Digital mapping of soil salinization based on Sentinel-1 and Sentinel-2 data combined with machine learning algorithms

Ma, Guolin; Ding, Jianli; Han, Lijng; Zhang, Zipeng; Ran, Si

doi:10.1016/j.regsus.2021.06.001

Cited by 45 publications

(37 citation statements)

References 74 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…All three models utilize the train_test_split function in the Sklearn module of the machine learning library in the Python 3.7 programming language to randomly divide the data into a modeling set (70%, n = 30) and a validation set (30%, n = 15), and fix the selected dataset with the random_state function. Before building the prediction model, the important parameters in the model need to be optimized (hyperparameter optimization) to improve the model performance [72]. Therefore, this study applies the grid search method to optimize the hyperparameters of the model.…”

Section: Modeling Of Total Nitrogen Monitoringmentioning

confidence: 99%

Evaluation of Total Nitrogen in Water via Airborne Hyperspectral Data: Potential of Fractional Order Discretization Algorithm and Discrete Wavelet Transform Analysis

Liu

Ding

et al. 2021

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Controlling and managing surface source pollution depends on the rapid monitoring of total nitrogen in water. However, the complex factors affecting water quality (plant shading and suspended matter in water) make direct estimation extremely challenging. Considering the spectral response mechanisms of emergent plants, we coupled discrete wavelet transform (DWT) and fractional order discretization (FOD) techniques with three machine learning models (random forest (RF), bagging algorithm (bagging), and eXtreme Gradient Boosting (XGBoost)) to mine this potential spectral information. A total of 567 models were developed, and airborne hyperspectral data processed with various DWT scales and FOD techniques were compared. The effective information in the hyperspectral reflectance data were better emphasized after DWT processing. After DWT processing the original spectrum (OR), its sensitivity to TN in water was maximally improved by 0.22, and the correlation between FOD and TN in water was optimally increased by 0.57. The transformed spectral information enhanced the TN model accuracy, especially for FOD after DWT. For RF, 82% of the model R2 values improved by 0.02~0.72 compared to the model using FOD spectra; 78.8% of the bagging values improved by 0.01~0.53 and 65.0% of the XGBoost values improved by 0.01~0.64. The XGBoost model with DWT coupled with grey relation analysis (GRA) yielded the best estimation accuracy, with the highest precision of R2 = 0.91 for L6. In conclusion, appropriately scaled DWT analysis can substantially improve the accuracy of extracting TN from UAV hyperspectral images. These outcomes may facilitate the further development of accurate water quality monitoring in sophisticated global waters from drone or satellite hyperspectral data.

show abstract

Section: Modeling Of Total Nitrogen Monitoringmentioning

confidence: 99%

Evaluation of Total Nitrogen in Water via Airborne Hyperspectral Data: Potential of Fractional Order Discretization Algorithm and Discrete Wavelet Transform Analysis

Liu

Ding

et al. 2021

Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…Accurate monitoring and prediction of soil salinity are essential for sustainable development, land management, water quality, and agricultural activities, especially in arid and semi-arid regions 106 . Therefore, other criteria to examine the performance of applied hybrid and standalone ML models under different scenarios for predicting groundwater salinity are the control rug and density distribution.…”

Section: Discussionmentioning

confidence: 99%

“…According to the results of the comparison, the SGT algorithm was found most suitable for predicting the soil salinity in three distinct regions. Ma et al 106 applied XGBoost (extreme gradient boosting), CART, and RF models to predict the soil salinity in the Ogan-Kuqa river oasis in China using remote sensing and topographical observations. They found that the XGBoost model achieved better prediction (R 2 = 0.68, RMSE = 10.56 dS m −1 ) than CART (R 2 = 0.57, RMSE = 12.20 dS m −1 ), and RF (R 2 = 0.63, RMSE = 11.41 dS m −1 ) models.…”

Section: Discussionmentioning

confidence: 99%

Computational assessment of groundwater salinity distribution within coastal multi-aquifers of Bangladesh

