Vegetation Biomass Research Articles

Aboveground Biomass Estimation and Time Series Analyses in Mongolian Grasslands Utilizing PlanetScope Imagery

Mongolia, situated in central Asia and bordered by Russia to the north and China to the south, experiences a semi-arid climate across most of its territory. Grasslands are pivotal in Mongolia’s agricultural sustainability and food security, facing rapid changes in the last two decades that underscore the ongoing need for innovative approaches to assess vegetation conditions. This study aims to evaluate grassland biomass measurement and prediction through the analysis of high-resolution satellite data. By conducting a time series assessment of grazing-induced changes in vegetation dynamics at the long-term monitoring sites of the Botanic Garden and Research Institute, Mongolian Academy of Sciences, we seek to refine our understanding. The investigation covers biomass estimation across various Mongolian grassland landscapes, encompassing desert, steppe, and mountain regions. Spanning the grassland growing season from May 2020 to October 2023, the research leveraged diverse ground data types, including surface reflectance measurements, geographic coordinates for satellite data correction, and aboveground dry biomass. These components were instrumental in developing a biomass estimation model reliant on establishing correlations between the satellite-derived Normalized Difference Vegetation Index and biomass. The predicted biomass facilitated the time series map analysis and dynamic analysis. The PlanetScope surface reflectance correlates strongly at 0.97 with field measurements, indicating robust relations. Biomass and the Normalized Difference Vegetation Index show correlations of 0.82 for dry grassland, 0.80 for mountain grassland, and 0.65 for desert grassland, with a combined correlation coefficient of 0.62, revealing distinct characteristics across these grasslands. Time series dynamic analysis reveals rising biomass differences between grazed and ungrazed areas, suggesting potential grassland degradation. Variations in the slope coefficient of biomass differences among grassland types indicate differing degradation patterns, emphasizing the need for effective grazing management practices to sustain and conserve Mongolian grasslands. This highlights the potential of remote sensing in monitoring and managing grassland ecosystems.

Technical Note: Revisiting the general calibration of cosmic-ray neutron sensors to estimate soil water content

Abstract. Cosmic-ray neutron sensing (CRNS) is becoming increasingly popular for monitoring soil water content (SWC). To retrieve SWC from observed neutron intensities, local measurements of SWC are typically required to calibrate a location-specific parameter, N0, in the corresponding transfer function. In this study, we develop a generalized conversion function that explicitly takes into account the different factors that govern local neutron intensity. Thus, the parameter N0 becomes location independent, i.e. generally applicable. We demonstrate the feasibility of such a “general calibration function” by analysing 75 CRNS sites from four recently published datasets. Given the choice between the two calibration strategies – local or general – users will wonder which one is preferable. To answer this question, we estimated the resulting uncertainty in the SWC by means of error propagation. While the uncertainty in the local calibration depends on both the local reference SWC itself and its error, the uncertainty in the general calibration is mainly governed by the errors in vegetation biomass and soil bulk density. Our results suggest that a local calibration – generally considered best practice – might often not be the best option. In order to support the decision which calibration strategy – local or general – is actually preferable in the user-specific application context, we provide an interactive online tool that assesses the uncertainty in both options (https://cosmic-sense.github.io/local-or-global, last access: 23 February 2024).

Long‐term conservation practice in litchi (Litchi chinensis L.) cultivation improves the crop productivity and soil health of degraded lands

