Assessing agricultural salt-affected land using digital soil mapping and hybridized random forests

Nabiollahi, Kamal; Taghizadeh–Mehrjardi, Ruhollah; Shahabi, Aram; Heung, Brandon; Amirian‐Chakan, Alireza; Davari, Masoud; Scholten, Thomas

doi:10.1016/j.geoderma.2020.114858

Cited by 70 publications

(26 citation statements)

References 51 publications

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“…The blue, green, red, and near-infrared bands were employed as spectral indices to further calculate other remote sensing indexes (Table S1). The efficiency of these indexes in predicting soil EC or SOC stock was confirmed by previous studies [26,27,29,30]. It should be noted that the sensors and band width differences among Landsat 5, 7, and 8 may cause uncertainty regarding the consistency of temporal predictions [56].…”

Section: Environmental Covariatessupporting

confidence: 71%

“…This process could be substantially accelerated by intense groundwater exploitation in extremely arid regions characterized by limited rainfall entering the aquifer, since almost all groundwater used for irrigation contained some dissolved salts [20,66]. In addition, soil salinization from irrigation water was also significantly strengthened by poor drainage and the use of saline water for irrigation [18,19,30], which was quite common in the lower reaches of the Shiyang River Basin [46].…”

Section: Spatial Patterns Of Soil Salinity and Soc Stockmentioning

confidence: 99%

“…However, most statistical regression models are characterized by large errors in predicting higher or lower values for regions characterized by large spatial variability. In contrast, more accurate estimations can be made by machine learning models, due to their superior capability in detecting the nonlinear relationships between soil salinity and environmental covariates [26,29,30].…”

Section: Introductionmentioning

confidence: 99%

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The Role of Soil Salinization in Shaping the Spatio-Temporal Patterns of Soil Organic Carbon Stock

Zhang

Liu

et al. 2022

Remote Sensing

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Soil salinization is closely related to land degradation, and it is supposed to exert a significant negative effect on soil organic carbon (SOC) stock dynamics. This effect and its mechanism have been examined at site and transect scales in previous studies while over a large spatial extent, the salinity-induced changes in SOC stock over space and time have been less quantified, especially by machine learning and remote sensing techniques. The main focus of this study is to answer the following question: to what extent can soil salinity exert an additional effect on SOC stock over time at a larger spatial scale? Thus, we employed the extreme gradient boosting models (XGBoost) combined with field site-level measurements from 433 sites and 41 static and time-varying environmental covariates to construct methods capable of quantifying the salinity-induced SOC changes in a typical inland river basin of China between the 1990s and 2020s. Results showed that the XGBoost models performed well in predicting the soil electrical conductivity (EC) and SOC stock at 0–20 cm, with the R2 value reaching 0.85 and 0.81, respectively. SOC stock was found to vary significantly with increasing soil salinity following an exponential decay function (R2 = 0.27), and salinity sensitivity analysis showed that soils in oasis were expected to experience the largest carbon loss (−137.78 g m−2), which was about 4.84, 14.37, and 25.95 times higher than that in the saline, bare, and sandy land, respectively, if the soil salinity increased by 100%. In addition, the decrease in the soil salinity (−0.32 dS m−1) from the 1990s to the 2020s was estimated to enhance the SOC stock by 0.015 kg m−2, which contributed an additional 10% increase to the total SOC stock enhancement. Overall, the proposed methods can be applied for quantification of the direction and size of the salinity effect on SOC stock changes in other salt-affected regions. Our results also suggest that the role of soil salinization should not be neglected in SOC changes projection, and soil salinization control measures should be further taken into practice to enhance soil carbon sequestration in arid inland river basins.

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Section: Environmental Covariatessupporting

confidence: 71%

Section: Spatial Patterns Of Soil Salinity and Soc Stockmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Role of Soil Salinization in Shaping the Spatio-Temporal Patterns of Soil Organic Carbon Stock

Zhang

Liu

et al. 2022

Remote Sensing

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“…However, it has limitations in dealing with nonlinear relationships between response and predictor variables, which are often present in heterogeneous agricultural landscapes (Khanal et al., 2018). Various machine learning methods, including extreme learning machine (ELM) (Xiao et al., 2021), random forest (Nabiollahi et al., 2021; Wang, Peng, et al., 2022), and support vector machine (Taghizadeh‐Mehrjardi et al., 2021), have proven to be successful in soil salinity monitoring. These methods have demonstrated superiority over traditional statistical models (Barzegar et al., 2018; Khanal et al., 2018; Zhou et al., 2020) in accurately assessing and predicting soil salinity concentrations.…”

Section: Introductionmentioning

confidence: 99%

Soil salinity monitoring model based on the synergistic construction of ground‐UAV‐satellite data

