# Monitoring Cropland Abandonment in Hilly Areas with Sentinel-1 and Sentinel-2 Timeseries
## 1 Introduction
[24] employed Sentinel-1 and Sentinel-2 on the GEE platform to map the fallow land in the agro-pastoral mixed areas in northern China. The study showed that, for areas with strong heterogeneity, the monthly scale synthetic image was favored in terms of classification accuracy. Generally speaking, the most peculiar characteristics of hilly areas include scattered planting, relatively fewer sunlight hours, and lower agricultural input-output ratio, where the cropland is more likely to be abandoned (especially in China). Using remote sensing technology to extract abandoned cropland is often subject to serious cloud interference, and temporal and spatial images with higher resolution become crucial. Therefore, remote sensing images must be properly processed to achieve higher usability. For optical images, spatiotemporal data fusion is the most advanced method to improve the availability of image, including unmixing-based [25], weight function-based [26], Bayesian-based [27], learning-based [28], and hybrid methods [29]. However, these algorithms are difficult to be implemented on GEE due to its limitations. The fill-and-fit (FF) approach is the most commonly used method to repair optical remote sensing images disturbed by clouds, especially for land use and land cover [30; 31; 32]. [PERSON] et al. [33] filled the Landsat timeseries with MODIS interpolation and reconstructed the Landsat dataset with high timeseries using Savitzky-Golay filtering. This method had good performance in the Coleambally irrigated areas in Australia and the Taian cultivated areas in China. However, this method may not be applicable due to the large differences in spectral and spatial resolution between MODIS and Sentinel-2 [34]. [PERSON] et al. [35] developed a MODIS-EVI image reconstruction method using weighted Whittaker with dynamic parameter \(\lambda\) in the spatial domain (wWHd), but the \(\lambda\) of this method did not apply to Sentinel-2 imagery. [PERSON] et al. [36] used gap filling and Savitzky-Golay filtering with adjacent timeseries remote sensing imagery for good collection, which allowed mapping the cropping intensity on GEE successfully. For SAR backscatter imagery, the rugged terrain and side-looking SAR imaging geometry causes radiometric distortion. To minimize the loss of information content and user freedom to adapt, as well as to optimize the preprocessing to specific application needs, the processing of the Sentinel-1 SAR backscatter data ingested in GEE is limited to thermal noise removal, data calibration, multi-looking, and range-Doppler terrain correction. For a wider range of monitoring and mapping needs, [PERSON] et al. [37; 38] upgraded the preprocessing framework on GEE, including additional border noise correction, speckle filtering, and radiometric terrain normalization. The framework has been successfully used in the studies of land use and land cover [39; 40]. Currently, monitoring the abandoned rainfed cropland and irrigated cropland remains an enormous challenge in hilly areas. In response to the problem, a new approach was proposed for extracting abandoned cropland using Sentinel-1 and Sentinel-2 imageries on the GEE platform, including the following aspects: (1) performing a series of processing on Sentinel-1 and Sentinel-2 imageries on the GEE platform to improve the availability of imagery; (2) combining multisource data and the developed timeseries viewer tool of GEE platform to obtain reliable samples; (3) using the processed imageries, the acquired samples, and the random forest classifier to map the annual land-cover distribution; (4) constructing the evaluation system of cropland abandonment and mapping the spatiotemporal distribution of abandoned rainfed cropland and irrigated cropland; (5) validating and evaluating the distribution results concerning the cropland abandonment.