Uninterrupted optical image time series are crucial for the timely monitoring of agricultural land changes. However, the continuity of such time series is often disrupted by clouds. In response to this challenge, we propose a deep learning method that integrates cloud-free optical (Sentinel-2) observations and weather-independent (Sentinel-1) Synthetic Aperture Radar (SAR) data, using a combined Convolutional Neural Network (CNN)-Recurrent Neural Network (RNN) architecture to generate continuous Normalized Difference Vegetation Index (NDVI) time series. We emphasize the significance of observation continuity by assessing the impact of the generated time series on the detection of grassland mowing events. We focus on Lithuania, a country characterized by extensive cloud coverage, and compare our approach with alternative interpolation techniques (i.e., linear, Akima, quadratic). Our method surpasses these techniques, with an average MAE of 0.024 and R^2 of 0.92. It not only improves the accuracy of event detection tasks by employing a continuous time series, but also effectively filters out sudden shifts and noise originating from cloudy observations that cloud masks often fail to detect.

翻译：连续无云光学影像时间序列对农业用地变化的及时监测至关重要。然而，这类时间序列的连续性常因云层干扰而中断。针对这一挑战，我们提出一种深度学习方法，通过融合无云光学（Sentinel-2）观测与不受天气影响的合成孔径雷达（SAR）数据（Sentinel-1），采用卷积神经网络（CNN）-循环神经网络（RNN）混合架构生成连续的归一化差分植被指数（NDVI）时间序列。我们通过评估生成的时间序列对草地割草事件检测的影响，强调观测连续性的重要意义。研究聚焦于云覆盖广泛的立陶宛，将本方法与多种插值技术（线性插值、Akima插值、二次插值）进行对比。结果表明，本方法平均绝对误差（MAE）为0.024，决定系数（R²）为0.92，显著优于对比方法。该方法不仅通过使用连续时间序列提升了事件检测任务的精度，还能有效过滤因云掩膜难以识别而残留的云层观测导致的突变和噪声。