Peatlands are one of the most important ecosystems due to their biodiversity and abundant organic compounds; therefore, it is important to observe how different plant species in peatlands react to changing environmental conditions. Sphagnum spp. are the main component of peatlands and are considered as the creator of conditions favorable for carbon storage in the form of peat. Sphagnum angustifolium and Sphagnum fallax are taxonomically very close species. To examine their adaptability to climate change, we studied the morphology and pigment content of these two species from environmental manipulation sites in Poland, where the environment was continuously manipulated for temperature and precipitation. The warming of peat was induced by using infrared heaters, whereas total precipitation was reduced by a curtain that cuts the nighttime precipitation. Morphology of S. angustifolium stayed under climate manipulation relatively stable. However, the main morphological parameters of S. fallax were significantly affected by precipitation reduction. Thus, this study indicates S. angustifolium is better adapted in comparison to S. fallax for drier and warmer conditions.

Hyperspectral remote sensing (RS) provides unique possibilities to monitor peatland vegetation traits and their temporal dynamics at a fine spatial scale. Peatlands provide a vital contribution to ecosystem services by their massive carbon storage and wide heterogeneity. However, monitoring, understanding, and disentangling the diverse vegetation traits from a heterogeneous landscape using complex RS signal is challenging, due to its wide biodiversity and distinctive plant species composition. In this work, we aim to demonstrate, for the first time, the large heterogeneity of peatland vegetation traits using well-established vegetation indices (VIs) and Sun-Induced Fluorescence (SIF) for describing the spatial heterogeneity of the signals which may correspond to spatial diversity of biochemical and structural traits. SIF originates from the initial reactions in photosystems and is emitted at wavelengths between 650–780 nm, with the first peak at around 687 nm and the second peak around 760 nm. We used the first HyPlant airborne data set recorded over a heterogeneous peatland area and its surrounding ecosystems (i.e., forest, grassland) in Poland. We deployed a comparative analysis of SIF and VIs obtained from differently managed and natural vegetation ecosystems, as well as from diverse small-scale peatland plant communities. Furthermore, spatial relationships between SIF and VIs from large-scale vegetation ecosystems to small-scale peatland plant communities were examined. Apart from signal variations, we observed a positive correlation between SIF and greenness-sensitive VIs, whereas a negative correlation between SIF and a VI sensitive to photosynthesis was observed for large-scale vegetation ecosystems. In general, higher values of SIF were associated with higher biomass of vascular plants (associated with higher Leaf Area Index (LAI)). SIF signals, especially SIF₇₆₀, were strongly associated with the functional diversity of the peatland vegetation. At the peatland area, higher values of SIF₇₆₀ were associated with plant communities of high perennials, whereas, lower values of SIF₇₆₀ indicated peatland patches dominated by Sphagnum. In general, SIF₇₆₀ reflected the productivity gradient on the fen peatland, from Sphagnum-dominated patches with the lowest SIF and fAPAR values indicating lowest productivity to the Carex-dominated patches with the highest SIF and fAPAR values indicating highest productivity.

One of the biggest problems facing agriculture is the occurrence of droughts. Due to ongoing climate change, many regions of the world are exposed to increasingly frequent and prolonged water shortages. The situation may significantly reduce production and the quality of many crops in the Poaceae family, including crucial cereals. Therefore, it is important to find solutions that can help adapt plants to the drought phenomenon and reduce its negative effects. One measure that could potentially improve the condition of plants and help them survive under water deficit conditions is the use of antitranspirants (AT), which are products that reduce transpiration. Antitranspirants are divided into three groups: film-forming, metabolic, and reflective types. This review aimed to the current state of knowledge on the effects of selected AT applications on Poaceae plants under drought conditions. It demonstrated that AT, in many cases, mitigates the negative effects of drought on crops such as maize, wheat, or rice, which are crucial for global food security. Furthermore, AT often improved growth and yield parameters. These results are particularly relevant for countries that are important cereals producers and are more vulnerable to droughts in the future. However, it should be noted that the results obtained often depend on several factors, such as plant species, environment, type of antitranspirant, and applied dose. Therefore, it is advisable to measure further the effects of AT on plants under drought-stress conditions.

The hysteresis of the seasonal relationships between vegetation indices (VIs) and gross ecosystem production (GEP) results in differences between these relationships during vegetative and reproductive phases of plant development cycle and may limit their applicability for estimation of croplands productivity over the entire season. To mitigate this problem and to increase the accuracy of remote sensing-based models for GEP estimation we developed a simple empirical model where greenness-related VIs are multiplied by the leaf area index (LAI). The product of this multiplication has the same seasonality as GEP, and specifically for vegetative periods of winter crops, it allowed the accuracy of GEP estimations to increase and resulted in a significant reduction of the hysteresis of VIs vs. GEP. Our objective was to test the multiyear relationships between VIs and daily GEP in order to develop more general models maintaining reliable performance when applied to years characterized by different climatic conditions. The general model parametrized with NDVI and LAI product allowed to estimate daily GEP of winter and spring crops with an error smaller than 14%, and the rate of GEP over- (for spring barley) or underestimation (for winter crops and potato) was smaller than 25%. The proposed approach may increase the accuracy of crop productivity estimation when greenness VIs are saturating early in the growing season.