Mapping paddy rice in Jiangsu Province, China, based on phenological parameters and a decision tree model
Mapping paddy rice in Jiangsu Province, China, based on phenological parameters and a decision tree model
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DOI：
10.1007/s11707-018-0723-y
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发表时间：
2018-11
影响因子：
2
通讯作者：
Jianhong Liu;Le Li;Xin Huang;Yongmei Liu;Tongsheng Li
中科院分区：
文献类型：
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作者：
Jianhong Liu;Le Li;Xin Huang;Yongmei Liu;Tongsheng Li
在中国目前的种植结构下，及时准确地绘制水稻种植面积图至关重要。提出了一种结合物候参数和决策树模型的水稻制图方法。根据中分辨率成像光谱仪（MODIS）增强植被指数（EVI）时间序列和地表水指数（LSWI）时间序列的分析，开发了6个物候参数来识别水稻面积。6个物候参数考虑了不同土地覆盖类型在特定的物候期（EVI 1和EVI 2），一半或整个水稻生长周期（LSWI 1和LSWI 2），和LSWI时间序列的形状（KurtosisLSWI和SkewnessLSWI）的性能。根据不同土地覆被类型在成对物候参数空间中的潜在可分性，设计了一个层次决策树模型对水稻区进行分类。结果表明，决策树模型对LSWI 1、LSWI 2和偏度LSWI的敏感性高于其它物候参数。以（0.4，0.42，9，19，1.5，-1.7，0.0）为最优阈值，生成了2015年江苏省水稻地图，总精度为93.9%。MODIS水稻图与国家土地覆盖数据集项目的水稻地比例图基本一致，但由于土地利用定义不同以及MODIS无法在碎片水平上绘制水稻图，因此存在区域差异。MODIS水稻地图与县级农业统计数据的相关性较高（R2= 0.85）。研究结果表明，基于物候参数的水稻制图算法可以应用于更大的空间尺度。
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 (EVI1and EVI2), one-half of or the entire rice growing cycle (LSWI1and LSWI2), and the shape of the LSWI time series (KurtosisLSWIand 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 SkewnessLSWIthan 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.