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Mapping paddy rice in Jiangsu Province, China, based on phenological parameters and a decision tree model

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Abstract

Timely and accurate mapping of rice planting areas is crucial under China’s current cropping structure. This study proposes a new paddy rice mapping method by combining phenological parameters and a decision tree model. Six phenological parameters were developed to identify paddy rice areas based on the analysis of the Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) time series and the Land Surface Water Index (LSWI) time series. The six phenological parameters considered the performance of different land cover types during specific phenological phases (EVI1 and EVI2), one-half of or the entire rice growing cycle (LSWI1 and LSWI2), and the shape of the LSWI time series (KurtosisLSWI and SkewnessLSWI). A hierarchical decision tree model was designed to classify paddy rice areas according to the potential separability of different land cover types in paired phenological parameter spaces. Results showed that the decision tree model was more sensitive to LSWI1, LSWI2, and SkewnessLSWI than the other phenological parameters. A paddy rice map of Jiangsu Province for 2015 was generated with an optimal threshold set of (0.4, 0.42, 9, 19, 1.5, –1.7, 0.0) with a total accuracy of 93.9%. The MODIS-derived paddy rice map generally agreed with the paddy land fraction map from the National Land Cover Dataset project, but there were regional discrepancies because of their different definitions of land use and the inability of MODIS to map paddy rice at a fragmental level. The MODIS-derived paddy rice map showed high correlation (R2= 0.85) with county-level agricultural statistics. The results of this study indicate that the phenological parameter-based paddy rice mapping algorithm could be applied at larger spatial scales.

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Reference

  • Bouman B (2009). How much water does rice use? Rice Today, (1): 15

    Article  Google Scholar 

  • Bradley B A, Mustard J F (2008). Comparison of phenology trends by land cover class: a case study in the Great Basin, USA. Glob Change Biol, 14(2): 334–346

    Article  Google Scholar 

  • Dong J, Xiao X (2016). Evolution of regional to global paddy rice mapping methods: a review. ISPRS J Photogramm Remote Sens, 119: 214–227

    Article  Google Scholar 

  • Dvorak W S (2012). Water use in plantations of eucalypts and pines: a discussion paper from a tree breeding perspective. Int Rev, 14(1): 110–119

    Google Scholar 

  • Friedl M A, Brodley C E (1997). Decision tree classification of land cover from remotely sensed data. Remote Sens Environ, 61(3): 399–409

    Article  Google Scholar 

  • Friedl M A, Sulla-Menashe D, Tan B, Schneider A, Ramankutty N, Sibley A, Huang X (2010). MODIS Collection 5 global land cover: algorithm refinements and characterization of new datasets. Remote Sens Environ, 114(1): 168–182

    Article  Google Scholar 

  • Frolking S, Qiu J, Boles S, Xiao X, Liu J, Zhuang Y, Li C, Qin X (2002). Combining remote sensing and ground census data to develop new maps of the distribution of rice agriculture in China. Global Biogeochem Cycles, 16(4): 1091

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Jensen J R (2004). Introductory Digital Image Processing: A Remote Sensing Perspective (3nd ed). Englewood Cliffs: Prentice Hall

    Google Scholar 

  • Kimball J S, McDonald K C, Running S W, Frolking S E (2004). Satellite radar remote sensing of seasonal growing seasons for boreal and subalpine evergreen forests. Remote Sens Environ, 90(2): 243–258

    Article  Google Scholar 

  • Labus M P, Nielsen G A, Lawrence R L, Engel R, Long D S (2002). Wheat yield estimates using multi-temporal NDVI satellite imagery. Int J Remote Sens, 23(20): 4169–4180

    Article  Google Scholar 

  • Liu J, Kuang W, Zhang Z, Xu X, Qin Y, Ning J, Zhou W, Zhang S, Li R, Yan C, Wu S, Shi X, Jiang N, Yu D, Pan X, Chi W (2014a). Spatiotemporal characteristics, patterns, and causes of land-use changes in China since the late 1980s. J Geogr Sci, 24(2): 195–210

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Liu J, Pan Y, Zhu X, Zhu W (2014b). Using phenological metrics and the multiple classifier fusion method to map land cover types. J Appl Remote Sens, 8(1): 083691

    Article  Google Scholar 

  • Liu J, Zhu W, Cui X (2012). A Shape-matching Cropping Index (CI) mapping method to determine agricultural cropland intensities in China using MODIS time-series data. Photogramm Eng Remote Sensing, 78(8): 829–837

    Article  Google Scholar 

  • Loveland T R, Belward A S (1997). The IGBP-DIS global 1 km land cover data set DISCover: first results. Int J Remote Sens, 18(15): 3289–3295

    Article  Google Scholar 

  • Matthews H D, Caldeira K (2007). Transient climate-carbon simulations of planetary geoengineering. Proc Natl Acad Sci USA, 104(24): 9949–9954

    Article  Google Scholar 

  • Otukei J R, Blaschke T (2010). Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. Int J Appl Earth Obs Geoinf, 12(Supplement 1): S27–S31

