Advertisement

Frontiers of Earth Science

, Volume 13, Issue 1, pp 111–123 | Cite as

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

  • Jianhong LiuEmail author
  • Le Li
  • Xin Huang
  • Yongmei Liu
  • Tongsheng Li
Research Article
  • 13 Downloads

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.

Keywords

phenological parameter paddy rice MODIS EVI LSWI decision tree 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

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).

Reference

  1. Bouman B (2009). How much water does rice use? Rice Today, (1): 15CrossRefGoogle Scholar
  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): 334–346CrossRefGoogle Scholar
  3. Dong J, Xiao X (2016). Evolution of regional to global paddy rice mapping methods: a review. ISPRS J Photogramm Remote Sens, 119: 214–227CrossRefGoogle Scholar
  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): 110–119Google Scholar
  5. Friedl M A, Brodley C E (1997). Decision tree classification of land cover from remotely sensed data. Remote Sens Environ, 61(3): 399–409CrossRefGoogle Scholar
  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): 168–182CrossRefGoogle Scholar
  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): 1091CrossRefGoogle Scholar
  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): 195–213CrossRefGoogle Scholar
  9. Jensen J R (2004). Introductory Digital Image Processing: A Remote Sensing Perspective (3nd ed). Englewood Cliffs: Prentice HallGoogle Scholar
  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): 243–258CrossRefGoogle Scholar
  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): 4169–4180CrossRefGoogle Scholar
  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): 195–210CrossRefGoogle Scholar
  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): 442–456CrossRefGoogle Scholar
  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): 083691CrossRefGoogle Scholar
  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): 829–837CrossRefGoogle Scholar
  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): 3289–3295CrossRefGoogle Scholar
  17. Matthews H D, Caldeira K (2007). Transient climate-carbon simulations of planetary geoengineering. Proc Natl Acad Sci USA, 104(24): 9949–9954CrossRefGoogle Scholar
  18. 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–S31Google Scholar
  19. Ozdogan M, Woodcock C E (2006). Resolution dependent errors in remote sensing of cultivated areas. Remote Sens Environ, 103(2): 203–217CrossRefGoogle Scholar
  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): 554–565CrossRefGoogle Scholar
  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): 232–242CrossRefGoogle Scholar
  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): 13–23CrossRefGoogle Scholar
  23. Pongratz J, Lobell D, Cao L, Caldeira K (2012). Crop yields in a geoengineered climate. Nat Clim Chang, 2(2): 101–105CrossRefGoogle Scholar
  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): 380–387CrossRefGoogle Scholar
  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): 254–263CrossRefGoogle Scholar
  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): 276–285CrossRefGoogle Scholar
  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: 220–233CrossRefGoogle Scholar
  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: 79–86CrossRefGoogle Scholar
  29. Robock A, Oman L, Stenchikov G L (2008). Regional climate responses to geoengineering with tropical and arctic SO2 injections. J Geophys Res, 113: D16101CrossRefGoogle Scholar
  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): 34–46CrossRefGoogle Scholar
  31. Story M, Congalton R G (1986). Accuracy assessment: a user’s perspective. Photogramm Eng Remote Sensing, 52(3): 397–399Google Scholar
  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): 398–407CrossRefGoogle Scholar
  33. 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): 1096–1116CrossRefGoogle Scholar
  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): 95–113CrossRefGoogle Scholar
  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): 480–492CrossRefGoogle Scholar
  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: 157–171CrossRefGoogle Scholar
  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): 1085–1094CrossRefGoogle Scholar
  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): 8030–8042CrossRefGoogle Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Jianhong Liu
    • 1
    • 2
    Email author
  • Le Li
    • 3
  • Xin Huang
    • 2
  • Yongmei Liu
    • 1
    • 2
  • Tongsheng Li
    • 1
    • 2
  1. 1.Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying CapacityNorthwest UniversityXi’anChina
  2. 2.College of Urban and Environmental ScienceNorthwest UniversityXi’anChina
  3. 3.Guangdong Research Center of Smart Homeland EngineeringSouth China Normal UniversityGuangzhouChina

Personalised recommendations