AbstractA long‐term experiment (1995–2021) was conducted on litchi (Litchi chinensis L.) with and without conservation practices, that is, litchi under clean cultivation (LCC) (recommended doses of fertilizers without intercrops and mulch) and litchi with conservation practice (LCP) (micro‐site improvement, intercrops, and organic mulching) on degraded lands. The study aimed to evaluate the long‐term impact of litchi‐based land uses on vegetative growth (plant height, canopy volume, fruit yield, and litter production), soil moisture, biomass production, carbon stocks (CS), and soil qualities (enzyme activities, microbial counts, and soil fertility). The results revealed that LCP improved vegetative growth, yields, and soil moisture over LCC. The total dry biomass was observed 91.6–103.9, stored CS; 43.6–49.4, emitted CS; 7.04–9.40, mitigated CS; 30.1–30.5, soil CS; 35.7–39.9, total CS (soil + vegetation); 65.9–70.5 Mg ha−1 and carbon dioxide mitigation; 88.3–112.1 Mg ha−1 in LCC and LCP land uses, respectively. Further, in LCP, beta‐glucosidase, acid phosphatase, alkaline phosphatase, and dehydrogenase activities increased by 17.2%, 20.4%, 19.8%, and 24.8%, respectively, in surface soil compared with LCC in 2021. Likewise, the microbial density of bacteria, fungi, and actinomycetes was also observed to be 20.2%, 41.4%, and 19.5% higher, respectively, in LCP land use in 2021 over 2015. Similarly, available N, P, and K increased by 19.9%, 75.3%, and 36.8% under LCP from 1997 to 2021. Hence, litchi‐based land use with conservation practices can be advocated to rehabilitate and improve the soil health of degraded lands and enhance ecosystem services in similar edaphoclimatic conditions.

Ecological responses to hydrological connectivity in grassland riparian zones: Insights from vegetation and ground-dwelling arthropods

Riparian wetlands have suffered from degradation due to global climate change and human activities, which can alter flora and fauna community patterns and disrupt material cycles in the riparian zones. Hydrological connectivity identified by functional and structural connectivity is an important driving force of riparian ecosystems. However, the role of hydrological connectivity in linking riparian hydrology and ecology remains unclear, especially in dryland rivers. By taking the riparian zone of the Xilin River in Eurasian steppe as an example, the functional connectivity was represented by the groundwater depth in the riparian zones. The structural connectivity was quantified by integrating the soil, and vegetation properties of the riparian zone. The structural connectivity decreased from upstream to downstream. Laterally, the highest structural connectivity was found in the riparian zone 25 m away from the river channel. The abundance of three groups of ground-dwelling arthropods (except Araneae) showed a threshold behavior in response to the functional connectivity, with the highest abundance occurring in the medium level of functional connectivity. Both vegetation biomass and ground-dwelling arthropod abundance were significantly and positively correlated to the structural connectivity strength. The results of structural equation models (SEMs) also indicated that structural connectivity was a key factor affecting vegetation and ground-dwelling arthropod abundance. The results underscore the essential function of hydrological connectivity in maintaining the biodiversity in the riparian zones. The study provides a scientific reference of riparian-zone restoration based on hydrological connectivity.

Natural vegetation biomass and the dimension of forest quality in tropical agricultural landscapes.

Forest cover has been a pivotal indicator of biological conservation and carrying capacity for wildlife in forest ecoregions. Such a relationship underpins policies focused on the extension of protected lands. Here, we estimate aboveground biomass (AGB) as a proxy for habitat quality in seminatural rural patches and provide a comparison with approaches that only consider forest cover. We hypothesize that recommendations for biological conservation in agricultural landscapes are substantially improved if habitat quality is also taken into account, and thus consider the possibility of forest quality being modulated by land-use amount, type, and age. We assessed AGB in a densely farmed Brazilian region using a straightforward approach designed to be affordable at large scales, focusing on two expanding and contrasting land uses: sugarcane, and eucalyptus plantations. At a detailed scale, we confirmed through field surveys and AGB estimation using 3D-multispectral imagery (i.e., AGB = 0.842 × vegetation heightNDVI+1) that AGB variation could be predicted with forest degradation classes that are visually distinguishable with high-resolution images: 9.33 t ha-1 (90% predictive intervals [PI] = [3.23, 26.97]) in regenerating fields (RF), 31.12 t ha-1 (90% PI = [10.77, 89.90]) in pioneer woods (PW), and 149.04 t ha-1 (90% PI = [51.59, 430.58]) in dense forests (DF). Applying these values to land units sampled across the study region, we found an average land use of 88.5%, together with 11.5% of land set aside for conservation, which reduced AGB to less than 4.2% of its potential (averages of 5.85 t ha-1 in sugarcane-dominated areas and 6.56 t ha-1 in eucalyptus-dominated areas, with secondary forests averaging 149.04 t ha-1). This imbalance between forest cover and AGB resulted from forest quality decay, which was similarly severe among land-use types, ages, and extensions. Therefore, the shortage of trophic resources is likely more critical to wildlife than spatial limitations in vastly deforested tropical ecoregions, where AGB and carbon sinks can be more than doubled just by restoring forests in lands currently spared by agriculture.

