HS6.7

Many studies have demonstrated the value of microwave remote sensing for soil and vegetation applications in agricultural and natural systems. The sensitivity of microwave observables to internal water content makes them suitable for monitoring changes in above ground biomass associated with growth, seasonality and land cover change. In recent years, microwave observations have increasingly been used to monitor changes in vegetation water content associated with water status. Here, novel experimental data from field campaigns and analyses of satellite data records will be synthesized to provide a perspective on the current and future use of radar for monitoring vegetation water dynamics.

Ground-based radar and in-situ data will be used to illustrate the sensitivity of sub-daily radar data to detect the subtle response of the vegetation to variations in moisture supply and demand. These data will also be used to highlight the sensitivity of radar observables to surface canopy water (dew and/or interception). On the one hand, it will be shown that surface canopy water can have a confounding effect on vegetation parameter retrieval. On the other hand, microwaves can provide valuable information on this quantity of considerable interest in hydrology and land-atmosphere interactions.

 Analyses of existing satellite data records (ASCAT, Sentinel-1) will be used to show that the opportunities and challenges identifiable at field scale translate to the footprint scale. Furthermore, they will be used to outline a way forward. Future microwave missions offer unprecedented diversity in terms of sensors (in terms of frequency, polarization, viewing geometry, observation technique), as well as finer spatial and temporal resolution. To make optimal use of these new capabilities, we need to be willing to revisit our fundamental understanding of the factors affecting microwave interactions with vegetation. Finally, it will be argued that the use of machine learning can facilitate extracting the full information content of microwave observations by providing a means to reconcile microwave observables with land surface model states and parameters.

How to cite: Steele-Dunne, S.: A perspective on the current and future use of satellite radar observations for monitoring vegetation water dynamics, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11542, https://doi.org/10.5194/egusphere-egu22-11542, 2022.

Leaf and canopy temperature have long been recognized as an important indicators of plant water status. Recently, unmanned aerial vehicles (UAVs) became arguably the superior platform to acquire leaf temperatures due to their low cost and high spatiotemporal resolution and flexibility compared to satellite platforms. However, when interested in absolute leaf temperatures of individual leaves, the resolution of thermal cameras is often not sufficient and UAV overflight height needs to be adjusted. This causes heterogeneous forests to become inherently complex structures with great challenges for generating thermal orthomosaics. In addition, currently applied uncooled thermal sensors are affected by their ambient conditions causing temperature readings to drift during flight operation.
To address these issues, we employed a dual camera setup consisting of a visible and thermal sensor to aid the geometric calibration of the thermal sensor. To account for the temperature drift, we developed an alternative flight planning approach: During the UAV mapping mission ground temperature references are repeatedly captured from above forest clearings to estimate temperature drift and continuously adjust temperature calibration. We compare our temperature calibration approach to the default camera calibration and to a simple pre- and post-flight calibration method under different atmospheric conditions (temperature, wind and cloud coverage). The geometric accuracy of the forests thermal orthomosaics is validated against ground control points.
Accurate calibrated canopy temperatures will allow to compare canopy temperature differences while also providing uncertainty estimates of the temperature data. High resolution thermal maps at the forest leaf scale will open up the possibility to analyze plant water status during seasonal dry forest changes.

How to cite: Gerchow, M., Kühnhammer, K., Iraheta, A., and Beyer, M.: UAV based thermal imaging at the leaf scale – A case study in a tropical dry forest, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12339, https://doi.org/10.5194/egusphere-egu22-12339, 2022.

How to cite: Greimeister-Pfeil, I., Wagner, W., Quast, R., Hahn, S., Steele-Dunne, S., and Vreugdenhil, M.: Disentangling soil moisture and vegetation effects on the ASCAT backscatter-incidence angle relationship, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5128, https://doi.org/10.5194/egusphere-egu22-5128, 2022.

