Filtering approaches on Global Ecosystem Dynamics Investigation (GEDI) data differ considerably across existing studies and it is yet unclear which method is the most effective. We conducted an in‐depth analysis of GEDI's vertical accuracy in mapping terrain and canopy heights across three study sites in temperate forests and grasslands in Spain, California, and New Zealand. We started with unfiltered data (2,081,108 footprints) and describe a workflow for data filtering using Level 2A parameters and for geolocation error mitigation. We found that retaining observations with at least one detected mode eliminates noise more effectively than sensitivity. The accuracy of terrain and canopy height observations depended considerably on the number of modes, beam sensitivity, landcover, and terrain slope. In dense forests, a minimum sensitivity of 0.9 was required, while in areas with sparse vegetation, sensitivity of 0.5 sufficed. Sensitivity greater than 0.9 resulted in an overestimation of canopy height in grasslands, especially on steep slopes, where high sensitivity led to the detection of multiple modes. We suggest excluding observations with more than five modes in grasslands. We found that the most effective strategy for filtering low‐quality observations was to combine the quality flag and difference from TanDEM‐X, striking an optimal balance between eliminating poor‐quality data and preserving a maximum number of high‐quality observations. Positional shifts improved the accuracy of GEDI terrain estimates but not of vegetation height estimates. Our findings guide users to an easy way of processing of GEDI footprints, enabling the use of the most accurate data and leading to more reliable applications.

Measuring and mapping vegetation structure is essential for understanding the functioning of terrestrial ecosystems and for informing environmental policies. Recent years have seen a growing demand for high‐resolution data on vegetation structure, driving their prediction at fine resolutions (1–30 m) at state, continental, and global spatial extents by combining satellite data with machine learning. As these initiatives expand, it is crucial to actively discuss the quality and usability of these products. Here, we briefly summarize current efforts to map vegetation structure and show that continental‐to‐global canopy height models (CHMs) exhibit significant errors in canopy heights compared to national airborne laser scanning (ALS) data. We recommend that regions with abundant ALS data, such as Europe, prioritize using ALS‐based canopy height metrics rather than relying on less accurate predictions from satellite products. Despite variations in ALS data characteristics, such as temporal inconsistencies and differences in acquisition characteristics and classification accuracy, the generation of spatially contiguous canopy height products in raster format at fine spatial resolution is necessary and feasible. This requires coordinating efforts for data and survey harmonization, developing standardized processing pipelines and continent‐wide ALS products, and ensuring free access for research and environmental policy. We show that ALS data now cover most of Europe, with newer surveys achieving higher point densities, improving their suitability for vegetation mapping. Beyond numerous applications in forestry, ecology, and conservation, such data sets are crucial for calibrating future Earth Observation missions, making them essential for producing reliable and accurate global, fine‐resolution vegetation structure data.

Prompt estimation of phytoplankton biomass is critical in determining the ecological quality of freshwaters. Remote Sensing (RS) may provide new opportunities to integrate with situ traditional monitoring techniques. Nonetheless, wide regional and temporal variability in freshwater optical constituents makes it difficult to design universally applicable RS protocols. Here, we assessed the potential of two neural networks-based models, namely the Case 2 Regional CoastColour (C2RCC) processor and the Mixture Density Network (MDN), applied to MSI Sentinel-2 data for monitoring Chlorophyll (Chl) content in three monomictic volcanic lakes while accounting for the effect of their specific water circulation pattern on the remotely-sensed and in situ data relation. Linear mixed models were used to test the relationship between the remote sensing indices calculated through C2RCC (I_NN) and MDN (I_MDN), and in situ Chl concentration. Both indices proved to explain a large portion of the variability in the field data and exhibited a positive and significant relationship between Chl concentration and satellite data, but only during the mixing phase. The significant effect of the water circulation period can be explained by the low responsiveness of the RS approaches applied here to the low phytoplankton biomass, typical of the stratification phase. Sentinel-2 data proved their valuable potential for the remote sensing of phytoplankton in small inland water bodies, otherwise challenging with previous sensors. However, caution should be taken, since the applicability of such an approach on certain water bodies may depend on hydrological and ecological parameters (e.g., thermal stratification and seasonal nutrient availability) potentially altering RS chlorophyll detection by neural networks-based models, despite their alleged global validity.

Inland freshwaters are of great importance for human health and activities, but major stressors such as nutrient pollution, deforestation, and urbanization are compromising their status. Water quality degradation and freshwater ecosystem preservation are current issues worldwide requiring frequent and efficient monitoring protocols. The increasing need for large amounts of data to comply with national and international regulations on water quality monitoring highlights traditional procedures limits. Therefore, the purpose of the present study is to investigate the potential of alternative and rapid methods for chlorophyll concentration surveys in freshwaters. The Phyto-PAM (pulse amplitude-modulated) instrument and the Case-2 Regional Coast Colour (C2RCC) satellite image processor were selected to estimate chlorophyll concentration in the surface waters of Lake Albano (Central Italy), selected as a pilot area for the project BLOOWATER (Water JPI 2018 Joint Call Closing the Water Cycle Gap). The correlation tests’ results indicate significant relations with chlorophyll data measured spectrophotometrically, confirming the suitability of both methods for chlorophyll retrieval. However, the relatively low strength of the correlation between remotely sensed and spectrophotometric data (r = 0.57, p < 2.2 × 10⁻¹⁶) was not as satisfactory as with Phyto-PAM values (r = 0.97, p = 1.2 × 10⁻⁴). Even though the techniques in this study proved to be promising in the water body under investigation, their current limitations suggest the need for further calibration and integration with other systems (e.g., unmanned aerial vehicles).

Beyond the importance of ponds for aquatic and terrestrial life, pond networks seem to be crucial to providing a vital spatial resource in response to global climate change for all migrating and spreading taxa. Additionally, ponds offer sustainable solutions to issues of concern in water management, such as nutrient retention, rainfall interception, or carbon sequestration. Although the ecological role of shallow waters seems clear, significant work must be performed to set future guidelines and actions towards their conservation. The main aims of the present study are to (i) georeference all small temporary wetlands within the Tyrrhenian central Italy coastal area, (ii) evaluate their hydroperiod, and (iii) calculate their surface size variability. We found 137 wetlands, 53 of which were temporary and contained listed habitats. Each wetland’s status was assessed in relation to land use and proximity to stressors (e.g., urban centres, railways, roads) while observing the relationship between pond occurrence, lithology, and permeability. Amongst the detected wetlands, we selected and monitored 21 temporary ponds (homogeneously distributed within the study area) for 12 months using images collected by the non-professional drone Parrot Bebop 2. All images were then acquired in ArcGIS to georeference all temporary ponds. The analysis confirmed that the majority of the surveyed ponds are in close proximity to roads and tracks, which might have significant impacts on the preservation of such fragile habitats. Moreover, despite the wide variability of hydroperiod duration, the greater part of the pools fill with water in autumn and dry in summer, in alignment with the Mediterranean climate. This preliminary study allowed for the creation of the first temporary ponds’ database, which is useful for monitoring their status in central Italy and planning further studies to assess eventual detrimental effects caused by human-mediated activities.