# Assessing the Long-Term Evolution of Abandoned Salinized Farmland via Temporal Remote Sensing Data
## 5 Discussion  
Supervised machine learning algorithms have shown a solid capability to learn abstract features in nonlinear systems [40]. However, the input of these algorithms should be a uniformly formatted array. In other words, the shape of the input should be the same in the training and the testing phases [21]. Formatting the time-series input for remote sensing data with high temporal resolution (e.g., MODIS-based products) is applicable via cloud and cloud shadow compensation technology [41]. For high spatial resolution products (e.g., Landsat-based products), contamination from clouds heavily affects the consistency of formatted VI time series in different years (the number of available imageries varied depending on the climate condition of that year). Directly extending the short time-series data through time interpolation introduces large uncertainty in the testing phase, which hampers the performance of fine-scale surface classification by the supervised method. To test the applicability of the supervised algorithms for land cover classification using time-series data with varying lengths (Landsat imageries), we examined one of the widely used algorithms, the random forest, to perform the task. The training samples were manually selected in specific years (years with more cloud-free data available) and tested in other years (results not shown). The trained model indeed performed well for years with more available time-series data. Unfortunately, it failed for years with short time series. In contrast, unsupervised algorithms, such as \(K\)-means clustering, classify the samples by learning the distinct features from the samples themselves instead of the training set. Then, the clustering results are labelled automatically by prior rules. The advantages of \(K\)-means clustering make it deployable on any dataset with different lengths of time series. The results confirmed that this method showed stable performance and worked well in both cloudy years and less cloudy years. A significant desalinization process was observed during the last thirty years, while the area of extremely saline conditions was reduced by approximately 50% and the area of highly saline conditions was reduced by approximately 70% (Figure 10). These results were deduced from the EC estimates of bare soil during the fallow season based on an established SI-EC relationship. However, the distribution of collected soil samples was imbalanced where the samples with extremely high EC were sparse. As a result, the prediction error of the established SI-EC relationship may be large in extremely saline areas. The only way to improve the robustness of the SI-EC relationship is to involve more available data. Therefore, more soil samples will be collected in the study area in future studies. In addition, Sentinel-2 imageries were used to build the EC estimation model because of the high spatial heterogeneity of the surface of the study area; therefore, remote sensing data with high resolution are needed to match the ground samples. However, the scale effect may reduce model performance when the SI-EC relationship is used in imagery with a lower resolution relationship (caused by the mixed pixels). Therefore, EC estimates may be more accurate on relatively uniform land with a smaller scale effect. When a field is affected by high-level salinity, this reduces the yield of most crops and causes the field to be abandoned. Abandoned salinized farmland acts as an evaporative sink, resulting in a lower groundwater depth than the cropped area where constant irrigation increases the water table. Consequently, the salty water is drained from the cropped area into the abandoned salinized farmland, which mitigates the salinity in the cropping area. This is the concept of \"dry drainage\" [42]. The higher gradient of the water table in the dry drainage system transforms more salt into abandoned salinized farmland, which may increase its saline degree. The soil hardens in the abandoned salinized farmland when its saline degree is extremely high.