# Monitoring Cropland Abandonment in Hilly Areas with Sentinel-1 and Sentinel-2 Timeseries
## 2 Materials and Methods
### Method
#### 2.3.2 Sentinel-1 and Sentinel-2 Imgeries Processing  
There is a need for more advanced timeseries analyses on short-term phenological characteristics to extract cropland abandonment in hilly areas. The monthly interval composition method is commonly used in land-cover classification and phenological characteristic analysis. It can effectively utilize the cloudless pixels of the image during the period and reduce interference of clouds and shadows [8; 15; 24]. Furthermore, we mappedthe spatial distribution and calculated the number of available pixels each month in the study area (Figure 3). Sentinel-1 imagery was preprocessed with thermal noise removal, data calibration, multi-looking, and range-Doppler terrain correction to a resolution of 10 m before being integrated into the GEE data pool. Even with such corrections applied, Sentinel-1 imagery suffered from speckle noise and radiometric distortion. Speckle noise is unique to SAR imagery and is caused by backscatter interferences between adjacent returns. Although images affected by speckle noise show large-scale distinguishable features, they may be seriously compromised at smaller scales. It has been shown that speckle noise may create confusion in machine learning algorithms [47]. Radiometric distortions over rugged terrain within the backscatter products on GEE originate from the side-looking SAR imaging geometry. Such distortions are strong enough to exceed weaker differences of the signal due to variation in land cover [48]. It is, therefore, necessary to account for these effects during the generation of higher-level backscatter products to enable a variety of land applications [49]. In this study, we processed Sentinel-1 on GEE by selecting instrument mode (IW), transmitter-receiver polarization (VV, VH), and ascending orbits. Furthermore,  
Figure 2: Flowchart of extracting abandoned cropland in hilly areas. the framework proposed by [PERSON] was used to remove additional border noise, reduce speckle noise, and normalize radiometric terrain (code available at: [[https://github.com/adugnag/gege_s1_ard](https://github.com/adugnag/gege_s1_ard)]([https://github.com/adugnag/gege_s1_ard](https://github.com/adugnag/gege_s1_ard)), accessed on 22 June 2022). Level-2A Sentinel-2 surface reflectance (SR) imagery was processed with calibration and atmospheric correction before being integrated into the GEE data pool. In this study, Sentinel-2 imagery was processed by the removal of the interference of cloud and shadow, linear fit interpolation, and Savitzky-Golay filtering. Removing the interference of cloud and shadow is an important step, and a reasonable method can greatly reduce the error in cloudy areas. The bands of SCL, MSK_CLDPRB, and MSK_SNWPRB included in Sentinel-2 were used to remove cloud and shadow. Preliminary experiments informed that when the thresholds of the MSK_CLDPRB band and MSK_SNWPRB band were larger than 20, the removal of some clouds was poor (especially thin clouds). However, when the thresholds of the MSK_CLDPRB band and MSK_SNWPRB band were larger than 10, some urban areas were misidentified as clouds. In addition, the shadow and cirrus values of the SCL band 3 three and 10. By comprehensively using the MSK_CLDPRB band, MSK_SNWPRB band, and SCL band, the thresholds of the MSK_CLDPRB band and MSK_SNWPRB band were set to 15. As a result, the value of the SCL band was no longer equal to 3 and 10, and the optimal cloud removal was achieved. Linear fit interpolation was carried out to fill missing values by properties of \"system:time_start\". The code of removing cloud and shadow, and the interpolating value linear fit was akin to the example provided by the SpatialThoughts official website: [[https://spatialthoughts.com/2021/11/08/temporal-interpolation-gee](https://spatialthoughts.com/2021/11/08/temporal-interpolation-gee)]([https://spatialthoughts.com/2021/11/08/temporal-interpolation-gee](https://spatialthoughts.com/2021/11/08/temporal-interpolation-gee)), accessed on 22 June 2022.