Soil salinity dynamics in arid oases during irrigated and non‐irrigated seasons

Zhu, Chuanmei; Ding, Jianli; Zhang, Zipeng; Wang, Jinjie; Chen, Xiangyue; Han, Lijing; Shi, Haobo; Wang, Jingzhe

doi:10.1002/ldr.4632

Cited by 9 publications

(4 citation statements)

References 35 publications

(48 reference statements)

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“…Considering the temporal variability of soil salinity, the use of advanced sensor technologies for precision agriculture applications in the future [89], both in the study area and in similar regions, can be used to optimize the growing conditions [90]. Especially in arid regions where irrigated agriculture is practiced, Zhu et al [91] emphasized the importance of creating soil salinity maps in terms of changes in soil salinity during the irrigated or non-irrigated period to understand the main mechanisms causing soil salinity.…”

Section: Discussionmentioning

confidence: 99%

Application of Machine Learning Algorithms for Digital Mapping of Soil Salinity Levels and Assessing Their Spatial Transferability in Arid Regions

Sulieman,

Kaya,

Elsheikh

et al. 2023

Land

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A comprehensive understanding of soil salinity distribution in arid regions is essential for making informed decisions regarding agricultural suitability, water resource management, and land use planning. A methodology was developed to identify soil salinity in Sudan by utilizing optical and radar-based satellite data as well as variables obtained from digital elevation models that are known to indicate variations in soil salinity. The methodology includes the transfer of models to areas where similar conditions prevail. A geographically coordinated database was established, incorporating a variety of environmental variables based on Google Earth Engine (GEE) and Electrical Conductivity (EC) measurements from the saturation extract of soil samples collected at three different depths (0–30, 30–60, and 60–90 cm). Thereafter, Multinomial Logistic Regression (MNLR) and Gradient Boosting Algorithm (GBM), were utilized to spatially classify the salinity levels in the region. To determine the applicability of the model trained at the reference site to the target area, a Multivariate Environmental Similarity Surface (MESS) analysis was conducted. The producer’s accuracy, user’s accuracy, and Tau index parameters were used to evaluate the model’s accuracy, and spatial confusion indices were computed to assess uncertainty. At different soil depths, Tau index values for the reference area ranged from 0.38 to 0.77, whereas values for target area samples ranged from 0.66 to 0.88, decreasing as the depth increased. Clay normalized ratio (CLNR), Salinity Index 1, and SAR data were important variables in the modeling. It was found that the subsoils in the middle and northwest regions of both the reference and target areas had a higher salinity level compared to the topsoil. This study highlighted the effectiveness of model transfer as a means of identifying and evaluating the management of regions facing significant salinity-related challenges. This approach can be instrumental in identifying alternative areas suitable for agricultural activities at a regional level.

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Section: Discussionmentioning

confidence: 99%

Application of Machine Learning Algorithms for Digital Mapping of Soil Salinity Levels and Assessing Their Spatial Transferability in Arid Regions

Sulieman,

Kaya,

Elsheikh

et al. 2023

Land

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“…Soil carbon stock is strongly disturbed by human activities, due to the process of oasis creation, and can be adjusted in a short time scale. The change in land-use type, the input of exogenous substances, and the intervention of seasonal human activities have an impact on the soil organic carbon budget and carbon sequestration potential in arid oases [5]. Due to its special geographical location and climatic conditions, SOC is transformed into SIC [6] through the micro-carbon cycle system of "SOC → CO 2 → SIC", which is an important way to transform soil carbon stock in arid and semi-arid regions.…”

Section: Introductionmentioning

confidence: 99%

The Oasisization Process Promotes the Transformation of Soil Organic Carbon into Soil Inorganic Carbon

Tang,

Gong,

et al. 2024

Land

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The dynamic fluctuations in the soil organic carbon (SOC) stock, a fundamental part of the terrestrial ecosystem’s carbon stock, are critical to preserving the global carbon balance. Oases in arid areas serve as critical interfaces between oasis ecosystems and deserts, with land use changes within these oases being key factors affecting soil organic carbon turnover. However, the response of the soil SOC-CO2-SIC (soil inorganic carbon) micro-carbon cycle to oasis processes and their underlying mechanisms remains unclear. Five land-use types in the Alar reclamation area—cotton field (CF), orchard (OR), forest land (FL), waste land (WL), and sandy land (SL)—were chosen as this study’s research subjects. Using stable carbon isotope technology, the transformation process of SOC in the varieties of land-use types from 0 to 100 cm was quantitatively analyzed. The results showed the following: (1) The SOC of diverse land-use types decreased with the increase in soil depth. There were also significant differences in SIC-δ13C values among the different land-use types. The PC(%) (0.73 g kg−1) of waste land was greatly higher than that of other land-use types (p < 0.05) (factor analysis of variance). (2) The CO2 fixation in cotton fields, orchards, forest lands, and waste land primarily originates from soil respiration, whereas, in sandy lands, it predominantly derives from atmospheric sources. (3) The redundancy analysis (RDA) results display that the primary influencing factors in the transfer of SOC to SIC are soil water content, pH, and microbial biomass carbon. Our research demonstrates that changes in land use patterns, as influenced by oasis processes, exert a significant impact on the conversion from SOC to SIC. This finding holds substantial significance for ecological land use management practices and carbon sequestration predictions in arid regions, particularly in the context of climate change.

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“…However, traditional methods of soil salinity assessment, such as collecting soil samples and conducting laboratory analyses, are both time-consuming and expensive. [1,2]. In this regard, the utilization of modern technologies is crucial, and one such technology is the integration of Geographic Information Systems (GIS) and Remote Sensing (RS) technologies.…”

Section: Introductionmentioning

confidence: 99%

Analysis of soil salinity in irrigated agricultural land using remote sensing data: case study of Chinoz district in Uzbekistan

Oymatov,

Teshaev,

Makhsudov

et al. 2023

E3S Web of Conf.

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Soil salinity is a serious agricultural concern in Uzbekistan, causing plant growth to be hampered and crop productivity to be diminished. This issue is especially prevalent in semi-desert and desert regions, compounding problems such as soil erosion, land degradation, subsidence, corrosion, and poor groundwater quality. On the other hand, Geographic Information Systems (GIS) and Remote Sensing (RS) technologies provide more efficient, cost-effective, and timely tools and procedures for mapping soil salinity. Different indices and methods can be used to detect and quantify soil salinity levels using the spectral information acquired by the Landsat-8 OLI sensor. Among these are the Normalized Difference Salinity Index (NDSI) and the Normolazed Difference Vegetation Index (NDVI). GIS software integrates satellite imagery with auxiliary data such as soil type and topography, allowing for a thorough assessment of soil salinity distribution over the research area. Compared to traditional methods, integrating remote sensing data with GIS analysis provides a more efficient and cost-effective approach to soil salinity assessment. The findings of this study will help us understand the distribution of soil salinity in the study area and provide insights for decision-making processes connected to sustainable land management.

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Soil salinity dynamics in arid oases during irrigated and non‐irrigated seasons

Cited by 9 publications

References 35 publications

Application of Machine Learning Algorithms for Digital Mapping of Soil Salinity Levels and Assessing Their Spatial Transferability in Arid Regions

Application of Machine Learning Algorithms for Digital Mapping of Soil Salinity Levels and Assessing Their Spatial Transferability in Arid Regions

The Oasisization Process Promotes the Transformation of Soil Organic Carbon into Soil Inorganic Carbon

Analysis of soil salinity in irrigated agricultural land using remote sensing data: case study of Chinoz district in Uzbekistan

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