Heavy metal stress can lead to morphological and physiological variations in crops. We aimed to distinguish heavy metal stress levels based on the variations of morphological and physiological parameters from radiative transfer and statistical models. Sentinel-2 satellite images and in situ measured data were collected from heavy metal-contaminated soils of rice growing areas in Zhuzhou City, Hunan Province, China. The chlorophyll content (chlorophyll a + chlorophyll b, Cab) and leaf area index (LAI) were calculated using a PROSAIL radiative transfer model and the multilayer perceptron algorithm. A two-dimensional feature space was established from Cab-LAI. Furthermore, a normalized heavy metal stress index (HMSI) from the established Cab-LAI theoretical triangular model was explored to distinguish heavy metal stress levels in rice. The results indicated that (i) the PROSAIL and artificial neural network algorithm were successful at deriving physiological parameters with high estimation accuracy. Pearson's correlation coefficient between the predicted and measured Cab was 0.85; (ii) the correlation between the measured concentration of cadmium in the soil and the HMSI was 0.84, indicating that it is a good indicator of rice damage caused by heavy metal stress, with the maximum HMSI occurring in rice subjected to high pollution; and (iii) high pollution occurred on both sides of the Xiangjiang River, whereas moderate pollution mainly existed around the heavily polluted areas. Areas with non-pollution and mild pollution were distributed over most of the study area. Combining rice Cab with LAI is a feasible method to determine the distribution of rice heavy metal stress levels over a large area.

An extensive discoloration (yellowing, browning), and defoliation (leaf loss) were observed in Slovak forests during the summer of 2022. These phenomena are attributed to the combination of very low atmospheric precipitation and extremely high air temperatures from June to early August. In this study, the deterioration of forest health was analysed by comparing the image classification of Sentinel-2 satellite data from the year of intense drought occur-rence, 2022, with that from a referenced year without drought occurrence, 2020. The results indicated that in 2022, the proportion of heavily damaged stands with defoliation exceeding 50% doubled, reaching 19.3% (417,000 ha), and an area of 223,000 ha experienced an increase in defoliation by 30% or more. The damage exhibited an uneven spatial distribution, with the most significant impact observed in the western and southern parts of central Slovakia, as well as partially in the southern part of eastern Slovakia. Further GIS analyses revealed that forests growing on slopes with southern aspects suffered more severe damage than with northern exposures. However, the difference between the most damaged forests with south-southeast exposure (12.2%) and the least damaged ones with north-northwest exposure (8.2%) was only 4%. The level of damage gradually decreased with increasing altitude. Nevertheless, compared to previous studies, the damage was significantly manifested even in the fourth forest vegetation zone, up to an elevation of approximately 800 m. Regarding soil texture, which influences the water regime, the damage gradually decreased with decreasing sand content, ranging from sandy soils (17.5%) to clayey soils (6.6%).