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

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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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