## 1 Introduction
In order to obtain an accurate LULC map, a feasible way is to directly use time series NDVI for land-cover classification and then extract changed areas using a post-processing method. In addition, there are many studies devoted to fusing different satellite data. This method can fully integrate the advantages of spatial resolution and temporal resolution of different satellites so as to obtain a high-quality NDVI image after fusion [20]. Using fused time-series NDVI combined with original satellite images and phenological parameters extracted from NDVI can effectively improve the accuracy of land-cover classification. Nevertheless, it still sometimes fails to achieve good results due to the missing information of some months or regions and the deviation of model regression fitting [21; 22] when compared with using other satellite data with a lower temporal resolution and adopting time-series fusion for time-series analysis. The time series NDVI derived from PlanetScope is realistic and avoids complex data processing, which can obtain a better result in a more efficient way. Classical machine-learning classifiers mainly include MLC [23], Decision Tree [24], Random Forest [25], and Support Vector Machine [26]. These methods are based on the image texture, spectrum, and other information for classification. There are many studies comparing classical machine-learning approaches [27; 28]. In the field of urban change detection, these machine-learning methods have been used by many scholars and have achieved relatively good results [29; 30]. However, these shallow machine-learning methods cannot fully learn the deep semantic features of each land-cover type in remote-sensing images; it is often difficult to obtain good classification results for areas with complex land-cover information. In recent years, deep learning has made great progress. Compared with classical machine learning, deep learning has the advantages of being able to adaptively extract the most relevant features for classification tasks, fully excavate deeper feature information, and effectively obtain feature variables expressing land-cover information. Therefore, this technology has also been introduced into the field of LUCC surveys, which has become a popular research topic in the field of remote sensing [31; 32]. At present, commonly used deep-learning models include convolutional neural networks (CNNs), recurrent neural networks (RNNs), and fully convolutional networks (FCN). Compared with CNN, FCNs solve the limitation of the fixed size of input and output images of CNN. In addition, it can reduce redundant calculations and improve the efficiency of large-scale LUCC mapping [33]. Classical FCN networks include U-Net, Deep-LAB, and PSP-Net, among which U-Net has a symmetric structure and only uses a small amount of training data to achieve relatively high accuracy. Therefore, U-Net is widely used in the field of remote-sensing image classification [34]. A large number of deep-learning samples need to be manually selected, which is very time- and energy-consuming. In order to reduce the workload, this article used the preliminary classification result obtained by MLC and the original satellite images to manually edit to obtain samples for deep learning. In this article, a 12-month continuous time series NDVI image of the study area was acquired based on PlanetScope satellite products. The MLC and U-Net model were used for land-cover classification and change detection. Finally, the accuracy of the classification results was evaluated by satellite images and field survey data. The objectives of this study are: (1) to explore the effect of NDVI data from PlanetScope satellite products, which are rarely used at present in the application of real land-cover classification; (2) to analyze and compare the accuracy of MLC and U-Net models in LUCC detection using time series NDVI; (3) to propose a sample-selection method that selects samples according to MLC supervised classification results so as to improve the efficiency of deep-learning sample acquisition.