Remote sensing is a suitable candidate for monitoring rapid changes in Polar regions, offering high-resolution spectral, spatial and radiometric data. This paper focuses on the spectral properties of dominant plant species acquired during the first week of August 2015. Twenty-eight plots were selected, which could easily be identified in the field as well as on RapidEye satellite imagery. Spectral measurements of individual species were acquired, and heavy metal contamination stress factors were measured contemporaneously. As a result, a unique spectral library of dominant plant species, heavy metal concentrations and damage ratios were achieved with an indication that species-specific changes due to environmental conditions can best be differentiated in the 1401–2400 nm spectral region. Two key arctic tundra species, Cassiope tetragona and Dryas octopetala, exhibited significant differences in this spectral region that were linked to a changing health status. Relationships between field and satellite measurements were comparable, e.g., the Red Edge Normalized Difference Vegetation Index (RENDVI) showed a strong and significant relationship (R² = 0.82; p = 0.036) for the species Dryas octopetala. Cadmium and Lead were below detection levels while manganese, copper and zinc acquired near Longyearbyen were at concentrations comparable to other places in Svalbard. There were high levels of nickel near Longyearbyen (0.014 mg/g), while it was low (0.004 mg/g) elsewhere.

Heavy metals and radioactive compounds are potentially hazardous substances for plants, animals and humans in the Arctic. A good knowledge of the spatial variation of these substances in soil and primary producers, and their sources, is therefore essential. In the samples of lichen Thamnolia vermicularis, Salix polaris and Cassiope tetragona, and the soil samples collected in 2014 in Svalbard near Longyearbyen, the concentrations of the following heavy metals were determined: Mn, Ni, Cu, Zn, Cd, Pb and Hg, as well as the activity concentrations of the following: K-40, Cs-137, Pb-210, Pb-212, Bi-212, Bi-214, Pb-214, Ac-228, Th-231 and U-235 in the soil samples. The differences in the concentrations of the analytes accumulated in the different plant species and soil were studied using statistical methods. Sea aerosol was indicated as the source of Pb, Hg, Cs-137, Pb-210 and Th-231 in the studied area. A relatively high concentration of nickel was determined in the biota samples collected near Longyearbyen, compared to other areas of Svalbard. It was supposed that nickel may be released into the atmosphere as a consequence of the local coal mining around Longyearbyen.

We have compared historical changes in concentrations of the heavy metals Mn, Ni, Cu, Zn, Cd and Pb accumulated in samples from the Polish woodlands of Beskidy and Karkonosze (S, SE Poland) and the north-east regions of the country, versus the relatively little polluted areas of Spitsbergen of the Svalbard Archipelago. We have combined the results from literature with new results from 2014. The regions of Beskidy and Karkonosze were the most exposed to heavy metals deposition. However, from 1975 to 2014 there was a considerable decrease of concentrations of Cu, Zn, Cd and Pb at all Polish sites, clearly signifying improvement of environmental quality. For example, the average Cd concentration in mosses samples collected in Karkonosze decreased from 0.002 mg/g in 1975 to 0.0006 mg/g in 2014. It is interesting to observe relatively large concentrations of nickel in moss samples collected in 2014 in the Svalbard archipelago, in the vicinity of Longyearbyen (average 0.018 mg/g) which most likely originate from local mine waste piles.

Soil moisture is a very important parameter influencing many hydrological and climatic processes. It is also a key factor in agriculture, determining crop yields and thus influencing food security. It is crucial to model this variable for large areas with high spatial and temporal resolution and good accuracy. The aim of this study is to develop a soil moisture model for bare soils from Sentinel-1 SAR data that would be characterized by high spatial resolution and would be universal enough to be applicable to large areas of various soil types, textures, and large ranges of roughness. Over 800 soil moisture measurements from five study areas located in different parts of Poland were used. The work was performed on Sentinel-1 data registered between March 2024 and March 2025 using both backscattering and polarimetric analysis. The soil data were obtained from a 1:5000 scale soil map available online for Poland through the soil-agricultural geoportal. The results of machine learning modeling of soil moisture based on backscattering were relatively poor, with R² = 0.49 and 6.65% accuracy of volumetric water content in the soils. In the case of polarimetric channels, results were more or less the same. The best results were obtained by taking the silt and clay content (particles < 0.02 mm) in the soil into account. Volumetric water content accuracy of 5.27% with R² = 0.69 was thus achieved. The proposed solution seems to be a good alternative to soil moisture studies that take soil roughness into account due to its simplicity, good accuracy, and relatively easy availability of data necessary for model inversion. The analyses carried out showed that it can be used for exposed soils of very diverse roughness.

Soil moisture is a very important parameter influencing many hydrological and climatic processes. It is also a key factor in agriculture, determining crop yields and thus influencing food security. It is crucial to model this variable for large areas with high spatial and temporal resolution and good accuracy. The aim of this study is to develop a soil moisture model for bare soils from Sentinel-1 SAR data that would be characterized by high spatial resolution and would be universal enough to be applicable to large areas of various soil types, textures and large ranges of roughness. Over 800 soil moisture measurements from five study areas located in different parts of Poland were used. The work was performed on Sentinel-1 data registered between March 2024 and March 2025 using both backscattering and polarimetric analysis. The soil data were obtained from a 1:5000 scale soil map available online for Poland through the soil-agricultural geoportal. The results of Random Forest modeling of soil moisture based on backscattering were relatively poor with R2=0.46 and 7.15% soil moisture accuracy. In the case of polarimetric channels results were slightly better only. The best results were obtained taking into account the clay content (particles < 0.002mm) in the soil. The soil moisture accuracy of 5.16% with R2=0.71 was achieved.