Jamei

Karbasi

Malik

et al. 2022

Sci Rep

View full text Add to dashboard Cite

The rising salinity trend in the country’s coastal groundwater has reached an alarming rate due to unplanned use of groundwater in agriculture and seawater seeping into the underground due to sea-level rise caused by global warming. Therefore, assessing salinity is crucial for the status of safe groundwater in coastal aquifers. In this research, a rigorous hybrid neurocomputing approach comprised of an Adaptive Neuro-Fuzzy Inference System (ANFIS) hybridized with a new meta-heuristic optimization algorithm, namely Aquila optimization (AO) and the Boruta-Random forest feature selection (FS) was developed for estimating the salinity of multi-aquifers in coastal regions of Bangladesh. In this regard, 539 data samples, including ten water quality indices, were collected to provide the predictive model. Moreover, the individual ANFIS, Slime Mould Algorithm (SMA), and Ant Colony Optimization for Continuous Domains (ACOR) coupled with ANFIS (i.e., ANFIS-SMA and ANFIS-ACOR) and LASSO regression (Lasso-Reg) schemes were examined to compare with the primary model. Several goodness-of-fit indices, such as correlation coefficient (R), the root mean squared error (RMSE), and Kling-Gupta efficiency (KGE) were used to validate the robustness of the predictive models. Here, the Boruta-Random Forest (B-RF), as a new robust tree-based FS, was adopted to identify the most significant candidate inputs and effective input combinations to reduce the computational cost and time of the modeling. The outcomes of four selected input combinations ascertained that the ANFIS-OA regarding the best accuracy in terms of (R = 0.9450, RMSE = 1.1253 ppm, and KGE = 0.9146) outperformed the ANFIS-SMA (R = 0.9406, RMSE = 1.1534 ppm, and KGE = 0.8793), ANFIS-ACOR (R = 0.9402, RMSE = 1.1388 ppm, and KGE = 0.8653), Lasso-Reg (R = 0.9358), and ANFIS (R = 0.9306) models. Besides, the first candidate input combination (C1) by three inputs, including Cl− (mg/l), Mg2+ (mg/l), Na+ (mg/l), yielded the best accuracy among all alternatives, implying the role importance of (B-RF) feature selection. Finally, the spatial salinity distribution assessment in the study area ascertained the high predictability potential of the ANFIS-OA hybrid with B-RF feature selection compared to other paradigms. The most important novelty of this research is using a robust framework comprised of the non-linear data filtering technique and a new hybrid neuro-computing approach, which can be considered as a reliable tool to assess water salinity in coastal aquifers.

show abstract

“…Therefore, the best estimation model obtained in this study would not be applicable in other seasons; further research is required to extend the models seasonally. In addition, we used only linear regression to construct the estimation models; however, many studies have confirmed that machine-learning-based modelling methods, such as support vector machine, back propagation neural network, and random forest algorithms, also perform well in soil prediction [ 24 , 58 , 59 ]. These methods will be evaluated in subsequent research.…”

Section: Discussionmentioning

confidence: 99%

“…From then, satellite remote sensing images have been widely used in large-scale SSC and SOM estimation for their convenient acquisition, easy processing, and large coverage area [ 22 , 23 ]. Ma et al used Sentinel-1A and Sentinel-2A data to retrieve the distribution map of soil salinization in the Ogan-Kuqa River Oasis located in the Tarim Basin in Xinjiang, China [ 24 ]. Zhai predicted the spatial distribution of SOM in the wetland of Gao’an Research Area and Anyi County of China by combing data from Landsat-8 and GF-1 [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Soil Salt Content and Organic Matter on Arable Land in the Yellow River Delta by Combining UAV Hyperspectral and Landsat-8 Multispectral Imagery

Sun

Jiang

et al. 2022

Sensors

View full text Add to dashboard Cite

Rapid and large-scale estimation of soil salt content (SSC) and organic matter (SOM) using multi-source remote sensing is of great significance for the real-time monitoring of arable land quality. In this study, we simultaneously predicted SSC and SOM on arable land in the Yellow River Delta (YRD), based on ground measurement data, unmanned aerial vehicle (UAV) hyperspectral imagery, and Landsat-8 multispectral imagery. The reflectance averaging method was used to resample UAV hyperspectra to simulate the Landsat-8 OLI data (referred to as fitted multispectra). Correlation analyses and the multiple regression method were used to construct SSC and SOM hyperspectral/fitted multispectral estimation models. Then, the best SSC and SOM fitted multispectral estimation models based on UAV images were applied to a reflectance-corrected Landsat-8 image, and SSC and SOM distributions were obtained for the YRD. The estimation results revealed that moderately salinized arable land accounted for the largest proportion of area in the YRD (48.44%), with the SOM of most arable land (60.31%) at medium or lower levels. A significant negative spatial correlation was detected between SSC and SOM in most regions. This study integrates the advantages of UAV hyperspectral and satellite multispectral data, thereby realizing rapid and accurate estimation of SSC and SOM for a large-scale area, which is of great significance for the targeted improvement of arable land in the YRD.

show abstract

Digital mapping of soil salinization based on Sentinel-1 and Sentinel-2 data combined with machine learning algorithms

Cited by 45 publications

References 74 publications

Evaluation of Total Nitrogen in Water via Airborne Hyperspectral Data: Potential of Fractional Order Discretization Algorithm and Discrete Wavelet Transform Analysis

Evaluation of Total Nitrogen in Water via Airborne Hyperspectral Data: Potential of Fractional Order Discretization Algorithm and Discrete Wavelet Transform Analysis

Computational assessment of groundwater salinity distribution within coastal multi-aquifers of Bangladesh

Estimation of Soil Salt Content and Organic Matter on Arable Land in the Yellow River Delta by Combining UAV Hyperspectral and Landsat-8 Multispectral Imagery

Contact Info

Product

Resources

About