基于水稻物候参数及面向对象算法的稻田识别

水稻是中国主要的粮食作物之一，实时且准确获取稻田区域及其空间分布特征是指导和管理农业生产的基础，对于保障国家粮食安全具有重要意义。但传统遥感变量与基于像元的机器学习分类算法在准确识别破碎度较高的稻田方面存在较大挑战。物候参数能够反映不同植被的生长动态，在识别稻田方面具有较大的应用潜力。面向对象的随机森林分类可以有效避免“椒盐”现象，提高稻田的分类精度。鉴于此，该研究以中国南方典型山地丘陵区——福建省漳州市稻田为研究对象，基于归一化植被指数、改进归一化水体指数、土壤调节植被指数、垂直极化后向散射系数、交叉极化后向散射系数和物候参数等多个遥感变量，利用面向对象的随机森林分类算法识别稻田，验证和分析物候参数与面向对象的随机森林分类法在提高南方复杂地形区稻田识别精度方面的有效性。结果表明：1）福建省漳州市稻田的最高识别精度为94.47%，Kappa系数为0.92，传统遥感变量、物候参数及面向对象的随机森林分类算法在准确识别破碎度较高的稻田方面具有协同优势；2）物候参数在表征植被生长与植被类型差异等方面具有显著优势，相较于仅基于传统遥感变量的试验组，物候参数与传统遥感变量的组合能够将稻田识别总体精度提高8.78～9.36个百分点；3）对于复杂地形区破碎度较高的稻田，面向对象的随机森林分类方法能够清晰明确地勾勒出稻田的形状与边界信息，且能够有效避免“椒盐”现象，相较于基于像元的分类方法，面向对象的分类法可将稻田识别精度提高0.58～1.53个百分点，因此，更适用于复杂地形区破碎农田的遥感提取。该研究结果可提高福建省漳州市稻田制图产品的应用价值，也可为中国南方复杂地形区稻田识别精度的进一步提高提供参考。

Paddy field identification using rice phenological parameters and object-oriented algorithm

Rice is one of the main food crops in China. It is of great importance for the timely, rapid, and accurate acquisition of rice field area and its spatial distribution in agricultural production, particularly for national food security. However, the traditional classification variables of remote sensing and pixel-based machine learning have posed great challenges in identifying the paddy fields with high fragmentation. The phenological parameters have been used to represent the growth dynamics of vegetation. Object-oriented random forests can also effectively avoid the ''salt and pepper'' noise for the high accuracy of paddy fields. In this study, the object-oriented algorithm was selected to identify the paddy field, according to the rice phenological parameters. The research area was taken as the paddy field in Zhangzhou City, Fujian Province, a typical mountainous and hilly area in southern China. Firstly, the recursive feature elimination (RFE) and variance inflation factor (VIF) were applied to optimize the remote sensing variables. As a result, the normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI), soil-adjusted vegetation index (SAVI), vertical transmit/vertical receive (VV), vertical transmit/horizontal receive (VH), and six phenological parameters were taken to construct the classification model. Secondly, the object-oriented and pixel-based random forest was used to identify the paddy fields. Three combinations of the variables were used as the input parameters of the classification model. The effectiveness of phenological parameters and the object-oriented random forest were verified to improve the accuracy of paddy field identification in the complex terrain areas in southern China. The results are as follows: 1) The highest identification accuracy of paddy fields was 94.47%, where the Kappa coefficient was 0.92. A better performance was achieved to identify the paddy fields using the remote sensing variables, phenological parameters, and object-oriented random forest; 2) The phenological parameters shared the significant advantages to characterizing the vegetation growth among various types. Thus, the phenological parameters and remote sensing variables were combined to improve the accuracy of paddy field identification by 9.45%-9.91%, compared with the experimental group with remote sensing variables only; 3) The object-oriented random forest was outstanding defined the shape and boundary of paddy fields with a high degree of fragmentation in the complex terrain areas. The artifact of ''salt and pepper'' was effectively avoided. The object-oriented classification improved the accuracy of paddy fields by 0.69%-1.62%, compared with the traditional pixel-based one. Therefore, the improved model was more suitable for the extraction of fragmented farmland in the complex topographic areas using remote sensing images. The finding can provide a strong reference to further improve the accuracy of paddy field mapping products in complex terrain areas.