Accurate and high-resolution spatio-temporal information about crop phenology obtained from Synthetic Aperture Radar (SAR) data is an essential component for crop management and yield estimation at a local scale. Crop growth monitoring studies seldom exploit complete polarimetric information contained in dual-pol GRD SAR data. In this study, we propose three polarimetric descriptors: the pseudo scattering-type parameter (${\theta }_c$), the pseudo scattering entropy parameter ($H_c$), and the co-pol purity parameter ($m_c$) from dual-pol S1 GRD SAR data. We also introduce a novel unsupervised clustering framework using $H_c$ and ${\theta }_c$ with six clustering zones to represent various scattering mechanisms. We implemented the proposed algorithm on the cloud-based Google Earth Engine (GEE) platform for Sentinel-1 SAR data. We have shown the sensitivity of these descriptors over a time series of data for wheat and canola crops at a test site in Canada. From the leaf development stage to the flowering stage for both crops, the pseudo scattering-type parameter ${\theta }_c$ changes by approximately 17°. Moreover, within the entire phenology window, both $m_c$ and $H_c$ varies by about 0.6. The effectiveness of ${\theta }_c$ and $H_c$ to cluster the phenological stages for the two crops is also evident from the clustering plot. During the leaf development stage, about 90% of the sampling points were clustered into the low to medium entropy scattering zone for both the crops. Throughout the flowering stage, the entire cluster shifted into the high entropy vegetation scattering zone. Finally, during the ripening stage, the clusters of sample points were split between the high entropy vegetation scattering zone and the high entropy distributed scattering zone, with $>55\%$ of the sampling points in the high entropy distributed scattering zone. This innovative clustering framework will facilitate the operational use of S1 GRD SAR data for agricultural applications.

从合成孔径雷达 (SAR) 数据获得的关于作物物候的准确和高分辨率时空信息是当地规模作物管理和产量估算的重要组成部分。作物生长监测研究很少利用双极化 GRD SAR 数据中包含的完整极化信息。在这项研究中，我们提出了三个极化描述符：伪散射型参数（${\theta }_C$)，伪散射熵参数 ($H_C$)，以及 co-pol 纯度参数 (${米}_C$) 来自双极化 S1 GRD SAR 数据。我们还介绍了一种新颖的无监督聚类框架，使用$H_C$ 和 ${\theta }_C$有六个聚类区域来代表各种散射机制。我们在基于云的谷歌地球引擎 (GEE) 平台上为 Sentinel-1 SAR 数据实现了所提出的算法。我们已经在加拿大的一个试验场展示了这些描述符对小麦和油菜作物的时间序列数据的敏感性。两种作物从叶片发育阶段到开花阶段，伪散射型参数${\theta }_C$变化约 17°。此外，在整个物候窗口内，${米}_C$ 和 $H_C$变化约 0.6。的有效性${\theta }_C$ 和 $H_C$从聚类图中也可以明显看出对两种作物的物候阶段进行聚类。在叶片发育阶段，大约 90% 的采样点聚集在两种作物的低到中熵散射区。在整个开花阶段，整个集群转移到高熵植被散射区。最后，在成熟阶段，样本点簇在高熵植被散射区和高熵分布散射区之间分裂，其中$>55\%$高熵分布散射区中的采样点。这种创新的聚类框架将促进 S1 GRD SAR 数据在农业应用中的操作使用。