Soil toxic elements determination using integration of Sentinel-2 and Landsat-8 images: Effect of fusion techniques on model performance

Khosravi, Vahid; Gholizadeh, Asa; Saberioon, Mohammadmehdi

doi:10.1016/j.envpol.2022.119828

Cited by 12 publications

(3 citation statements)

References 70 publications

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“…This comprehensive view is essential in the era of precision medicine, where treatments are increasingly tailored to the individual characteristics of each patient. Multimodal data fusion allows for the correlation of symptoms with imaging and audio findings, leading to more precise diagnoses, better prediction of disease progression, and more effective treatment planning [6,7].…”

Section: Enhanced Diagnostic Accuracy and Comprehensive Carementioning

confidence: 99%

Harnessing the Power of Multimodal Data: Medical Fusion and Classification

Bhushan Rajendra Nandwalkar

2024

anvi

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In the field of medical diagnosis, combining different types of information like text, images, and audio is a big step forward in making patient assessments more accurate. This research introduces an innovative method to bring together and categorize these different types of data. This method fills an important gap in current research [50, 54]. Proposed approach focuses on turning each type of data—text, images, and audio—into useful numbers. Text data is processed to extract meaning and context, while images are analysed using advanced computer techniques to capture important visual details. We also carefully examine audio data to extract important sound features, which is often overlooked but can be a valuable source of diagnostic information [48]. What makes our method special is how we combine these different types of data. We designed a strategy to blend these diverse sets of numbers into a single, enriched representation. This approach keeps the unique characteristics of each data type intact while harnessing their combined power for diagnosis [22, 29]. After combining the data, we use a well-chosen classification model that's known for its ability to make sense of complex data, especially in medical diagnosis scenarios [67, 71]. Proposed approach is rigorously assessing our method using a set of strong metrics that measure not only how accurate it is but also how reliable and valid it is for diagnosis [90, 94]. The results of this study mark a significant step forward in the field of combining different types of data, showing how it can greatly improve medical diagnosis. This method has the potential to revolutionize healthcare, enabling more precise and comprehensive data-driven decisions [143, 156].

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Section: Enhanced Diagnostic Accuracy and Comprehensive Carementioning

confidence: 99%

Harnessing the Power of Multimodal Data: Medical Fusion and Classification

Bhushan Rajendra Nandwalkar

2024

anvi

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“…It has been shown that mercury contamination affects reflection in the visible, near-infrared (NIR) and shortwave infrared (SWIR) regions of the electromagnetic spectrum, enabling digital mapping using RSD [23,24]. Thus, the use of RSD as an explanatory variable for the digital mapping of HM and potentially toxic elements has been presented in many works [25][26][27][28]. However, the application of RSD for such tasks in urbanized areas is not an easy process.…”

Section: Introductionmentioning

confidence: 99%

Mercury Prediction in Urban Soils by Remote Sensing and Relief Data Using Machine Learning Techniques

Suleymanov

Kulagin

et al. 2023

Remote Sensing

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This article aims to explore the use of machine learning (ML) methods for mapping the distribution of mercury (Hg) content in topsoil, using the city of Ufa (Russia) and adjacent areas as an example. For this purpose, a soil dataset of 250 points sampled from a 0–20 cm depth on different land uses, including residential, industrial and undisturbed (forests and parks), was used. Random Forest (RF), Extreme Gradient Boosting (XGboost), Cubist and k-Nearest Neighbor (kNN) ML techniques were employed to model and map the Hg concentrations. We used remote sensing data (RSD) and topographic attributes as explanatory variables. ML models were calibrated and validated using the leave-one-out cross-validation approach. The Hg content varied from 0.005 to 0.58 mg/kg and was characterized by very high variability. According to the MAE and RMSE metrics, the RF method resulted in the most accurate spatial prediction for the Hg content (0.029 and 0.065 mg/kg, respectively), while the XGBoost approach showed the lowest prediction efficiency (0.032 and 0.073 mg/kg, respectively). The results showed that the slope map, spectral index MSI and Sentinel-2A band B11 were the key variables in explaining the variability of Hg content. We found that higher uncertainty values of soil Hg were found in croplands, urban residential and industrial areas, which supports the view that spatial modelling of HM in urban landscapes is challenging. The present study provides insights into the potential of digital soil mapping techniques in combination with RSD and terrain variables for identifying areas at risk of Hg contamination in urban areas, which can inform land-use planning and management strategies to protect human health and the environment.

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“…In comparison, multispectral remote sensing images offer wider spatial coverage, fast access, low cost, and rich information [7]. Researchers have utilized multispectral remote sensing data, such as Landsat, to investigate soil heavy metal pollution in critical regions [8]. However, traditional regression models used for heavy metal prediction often struggle to achieve a high inversion accuracy due to the weak spectral characteristics of soil heavy metals, the limited number of bands, and the low spectral resolution of Landsat images [9].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Pb and Cd Content in Soil Using Sentinel-2A Multispectral Images Based on Ensemble Learning

Xie

Liu

et al. 2023

Remote Sensing

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With the increasing economic growth in developing nations, soil heavy metal pollution has become a growing concern. Monitoring the heavy metal concentration in soil through remote sensing is crucial for safeguarding the ecological environment. However, the current indoor spectral measurement method has limitations, such as the discrete soil sampling space and weak spectral characteristics of soil heavy metals, leading to a poor robustness of remote sensing inversion models. This study presents a novel approach to address these challenges by incorporating a spatial feature of pollution sources and sinks to evaluate the spatial factors affecting pollutant diffusion and concentration. An integrated learning model, combining spatial and spectral information, is developed to estimate heavy metal content in soil using Sentinel-2A satellite data. A total of 235 soil samples were collected in Jiyuan, China, and the effective spectral transformation characteristics of Sentinel-2A data were screened. The impact of spectral characteristics, topographic characteristics, and spatial characteristics on retrieving soil heavy metal lead (Pb) and cadmium (Cd) content were analyzed. The optimal inversion method was determined through various integrated learning models, and the spatial distribution of heavy metals Pb and Cd was mapped. The results indicate that the accuracy of the inversion model was significantly improved by incorporating terrain features and spatial features of pollution sources. The Blending integrated learning method showed a 65.9% and 73.2% reduction in the RMSE of Pb and Cd, respectively, compared to other regression models. With R2 values of 0.9486 and 0.9489 for Pb and Cd, respectively, and a MAPE less than 0.2, the Blending model demonstrated high prediction accuracy.

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Soil toxic elements determination using integration of Sentinel-2 and Landsat-8 images: Effect of fusion techniques on model performance

Cited by 12 publications

References 70 publications

Harnessing the Power of Multimodal Data: Medical Fusion and Classification

Harnessing the Power of Multimodal Data: Medical Fusion and Classification

Mercury Prediction in Urban Soils by Remote Sensing and Relief Data Using Machine Learning Techniques

Estimation of Pb and Cd Content in Soil Using Sentinel-2A Multispectral Images Based on Ensemble Learning

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