Jia,

Chen,

Liu

et al. 2023

Soil Use and Management

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Soil salinization poses a significant constraint on the sustainable development of agriculture. While satellite remote‐sensing data enables salinity monitoring over large spatial scales, its coarse resolution limits monitoring accuracy. On the other hand, unmanned aerial vehicle (UAV) remote‐sensing data offers greater accuracy in salinity monitoring but covers a smaller area compared to satellite remote sensing. To address the need for both high precision and wide‐range monitoring, we developed a soil salinity monitoring model integrating ground, UAV, and satellite data. Field experiments were conducted in the Shahaoqu irrigation area, Inner Mongolia, China, from August 11 to 15, 2019. During this period, we collected satellite remote‐sensing images, UAV remote‐sensing images, and soil salinity data. Spectral bands from the remote‐sensing data were utilized to construct separate vegetation and salinity indices, which were further filtered using the variable importance in projection (VIP) algorithm. The soil salinity monitoring model was then constructed using the extreme learning machine (ELM) algorithm. Several soil salinity monitoring models were developed, including SSMM‐UAV (based on ground‐UAV data at a resolution of 6.5 cm), SSMM‐UAV‐upscaling (obtained by upscaling the results of SSMM‐UAV to a 16 m scale), SSMM‐satellite (based on ground‐satellite data at a 16 m scale), and SSMM‐UAV‐satellite (constructed using ground‐UAV‐satellite data at a 16 m scale). The results revealed that SSMM‐UAV accurately monitored soil salinity at the UAV scale, with R2 values exceeding .81 and RMSEs below 0.11% for both the model modelling set and validation set. SSMM‐UAV‐upscaling demonstrated consistency with SSMM‐UAV and effectively represented salinity conditions at the 16 m scale. In contrast, SSMM‐satellite exhibited inferior performance, with R2 values of .42 and .32 for the modelling set and validation set, respectively, and RMSEs of 0.15% and 0.17%, respectively. By incorporating ground‐UAV‐satellite data, SSMM‐UAV‐satellite improved the R2 of SSMM‐satellite by more than .09 and reduced the RMSEs by at least 0.08%. Furthermore, the area covered by satellite data was ca. 85 times larger than that covered by UAV data. The synergistic use of UAV and satellite data in salinity monitoring enhances the accuracy of satellite remote sensing and expands the monitoring range of UAV remote sensing. The findings of this study provide a reference for high precision and large‐scale salt monitoring through the synergistic integration of ground, UAV, and satellite data.

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“…Using Landsat images allows for assessing the soil salinity features. The Landsat images are the best to capture soil salinity extent with different salinity levels [35][36][37][38][39][40][41]. In Egypt, several studies have also been conducted to map soil salinity using remote sensing and GIS datasets and showed reliable soil salinity results [42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57].…”

Section: Introductionmentioning

confidence: 99%

Quantitative Estimation of Saline-Soil Amelioration Using Remote-Sensing Indices in Arid Land for Better Management

Aboelsoud

AbdelRahman

Kheir

et al. 2022

Land

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Soil salinity and sodicity are significant issues worldwide. In particular, they represent the most dominant types of degraded lands, especially in arid and semi-arid regions with minimal rainfall. Furthermore, in these areas, human activities mainly contribute to increasing the degree of soil salinity, especially in dry areas. This study developed a model for mapping soil salinity and sodicity using remote sensing and geographic information systems (GIS). It also provided salinity management techniques (leaching and gypsum requirements) to ameliorate soil and improve crop productivity. The model results showed a high correlation between the soil electrical conductivity (ECe) and remote-sensing spectral indices SIA, SI3, VSSI, and SI9 (R2 = 0.90, 0.89, 0.87, and 0.83), respectively. In contrast, it showed a low correlation between ECe and SI5 (R2 = 0.21). The salt-affected soils in the study area cover about 56% of cultivated land, of which the spatial distribution of different soil salinity levels ranged from low soil salinity of 44% of the salinized cultivated land, moderate soil salinity of 27% of salinized cultivated land, high soil salinity of 29% of the salinized cultivated land, and extreme soil salinity of 1% of the salinized cultivated land. The leaching water requirement (LR) depths ranged from 0.1 to 0.30 m ha−1, while the gypsum requirement (GR) ranged from 0.1 to 9 ton ha−1.

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Assessing agricultural salt-affected land using digital soil mapping and hybridized random forests

Cited by 70 publications

References 51 publications

The Role of Soil Salinization in Shaping the Spatio-Temporal Patterns of Soil Organic Carbon Stock

The Role of Soil Salinization in Shaping the Spatio-Temporal Patterns of Soil Organic Carbon Stock

Soil salinity monitoring model based on the synergistic construction of ground‐UAV‐satellite data

Quantitative Estimation of Saline-Soil Amelioration Using Remote-Sensing Indices in Arid Land for Better Management

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