    Google Scholar 

  • Ozdogan M, Woodcock C E (2006). Resolution dependent errors in remote sensing of cultivated areas. Remote Sens Environ, 103(2): 203–217

    Article  Google Scholar 

  • Pal M, Mather P M (2003). An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sens Environ, 86(4): 554–565

    Article  Google Scholar 

  • Pan Y, Li L, Zhang J, Liang S, Zhu X, Sulla-Menashe D (2012). Winter wheat area estimation from MODIS-EVI time series data using the crop proportion phenology index. Remote Sens Environ, 119(3): 232–242

    Article  Google Scholar 

  • Peng D, Huete A R, Huang J, Wang F, Sun H (2011). Detection and estimation of mixed paddy rice cropping patterns with MODIS data. Int J Appl Earth Obs Geoinf, 13(1): 13–23

    Article  Google Scholar 

  • Pongratz J, Lobell D, Cao L, Caldeira K (2012). Crop yields in a geoengineered climate. Nat Clim Chang, 2(2): 101–105

    Article  Google Scholar 

  • Potgieter A B, Apan A, Hammer G, Dunn P (2010). Early-season crop area estimates for winter crops in NE Australia using MODIS. ISPRS J Photogramm Remote Sens, 65(4): 380–387

    Article  Google Scholar 

  • Potgieter A B, Lawson K, Huete A R (2013). Determining crop acreage estimates for specific winter crops using shape attributes from sequential MODIS imagery. Int J Appl Earth Obs Geoinf, 23(8): 254–263

    Article  Google Scholar 

  • Pringle M J, Denham R J, Devadas R (2012). Identification of cropping activity in central and southern Queensland, Australia, with the aid of MODIS MOD13Q1 imagery. Int J Appl Earth Obs Geoinf, 19(1): 276–285

    Article  Google Scholar 

  • Qin Y, Xiao X, Dong J, Zhou Y, Zhu Z, Zhang G, Du G, Jin C, Kou W, Wang J, Li X (2015). Mapping paddy rice planting area in cold temperate climate region through analysis of time series Landsat 8 (OLI), Landsat 7 (ETM +) and MODIS imagery. ISPRS J Photogramm Remote Sens, 105: 220–233

    Article  Google Scholar 

  • Qiu B, Li W, Tang Z, Chen C, Qi W (2015). Mapping paddy rice areas based on vegetation phenology and surface moisture conditions. Ecol Indic, 56: 79–86

    Article  Google Scholar 

  • Robock A, Oman L, Stenchikov G L (2008). Regional climate responses to geoengineering with tropical and arctic SO2 injections. J Geophys Res, 113: D16101

    Article  Google Scholar 

  • Sakamoto T, Van Phung C, Kotera A, Nguyen K D, Yokozawa M (2009). 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

    Article  Google Scholar 

  • Story M, Congalton R G (1986). Accuracy assessment: a user’s perspective. Photogramm Eng Remote Sensing, 52(3): 397–399

    Google Scholar 

  • Tooke T R, Coops N C, Goodwin N R, Voogt J A (2009). Extracting urban vegetation characteristics using spectral mixture analysis and decision tree classifications. Remote Sens Environ, 113(2): 398–407

    Article  Google Scholar 

  • Wardlow B D, Egbert S L (2008). Large-area crop mapping using timeseries MODIS 250 m NDVI data: an assessment for the U.S. Central Great Plains. Remote Sens Environ, 112(3): 1096–1116

    Article  Google Scholar 

  • Xiao X, Boles S, Frolking S, Li C, 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

    Article  Google Scholar 

  • Xiao X, Boles S, Liu J, Zhuang D, Frolking S, Li C, 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

    Article  Google Scholar 

  • Zhang G, Xiao X, Dong J, Kou W, Jin C, Qin Y, Zhou Y, Wang J, Menarguez M A, Biradar C (2015). Mapping paddy rice planting areas through time series analysis of MODIS land surface temperature and vegetation index data. ISPRS J Photogramm Remote Sens, 106: 157–171

    Article  Google Scholar 

  • Zhu W, Pan Y, He H, Wang L, Mou M, Liu J (2012). A changing-weight filter method for reconstructing a high-quality NDVI time series to preserve the integrity of vegetation phenology. IEEE Trans Geosci Remote Sens, 50(4): 1085–1094

    Article  Google Scholar 

  • Zou J, Huang Y, Zheng X, Wang Y (2007). Quantifying direct N2O emissions in paddy fields during rice growing season in mainland China: dependence on water regime. Atmos Environ, 41(37): 8030–8042

    Article  Google Scholar 

Download references

Acknowledgements

This research was founded by the National Natural Science Foundation of China (Grant No. 41401494), China Postdoctoral Science Foundation (No. 2014M552475) and Foundation of Shaanxi Educational Committee (No. 14JK1745).

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Correspondence to Jianhong Liu.

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Liu, J., Li, L., Huang, X. et al. Mapping paddy rice in Jiangsu Province, China, based on phenological parameters and a decision tree model. Front. Earth Sci. 13, 111–123 (2019). https://doi.org/10.1007/s11707-018-0723-y

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  • DOI: https://doi.org/10.1007/s11707-018-0723-y

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