# Assessing the Long-Term Evolution of Abandoned Salinized Farmland via Temporal Remote Sensing Data
## 1 Introduction
The difference in the temporal VI patterns among non-vegetative areas, such as dune area and abandoned salimized farmland, is small. As a result, insufficient training samples can result in the overfitting problem of supervised algorithms, limiting the performance of these methods for abandoned salinized farmland detection. Most previous studies have detected cropland that was abandoned mainly due to its poor topography, long distance to markets, or poor soil fertility. Approaches to detecting abandoned salinized farmland have not been well explored. [PERSON] et al. [10] distinguished saline soil and dune areas using the thermal band because dune areas have higher surface temperatures due to limited evaporation and lower thermal capacity. However, separating the salt-affected soil and other non-vegetative areas (e.g., urban areas) was difficult due to the similar vegetation index value and surface temperature. Strong saline soil shows higher reflectance in the visible and NIR wavelength ranges than less salt-affected areas [11]. Utilizing the reflection characteristics of saline soil, various soil salinity indices (SI) have been designed to assess the degree of soil salinity [12; 25; 26]. The SI is preferred to quantify the soil salinity in uncropped areas to bypass the influence of vegetation, indicating that it has a limited ability to detect abandoned salinized farmland. Moreover, distinguishing non-vegetative surfaces using the SI has not been discussed much. Previous studies have mainly focused on limited types of land-use classification or evaluating the soil salinity degree from non-vegetative areas. Most of them failed to identify abandoned salinized farmland, which is crucial in gathering salt from surrounding cropping areas [3]. [PERSON] et al. [10] detected abandoned salinized farmland using monopemporal imagery via supervision classification. However, mono-temporal data provided limited information for the target and may be influenced by the timing of detection. Therefore, a robust and accurate method that can detect abandoned salinized farmland and evaluate the soil salinity degree simultaneously from temporal remotely sensed data is needed to assess the salinization process over a larger area. In this study, a method based on the concept of decision trees was developed to monitor the long-term dynamics of salinization in the Hetao Irrigation District, an irrigation system that is over two thousand years old in North China. The direction of the salinization process can be affected by both natural and human factors, while a shallower groundwater table with higher evaporation demands is prone to salinization. In contrast, deeper groundwater levels with sufficient infiltration water (e.g., rainfall and irrigation) show positive signs of desalilization [27; 28; 29]. Human-related activities may be the dominant factor in determining the direction of salinization for human-dominated land use [30]. However, sometimes the human impacts caused by socioeconomic factors can be spontaneous, making such impacts difficult to predict. For example, a large-scale shift of the cropping structure may disturb the previous balance of the water-salt system, thereby affecting the salinization process. Therefore, the objectives of this study were: (1) to assess the spatio-temporal patterns of the abandoned salinized farmland via remote sensing data backed by a robust and accurate method; and (2) to analyze the influence factors that affect the long-term dynamics of the salinization process in Hetao.