Vegetation Classification and a Biomass Inversion Model for Wildfires in Chongli Based on Remote Sensing Data

Vegetation classification, biomass assessment, and wildfire dynamics are interconnected wildfire-ecosystem components. The Chongli District, located in Zhangjiakou City, was the venue for skiing at the 2022 Winter Olympics. Its high mountains and dense forests create a unique environment. The establishment of alpine ski resorts highlighted the importance of comprehensive forest surveys. Understanding vegetation types and their biomass is critical to assessing the distribution of local forest resources and predicting the likelihood of forest fires. This study used satellite multispectral data from the Sentinel-2B satellite to classify the surface vegetation in the Chongli District through K-means clustering. By combining this classification with a biomass inversion model, the total biomass of the survey area can be calculated. The biomass inversion equation established based on multispectral remote sensing data and terrain information in the Chongli area have a strong correlation (shrub forest R2 = 0.811, broadleaf forest R2 = 0.356, coniferous forest R2 = 0.223). These correlation coefficients are key indicators for our understanding of the relationship between remote sensing data and actual vegetation biomass, reflecting the performance of the biomass inversion model. Taking shrubland as an example, a correlation coefficient as high as 0.811 shows the model’s ability to accurately predict the biomass of this type of vegetation. In addition, through multiple linear regression, the optimal shrub, broadleaf, and coniferous forest biomass models were obtained, with the overall accuracy reaching 93.58%, 89.56%, and 97.53%, respectively, meeting the strict requirements for survey accuracy. This study successfully conducted vegetation classification and biomass inversion in the Chongli District using remote sensing data. The research results have important implications for the prevention and control of forest fires.

Effects of different ecological restoration methods on the soil physicochemical properties and vegetation community characteristics of the Baotou light rare earth tailings pond in Inner Mongolia, China.

This study investigated the soil physicochemical properties and vegetation community characteristics of the Baotou light rare earth tailings pond after 10years of aggregate spray seeding ecological restoration (S1) and ordinary soil spray seeding ecological restoration (S2), and the naturally restored dam slope area without human intervention (S3). The results showed that the vegetation community of S1 was dominated by Caragana korshinskii Kom, and its importance and abundance values were 0.40 and 38.4, respectively, while the vegetation communities of S2 and S3 mainly comprised herbaceous plants. Additionally, the vegetation biomass of S1 was significantly higher than that of S2 and S3 by 215.20% and 1345.76%, respectively, and the vegetation diversity index of S1 was the highest among the three treatment groups. The soil porosity (SP), water content (W), electrical conductivity (EC), and available K were significantly improved in S1, while soil bulk density (BD) was significantly reduced compared with that of S2 and S3. In addition, redundancy analysis revealed that SP, EC, W, and K positively correlate with the biomass, Shannon, Pielou, Simpson, and Marglef indices. Principal component analysis further showed that the comprehensive score of S1 (0.983) was higher than that of S2 (- 0.261) and S3 (- 0.648). Collectively, these findings indicate that appropriate ecological restoration can improve soil structure and vegetation community characteristics, thereby accelerating vegetation restoration, ultimately increasing the stability of the ecosystem.

U.S. cereal rye winter cover crop growth database

Winter cover crop performance metrics (i.e., vegetative biomass quantity and quality) affect ecosystem services provisions, but they vary widely due to differences in agronomic practices, soil properties, and climate. Cereal rye (Secale cereale) is the most common winter cover crop in the United States due to its winter hardiness, low seed cost, and high biomass production. We compiled data on cereal rye winter cover crop performance metrics, agronomic practices, and soil properties across the eastern half of the United States. The dataset includes a total of 5,695 cereal rye biomass observations across 208 site-years between 2001–2022 and encompasses a wide range of agronomic, soils, and climate conditions. Cereal rye biomass values had a mean of 3,428 kg ha−1, a median of 2,458 kg ha−1, and a standard deviation of 3,163 kg ha−1. The data can be used for empirical analyses, to calibrate, validate, and evaluate process-based models, and to develop decision support tools for management and policy decisions.

