Estimating soil salinity under sunflower cover in the Hetao Irrigation District based on unmanned aerial vehicle remote sensing

Cui, Xin; Han, Wenting; Zhang, Huihui; Cui, Jiawei; Ma, Weitong; Zhang, Liyuan; Li, Guang

doi:10.1002/ldr.4445

Cited by 20 publications

(9 citation statements)

References 47 publications

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“…Similar to the present study, the wasteland in the study area continued to accumulate salts under salt recharge from groundwater, and salt accumulation reached a maximum by the end of the reproductive period. Cui et al [38] analyzed the relationship between soil salinity and groundwater in the Yao Ba Oasis and found a significant correlation between surface soil salt fractions, salt content, and groundwater. Wang et al [39] indicated that groundwater depth and salinity are the main factors affecting soil salinization in river oases.…”

Section: Discussionmentioning

confidence: 99%

Water and Salinity Variation along the Soil Profile and Groundwater Dynamics of a Fallow Cropland System in the Hetao Irrigation District, China

Hou,

Miao,

Shi

et al. 2023

Water

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Managing soil salinity has always been a difficult problem for agriculture. Balancing water and salt while maintaining crop quality and yield is a key issue for agricultural sustainability. The Hetao lrrigation District in China has a complex mix of cultivated and uncultivated land which plays a crucial role in soil salinization processes. To investigate the dynamic properties of soil moisture and salinity, soil ions and groundwater, cultivated and fallow soils in the Hetao lrrigation District were analyzed, side by side, using a combination of field and laboratory tests, with data processed using univariate and multivariate statistical approaches. The results showed that soil moisture increased with increasing soil depth in both cultivated and fallow soils. Salinity showed an increasing trend in 2022 and 2023 from April to September. The soil ions were mainly sulfate in the cultivated soils and chloride in the fallow soils. The characteristic factors affecting salt accumulation in cultivated soils are Na++K+, Cl−, SSC, SO42−, HCO3−, and pH, and the characteristic factors affecting salt accumulation in fallow soils are Na++K+, Cl−, SSC, HCO3−, and pH. Water table depth varied with irrigation and precipitation and was strongly influenced by external environmental factors. Groundwater salinity remained stable throughout the study period. This study provides a theoretical basis for the prevention and control of soil salinization in arid and semiarid areas through the “dry drainage salt” measure.

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Section: Discussionmentioning

confidence: 99%

Water and Salinity Variation along the Soil Profile and Groundwater Dynamics of a Fallow Cropland System in the Hetao Irrigation District, China

Hou,

Miao,

Shi

et al. 2023

Water

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“…Using optical remote sensing to determine the crop growth status and indirectly inverting soil salt content (SSC) is still a commonly used monitoring method. It mainly establishes a certain relationship among the reflectance, spectral index, and SSC and then estimates soil salt in the crop root zone [44,45]. However, the optical remote sensing information is complex, and if the redundant spectral information is not screened, the stability and accuracy of the monitoring model will be affected [46].…”

Section: Comparison Of Variable Filtering Methodsmentioning

confidence: 99%

Estimation of Soil Salt Content at Different Depths Using UAV Multi-Spectral Remote Sensing Combined with Machine Learning Algorithms

