Estimation and mapping of surface soil properties in the Caucasus Mountains, Azerbaijan using high-resolution remote sensing data

Mammadov, Elton; Nowosad, Jakub; Gläßer, Cornelia

doi:10.1016/j.geodrs.2021.e00411

Cited by 20 publications

(21 citation statements)

References 57 publications

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“…On the other hand, the kriging method is a geostatistical method that provides the best linear unbiased estimate by incorporating spatial correlation defined as empirical variogram [19,24,25]. Although OK has the disadvantages of being computationally demanding [26] and not being suitable for generating sufficient covariate information [24], it has recently been synthesized with RF or support vector machine (SVM) as an approach for interpolating residuals to achieve less sensitivity to data variance and provide geo-referenced features [27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

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Interpolation-Based Fusion of Sentinel-5P, SRTM, and Regulatory-Grade Ground Stations Data for Producing Spatially Continuous Maps of PM2.5 Concentrations Nationwide over Thailand

et al. 2022

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Atmospheric pollution has recently drawn significant attention due to its proven adverse effects on public health and the environment. This concern has been aggravated specifically in Southeast Asia due to increasing vehicular use, industrial activity, and agricultural burning practices. Consequently, elevated PM2.5 concentrations have become a matter of intervention for national authorities who have addressed the needs of monitoring air pollution by operating ground stations. However, their spatial coverage is limited and the installation and maintenance are costly. Therefore, alternative approaches are necessary at national and regional scales. In the current paper, we investigated interpolation models to fuse PM2.5 measurements from ground stations and satellite data in an attempt to produce spatially continuous maps of PM2.5 nationwide over Thailand. Four approaches are compared, namely the inverse distance weighted (IDW), ordinary kriging (OK), random forest (RF), and random forest combined with OK (RFK) leveraging on the NO2, SO2, CO, HCHO, AI, and O3 products from the Sentinel-5P satellite, regulatory-grade ground PM2.5 measurements, and topographic parameters. The results suggest that RFK is the most robust, especially when the pollution levels are moderate or extreme, achieving an RMSE value of 7.11 μg/m3 and an R2 value of 0.77 during a 10-day long period in February, and an RMSE of 10.77 μg/m3 and R2 and 0.91 during the entire month of March. The proposed approach can be adopted operationally and expanded by leveraging regulatory-grade stations, low-cost sensors, as well as upcoming satellite missions such as the GEMS and the Sentinel-5.

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Section: Introductionmentioning

confidence: 99%

“…It can overcome the limitation of RF in that it does not account for geo-referenced data [25]. Past studies which have compared the accuracy of RF and RFK [22,30], or RK and RFK [29], concluded that the RFK presented a better ability to map non-linear complex relations.…”

Section: Introductionmentioning

confidence: 99%

Interpolation-Based Fusion of Sentinel-5P, SRTM, and Regulatory-Grade Ground Stations Data for Producing Spatially Continuous Maps of PM2.5 Concentrations Nationwide over Thailand

et al. 2022

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“…However, the importance scores and correlations between SOC and remote sensing spectral indices demonstrated strong variations in different months (Figures 2 and 3). It suggested that some research utilizing one single remote sensing image for SOC prediction (Mammadov et al, 2021; Sankey et al, 2021) may not obtain the optimal model prediction performance. Generally, spectral indices in the wet season had higher correlations with SOC compared with those in the dry season (Figure 2), and more spectral indices in wet season than those in the dry season were identified as relevant variables with SOC (Figure 3).…”

Section: Discussionmentioning

confidence: 99%

Effects of multi‐temporal environmental variables on SOC spatial prediction models in coastal wetlands of a Chinese delta

Bai

et al. 2022

Land Degrad Dev

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Mapping soil organic carbon (SOC) distributions in coastal wetlands plays an important role in assessing ecosystem services and investigating the global carbon cycle. Little research has explored the effects of environmental variables with seasonal variations on digital soil mapping (DSM). Our research utilized machine learning methods and established multiple prediction models of SOC based on multi‐temporal data from dry and wet seasons, and mono‐temporal data from April. The results showed that the relationships between SOC and environmental variables in different months varied significantly in coastal wetlands of the Yellow River Delta (YRD). In general, the environmental variables in the wet season showed stronger correlations and higher importance scores with SOC compared with those in the dry season. In addition, SOC prediction models in wet season and April had stronger prediction performance compared with those in the dry season. As a result, data fusion of multi‐temporal data did not necessarily contribute to the model performance enhancement. Relative homogenous soil‐landscape attributes and spectral characteristics in dry season could not accurately explain the strong spatial variation of SOC in this area, and it might be the major reason that caused the stronger model performance of soil prediction models in wet season than those in dry season. Therefore, the accurate spatial prediction of soil properties requires the characterization of the seasonal dynamics of soil‐landscape relationships. In general, the findings of this research demonstrated that seasonal variation of environmental variables should be considered in the establishment of a DSM model in coastal wetland.

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“…As a future work the idea is to work in two directions. From one side we wish to add to our input other measures such as the features extracted from the Digital Elevation Model (Mammadov et al (2021); Khanal et al (2018)). On the other side we wish to experiment the powerful of our approach on real aerial hyperspectral data.…”

Section: Discussionmentioning

confidence: 99%

A deep scalable neural architecture for soil properties estimation from spectral information

Piccoli¹,

Rossini²,

Colombo³

et al. 2022

Preprint

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In this paper we propose an adaptive deep neural architecture for the prediction of multiple soil characteristics from the analysis of hyperspectral signatures. The proposed method overcomes the limitations of previous methods in the state of art: (i) it allows to predict multiple soil variables at once; (ii) it permits to backtrace the spectral bands that most contribute to the estimation of a given variable; (iii) it is based on a flexible neural architecture capable of automatically adapting to the spectral library under analysis. The proposed architecture is experimented on LUCAS, a large laboratory dataset and on a dataset achieved by simulating PRISMA hyperspectral sensor. 'Results, compared with other state-of-the-art methods confirm the effectiveness of the proposed solution.

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Estimation and mapping of surface soil properties in the Caucasus Mountains, Azerbaijan using high-resolution remote sensing data

Cited by 20 publications

References 57 publications

Interpolation-Based Fusion of Sentinel-5P, SRTM, and Regulatory-Grade Ground Stations Data for Producing Spatially Continuous Maps of PM2.5 Concentrations Nationwide over Thailand

Interpolation-Based Fusion of Sentinel-5P, SRTM, and Regulatory-Grade Ground Stations Data for Producing Spatially Continuous Maps of PM2.5 Concentrations Nationwide over Thailand

Effects of multi‐temporal environmental variables on SOC spatial prediction models in coastal wetlands of a Chinese delta

A deep scalable neural architecture for soil properties estimation from spectral information

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