Spatiotemporal Variation and Future Predictions of Soil Salinization in the Werigan–Kuqa River Delta Oasis of China

Abstract

Soil salinization is a serious global issue; by 2050, without intervention, 50% of the cultivated land area will be affected by salinization. Therefore, estimating and predicting future soil salinity is crucial for preventing soil salinization and investigating potential arable land resources. In this study, several machine learning methods (random forest (RF), Light Gradient Boosting Machine (LightGBM), Gradient Boosting Decision Tree (GBDT), and eXtreme Gradient Boosting (XGBoost)) were used to estimate the soil salinity in the Werigan–Kuqa River Delta Oasis region of China from 2001 to 2021. The cellular automata (CA)–Markov model was used to predict soil salinity types from 2020 to 2050. The LightGBM method exhibited the highest accuracy, and the overall prediction accuracy of the methods had the following order: LightGBM > RF > GBRT > XGBoost. Moderately saline, severely saline, and saline soils were dominant in the east and south of the research area, while non-saline and mildly saline soils were widely distributed in the inner oasis area. A marked decreasing trend in the soil salt content was observed from 2001 to 2021, with a decreasing rate of 4.28 g/kg·10 a−1. The primary change included the conversion of mildly and severely saline soil types to non-saline soil. The generalized difference vegetation index (51%), Bio (30%), and temperature vegetation drought index (27%) had the greatest influence, followed by variables associated with soil attributes (soil organic carbon and soil organic carbon stock) and terrain (topographic wetness index, slope, aspect, curvature, and topographic relief index). Overall, the CA–Markov simulation resulted exhibited suitable accuracy (kappa = 0.6736). Furthermore, areas with non-saline and mildly saline soils will increase while areas with other salinity levels will continue to decrease from 2020 to 2050. From 2046 to 2050, numerous areas with saline soil will be converted to non-saline soil. These results can provide support for salinization control, agricultural production, and soil investigations in the future. The gradual decline in soil salinization in the research area in the past 20 years may have resulted from large-scale land reclamation, which has turned saline alkali land into arable land and is also related to effective measures taken by the local government to control salinization.

The Relationship between the Carbon Fixation Capacity of Vegetation and Cultivated Land Expansion and Its Driving Factors in an Oasis in the Arid Region of Xinjiang, China

“…It is situated in the alluvial and diluvial plain zone at the southern foothills of the Tianshan Mountains in the central-northern part of the Tarim Basin, between 81 • 28 ′ 30 ′′ E and 84 • 05 ′ 06 ′′ E longitude, and 39 • 29 ′ 51 ′′ N and 42 • 38 ′ 01 ′′ N latitude. The elevation of the plain area is between 976 and 1030 m, belonging to a continental warm temperate arid climate [41]. The average annual temperature is 11.6 • C, with cold winters, long frost-free periods, and scarce precipitation.…”

“…Referring to the "Yunnan Province Forest Resource Planning and Design Survey Operation Rules", the slope was divided into six levels and the aspect was divided into nine directions [45,46]. Using the reclassification tool in ArcGIS, the slope was divided into six categories (in degrees): gentle slope (0-5), mild slope (6)(7)(8)(9)(10)(11)(12)(13)(14)(15), moderate slope (16)(17)(18)(19)(20)(21)(22)(23)(24)(25), steep slope (26)(27)(28)(29)(30)(31)(32)(33)(34)(35), sharp slope (36)(37)(38)(39)(40)(41)(42)(43)(44)(45), and dangerous slope (≥46). The remaining six variables were converted from continuous to categorical using the natural break point classification method (Table 1).…”

A longitudinal analysis of soil salinity changes using remotely sensed imageries

“…These studies show that the L8 satellite image is more effective for monitoring soil salinity than the S2 image 13 , 18 – 20 . Also, researchers have employed RF 21 , XGBoost 22 , Decision Tree (DT) 23 , and SVR 24 , 25 as predictive tools in their investigations, and these models consistently demonstrated robust and reliable performance in the context of soil salinity prediction.…”

Using Different Extraction Methods to Estimate Soil Salinity and Salt Type Changes and Their Effects on Soil Inorganic Carbon in Plowed Desert–Sierozem Soil

“…The soil salinity content and characteristics directly affect crop growth, soil carbon sequestration, and ecosystem security [5][6][7]. The salinity in the profiles of desertsierozem soils varies greatly during irrigated maturation, and farmed desert-sierozem soils show a reverse soil salinity succession process, i.e., the salts in the soils gradually decrease with an increasing duration of cultivation [8]. It was found that the longer the cultivation period, the more significant the reverse change in soil salinity, and the earlier the farmland is removed from the influence of the groundwater table, the earlier the reverse salinity succession process occurs and the more complete the soil desalination is [9].…”