Soil salinity prediction and mapping by machine learning regression in <scp>C</scp>entral <scp>M</scp>esopotamia, <scp>I</scp>raq

Wu, Weicheng; Zucca, Claudio; Muhaimeed, Ahmad S.; Al-Shafie, Waleed M.; Al-Quraishi, Ayad M. Fadhil; Nangia, Vinay; Zhu, Min; Liu, Guangping

doi:10.1002/ldr.3148

Cited by 93 publications

(64 citation statements)

References 40 publications

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“…Nevertheless, radar-derived map predicted better salinity in halophytes. Another comparison was taken with the results derived by machine learning regression, in particular, Randon Forest Regression (RFR) using the combined optical and radar dataset by Wu et al (2018). We noted that the latter has a better mapping accuracy as it has taken both advantages of optical and radar data.…”

Section: Discussionmentioning

confidence: 99%

“…The Landsat 5 TM images were radiometrically calibrated to convert Digital Number (DN) of pixel into radiance, and then the FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) model was applied to remove the atmospheric effects (Wu et al 2014a, 2014b, Wu et al 2018.…”

Section: Tm Image Processingmentioning

confidence: 99%

“…As revealed in our recent study (Wu et al 2018), the relevant vegetation indices required for vegetation removal on the radar backscattering coefficients from TM images are the Normalized Difference Vegetation Index (NDVI), the Generalized Difference Vegetation Index (GDVI, Wu 2014) with power number of 2 and 3 (denoted respectively GDVI2 and GDVI3). Vegetation fraction or fractional vegetation cover ( f r ), the rescaled NDVI (Gutman andIgnatov 1998, Wu 2014) was also derived from the TM images.…”

Section: Tm Image Processingmentioning

confidence: 99%

“…The Level 1.5 radar product has been geometrically rectified, and its spatial deformation induced from landform and variation of incidence angle also corrected, and pixels were resampled to 12.5 m in size by the provider. The DNs of the two polarization bands, HH and HV, were respectively calibrated and converted into backscattering coefficients ( and A and B, among a number of the proposed pairs, Wu et al (2018) found that the 2 nd case of Dabrowska-Zielinska et al (2007) designed for ALOS L-band radar data performed best, i.e., A=0.0045, B=0.4179. This pair of parameters was selected for our further analysis.…”

Section: L-band Radar Processingmentioning

confidence: 99%

“…Quantification and mapping of soil salinity in space and time to provide relevant advice for decision-makers and land managers have become a pressing issue. For this purpose, optical remote sensing has been successfully utilized for soil salinity mapping in the past decades (Dwivedi and Rao 1992, Mougenot et al 1993, Metternicht and Zinck 2003, Farifteh et al 2006, Farifteh et al 2007, Eldeiry and Garcia 2010, Wu et al 2014aand 2014b, Gorji et al 2015, Scudiero et al 2015, Ivushkin et al 2017, Bannari et al 2018, Wu et al 2018, Paliwal et al 2019. Mature approaches have been proposed, for example, best band combination, multiyear maxima-based multivariate modeling and quantification, shortwave infrared (SWIR) band-based discrimination, and machine learning regression, etc…”

Section: Introductionmentioning

confidence: 99%

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Using L-band radar data for soil salinity mapping—a case study in Central Iraq

Muhaimeed

Al-Shafie

et al. 2019

Environ. Res. Commun.

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Soil salinization is a critical environmental problem for dryland agriculture. Mapping its distribution and severity in space and time is essential for agricultural management and development. Recently, remote sensing technology has been widely applied in such mapping but mostly using optical remote sensing data. In conjunction with the field surveys, this case study was aimed at developing an operational approach for this purpose by employing ALOS (Advanced Land Observing Satellite) L-band radar data with support of Landsat 5 TM (Thematic Mapper) imagery acquired at almost the same time. The test was conducted in the Mussaib site in Central Iraq. The innovative procedure involved was the removal or minimization of the impact of vegetation cover and moisture on the backscattering coefficients by Water Cloud Model. The results revealed a strong correlation between the corrected backscattering coefficients of soil and the measured soil salinity (R 2 =0.565-0.677). The radar-based salinity models developed through multivariate linear regression (MLR) analysis were able to predict salinity with reliability of 70.05%. In conclusion, it is possible to use radar data for soil salinity prediction and mapping in dry environment.

