Monitoring salinity in bare soil based on Sentinel-1/2 image fusion and machine learning

He, Yujie; Zhang, Zhitao; Xiang, Ru; Ding, Bingjun; Du, Ruiqi; Yin, Haoyuan; Chen, Yinwen; Ba, Yalan

doi:10.1016/j.infrared.2023.104656

Cited by 9 publications

(5 citation statements)

References 37 publications

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“…Various regression models were used, like polynomial, random forest, linear regression, and exponential regression. Similar recent studies (2023) have highlighted the efficacy of various regression algorithms, like random forest with R 2 = 0.80 [24] and the PLSR model with R 2 = 0.66 [46], for predicting soil salinity in semi-arid and arid regions. Notably, in our study, the exponential regression model, due to its accurate fitting R 2 = 0.75 and low root mean square error (RMSE) = 0.47 ds/m, was selected to predict soil salinity based on ground truth measurements and Sentinel-1 SAR data.…”

Section: Discussionsupporting

confidence: 60%

Evaluating Drought Effects on Soil: Innovative Soil Salinity Monitoring via SAR Data, Sentinel-2 Imagery, and Machine Learning Algorithms in Kerkennah Archipelago

Hihi,

Katlane,

Kilani

et al. 2023

Atmosphere

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The Kerkennah archipelago in Tunisia is one of the most vulnerable areas where the influence of climate change is undeniable. Soil salinization has emerged as a major consequence of climate variation on this island. In this study, remote sensing techniques were implemented to develop a model for predicting soil salinity from satellite images. Machine learning algorithms, Sentinel-1 and Sentinel-2 data, and ground truth measurements were used to estimate soil salinity. Several algorithms were considered to achieve accurate findings. These algorithms are categorized as polynomial regression, random forest regression, exponential regression, and linear regression. The results demonstrate that exponential regression is the pre-eminent algorithm for estimating soil salinity with high predictive accuracy of R2 = 0.75 and RMSE = 0.47 ds/m. However, spatiotemporal soil salinity maps reveal distinct and clear distribution patterns, highlighting salty areas (i.e., sebkhas) and agricultural parcels. Thus, through the model, we explore areas of moderately high salinity within agricultural lands that could be affected by irrigation practices. The present work demonstrates a reliable model for soil salinity monitoring in the Kerkennah archipelago and inspires more successful technologies such as remote sensing and machine learning to improve the estimation of soil salinity in climate-affected vulnerable areas.

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Section: Discussionsupporting

confidence: 60%

Evaluating Drought Effects on Soil: Innovative Soil Salinity Monitoring via SAR Data, Sentinel-2 Imagery, and Machine Learning Algorithms in Kerkennah Archipelago

Hihi,

Katlane,

Kilani

et al. 2023

Atmosphere

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“…These methods all belong to ensemble learning, which is a technique that can improve the generalization ability and robustness of a single learner by combining the prediction results of multiple base learners [38]. These methods have been used by numerous studies to estimate soil salinization, whose results indicate that these methods have high accuracy and strong robustness [39][40][41][42]. However, few studies compare the accuracy of these methods at the same time.…”

Section: Methodsmentioning

confidence: 99%

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

He,

Ding,

Huang

et al. 2023

Sustainability

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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.

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“…Presently, the predominant approach to soil salinity monitoring primarily involves the direct inversion of bare soil. In contrast, assessing soil salinity beneath vegetative cover remains a topic requiring more comprehensive exploration and discussion [16,17].…”

Section: Introductionmentioning

confidence: 99%

Inversion of Soil Salinity in the Irrigated Region along the Southern Bank of the Yellow River Using UAV Multispectral Remote Sensing

Wang,

Qu,

Yang

et al. 2024

Agronomy

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Soil salinization is a global issue confronting humanity, imposing significant constraints on agricultural production in the irrigated regions along the southern bank of the Yellow River. This, in turn, leads to the degradation of the ecological environment and inadequate grain yields. Hence, it is essential to explore the magnitude and spatial patterns of soil salinization to promote efficient and sustainable agricultural development. This study carried out a two-year surface soil sampling experiment encompassing the periods before spring irrigation and the budding, flowering, and maturity stages of sunflower fields in the irrigated area along the southern bank of the Yellow River. It employed deep learning in conjunction with multispectral remote sensing conducted by UAV to estimate soil salinity levels in the sunflower fields. Following the identification of sensitive spectral variables through correlation analysis, we proceeded to model and compare the accuracy and stability of various models, including the deep learning Transformer model, traditional machine learning BP neural network (BPNN), random forest model (RF), and partial least squares regression model (PLSR). The findings indicate that the precision of soil salinity content (SSC) retrieval in saline–alkali land can be significantly enhanced by incorporating the RE band of UAV data. Four SSC inversion models were developed using the most suitable spectral variables, resulting in precise soil salinity inversion. The model order based on accuracy and stability was Transformer > BPNN > RF > PLSR. Notably, the Transformer model achieved a prediction accuracy exceeding 0.8 for both the training and test datasets, as indicated by R2 values. The precision order of the soil salinity inversion model in each period is as follows: before spring irrigation > budding period > maturity period > flowering stages. Additionally, the accuracy is higher in the bare soil stage compared to the crop cover stage. The Transformer model exhibited RMSE and R2 values of 2.41 g kg−1 and 0.84 on the test datasets, with the salt inversion results aligning closely with field-measured data. The results showed that the Transformer deep learning model integrated with RE band data significantly enhances the precision and efficiency of soil salinity inversion within the irrigated regions along the south bank of the Yellow River.

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Monitoring salinity in bare soil based on Sentinel-1/2 image fusion and machine learning

Cited by 9 publications

References 37 publications

Evaluating Drought Effects on Soil: Innovative Soil Salinity Monitoring via SAR Data, Sentinel-2 Imagery, and Machine Learning Algorithms in Kerkennah Archipelago

Evaluating Drought Effects on Soil: Innovative Soil Salinity Monitoring via SAR Data, Sentinel-2 Imagery, and Machine Learning Algorithms in Kerkennah Archipelago

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

Inversion of Soil Salinity in the Irrigated Region along the Southern Bank of the Yellow River Using UAV Multispectral Remote Sensing

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