Hydrological processes are known as driving forces in structuring wetland plant communities, but still specific relationships are not always well understood. The dynamic, seasonally inundated wetland at Poyang Lake (less than 1000  km² in the dry season and more than 3000 km² in the wet season), the largest freshwater lake in China, underpinnes critical regional ecosystem services (e.g. flood water retention, water supply and biodiversity conservation). However, recent drier conditions of Poyang Lake are having a profound impact on its wetland vegetation leading to degradation of the entire wetland ecosystem, thereby also threatening the winter habitat of migratory birds. We aim to develop an integrated framework to quantitatively investigate the spatial distribution of major herbaceous communities that provide habitat for the migratory birds in response to Poyang Lake flood inundation. First, ground references are obtained from a combination of drone imagery and field surveys as an input for the wetland herbaceous community classification model. Our classification model is based on a machine learning technique applied to Sentinel-2 satellite data. This new search strategy provides an accurate classification based on the more optimal input variables and model parameters gained simultaneously. Secondly, based on the dynamic changes in water levels since 2000, we statistically evaluate the key environmental drivers of the hydrological regime on the spatial distribution of the wetland vegetation communities. This showed that: 1) different plant communities exhibited varying tolerance to flood inundation and 2) two key factors, i.e., average water depth and average duration of the inundation events, were found to be able to characterize the communities’ tolerance independently. For example, Carex cinerascens Ass. which had the widest inundation stress tolerance, being adapted to an inundation duration of 120~230 d and depth of 1.5~1.7 m, accounted for the largest herbaceous community (>27% cover) within the entire study area. Different survival strategies to inundation stress, such as dormancy and morphological restructuring, can explain the varying tolerance of plant species/communities. Our work elucidated the linkages between hydrological processes and herbaceous plants’ distribution in wetlands, and the approach can be readily applied at small to large catchment scales and provides a straightforward practical tool to predict the possible responses of the lake wetland vegetations to potential hydrological changes.

How to cite: Huang, W., Hu, T., Mao, J., Montzka, C., Bol, R., Wan, S., and Yue, J.: Hydrological drivers of the spatial distribution of herbaceous wetland communities at Poyang Lake, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1737, https://doi.org/10.5194/egusphere-egu22-1737, 2022.

The hydrologic cycle in Switzerland relies heavily on snowmelt sustaining streamflows during spring and summer. Climate warming will shrink the regional coverage of seasonal snowpacks thereby leading to an earlier onset of snowmelt, which in turn will alter streamflow regimes. However, the effects of changes in snow regimes on Alpine vegetation are largely unknown. In this context, it is imperative to understand how much streamflow and vegetation water uptake depend on different precipitation phases (rainfall versus snowfall), and what factors control the relative proportion of rainfall and snowfall that are ultimately used by vegetation (versus that flow to streams).

In this presentation, we use stable water isotopes to assess seasonal origin of waters used by Alpine trees vs water flowing into the nearby stream across different sites in Switzerland. We then correlate remote sensing based plant water abundance indicators (NDVI, NDWI, VOD) against long term streamflow records to assess how strongly waters flowing into streams are decoupled from waters taken up by vegetation, and how this decoupling varies across space and time. Using these results, we propose a theoretical framework that explains the phenomenon of “drought paradox”, where precipitation deficits during periods of drought disproportionally impact streamflow generation over vegetation in the Swiss Alps.

How to cite: Beria, H. and Kirchner, J. W.: Partitioning of rainfall and snowmelt between trees and streams in the Swiss Alps, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11165, https://doi.org/10.5194/egusphere-egu22-11165, 2022.

The frequency and the intensity of drought events have increased during the past decades in some regions, yet the implications of drought for the terrestrial vegetation functioning are not fully understood. In particular, drought in related studies has often been characterized by meteorological conditions rather than the actual soil moisture deficit. Further, previous research focused predominantly on the structural vegetation response, such that the large-scale physiological response remains poorly understood.

Here, we analyze and compare the vegetation’s physiological and structural responses to drought across the globe using high-resolution daily TROPOMI sun-induced fluorescence (SIF) as a proxy for productivity, short-wavelength vegetation optical depth (VOD) as a proxy for canopy water conditions and biomass, and near-infrared reflectance of terrestrial vegetation (NIRv) during the period March 2018 - August 2021. Taking advantage of an extended soil moisture record (1979-2021, ERA5-Land reanalysis) we identify and focus on regions where severe soil moisture droughts occurred during our relatively short analysis period. Therein, we quantify and compare the amounts of SIF, VOD and NIRv changes during the early and late drought stages as well as for the recovery period. We also compute the vegetation response to short-term soil moisture changes, i.e. the vegetation sensitivity to short-term soil moisture and the respective changes during the course of droughts. The absolute changes of vegetation indices allow to disentangle physiological and structural responses, while the sensitivity analysis can quantify vegetation responses straightforward to water limitation by accounting for meteorological forcings. To infer vegetation sensitivity, we train random forest regression models at each grid cell, and apply the SHapley Additive exPlanations (SHAP) method to isolate the influence of soil moisture on vegetation from that of other meteorological variables such as temperature, solar radiation, precipitation and vapor pressure deficit. 

