# Assessing the Long-Term Evolution of Abandoned Salinized Farmland via Temporal Remote Sensing Data
## 1 Introduction  
Approximately 19.5% of irrigated land worldwide is affected by salt, posing a threat to food security and economic development [1]. Secondary salinity, a direct result of human interaction with the land due to poor management practices, is a prominent land degradation process in agricultural land, especially in arid and semiarid areas with shallow groundwater depth [2, 3]. An improper irrigation and fertilization schedule introduces an amount of dissolved salt into the waterlogged area, where salinization occurs due to the capillary ascent of groundwater with high salt content [4]. Salt-affected soil limits growth and reduces the yield of a wide range of crops [5, 6]. As the degree of salinization increaseswithout intervention, the salt-affected area may turn into a wasteland, and eventually the soil saline content is beyond the salinity tolerance of crops. As an evaporation sink, abandoned salimized farmland plays an essential role in maintaining sustainable farming by gathering salt from the surrounding cropping area [3]. Traditionally, evaluating the area of salt-affected areas and wasteland areas relies on human surveys, which are laborious and ineffective at the regional scale. Detecting salt-affected farmland in the landscape and monitoring the processes of regional land use by remote sensing are essential for agricultural management, soil improvement, and policy making. With the rapid development of remote sensing technology, many efforts have been made to classify the land-cover type and degree of soil salinity using mono-temporal and time-series remote sensing products [7; 8; 9; 10; 11; 12]. In general, methods for land-cover classification can be split into two groups: (1) spectrum-based approaches; and (2) visual-based approaches. Spectrum-based approaches distinguish the land surface based on the difference in reflected spectral signatures or the features of temporal dynamics. For instance, distinguishing vegetation area from bare soil is easy when using a predefined threshold of a vegetation index (VI) (such as the normalized difference vegetation index, NDVI) using mono-temporal imagery. However, classifying similar land types (such as kinds of vegetative areas) using mono-temporal VI information is troublesome. Time-series VI data were used to tackle this problem by introducing the features in the time dimension into the classification task [7]. For example, [PERSON] et al. [13] classified crops using a time-series VI trajectory via the curve-fitting method. Supervised algorithms, such as support vector machine (SVM), random forest (RF), and neural networks (NNs), have shown superior capability in modelling high-degree nonlinear problems in land-use classification [14; 15] and cropland abandonment detection [16; 17; 18]. [PERSON] et al. [17] used machine learning algorithms (SVM and RF) to successfully distinguish abandoned cropland from a cropland dataset. However, abandoned cropland can be easily confused with spectrally similar land types in a regional task. To address this problem, [PERSON] et al. [18] developed an RF-based model to detect abandoned cropland using several indices such as the bare soil index (BSI), normalized burn ratio (NBR), and NDVI time series as input. With the boom of computer vision technologies, the classification of the land-use type can be achieved by vision-based approaches using merely mono-temporal remote sensing data [19; 20]. Convolutional neural networks (CNNs), one of the visual-based deep learning approaches, have been widely used to classify Earth's surface by extracting abstract textural features in imagery [21]. Many successful applications of CNNs have been reported in recent years in crop type classification [22], tree species classification [23], and abandoned cropland detection [24]. However, supervised machine learning algorithms require a wealth of labelled training data which is labor intensive and expensive, and CNNs require remotely sensed imagery with high spatial resolution.