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

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 (Kurtosis_LSWI and Skewness_LSWI). 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 Skewness_LSWI 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.

Keywords

phenological parameter / paddy rice / MODIS / EVI / LSWI / decision tree

References

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

[2] 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

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

[4] 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

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

[6] 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

[7] 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

[8] 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

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

[10] 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

[11] 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

[12] 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

[13] 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

[14] 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

[15] 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

[16] 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

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

[18] Otukei J R, Blaschke T (20 10). 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

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

[20] 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

[21] 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

[22] 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

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

[24] 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

[25] 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

[26] 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

[27] 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

[28] 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

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

[30] 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

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

[32] 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

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

[34] 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

[35] 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

[36] 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

[37] 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

[38] 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

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