Remote sensing-based crop mapping using multispectral temporal images is a reliable source of crop status information. Reflectance in red-edge region can be incorporated in vegetation indices for better results as it heavily depends upon chlorophyll content in the leaves. This research work studies the effect of three Sentinel-2 red-edge bands on fuzzy classification of sunflower crop in Shahabad, Haryana, India. Fuzzy set theory was introduced in the image processing for handling the mixed pixel problems. Supervised modified possibilistic c -means (MPCM) classification approach has been adopted for the identification of sunflower fields due to the capability of handling outliers, noises, extraction of single crop and coincident cluster problem. Classification approach was applied on four different modified temporal vegetation indices. The modified vegetation indices are generated by taking different combinations of red and red-edge reflectance bands in a controlled manner with NIR band. The best vegetation index and suitable red-edge band for the discrimination of sunflower crop were determined. Further, optimization of temporal date images to separate mapping of early sown, middle sown and late sown fields was also identified. From the results of this study, it has been proven that for temporal datasets red-edge-based indices are better than the standard indices for distinguishing between different crops while applying the MPCM classification method.

中文翻译：

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模糊分类中红边区域的影响：以向日葵作物为例

使用多光谱时间图像的基于遥感的作物制图是作物状态信息的可靠来源。红边区域的反射率可以纳入植被指数以获得更好的结果，因为它在很大程度上取决于叶子中的叶绿素含量。本研究工作研究了三个 Sentinel-2 红边带对印度哈里亚纳邦沙哈巴德向日葵作物模糊分类的影响。在图像处理中引入了模糊集理论来处理混合像素问题。由于处理异常值、噪声、提取单一作物和重合聚类问题的能力，已采用监督改进的可能性 c 均值 (MPCM) 分类方法来识别向日葵田。分类方法应用于四种不同的修正时间植被指数。修改后的植被指数是通过以受控方式与 NIR 波段采用红色和红边反射波段的不同组合来生成的。确定了区分向日葵作物的最佳植被指数和适宜的红边带。此外，还确定了优化时间日期图像以分离早播、中播和晚播田的映射。从这项研究的结果来看，已经证明，对于时间数据集，基于红边的指数在应用 MPCM 分类方法时在区分不同作物方面优于标准指数。确定了区分向日葵作物的最佳植被指数和适宜的红边带。此外，还确定了优化时间日期图像以分离早播、中播和晚播田的映射。从本研究的结果可以看出，对于时间数据集，基于红边的指数在应用 MPCM 分类方法时，在区分不同作物方面优于标准指数。确定了区分向日葵作物的最佳植被指数和适宜的红边带。此外，还确定了优化时间日期图像以分离早播、中播和晚播田的映射。从本研究的结果可以看出，对于时间数据集，基于红边的指数在应用 MPCM 分类方法时，在区分不同作物方面优于标准指数。