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.

The aim of this study was to follow the response to drought stress in a Poa pratensis canopy exposed to various levels of soil moisture deficit. We tracked the changes in the canopy reflectance (450–2450 nm) and retrieved vegetation properties (Leaf Area Index (LAI), leaf chlorophyll content (Cab), leaf water content (Cw), leaf dry matter content (Cdm) and senescent material (Cs)) during a drought episode. Spectroscopic techniques and radiative transfer model (RTM) inversion were employed to monitor the gradual manifestation of drought effects in a laboratory setting. Plots of 21 cm × 14.5 cm surface area with Poa pratensis plants that formed a closed canopy were divided into a well-watered control group and a group subjected to water stress for 36 days. In a regular weekly schedule, canopy reflectance and destructive measurements of LAI and Cab were taken. Spectral analysis indicated the first sign of stress after 4–5 days from the start of the experiment near the water absorption bands (at 1930 nm, 1440 nm) and in the red (at 675 nm). Spectroscopic techniques revealed plant stress up to 6 days earlier than visual inspection. Of the water stress-related vegetation indices, the response of Normalized Difference Water Index (NDWI_1241) and Normalized Photochemical Reflectance Index (PRI_norm) were significantly stronger in the stressed group than the control. To observe the effects of stress on grass properties during the drought episode, we used the RTMo (RTM of solar and sky radiation) model inversion by means of an iterative optimization approach. The performance of the model inversion was assessed by calculating R² and the Normalized Root Mean Square Error (RMSE) between retrieved and measured LAI (R² = 0.87, NRMSE = 0.18) and Cab (R² = 0.74, NRMSE = 0.15). All parameters retrieved by model inversion co-varied with soil moisture deficit. However, the first strong sign of water stress on the retrieved grass properties was detected as a change of Cw followed by Cab and Cdm in the earlier stages. The results from this study indicate that the spectroscopic techniques and RTMo model inversion have a promising potential of detecting stress on the spectral reflectance and grass properties before they become visibly apparent.

Many European countries rely on groundwater for public and industrial water supply. Due to a scarcity of near-real-time water table depth (wtd) observations, establishing a spatially consistent groundwater monitoring system at the continental scale is a challenge. Hence, it is necessary to develop alternative methods for estimating wtd anomalies (wtd_a) using other hydrometeorological observations routinely available near real time. In this work, we explore the potential of Long Short-Term Memory (LSTM) networks for producing monthly wtd_a using monthly precipitation anomalies (pr_a) as input. LSTM networks are a special category of artificial neural networks that are useful for detecting a long-term dependency within sequences, in our case time series, which is expected in the relationship between pr_a and wtd_a. In the proposed methodology, spatiotemporally continuous data were obtained from daily terrestrial simulations of the Terrestrial Systems Modeling Platform (TSMP) over Europe (hereafter termed the TSMP-G2A data set), with a spatial resolution of 0.11^∘, ranging from the years 1996 to 2016. The data were separated into a training set (1996–2012), a validation set (2013–2014), and a test set (2015–2016) to establish local networks at selected pixels across Europe. The modeled wtd_a maps from LSTM networks agreed well with TSMP-G2A wtd_a maps on spatially distributed dry and wet events, with 2003 and 2015 constituting drought years over Europe. Moreover, we categorized the test performances of the networks based on intervals of yearly averaged wtd, evapotranspiration (ET), soil moisture (θ), snow water equivalent (S_w), soil type (S_t), and dominant plant functional type (PFT). Superior test performance was found at the pixels with wtd < 3 m, ET > 200 mm, θ>0.15 m³ m⁻³, and S_w<10 mm, revealing a significant impact of the local factors on the ability of the networks to process information. Furthermore, results of the cross-wavelet transform (XWT) showed a change in the temporal pattern between TSMP-G2A pr_a and wtd_a at some selected pixels, which can be a reason for undesired network behavior. Our results demonstrate that LSTM networks are useful for producing high-quality wtd_a based on other hydrometeorological data measured and predicted at large scales, such as pr_a. This contribution may facilitate the establishment of an effective groundwater monitoring system over Europe that is relevant to water management.