Growth and Nutritional Responses of Zucchini Squash to a Novel Consortium of Six Bacillus sp. Strains Used as a Biostimulant

The use of biostimulants consisting of plant growth-promoting rhizobacteria (PGPR) has been rapidly expanding in horticulture in recent years. In the current study, a novel mix of six Bacillus sp. strains (B. subtilis, B. pumilus, B. megaterium, B. amyloliquefaciens, B. velezensis, B. licheniformis) was tested as a PGPR biostimulant in two experiments with zucchini squash (Cucurbita pepo L.). The first experiment took place in greenhouse soil in winter, while the second experiment was conducted in an open field during summer. In both experiments, seeds of the local landrace “Kompokolokytho” and the commercial hybrid “ARO-800” were either inoculated or non-inoculated with the PGPR biostimulant. The application of the six Bacillus sp. strains increased both the vegetative growth and the yield of zucchini squash, and these effects were associated with significantly higher shoot phosphorus levels in both experiments and both genotypes. Furthermore, at the end of the cultivation, the colony-forming units of Bacillus sp. were appreciably higher in plants originating from inoculated compared to non-inoculated seeds, indicating that the tested mix of Bacillus sp. can be successfully applied through seed inoculation. “ARO-800” produced more vegetative and fruit biomass than “Κompokolokytho” under greenhouse cropping conditions, while in the open field crop, both genotypes performed equally. Presumably, this response occurred because “ARO-800” did not express its full yield potential in the open field due to stress imposed by the high summer temperatures, while the local landrace, which is traditionally grown in open fields, may be more resilient to stress conditions frequently encountered in open fields.

Deep Learning Model Effectiveness in Forecasting Limited-Size Aboveground Vegetation Biomass Time Series: Kenyan Grasslands Case Study

Timely forecasting of aboveground vegetation biomass is crucial for effective management and ensuring food security. However, research on predicting aboveground biomass remains scarce. Artificial intelligence (AI) methods could bridge this research gap and provide early warning to planners and stakeholders. This study evaluates the effectiveness of deep learning (DL) algorithms in predicting aboveground vegetation biomass with limited-size data. It employs an iterative forecasting procedure for four target horizons, comparing the performance of DL models—multi-layer perceptron (MLP), long short-term memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN), and CNN-LSTM—against the traditional seasonal autoregressive integrated moving average (SARIMA) model, serving as a benchmark. Five limited-size vegetation biomass time series from Kenyan grasslands with values at 15-day intervals over a 20-year period were chosen for this purpose. Comparing the outcomes of these models revealed significant differences (p < 0.05); however, none of the models proved superior among the five time series and the four horizons evaluated. The SARIMA, CNN, and CNN-LSTM models performed best, with the statistical model slightly outperforming the other two. Additionally, the accuracy of all five models varied significantly according to the prediction horizon (p < 0.05). As expected, the accuracy of the models decreased as the prediction horizon increased, although this relationship was not strictly monotonic. Finally, this study indicated that, in limited-size aboveground vegetation biomass time series, there is no guarantee that deep learning methods will outperform traditional statistical methods.

Characterisation of domestically discarded vegetable waste biomass ash and silica sand mixed with ordinary Portland cement concrete

Characterisation of domestically discarded vegetable waste biomass ash and silica sand mixed with ordinary Portland cement concrete

Evaluation of toxic effect of monoterpene compounds on anaerobic digestion

Monoterpenes are antimicrobial compounds widely distributed in vegetable biomass, whose inhibitory potential for anaerobic digestion is underestimated. In this research, the toxic effect of limonene and fenchone, two of the main monoterpenes present in vegetable biomasses, and those of 4-terpineol, α-terpineol, and p-cymene, compounds described as main metabolites of limonene degradation, have been assessed. Methane production was totally inhibited at dosed of 1000 mg L−1 of fenchone and limonene and at 600 mg L−1 of p-cymene and 4-terpineol. Based on the methane production rate, the inhibition followed the next trend: α-terpineol < < fenchone < limonene ≈ p-cymene < 4-terpineol. Regardless of dosed concentration, monoterpenes were mostly degraded at the end of the experiment (>85%), except p-cymene at 600 mg L−1. Therefore, monoterpenes could entail a high risk of inhibition that can be aggravated by the difficulty to accurately follow their concentration and by the scarce information on their effect on anaerobic process.

