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Heavy metal contamination in soils and vegetation poses a significant problem due to its toxicity and persistence. Toxic effects on vegetation include not only impaired growth, reduced yields, and even plant death but also biodiversity loss and ecosystem degradation. Addressing this issue requires comprehensive monitoring and remediation efforts to mitigate the environmental, human health, and ecological impacts. This review examines the state-of-the-art methodologies and advancements in remote sensing applications for detecting and monitoring heavy metal contamination in soil and its subsequent effects on vegetation. By synthesizing the current research findings and technological developments, this review offers insights into the efficacy and potential of remote sensing for monitoring heavy metal contamination in terrestrial ecosystems. However, current studies focus on regression and AI methods to link spectral reflectances and indices to heavy metal concentrations, which poses limited transferability to other areas, times, spectral discretizations, and heavy metal elements. We conclude that one important way forward is the more thorough understanding and simulation of the related physico-chemical processes in soils and plants and their effects on the spectral signatures. This would offer a profound basis for remote sensing applications for individual circumstances and would allow disentangling heavy metal effects from other stressors such as droughts or soil salinity. [ABSTRACT FROM AUTHOR]

The ecological environment of the remote plateaus has been a challenge plaguing many countries due to global warming, increased human activities, and frequent occurrence of various heavy metal (HM) pollutants. This paper analyzes the primary factors and potential susceptible regions' characteristics related to soil HMs (As, Cd, Pb, Cr, Ni, and Zn) in the southern Tibet Plateau (TP) using Geo-detectors and a set of multi-source data from remote sensing and geographic and geological information. The geoaccumulation index showed that the As pollution was the most serious. The precipitation, pH, geological age (GA), and rock type (RT) were the most critical factors affecting HMs. Additionally, soil HMs were relatively unaffected by land use and clay. Based on the risk detector, the most key impact types (range) of the predominant factors of HM's sources were identified. For example, precipitation (100–200 mm), pH (7–7.5), DEM (>5500 m), RT (ophiolite), and GA (Ordovician) had the highest average concentration of As in each type (ranges) of the predominant factors. This work provides new data on the extent of soil HM contamination in the southern TP and predicts vulnerable areas of HM contamination, providing an important scientific basis for monitoring and managing HM in remote areas at high altitudes. [ABSTRACT FROM AUTHOR]

Heavy metal concentrations of Cd, As, Pb, Cu, Cr, and Hg were investigated for 86 soil samples in Jinzhou near the Bohai Sea in China, in order to identify what anthropological activities influenced their distribution levels. Ordinary cokriging (OCK) was utilized to map six heavy-metal distributions by incorporating their main environmental influencers. The resultant p values for the six OCK mapping models of 0–2.78% indicated good statistical significance of the models, and the relative mean absolute errors of 4.82–12.53% and relative root mean square errors of 6.23–18.21% indicated allowable predication precision for their concentrations. The contamination distributions by OCK mapping were then graded based on the standards of the China National Environmental Monitoring Center and the Chinese Environmental Protection Administration, which showed that Cu and As contaminations in parts of this area were over the natural level but not polluted, Cr contamination was omnipresent over the natural level in this area and even reached the polluted level in parts of this area. The graded contamination maps that were overlapped with land-use maps and Google satellite maps, as well as the verifications reported in literatures, enabled correlations of the different contamination levels of As, Cu, and Cr with human activities. Resultantly, it indicated that As and Cu contamination over the natural level may be related to agricultural planting and aquacultural activities along the coast of Bohai Bay, with the contaminants transported via watercourses; Cr contamination over the natural level may have been due to vehicle emissions; and, Cr pollution may have been from steel manufacturing and geochemical factories [ABSTRACT FROM AUTHOR]

Soil contamination by heavy metals is of particular concern, due to the direct negative impact on crop yield, food quality and human health. Although the conventional approach to monitor heavy metals relies on field sampling and lab analysis, the proliferation in the use of portable spectrometers has reduced the cost and time of investigation. However, discrepancies in spectral data from different spectrometers increase the modeling time and undermine the model accuracy for spatial mapping. This study, therefore, took advantage of the readily accessible Landsat 7 data to predict and map the spatiotemporal distribution of ten heavy metals (i.e., Sb, Pb, Ni, Mn, Hg, Cu, Cr, Co, Cd and As) over a 640 km2 area in Belgium. The Land Use/Cover Area Frame Survey (LUCAS) database of a region in north-eastern Belgium was used to retrieve variation in heavy metals concentrations over time and space, using the Landsat 7 imagery for four single dates in 2009, 2013, 2016 and 2020. Three regression methods, namely, partial least squares regression (PLSR), random forest (RF) and support vector machine (SVM) were used to model and predict the heavy metal concentrations for 2009. By comparing these models unbiasedly, the best model was selected for predicting and mapping the heavy metal distributions for 2013, 2016 and 2020. RF turned out to be the optimal model for 2009 with a coefficient of determination of prediction (R2P) and residual prediction deviation of prediction (RPDP) ranging from 0.62 to 0.92, and 1.23 to 2.79, respectively. The measured heavy metal distributions along the river floodplains, at the highlands and in the lowlands, were generally high, compared to their RF spatiotemporal predictions, which decreased over time. Increasing moisture contents in the floodplains adjacent to the river channels and the lowlands were the primary contributors to the reduction in the satellite reflectance spectra. However, topsoil erosion from rainfall, snowmelt as well as wind into the lowlands could have influenced the reduction in heavy metal spatiotemporal predicted values over time in the highlands. The spatiotemporal prediction maps produced for the heavy metals for the four different years revealed a good spatial similarity and consistency with the measured maps for 2009, which indicates their stability over the years. [ABSTRACT FROM AUTHOR]