Evaluation of Landsat 8 image pansharpening in estimating soil organic matter using multiple linear regression and artificial neural networks

Bouasria, Abdelkrim; Namr, Khalid Ibno; Rahimi, Abdelmejid; Ettachfini, El Mostafa; Rerhou, Badr

doi:10.1080/10095020.2022.2026743

Cited by 26 publications

(3 citation statements)

References 60 publications

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“…In this method, a linear regression model is established to fit the relationship between independent variables and dependent variables and predict the changing trend of dependent variables. Multiple linear regression is widely used in various fields [19][20][21], and it is a very practical multivariate data processing method. Through correlation analysis of a large number of sample data by SPSS 22.0, this method can intuitively and quickly obtain the linear relationship between independent variables and dependent variables, which is the simplest and most common regression model [22].…”

Section: Multiple Linear Regression Modelingmentioning

confidence: 99%

Research on Remote Sensing Geochemical Modeling Based on Surface to Surface Model

Cheng,

Li,

Tang

et al. 2023

J. Phys.: Conf. Ser.

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Remote sensing geochemistry is a simple, fast and economical advanced prospecting method, which carries out inversion and prediction of surface element content using the empirical model by regression or machine learning. The key problem faced by quantitative remote sensing is the low inversion accuracy of the model due to the mismatch of “point surface” information. How to overcome this problem? This paper proposes a “surface to surface” modeling method, which converts point data into surface data through Kriging interpolation to solve this problem. This paper uses geochemical interpolation data of Cu elements at different scales in the Qishitan gold mine area, Xinjiang, and ASTER remote sensing data to conduct geochemical modeling. In order to test the effect of Kriging on decreasing the scale effect, five sets of experiments were designed for comparison. The first four sets of sample data were interpolated according to different cell sizes, and the last set of data was not interpolated. The results show that the Kriging interpolation based on the ground resolution of the remote sensing image can effectively improve the accuracy of the remote sensing quantitative inversion model. When the square interpolation is close to the ground resolution of the used remote sensing data, the modeling accuracy gets the best value. This paper provides a new idea for improving the accuracy of remote sensing geochemical modeling.

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Section: Multiple Linear Regression Modelingmentioning

confidence: 99%

Research on Remote Sensing Geochemical Modeling Based on Surface to Surface Model

Cheng,

Li,

Tang

et al. 2023

J. Phys.: Conf. Ser.

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“…Atmospheric precipitation infiltrates into the aquifer through the air pocket (Figure 1 pathway ①) and becomes one of the important sources of groundwater recharge in a river basin. Groundwater receives recharge from precipitation and also recharges the Jing River and its tributaries in the form of lateral runoff (Figure 1 pathway ②), and groundwater discharge from the river is transformed into river runoff, which is important for safeguarding river runoff to maintain the ecological health of the Jing River and its tributaries [11][12].…”

Section: The "Three Waters" Conversion Relationship In a River Basinmentioning

confidence: 99%

Linear Regression for Water Pollution Control Planning under Support Vectors

2021

WPPCP

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China is facing a serious situation in water pollution prevention and control, and the uncertainty of regional development should be fully considered when formulating water pollution prevention and control planning, and its scientificity should be improved through effective mathematical methods. Water resources and water environment are closely related to human health, and China has not yet established a comprehensive set of water resources and environment monitoring network, so the water pollution prevention and control planning process is crucial to the implementation of water pollution control planning. Due to the relatively low level of economic development in China, the state attaches less importance to water pollution prevention and control issues, and therefore also has a certain impact on the water environment. In order to reduce the problems caused by water pollution, the Water Resources Management Committee has organised the preparation of water pollution control plans. This paper presents an empirical analysis of the support vector model for water pollution prevention and control planning in China. The results of the study show that the introduction of the support vector approach to water pollution prevention and control planning in China can effectively improve the scientific nature of the planning. This paper firstly introduces the support vector model, which is a multivariate model designed based on the support vector method, and can be used to analyse multiple indicators and conduct comprehensive analysis in the prediction process; finally, the planning scheme should be designed with full consideration of water pollution prevention and control planning to avoid conflicts between different water resources and environmental issues. However, there are many problems with the use of the support vector approach in water pollution prevention and control planning that warrant further research.

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“…Furthermore, Hengl et al [10] generated 30 m resolution pan-African maps detailing various soil nutrients, such as SOC, pH, total nitrogen (N), phosphorus (P), and potassium (K), among others, through the combination of diverse EO datasets and ensemble ML algorithms. Bouasria et al [11] explored the feasibility of utilizing pan-sharpened Landsat-8 imagery (15 m resolution) for SOM mapping via multiple linear regression and artificial neural networks. Similarly, Bouslihim et al [12] employed a Random Forest approach for SOM mapping using Landsat-8 imagery at a 30 m resolution.…”

Section: Introductionmentioning

confidence: 99%

Synergistic Use of Multi-Temporal Radar and Optical Remote Sensing for Soil Organic Carbon Prediction

Dahhani,

Raji,

Bouslihim

2024

Remote Sensing

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Exploring soil organic carbon (SOC) mapping is crucial for addressing critical challenges in environmental sustainability and food security. This study evaluates the suitability of the synergistic use of multi-temporal and high-resolution radar and optical remote sensing data for SOC prediction in the Kaffrine region of Senegal, covering over 1.1 million hectares. For this purpose, various scenarios were developed: Scenario 1 (Sentinel-1 data), Scenario 2 (Sentinel-2 data), Scenario 3 (Sentinel-1 and Sentinel-2 combination), Scenario 4 (topographic features), and Scenario 5 (Sentinel-1 and -2 with topographic features). The findings from comparing three different algorithms (Random Forest (RF), XGBoost, and Support Vector Regression (SVR)) with 671 soil samples for training and 281 samples for model evaluation highlight that RF outperformed the other models across different scenarios. Moreover, using Sentinel-2 data alone yielded better results than using only Sentinel-1 data. However, combining Sentinel-1 and Sentinel-2 data (Scenario 3) further improved the performance by 6% to 11%. Including topographic features (Scenario 5) achieved the highest accuracy, reaching an R2 of 0.7, an RMSE of 0.012%, and an RPIQ of 5.754 for the RF model. Applying the RF and XGBoost models under Scenario 5 for SOC mapping showed that both models tended to predict low SOC values across the study area, which is consistent with the predominantly low SOC content observed in most of the training data. This limitation constrains the ability of ML models to capture the full range of SOC variability, particularly for less frequent, slightly higher SOC values.

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Evaluation of Landsat 8 image pansharpening in estimating soil organic matter using multiple linear regression and artificial neural networks

Cited by 26 publications

References 60 publications

Research on Remote Sensing Geochemical Modeling Based on Surface to Surface Model

Research on Remote Sensing Geochemical Modeling Based on Surface to Surface Model

Linear Regression for Water Pollution Control Planning under Support Vectors

Synergistic Use of Multi-Temporal Radar and Optical Remote Sensing for Soil Organic Carbon Prediction

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