Cloud detection is the task of detecting cloud areas in remote sensing images (RSIs), and it has attracted extensive research interest. Recently, deep learning (DL)-based methods have been proposed and achieved great performance for cloud detection. However, due to the satellite's limitation in storage and memory, existing DL approaches, which suffer from extensive computation and large model size, are almost impossible to be deployed on satellites. To fill this gap, we target at studying effective and efficient cloud detection solutions that are suitable for satellites. In this article, we develop a lightweight autoencoder-based cloud detection method, namely, lightweight encoder-decoder cloud detection network (LWCDnet). In the encoder part, the designed novel lightweight dual-branch block (LWDBB) in the backbone extracts spatial and contextual information concurrently. Moreover, a lightweight feature pyramid module (LWFPM) is proposed to capture high-level multiscale contextual information. In the decoder part, the lightweight feature fusion module (LWFFM) compensates for the missing spatial and detail information from the encoder to the high-level feature maps. We evaluate the proposed method on two public datasets: LandSat8 and Moderate-Resolution Imaging Spectroradiometer (MODIS). Extensive experiments demonstrate that the proposed LWCDnet achieves comparable accuracy as the state-of-the-art cloud detection methods and lightweight semantic segmentation algorithms. In the meantime, LWCDnet has much less computation burden with smaller model size.