Cui,

Chen,

Han

et al. 2023

Remote Sensing

Self Cite

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Soil salinization seriously affects the sustainable development of agricultural production; thus, the timely, efficient, and accurate estimation of soil salt content (SSC) has important research significance. In this study, the feasibility of soil salt content retrieval using machine learning models was explored based on a UAV (unmanned aerial vehicle) multi-spectral remote sensing platform. First, two variable screening methods (Pearson correlation analysis and Grey relational analysis) are used to screen the characteristic importance of 20 commonly used spectral indices. Then, the sensitive spectral variables were divided into a vegetation index group, a salt index group, and a combination variable group, which represent the model. To estimate SSC information for soil depths of 0–20 cm and 20–40 cm, three machine learning regression models were constructed: Support Vector Machine (SVM), Random Forest (RF), and Backpropagation Neural Network (BPNN). Finally, the salt distribution map for a 0–20 cm soil depth was drawn based on the best estimation model. The results of experiments show that GRA is better than PCA in improving the accuracy of the estimation model, and the combination variable group containing soil moisture information performs best. The three machine learning models have achieved good prediction effects to some extent. The accuracy and stability of the model are considered comprehensively, the prediction effect of 0–20 cm is higher than that of 20–40 cm, and the validation set coefficient of determination (R2), Root-Mean-Square-Error (RMSE), and Mean Absolute Error (MAE) of the best inversion model are 0.775, 0.055, and 0.038, and the soil salt spatial map based on the optimal estimation model can reflect the salinization distribution in the study area. Therefore, this study shows that a UAV multi-spectral remote sensing platform combined with machine learning models can better monitor farmland soil salt content.

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“…Hence, two natural indicators, including temperature and precipitation, and four human indicators, including total power of agricultural machinery, total output value of the plantation industry, production of raw coal, and population, were selected to explore the driving factors behind oasis transitions. Pearson's correlation coefficient (r) was used to express the linear correlation between two variables [41]. In this study, we quantified the driving forces between driving factors and oasis changes using the r-value.…”

Section: Driving Forces For Oasis Transitionmentioning

confidence: 99%

Three Decades of Oasis Transition and Its Driving Factors in Turpan–Hami Basin in Xinjiang, China: A Complex Network Approach

Zhang,

Yan,

Zhang

et al. 2024

Remote Sensing

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As a predominant ecosystem-providing area and distinctive landscape in arid regions, an oasis plays an important role in maintaining land stability, human production, and daily activities. Studying the dynamics of oasis and its driving factors is vital to supporting arid regions’ sustainable development. As a typical mountain–desert–oasis landscape, the Turpan–Hami (Tuha) Basin, located in Xinjiang, China, includes complex interactions among different land types. For this study, we revealed the spatio-temporal patterns and transition processes of the oasis using a complex network method between 1990 and 2020 in the Tuha Basin. In the oasis transition network, the degree value, betweenness centrality, and average path length were calculated to express the transition relationship, key oasis type, and oasis structural stability, respectively. Six factors related to climate change and human actives were selected to investigate the driving forces behind oasis transitions, including the average temperature and precipitation in the growing season, the total power of agricultural machinery (TAMP), the production of raw coal (PRC), the total output value of the plantation industry (TPI), and the population (Pop). Our results show that the oasis area of the Tuha Basin, including the natural oasis and artificial oasis, all grew from 1990 to 2020, with the natural oasis expanding more than the artificial oasis. The transitions between oasis types became more frequent as the area of oasis land types increased throughout the study period. Grassland acted as the most important oasis type in the network, with the highest betweenness centrality, but its importance declined due to the increasing complexity of the oasis transition network from 1990 to 2020. The transitions between oasis types became simpler, and the oasis structural stability decreased. Through driving force analysis, the oasis changes showed a positive correlation with the temperature (p-value < 0.05, r = 0.88), and urbanization and industrialization factors prompted transitions to built-up areas and cropland from grassland and shrubland. In summary, our results suggest that to create a harmonious symbiotic relationship between the natural environment in dryland and human activities, preventing grassland degradation and excessive reclamation of land cover is an available way. Meanwhile, the protection of shrubland and water resources is also important. This study provided reference and theory support for promoting the sustainable development of oases.

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Estimating soil salinity under sunflower cover in the Hetao Irrigation District based on unmanned aerial vehicle remote sensing

Cited by 20 publications

References 47 publications

Water and Salinity Variation along the Soil Profile and Groundwater Dynamics of a Fallow Cropland System in the Hetao Irrigation District, China

Water and Salinity Variation along the Soil Profile and Groundwater Dynamics of a Fallow Cropland System in the Hetao Irrigation District, China

Estimation of Soil Salt Content at Different Depths Using UAV Multi-Spectral Remote Sensing Combined with Machine Learning Algorithms

Three Decades of Oasis Transition and Its Driving Factors in Turpan–Hami Basin in Xinjiang, China: A Complex Network Approach

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