# Monitoring Cropland Abandonment in Hilly Areas with Sentinel-1 and Sentinel-2 Timeseries
## 2 Materials and Methods
### Method
#### 2.3.4 Annual Land-Cover Classification  
The samples were split randomly in the ratio of 7:3 for training and validation respectively. The random forest (RF) classifier was selected to map both pre-classification and the secondary classification, as it has been successfully used in a variety of satellite-based applications including crop type mapping, with the valuable advantage of being intrinsically not prone to overfitting [70; 71; 72]. More importantly, the implementation of RF is available in  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Indicators** & **Expressions** & **References** \\ \hline GCVI & \(\frac{P_{\text{NIR}}}{P_{\text{Cour}}}-1\) & [52; 53; 54] \\ NDVI & \(\frac{P_{\text{NIR}}}{P_{\text{NIR}}}-P_{\text{Red}}\) & [1; 36; 52; 53; 55; 56; 57; 58] \\ EVI & \(2.5\times\frac{P_{\text{NIR}}}{P_{\text{NIR}}+0.0_{\text{NIR}}}-P_{\text{ Spike}}+1\) & [52; 53; 57; 59] \\ LSWI & \(\frac{P_{\text{NIR}}}{P_{\text{NIR}}+P_{\text{NIR}}}\) & [36; 52; 53; 55; 55; 57] \\ BSI & \(\frac{(P_{\text{NIR}}}{P_{\text{NIR}}}+P_{\text{Spike}}-P_{\text{Spike}})\) & [1; 52; 60] \\ NBR & \(\frac{P_{\text{NIR}}}{P_{\text{NIR}}+P_{\text{Spike}}}\) & [1; 56; 58] \\ NDWI & \(\frac{P_{\text{NIR}}}{P_{\text{NIR}}-P_{\text{Spike}}}\) & [15; 53; 58; 61] \\ VV & Single co-polarization, vertical & [53; 62; 63; 64] \\  & transmit/vertical receive & [62; 63; 64] \\ VH & Dual-band cross-polarization, vertical & [53; 62; 63; 64] \\  & transmit/horizontal receive & [62; 63; 64] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Spectral indices and their expressions from Sentinel-1 and Sentinel-2.  
GEE, allowing large-scale predictions at the pixel level. As with many other models, RF is sensitive to the choice of hyperparameters and the training data used [73]. On the basis of several fitting experiments, the number of random forest trees was set to 250 to achieve better accuracy in this study. Following pre-classification for three consecutive years, the spatial distribution of six categories of features was obtained, namely, irrigated cropland, rainfed cropland, woody vegetation, herbaceous vegetation, water bodies, and impervious surfaces. The total area of non-vegetation type cover (i.e., water bodies and impervious surfaces) for three consecutive years was masked so that only vegetation cover types remained. The samples with consistent results from three consecutive years of pre-classification were selected for secondary classification. During pre-classification, the non-vegetated areas of the image were masked, and stable and reliable training samples were obtained. Using the masked image and samples for classification, the spatial distribution of land cover was obtained for each year for irrigated cropland, rainfed cropland, woody vegetation, and herbaceous vegetation. Following pre-classification screening, accurate vegetation regions were identified, while the stable pixels obtained from the results of pre-classification for three consecutive years were expanded using the samples of secondary classification to increase the reliability of the samples. Through the above steps, the spatial distribution of the vegetation region in the study area was generated for three consecutive years.