Quantitative assessment of soil salinity using remote sensing data based on the artificial neural network, case study: Sharif Abad Plain, Central Iran

Habibi, Vahid; Hasan, Ahmadi; Jafari, Mohammad; Moeini, Abolfazl

doi:10.1007/s40808-020-01015-1

Cited by 11 publications

(3 citation statements)

References 54 publications

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“…Differences in models and algorithms, from simple linear regression to complex machine learning, may lead to inconsistent results from the same remote sensing data, and researchers have continuously tried to apply new methods to study soil salinity to improve the prediction accuracy. For example, multiple linear regression [33,34], neural network models [35], support vector machine models [36], partial least squares regression models [37], and Cubist models [38] have shown their potential for soil salinity studies. Other researchers have compared multiple models to select the best regional salinity inversion model [39][40][41][42].…”

Section: Introductionmentioning

confidence: 99%

Inversion of Soil Salinity Using Multisource Remote Sensing Data and Particle Swarm Machine Learning Models in Keriya Oasis, Northwestern China

et al. 2022

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Soil salinization is a global problem that damages soil ecology and affects agricultural development. Timely management and monitoring of soil salinity are essential to achieve the most sustainable development goals in arid and semi-arid regions. It has been demonstrated that Polarimetric Synthetic Aperture Radar (PolSAR) data have a high sensitivity to the soil dielectric constant and soil surface roughness, thus having great potential for the detection of soil salinity. However, studies combining PALSAR-2 data and Landsat 8 data to invert soil salinity information are less common. The particle swarm optimization (PSO) algorithm is characterized by simple operation, fast computation, and good adaptability, but there are relatively few studies applying it to soil salinity as well. This paper takes the Keriya Oasis as an example, proposing the PSO-SVR and PSO-BPNN models by combining PSO with support vector machine regression (SVR) and back-propagation neural network (BPNN) models. Then, PALSAR-2 data, Landsat 8 data, evapotranspiration data, groundwater burial depth data, and DEM data were combined to conduct the inversion study of soil salinity in the study area. The results showed that the introduction of PSO generated a satisfactory estimating performance. The SVR model accuracy (R2) improved by 0.07 (PALSAR-2 data), 0.20 (Landsat 8 data), and 0.19 (PALSAR+ Landsat data); the BP model accuracy (R2) improved by 0.03 (PALSAR-2 data), 0.24 (Landsat 8 data), and 0.12 (PALSAR+ Landsat data), and then combined with the model inversion plots, we found that PALSAR+ Landsat data combined with the PSO-SVR model could achieve better inversion results. The fine texture information of PALSAR-2 data can be used to better invert the soil salinity in the study area by combining it with the rich spectral information of Landsat 8 data. This study complements the research ideas and methods for soil salinization using multi-source remote sensing data to provide scientific support for salinity monitoring in the study area.

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Section: Introductionmentioning

confidence: 99%

Inversion of Soil Salinity Using Multisource Remote Sensing Data and Particle Swarm Machine Learning Models in Keriya Oasis, Northwestern China

et al. 2022

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“…But because these algorithms need to collect and organize more data, on the other hand, due to the fact that China's power distribution the system is minimally automated, with insufficient and backward measurements, making it difficult to collect the operational and structural parameters required for the calculations. This algorithm for improving energy flow is often difficult to implement, but there are many algorithms at home and abroad [7][8].…”

Section: Application Of Bp Neural Network In Damage Calculation Of Dnmentioning

confidence: 99%

Damage Calculation of Distribution Network Lines Relying on BP Neural Network

2020

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In order to better explore effective loss reduction methods and provide a basis for formulating scientific line loss indicators, this paper aims to study the distribution network (DN) line damage calculation relying on BP neural network. This paper makes an in-depth analysis of the existing non-destructive testing methods, and compares the advantages and disadvantages of various non-destructive testing methods in the home DN, such as low practicability, complex component distribution, and difficulty in collecting raw data. In addition, based on the analysis of the scientific method, management and various loss reduction measures of energy loss measurement, the current situation and existing problems of line loss measurement are analyzed from the perspective of line loss measurement data acquisition. In addition to bias methods and other aspects, this paper analyzes and summarizes research, especially bias measures, and provides many valuable insights from both technical and managerial perspectives. Experiments show that the simulation error is less than 1%. Compared with the traditional BP network, the learning rate of the algorithm in this paper is higher.

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“…Wang, et al [13] have proposed a new spectral index and have used a neural network to produce soil moisture and salinity inversion. Similarly, Habibi, et al [14] have investigated the quantitative assessment of soil salinity using remote sensing data based on the artificial neural network. Wang, et al [15] have evaluated three different machine learning algorithms (Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN)) for soil salinity mapping with Sentinel-2 MSI data.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Machine Learning Models for Soil Salinity Estimation Using Satellite Imagery

Avdan,

Kaplan,

Matci

et al. 2021

STEPGRAD

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Salinity is one of the most critical problems for agricultural lands. Soil salinity should be monitored with fast, economical and accurate data and methods. In this study, soil salinity was estimated using remote sensing data and machine learning algorithms, where fıve different methods were used, and the results were compared. As a study area, Alpu, Turkey has been selected. Within the scope of the study, on-site measurements were made in cultivation areas where there are different agricultural products such as beets, wheat, tomatoes, and corn in the district. The results show that machine learning algorithms and Planetscope images successfully determine soil salinity. Future studies will evaluate the methods by taking samples from different product classes and wet/arid lands.

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Quantitative assessment of soil salinity using remote sensing data based on the artificial neural network, case study: Sharif Abad Plain, Central Iran

Cited by 11 publications

References 54 publications

Inversion of Soil Salinity Using Multisource Remote Sensing Data and Particle Swarm Machine Learning Models in Keriya Oasis, Northwestern China

Inversion of Soil Salinity Using Multisource Remote Sensing Data and Particle Swarm Machine Learning Models in Keriya Oasis, Northwestern China

Damage Calculation of Distribution Network Lines Relying on BP Neural Network

Evaluating Machine Learning Models for Soil Salinity Estimation Using Satellite Imagery

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