In this study, the use of Sentinel-1 interferometric coherence data as a tool for crop monitoring has been explored. For this purpose, time series of images acquired by Sentinel-1 and 2 spanning 2017 have been analysed. The study site is an agricultural area in Sevilla, Spain, where 16 different crop species were cultivated during that year. The time series of 6-day repeat-pass coherence measured at each polarimetric channel (VV and VH), as well as their difference, have been compared to the NDVI and to the backscattering ratio (VH/VV) and other indices based on backscatter. The contribution of different decorrelation sources and the effect of the bias from the space-averaged sample coherence magnitude estimation have been evaluated. Likewise, the usage of 12 days as temporal baseline was tested. The study has been carried for three different orbits, characterised by different incidence angles and acquisition times. All results support using coherence as a measure for monitoring the crop growing season, as it shows good correlations with the NDVI ($R^2>0.7$), and its temporal evolution fits well the main phenological stages of the crops. Although each crop shows its own evolution, the performance of coherence as a vegetation index is high for most of them. VV is generally more correlated with the NDVI than VH. For crop types characterised by low plant density, this difference decreases, with VH even showing higher correlation values in some cases. For a few crop types, such as rice, the backscattering ratio outperforms the coherence in following the growth stages of the plants. Since both coherence and backscattering are directly computed from the radar images, they could be used as complementary sources of information for this purpose. Notably, the measured coherence performs well without the need of compensating the thermal noise decorrelation or the bias due to the finite equivalent number of looks.

在本研究中，探索了使用 Sentinel-1 干涉相干数据作为作物监测工具。为此，分析了 Sentinel-1 和 2 跨越 2017 年获取的时间序列图像。研究地点是西班牙塞维利亚的一个农业区，当年在那里种植了 16 种不同的作物。已将在每个偏振通道（VV 和 VH）处测量的 6 天重复通过相干性的时间序列及其差异与 NDVI 和反向散射比 (VH/VV) 以及基于反向散射的其他指标进行了比较. 已经评估了不同去相关源的贡献和来自空间平均样本相干幅度估计的偏差的影响。同样，测试了使用 12 天作为时间基线。该研究针对三个不同的轨道进行，其特点是不同的入射角和采集时间。所有结果都支持使用一致性作为监测作物生长季节的衡量标准，因为它与 NDVI 具有良好的相关性（$R^2>0.7$)，其时间演化与作物的主要物候阶段吻合较好。尽管每种作物都有自己的进化，但作为植被指数的连贯性对大多数作物来说都很高。VV 通常与 NDVI 的相关性高于 VH。对于以植物密度低为特征的作物类型，这种差异会减小，在某些情况下，VH 甚至显示出更高的相关值。对于一些作物类型，例如水稻，后向散射比在跟踪植物生长阶段方面的表现优于一致性。由于相干和后向散射都是直接从雷达图像中计算出来的，因此它们可以用作为此目的的补充信息源。尤其，