文献类型：期刊文献

英文题名：Classification of Paddy Rice Using a Stacked Generalization Approach and the Spectral Mixture Method Based on MODIS Time Series

作者：Zhang, Meng[1] Zhang, Huaiqing[2] Li, Xinyu[1] Liu, Yang[2] Cai, Yaotong[1] Lin, Hui[1]

第一作者：Zhang, Meng

通信作者：Lin, H[1]

机构：[1]Cent South Univ Forestry & Technol, Changsha 410004, Peoples R China;[2]Chinese Acad Forestry, Res Inst Forest Resources Informat Tech, Beijing 100091, Peoples R China

年份：2020

卷号：13

起止页码：2264-2275

外文期刊名：IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING

收录：;EI(收录号：20202408819606);Scopus(收录号：2-s2.0-85086232381);WOS:【SCI-EXPANDED(收录号:WOS:000542949600011)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 41901385, and in part by the China Postdoctoral Science Foundation under Grant 2019M652815.

语种：英文

外文关键词：Lakes; MODIS; Vegetation mapping; Wetlands; Indexes; Classification algorithms; Time series analysis; Moderate Resolution Imaging Spectralradiometer (MODIS) time series; paddy rice; phonological variables; spectral mixture; stack generalization

摘要：Paddy rice is a major stable food, accounting for about 20% world's food supply. And the rice paddy, an important artificial wetland type, plays an important role in the regional ecological environment. This study proposes a stacked generalization and spectral mixture approach to map paddy rice using coarse spatial resolution images [Moderate Resolution Imaging Spectralradiometer, (MODIS)]. By this method, the time series MODIS enhanced vegetation index images, phenological variables, land surface water index, elevation, and slope images are all employed to produce the optimal feature combination, which is then used to map paddy rice by the stacking algorithm. The validation experiment using the data of the Dongting Lake area showed that the proposed method can improve the overall accuracy of single classifiers, including the support vector machine, random forest, k-nearestneigbor (kNN), extreme gradient boosting (XGB), and decision tree. Stacking (XGB) achieves the highest overall accuracy (90.3%) and Kappa coefficient (0.86), which are 2.8% and 0.03 higher than that of using the single kNN classifier. Furthermore, its user accuracies for distinguishing double-cropping rice and single-season rice are 92.5% and 90.0%, respectively. In terms of the paddy rice classification accuracy, the stacking model is also superior to single classifiers. Moreover, the MODIS-derived rice map obtained by the stacked generalization approach and the spectral mixture method area has a large determination coefficient (R-2 = 0.9975) with the government statistic data. The results demonstrate the potential of the proposed method in using coarse spatial resolution images for large-scale paddy rice mapping.