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Section: Discussionmentioning

confidence: 99%

Section: Tm Image Processingmentioning

confidence: 99%

Section: Tm Image Processingmentioning

confidence: 99%

Section: L-band Radar Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using L-band radar data for soil salinity mapping—a case study in Central Iraq

Muhaimeed

Al-Shafie

et al. 2019

Environ. Res. Commun.

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Soil salinity estimation: Effects of microwave dielectric spectroscopy and important frequencies

Zhao

Ding

et al. 2023

Land Degrad Dev

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The microwave dielectric constant is a key bridge in establishing the relationship between microwave remote sensing and soil salinity (electrical conductivity, EC). However, the response between microwave dielectric spectrum type, frequency, and soil salinity is still unclear. The purpose of this study is to reveal the dielectric spectrum and frequency range closely related to soil salinity. In this study, 129 surface soil samples were collected, and soil EC and microwave dielectric spectrum (frequency range of 0.3–20 GHz) were measured. Four variable selection methods were used to select important spectral variables, and the relationship between these spectral variables and soil salt was constructed using a random forest model. The results showed that the combination of boldε′ (real part) and boldε′′ (imaginary part) revealed the most implicit information related to soil salinity. Considering the important spectral variables, the model accuracy was generally improved to the full‐dielectric spectrum model (the ratios of the performance to the interquartile distance (RPIQ) improved by 0.11–0.40); furthermore, the variable iterative space shrinkage (VISSA) method could most effectively select important spectral variables (RPIQ = 2.32). The selected spectral‐RF models could provide important frequencies for estimating soil salinity; moreover, the low‐frequency range (0.3–4 GHz) and new important frequency ranges (e.g., 7 and 15 GHz) able to respond to soil salinity were identified. This study provides a new strategy for monitoring the soil salinization risk using multiple frequencies microwave remote sensing techniques.

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Soil salinity estimation in Shule River Basin using support vector regression model

2023

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Soil salinization is a progressive degradation process that spreads globally and leads to a decline in soil fertility. Assessing the scale of salinization is crucial for sustainable agricultural development and saline‐land rehabilitation. In this study, we proposed a support vector regression spatial (SVR‐S) model that utilizes spatial dependence information to predict soil salinity over the irrigation area of the Shule River Basin in northwestern China. To investigate the performance of the SVR‐S model, 50 soil samples were collected in the field. Semivariograms of soil salinity (measured as total salt content and ion concentrations) were constructed to measure spatial dependence. The SVR‐S model was compared with the original SVR model and the geographically weighted regression (GWR) model regarding the salinity prediction ability. The soil salinity in the experimental area demonstrated a strong spatial dependence pattern. The SVR‐S model delivered a better performance than the SVR‐O and GWR. SVR‐S showed a correlation coefficient R of 0.87 and a root mean square error (RMSE) of 1.83%, while the performance of SVR‐O (R = 0.75, RMSE = 3.32%) and GWR (R = 0.73, RMSE = 3.47%) was comparatively poor. Topographic indices integrating spatial information contributed the most to the estimation of salinity in the study area. This study provides a new approach to integrating spatial information for accurate soil salinity mapping.

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Soil salinity prediction and mapping by machine learning regression in Central Mesopotamia, Iraq

Cited by 93 publications

References 40 publications

Using L-band radar data for soil salinity mapping—a case study in Central Iraq

Using L-band radar data for soil salinity mapping—a case study in Central Iraq

Soil salinity estimation: Effects of microwave dielectric spectroscopy and important frequencies

Soil salinity estimation in Shule River Basin using support vector regression model

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