A framework for determining the total salt content of soil profiles using time-series Sentinel-2 images and a random forest-temporal convolution network

Nan, Wang; Peng, Jie; Xue, Jie; Zhang, Xianglin; Huang, Jingyi; Biswas, Asim; He, Yong; Shi, Zhou

doi:10.1016/j.geoderma.2021.115656

Cited by 27 publications

(12 citation statements)

References 74 publications

(96 reference statements)

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“…Different environmental covariates maintained different effects on salinity in the two seasons and soil profiles (Figure 3). Most NDVI and salinity indices were good predictors of soil salinity, which is notably consistent with the results of previous studies on soil salinity at different regional scales (Peng et al, 2019; Wang et al, 2018; Wang et al, 2022; Wang, Chen, et al, 2021; Wei et al, 2021). In addition, various concerns regarding the contribution of environmental covariates to soil salinity were raised.…”

Section: Discussionsupporting

confidence: 90%

“…Further, seasonal differences in runoff also have a strong impact on soil salinization, particularly during the irrigation season, when changes in water volume affect salt transport. Previous studies have found that intensively irrigated agriculture accelerates the diffusion of deep soil salts as well as the accumulation of surface soil salts (Wang et al, 2022); however, patterns of soil salinity changes at the macroscopic scale have been understudied.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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Preventing soil salinization is key to the healthy development of agroecosystems in arid regions. Notably, long‐term anthropogenic irrigation accelerates secondary salinity accumulation in arid areas; however, the regional‐scale spatial patterns of soil salinity pre‐ and post‐irrigation season remain unclear. Accordingly, field observations over two periods (n = 182 samples), along with environmental covariates and the randomForest algorithm, were used to estimate the horizontal and vertical spatial distributions (12 soil depths) of soil salinity in the Weiku Oasis (0–60 cm) at a 30 m spatial resolution. The results showed that the prediction accuracy was greater for the non‐irrigated season than for the irrigated season. Moreover, soil salinity was low in the interior of the oasis, high in the desert oasis interlacing zone, and significantly decreased with increasing soil depth. Variable contributions indicated that the effects of SoilGrids variables on soil salinity increased with greater soil depth. During the irrigation season, an inflection point for salt aggregation was observed at ~55 cm for farmlands, grasslands, and shrubs, with the greatest variation seen in farmlands. The findings here provide insights into the improvement of irrigation techniques in arid zone agroecosystems.

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Section: Discussionsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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“…However, in-situ data was an essential requirement in case the visual observation was confused between similar vegetation types. Thus, we obtained the in-situ ground reference data of the experimental fields in Alar, South Xinjiang, in November 2019 [66]. Figure 12 shows the in-situ observation of agricultural field boundaries and the model output result.…”

Section: In-situ Observationmentioning

confidence: 99%

Extraction of Agricultural Fields via DASFNet with Dual Attention Mechanism and Multi-scale Feature Fusion in South Xinjiang, China

Wang

Zhang

et al. 2022

Remote Sensing

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Agricultural fields are essential in providing human beings with paramount food and other materials. Quick and accurate identification of agricultural fields from the remote sensing images is a crucial task in digital and precision agriculture. Deep learning methods have the advantages of fast and accurate image segmentation, especially for extracting the agricultural fields from remote sensing images. This paper proposed a deep neural network with a dual attention mechanism and a multi-scale feature fusion (Dual Attention and Scale Fusion Network, DASFNet) to extract the cropland from a GaoFen-2 (GF-2) image of 2017 in Alar, south Xinjiang, China. First, we constructed an agricultural field segmentation dataset from the GF-2 image. Next, seven evaluation indices were selected to assess the extraction accuracy, including the location shift, to reveal the spatial relationship and facilitate a better evaluation. Finally, we proposed DASFNet incorporating three ameliorated and novel deep learning modules with the dual attention mechanism and multi-scale feature fusion methods. The comparison of these modules indicated their effects and advantages. Compared with different segmentation convolutional neural networks, DASFNet achieved the best testing accuracy in extracting fields with an F1-score of 0.9017, an intersection over a union of 0.8932, a Kappa coefficient of 0.8869, and a location shift of 1.1752 pixels. Agricultural fields can be extracted automatedly and accurately using DASFNet, which reduces the manual record of the agricultural field information and is conducive to further farmland surveys, protection, and management.

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“…The main origins of salts in "primary" salinization are generally from precipitation, parent materials weathering, and the transport of saline dust while "secondary" salinization is mainly caused by intense agricultural activities [18][19][20]. The soil salinization processes are characterized by high dynamism [21,22]; thus, to fully evaluate the role of soil salinity in shaping the spatio-temporal patterns of SOC stock at a broad scale, updated spatial and temporal information of soil salinity with high resolution and precision should first be obtained.…”

Section: Introductionmentioning

confidence: 99%

“…Numerous researchers have proved that the combination of site-level soil measurements, remote sensing data, and machine learning models can enable cost-effective mapping of the spatio-temporal distribution of soil salinity from local to global scales [15,[22][23][24][25][26]. The advantages of remote sensing data make it possible to directly observe the dynamics of soil surface salinity at various scales and resolutions [26,27].…”

Section: Introductionmentioning

confidence: 99%

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.

show abstract

A framework for determining the total salt content of soil profiles using time-series Sentinel-2 images and a random forest-temporal convolution network

Cited by 27 publications

References 74 publications

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

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

Extraction of Agricultural Fields via DASFNet with Dual Attention Mechanism and Multi-scale Feature Fusion in South Xinjiang, China

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

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