Distribution Characteristics of Carbon Density in Plant–Soil System of Temperate Steppe and Temperate Desert in the Longzhong Loess Plateau

Grassland, as a key component of the carbon cycle in terrestrial ecosystems, is vital in confronting global climate change. Characterising the carbon density of grassland ecosystems in the Longzhong Loess Plateau is important for accurately assessing the contribution of grasslands to global climate change and achieving the goal of “peak carbon” and “carbon neutral”. In this study, the Longzhong Loess Plateau was used as the research object to explore changes in the plant–soil system carbon density in two grassland types by analysing the aboveground vegetation biomass carbon density, belowground vegetation biomass carbon density, 0–100 cm soil carbon density, and ecosystem carbon density of temperate steppe and temperate desert. The results showed that the vegetation biomass (standing and living, litter, and belowground biomass), soil, and ecosystem carbon densities of the temperate steppe were significantly higher than those of the temperate desert (p &lt; 0.05). Their carbon densities were 700.51, 7612.95, and 8313.45 g·m−2, respectively. The vertical distribution of belowground biomass and soil carbon density in the temperate steppe was significantly higher than that in the temperate desert. The overall trend of belowground biomass carbon density in the temperate steppe and temperate desert showed a gradual decrease, whereas soil carbon density showed a steady increase. More than 91% and 96% of the carbon was stored in soil in the temperate steppe and temperate desert, respectively, and the belowground biomass carbon stock accounted for more than 84% of the total biomass carbon pools in both temperate steppe and temperate desert. Temperate steppe has a significant effect in improving the carbon stock of grassland ecosystems, so ecological protection and restoration of grassland should be strengthened in the future to enhance the capacity of grassland to sequester carbon and increase sinks.

Short‐term biogeomorphology of a gravel‐bed river: Integrating remote sensing with hydraulic modelling and field analysis

AbstractIn recent decades, fluvial geomorphology and ecohydraulic research have extensively used field observations, remote sensing or hydrodynamic modelling to understand river systems. This study presents an innovative approach that combines field surveys, Light Detection and Ranging (LiDAR)‐based topographical and biomass analyses and model‐derived hydro‐morphodynamic geostatistics to examine short‐term biogeomorphological changes in the wandering gravel‐bed Orco River in Italy. Our primary hypothesis is that hydro‐morphological variables can be robust descriptors for riparian vegetation distribution. From a geomorphological perspective, our study confirms the prevalent wandering behaviour of the Orco River. Moreover, we identified a widening trend in braiding and anabranching sections, particularly downstream. This is evident because of hotspots of flood‐induced morphological reactivation and the redistribution of sediments from the riverbed to lateral bars, resulting in a multi‐thread pattern. Our analysis reveals a net increase in biomass during the observation period despite frequent flood disturbances. We attributed it to two opposing biogeomorphological dynamics: the reduced flow disturbance in some regions due to flood‐induced geomorphological changes and the self‐healing of lateral connectivity through river wandering. Such a net increase indicates that transitional rivers store carbon in the form of vegetation biomass due to their short‐term morphological instability and the different timescales between vegetation and morphological adjustments. Finally, we supported our initial hypothesis with three key findings: (i) a signature of vegetation not just on topography but also on hydro‐morphological conditions, summarised by inundation probability; (ii) the lower variance in vertical topographical changes in vegetated areas compared with bare ones; and (iii) the introduction of a new parameter, named inundation viscosity, derived from the product of mean bed shear stress and average inundation duration, as a discriminating factor for colonisation conditions. These results underscore the value of our comprehensive approach.

