# Monitoring Cropland Abandonment in Hilly Areas with Sentinel-1 and Sentinel-2 Timeseries
## 1 Introduction  
Cropland abandonment is a common type of land-cover change worldwide as a result of a range of social, economic, environmental, and terrain factors [1]. For example, the rapid development in developing countries, rural-to-urban immigration, advanced social security system, and attenuated social insurance function of cropland have led to cropland abandonment [2]. Similarly, environmental pollution, soil desertification, and terrain fragmentation may also lead to cropland abandonment. Correspondingly, cropland abandonment may cause a series of environmental and ecological problems. It increases the occurrence of natural disasters [3], threatens water resources [4], deteriorates the ecosystem, but improves soil stability [5], mitigates the pollution from agricultural chemicals [6], and creates new habitats for wildlife [7]. Moreover, rainfed cropland and irrigated cropland have different degrees of dependence on water resources, and their abandonment has different effects on the environment and ecology, especially in hilly areas. The spatiotemporal distribution of abandoned rainfed cropland and irrigated croplandis useful data for land management departments, whose land adjustment policies can be carried out according to local conditions. Remote sensing technology is a reliable scientific method for monitoring cropland abandonment [8]. It has been widely used to monitor large-scale cropland abandonment [9; 10; 11]. However, these methods are based on MODIS imagery, which is only applicable to monitor homogenized cropland abandonment. Some researchers used Landsat imagery for long-term monitoring of cropland abandonment, to increase the spatial resolution of classification results and reduce errors caused by mixed pixels [12; 13; 14]. Nevertheless, Landsat imagery is generated every 16 days at 30 m resolution, which may result in no available imagery for areas suffering from extended cloud cover. These aforementioned studies provided fundamental knowledge for the research on large-scale homogenized cropland abandonment. However, for the hilly areas, i.e., transition areas between plains and mountains, remote sensing imagery with higher temporal and spatial resolution is required. Previous studies illustrated that the extraction of short-term abandoned farmland by Sentinel-2 imagery could minimize the error caused by cloud interference and mixed pixels [15]. Our team also showed that a better spatial distribution of cropland abandonment could be obtained by using the spatiotemporal fusion of multisource optical imagery [8]. Unfortunately, large-scale extraction is limited with these methods due to relatively low processing efficiency. In addition, Sentinel-1 combined with Sentinel-2 was shown to improve the accuracy of land-cover classification [16]. Google Earth Engine (GEE) is a cloud platform that integrates massive remote sensing data and existing classification products, as well as a large number of functions and available algorithms, all of which facilitate the monitoring of large-scale land use and land cover [17]. Currently, the GEE-based change feature extraction includes global forest spatiotemporal change monitoring [18], global surface water spatiotemporal change monitoring [19], global land-cover mapping [20; 21], global photovoltaic distribution mapping [22], and global urban spatiotemporal distribution mapping [23]. Using the GEE platform to combine scientific algorithms and frameworks is an effective way to solve the problems of monitoring cropland abandonment in hilly areas. [PERSON] et al. [1] used timeseries Landsat images on the GEE platform to analyze the mechanism of cropland abandonment in China, Nepal, Iraq, Russia, and other countries. The results were satisfactory in most of these areas. Furthermore, they pointed out that extraction of abandoned cropland remains challenging for small-scale cropland and areas with strong heterogeneity. [PERSON] et al.