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.

利用深度学习方法，结合免云的光学观测和天气无关的合成孔径雷达数据，采用组合卷积神经网络 (CNN) - 循环神经网络 (RNN) 架构生成连续的归一化植被指数 (NDVI) 时间序列，该方法在连续观测序列中对事件检测任务的准确性进行了改进，并有效滤除由云观测引起的突变和噪声。