 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

[22] Okamoto K, Yamakawa S, Kawashima H (1998). Estimation of flood damage to rice production in North Korea in 1995. Int J Remote Sens, 19(2): 365–371

[23] Panigrahy S, Parihar J S (1992). Role of middle infrared bands of Landsat Thematic Mapper in determining the classification accuracy of rice. Int J Remote Sens, 13(15): 2943–2949

[24] Qiu J, Tang H, Frolking S, Boles S, Li C, Xiao X, Liu J, Zhuang Y, Qin X (2003). Mapping single-, double-, and triple-crop agriculture in China at 0.5°×0.5° by combining county-scale census data with a remote sensing-derived land cover map. Geocarto Int, 18(2): 3–13

[25] Rao P P N, Rao V R (1987). Rice crop identification and area estimation using remotely-sensed data from Indian cropping patterns. Int J Remote Sens, 8(4): 639–650

[26] Richards J A eds (1999). Remote Sensing Digital Image Analysis. Berlin: Springer-Verlag

[27] Sakamoto T, van Cao P, van Nguyen N, Kotera A, Yokozawa M (2009a). Agro-ecological interpretation of rice cropping systems in flood-prone areas using MODIS imagery. Photogramm Eng Remote Sensing, 75(4): 413–424

[28] Sakamoto T, Van Nguyen N, Ohno H, Ishitsuka N, Yokozawa M (2006). Spatio−temporal distribution of rice phenology and cropping systems in the Mekong Delta with special reference to the seasonal water flow of the Mekong and Bassac rivers. Remote Sens Environ, 100(1): 1–16

[29] Sakamoto T, Van Phung C, Kotera A, Van Nguyen K D, Yokozawa M (2009b). Analysis of rapid expansion of inland aquaculture and triple rice-cropping areas in a coastal area of the Vietnamese Mekong Delta using MODIS time-series imagery. Landsc Urban Plan, 92(1): 34–46

[30] Shalaby A, Tateishi R (2007). Remote sensing and GIS for mapping and monitoring land cover and land-use changes in the Northwestern coastal zone of Egypt. Appl Geogr, 27(1): 28–41

[31] Sun H, Huang J, Huete A R, Peng D, Zhang F (2009). Mapping paddy rice with multi-date moderate-resolution imaging spectroradiometer (MODIS) data in China. Journal of Zhejiang University SCIENCE A, 10: 1509–1522

[32] Tucker C J (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ, 8(2): 127–150

[33] Turner M D, Congalton R G (1998). Classification of multi-temporal SPOT-XS satellite data for mapping rice fields on a West African floodplain. Int J Remote Sens, 19(1): 21–41

[34] Van Nguyen N, Ferrero A (2006). Meeting the challenges of global rice production. Paddy and Water Environment, 4(1): 1–9

[35] Vermote E F, ElSaleous N, Justice C O, Kaufman Y J, Privette J L, Remer L, Roger J C, Tanre D (1997). Atmospheric correction of visible to middle-infrared EOS-MODIS data over land surfaces: background, operational algorithm and validation. J Geophys Res, D, Atmospheres, 102(D14): 17131–17141

[36] Xiao X M, Boles S, Frolking S, Li C S, Babu J Y, Salas W, Moore B III (2006). Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images. Remote Sens Environ, 100(1): 95–113

[37] Xiao X M, Boles S, Liu J Y, Zhuang D F, Frolking S, Li C S, Salas W, Moore B III (2005). Mapping paddy rice agriculture in southern China using multi-temporal MODIS images. Remote Sens Environ, 95(4): 480–492

[38] Xiao X M, Zhang Q Y, Braswell B, Urbanski S, Boles S, Wofsy S, Berrien M, Ojima D (2004). Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data. Remote Sens Environ, 91(2): 256–270

[39] Xie J (2013). Classification of wetlands using object-oriented method and multi-season remote sensing images in Sanjiang Plain. Dissertation for Master degree. Available from China knowledge Resource Integrated Database (in Chinese)

[40] 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

[41] Zhong L, Gong P, Biging G S (2014). Efficient corn and soybean mapping with temporal extendability: a multi-year experiment using Landsat imagery. Remote Sens Environ, 140: 1–13

[42] Zhu Z, Woodcock C E (2012). Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sens Environ, 118: 83–94

[43] Zhu Z, Woodcock C E, Olofsson P (2012). Continuous monitoring of forest disturbance using all available Landsat imagery. Remote Sens Environ, 122: 75–91

Acknowledgments

This study was supported by the NASA Land Use and Land Cover Change Program (NNX09AC39G, NNX11AJ35G), the US National Science Foundation EPSCoR program (NSF- 0919466), and the National Institutes of Health (1R01AI101028-01A1). The WorldView-2 high-resolution images were provided by NASA under the terms of the National Geospatial-Intelligence Agency’s (NGA) Nextview License Agreement. We thank the reviewers for their insightful comments on earlier versions of the manuscript. We would also like to thank Sarah Xiao for the English and grammar corrections.

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2015 Higher Education Press and Springer-Verlag Berlin Heidelberg