Precipitation- rather than temperature-driven pattern in belowground biomass and root:shoot ratio across the Qinghai-Tibet Plateau

The Qinghai-Tibet Plateau faces dramatic global change, which can greatly affect its plant growth, biomass accumulation, and carbon cycling. However, it is still unclear how belowground plant biomass, which is the major part of vegetation biomass on the plateau, changes with different environmental factors, impeding accurate prediction of ecosystem carbon cycling under future global change scenarios.To reveal the patterns of belowground biomass and root:shoot ratio with environmental factors in different vegetation types on the Qinghai-Tibet Plateau, we synthesized data for 158 sites from 167 publications, including 585 and 379 observations for above- and below-ground biomass, respectively. Data on temperature, precipitation, soil nitrogen content, evapotranspiration and solar radiation were collected from open databases.The results showed that precipitation, rather than temperature, was closely associated with other environmental factors including soil N and solar radiation. Also, both above- and below-ground biomass significantly increased with annual precipitation and its related environmental factors, while elevation-related coldness only slightly decreased aboveground biomass. In addition, the positive effect of precipitation on belowground biomass is more obvious in higher elevations (colder areas). As a result, root:shoot ratio significantly increased with precipitation in colder areas but not in warmer areas. Finally, the positive relationship between biomass and precipitation was stronger for dryer steppes than for wetter meadows and shrublands.Our findings indicate that precipitation, as well as the associated nitrogen availability and solar radiation, together are more important drivers than temperature for ecosystem productivity and biomass allocation on the Qinghai-Tibet Plateau. Given the heterogeneous trend of precipitation change on the plateau, productivity response to global change can be highly variable across different regions and vegetation types, which can consequently impact soil carbon dynamics and regional carbon cycling.

Development of a Pre-Automatized Processing Chain for Agricultural Monitoring Using a Multi-Sensor and Multi-Temporal Approach

Understanding crop types and their annual cycles is key to managing natural resources, especially when the pressures on these resources are attributable to climate change and social, environmental, and economic policies. In recent years, the space sector’s development, with programs such as Copernicus, has enabled a greater availability of satellite data. This study uses a multi-sensor approach to retrieve crop information by developing a Proof of Concept for the integration of high-resolution SAR imagery and optical data. The main goal is to develop a pre-automatized processing chain that explores the temporal dimension of different crop. Results are related to the advantage of using a multi-sensor approach to retrieve vegetation biomass and vertical structure for the identification of phenological stages and different crops. The novelty consists of investigating the multi-temporal pattern of radiometric indices and radar backscatter to detect the different phenological stages of each crop, identifying the Day of the Year (DoY) in which the classes showed greater separability. The current study could be considered a benchmark for the exploitation of future multi-sensor missions in downstream services for the agricultural sector, strengthening the evolution of Copernicus services.

Plant communities and potential phytoremediation species for resource utilization of abandoned drilling mud

Oil and gas development produces a large amount of abandoned drilling mud, which may be a source of pollution but may also be a potential resource. A quadrat sampling method was used in the Changqing Oilfield to investigate the plant community characteristics inside and outside abandoned drilling mud pits at different completion times. The importance value of plant showed that the natural succession of plant communities in the abandoned drilling mud pool could be divided into three stages: (1) 4–8 years after the completion of the well, Leymus secalinus, Calamagrostis epigeios with the importance values of more than 100, were dominant species in the initial construction stage; (2) 9–13 years after the succession, the intense competition stage of the plant community, although the importance value of plant inside the mud pit had decreased, it was still higher than the importance value of plant outside the mud pool. The importance values of L. secalinus and Artemisia scoparia outside the mud pool were 62.5 and 52.5, while those in the mud pool were 95 and 75, respectively; (3) 23–28 years after the succession, the gradual stabilization stage of the plant community, the importance value of plant was lower than that in the mud pool. Leymus secalinus, C. epigeios and A. scoparia could be used for phytoremediation of abandoned drilling soil for their higher importance values. Changes of Shannon–Wiener index, Pielou index, Community ecological dominance and vegetation biomass showed that the mud pit was suitable for the recovery and growth of the dominant species, improved the diversity of plant communities inside the mud pit compared with the stable plant community outside the mud pit. Abandoned drilling mud increased the content of nitrogen, potassium and trace elements in plants. While the variation coefficient of heavy metal content in plants inside and outside the mud pit was large, but the content of heavy metals in plants was within the normal range. Abandoned drilling mud has the potential for resource utilization on the premise of ensuring soil quality and safety. This study provides information on the comprehensive treatment, resource utilization, disposal economics and environmental safety of abandoned drilling mud.

