基于MODIS指数和随机森林的江西省早稻种植信息提取

尽早获取双季早稻的种植信息，对政府部门掌握全省水稻生产形势及制定粮食安全保障的相关政策方针具有重要意义。传统业务服务中，通常将水稻生长早期的多时相MODIS指数与阈值法相结合，对种植信息进行提取，但该方法主观性强，受人为及不同地区水稻物候期差异影响大，且存在混合像元等限制，机器学习算法可以较好解决此问题。因此，该研究提出一种结合水稻生长早期MODIS指数和随机森林的种植信息提取方法，基于江西省早稻生长早期多时相MODIS增强型植被指数（Enhanced Vegetation Index，EVI）、归一化植被指数（Normalized Difference Vegetation Index，NDVI）和地表水分指数（Land Surface Water Index，LSWI）的变化特征，利用随机森林算法构建早稻种植区域提取模型与丰度反演模型，提取全省早稻种植信息，并利用Sentinel-1A提取的验证样区与统计资料验证。结果表明，早稻种植区域及丰度的空间分布特征与Sentinel-1A提取的验证样区的空间特征基本一致，提取模型的分类精度为93.18%，丰度反演模型与样本数据的平均绝对误差、均方根误差和决定系数分别为0.07、0.10与0.86，且在高丰度种植区反演效果更优。与统计资料相比，全省早稻面积识别精度为92.33%。该研究解决了水稻种植信息提取中阈值选取合理性、混合像元与时效性限制等问题，为水稻生长早期种植信息的业务化提取提供一种参考方法，具有一定应用价值。

Extracting planting information of early rice using MODIS index and random forest in Jiangxi Province, China

Extracting planting information of rice as early as possible can be great significance in provincial agricultural production for the food security. MODIS data has been demonstrated to be superior in extracting planting information of rice at large scale due to short observation period and wide swath and easy image acquisition. The method of MODIS index during early growth period of rice combined with threshold value was usually used in the conventional provincial decision-making service to ensure the timeliness of service and convenient operation. However, the method is largely influenced by human and phenology difference of rice in various regions leading to highly subjective and poor stability. In addition, the mixed pixels were likely causing misestimation of the MODIS product. Random forest algorithm can make up the deficiency of threshold method due to the characteristics of less manual intervention and difficult over-fitting. In the study, an extraction method of rice planting information was proposed using MODIS index and random forest during early growth period of rice. Firstly, Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI) and Land Surface Water Index (LSWI) were selected to extract planting information based on the variation characteristics of MODIS index from seeding to jointing stage in Jiangxi Province, and then feature data sets were constructed to model. The models were built to extract planting area of early rice and to inverse planting abundance using random forest algorithm. Finally, the accuracy of the planting and the abundance maps of early rice were validated by the verification samples from the measured points, validation sample region obtained by the Sentinel-1A image and the statistical data from Jiangxi Provincial Bureau of Statistics. The results showed that using MODIS index during early growth period of rice and random forest was an effective way to extract plant information. The classification accuracy of early rice planting area extraction model was 93.18% with the Kappa coefficient of 0.915, and the mapping accuracy and user accuracy were 92.04% and 91.23%, respectively when matching the verification samples. Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), decision coefficient of abundance inversion model were 0.07, 0.104 and 0.855, respectively, and the better performance of the abundance inversion model was achieved in high abundance planting areas. The spatial distribution characteristics of early rice planting area and abundance were consistent with validation sample region. Compared with the statistical data, the model accuracy of early rice area was 92.33%. This method can ensure the timeliness of the service and solve the problem that the extraction of rice planting area in the conventional provincial decision-making service is greatly affected by the problems of rationality of threshold selection and mixed pixel, also have no complex operation. The finding can provide a reference to extract planting area of early rice during early growth period.