Tree species replacement from birch to spruce affects eukaryome in boreal forest soil

Large-scale replacements of native birch with spruce have been carried out in Western Norway for economic reasons. This tree species shift potentially affects biotic components such as the eucaryome, consisting of microscopic animals (Metazoa), protists and fungi, which are key players in the functioning of forest ecosystem. The impact on the belowground eukaryome and its interactions with vegetation and soil properties is not well assessed. We examined the impact of replacing native birch with Norway spruce plantations on the eukaryome of the boreal forest floor in Western Norway using 18S rDNA metabarcoding. The tree species shift from birch to spruce had significant impacts on the eukaryome at both taxonomic (Metazoa) and functional categories (phagotrophs, phototrophs, parasites and osmotrophs). The distinct differences in eukaryome communities were related to changes in understorey vegetation biomass and soil chemistry following the tree species shift. This had a negative effect on eukaryome richness, particularly affecting phagotrophs and parasites, while the opposite was observed for osmotroph richness. Our results indicated that the spruce plantations altered the eukaryome communities and their food-web patterns compared to what was found in the native birch forest soil. This information should be taken into consideration in forest management planning.

Mitigating the impact of dense vegetation on the Sentinel-1 surface soil moisture retrievals over Europe

ABSTRACT The C-band Synthetic Aperture Radar (SAR) on board of the Sentinel-1 satellites have a strong potential to retrieve Surface Soil Moisture (SSM). Using a change detection model to Sentinel-1 backscatter, an SSM product at a kilometre scale resolution over Europe could be established in the Copernicus Global Land Service (CGLS). Over areas with dense vegetation and high biomass. The geometry and water content influence the seasonality of the backscatter dynamics and hamper the SSM retrieval quality from Sentinel-1. This study demonstrates the effect of woody vegetation on SSM retrievals and proposes a masking method at the native resolution of Sentinel-1’s Interferometric Wide (IW) swath mode. At a continental 20 m grid, four dense vegetation masks are implemented over Europe in the resampling of the backscatter to a kilometre scale. The resulting backscatter is then used as input for the TUWien (TUW) change detection model and compared to both in-situ and modelled SSM. This paper highlights the potential of high-resolution vegetation datasets to mask for non-soil moisture-sensitive pixels at a sub-kilometre resolution. Results show that both correlation and seasonality of the retrieved SSM are improved by masking the dense vegetation at a 20 m resolution.

Sediment–vegetation interactions determine the fate of fluvial sediment in the upper reaches of a large estuary

AbstractFeedbacks between sediment and plants in the submersed aquatic vegetation (SAV) beds of the Susquehanna Flats help modulate sediment delivery into the upper Chesapeake Bay from its major tributary (Susquehanna River). Recent modeling work has shown that SAV can steer the river plume, directly controlling sediment transport and fate. However, transport mechanisms likely differ between flood and non‐flood conditions and depend on event timing. An opportunity to evaluate these insights in the field occurred with several flood events in summer 2018. Sediment and biomass samples were collected after the flood in the large, continuous main SAV bed of the Flats and in smaller patches to the west of the main bed to evaluate the effect of SAV bed size (width). Sediment characteristics (grain size, organic content) and deposition rates were compared to previous observations during non‐flood conditions in the main bed in 2014–2015 and in the smaller patches in 2019. Overall, sediment deposition rates could be predicted by an empirical model including sediment load, vegetation biomass, and SAV bed width, reflecting the role of vegetation in steering the Susquehanna River plume and modulating sediment input to the upper Chesapeake Bay. Understanding spatiotemporal patterns of sedimentation is essential for realistic estimates of sediment and nutrient (especially nitrogen and carbon) storage in freshwater SAV systems, which have not received as much attention as their saltwater counterparts.