Analyzing the absolute value changes of NIRv, SIF and VOD during the 2018 European drought and the 2020 Russian drought events, we find asynchronous responses of vegetation productivity and vegetation water content. This indicates different hydraulic regulation strategies in response to drought. Moving beyond these case studies, we quantified and averaged the vegetation sensitivity to soil moisture across many severe droughts across the globe, which reveals systematic sensitivity increases during the course of the drought. From an ecological perspective, this indicates increases in ecosystem vulnerability to drought, and can induce feedback in climate and ecosystem services. Vegetation responses to drought differ depending on different vegetation types, climate and soil conditions. In addition, we conduct carbon fluxes from eddy covariance measurements to confirm the vegetation responses to drought derived from Earth observation satellites. In summary, this study provides insights into vegetation responses to droughts at large spatial scales which can help to accurately quantify respective anomalies in terms of land carbon uptake to consequently improve climate projections.

How to cite: Li, W., Migliavacca, M., Reichstein, M., Forkel, M., Frankenberg, C., and Orth, R.: Revealing the drought response of large-scale vegetation physiology from multiple satellite-based observations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8239, https://doi.org/10.5194/egusphere-egu22-8239, 2022.

In the presence of uncertain initial conditions and model parameters coupled land surface model (LSM)- vegetation dynamic model (VDM) performance can be significantly improved by the assimilation of periodic observations of certain state variables, such as the soil moisture and normalized difference vegetation index (NDVI) as observed from satellite remote platforms.

The possibility to merge grass and tree NDVI observations and radar data with the model optimally for providing robust predictions of soil moisture and grass and tree leaf area index (LAI) in heterogenous ecosystems is demonstrated. We propose an assimilation approach that assimilates backscatter data from radar and NDVI from optical sensors through the Ensemble Kalman filter (EnKF) and provides a physics-based update of soil moisture and grass and tree LAI predicted by VDM. We used Sentinel 1 radar data for soil moisture, and Landsat 8 and Sentinel 2 optical data for NDVI. Soil moisture is predicted by the LSM, while the VDM predicts the LAI, which is strictly related to NDVI, through a field-estimated empirical relationship.

This approach, as with other common assimilation approaches, may fail when key model parameters, e.g. the saturated hydraulic conductivity of LSM and the maintenance respiration coefficient (m_a) of VDM, are estimated poorly. This leads to biased model errors producing a violation of a main assumption (model errors with zero mean) of the EnKF. For overcoming this model bias an innovative assimilation approach was developed, which accepts this violation in the early model run-times and dynamically calibrates all the components of the model parameter ensembles as a function of the persistent bias in soil moisture and LAI predictions, allowing to remove the model bias, restore the fidelity to the EnKF requirements and reduce the model uncertainty.

The proposed multiscale assimilation approach was tested in a Sardinian field site, a typical Mediterranean ecosystem characterized by strong heterogeneity of the vegetation and water limited conditions. The site is also a case study of the SWATCH European Research Project, and in this field a micrometeorological eddy-covariance based tower is operating from 2003.

The positive impact of the proposed assimilation approach on the soil water budget, evapotranspiration and CO₂ uptake predictions in the heterogenous ecosystem is demonstrated finally. 

How to cite: Gaspa, A., Corona, R., and Montaldo, N.: Multi Scale Assimilation of NDVI and radar data for soil moisture and Leaf Area Index Predictions in an Heterogeneous Mediterranean Ecosystem, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10048, https://doi.org/10.5194/egusphere-egu22-10048, 2022.

Increases in streamflow are often attributed to land-cover clearing (LCC) on the basis that it reduces soil infiltration capacity and increases surface runoff. Nonetheless, these changes can result from different hydrological mechanisms depending on the vegetation, and temporal and spatial scales. LCC triggers a series of changes in hydrological fluxes that have non-linear responses to precipitation and that were established upon the long-term balance with regional climatological, edaphic, and geological characteristics. We analysed streamflow and root zone water capacity (RZSC) to identify underlying relationships between stream dynamics and water consumption by plants. We used a time-series segmentation and residual trend analysis on streamflow and precipitation of high-order tributaries of the Tapajós River whose catchments underwent intense land-use changes over the past decades. We estimated the RZSC using the "Earth observation-based" mass-curve balance method by considering the annual land-cover changes over a >30-year period. We show that the reduction in the RZWC caused by changes in the water consumption by plants over the dry season is tightly associated with the increased baseflow contribution to rivers. Finally, we analysed gross primary productivity (GPP) and ET estimates generated by a model based on eco-evolutionary optimality that integrates the water and carbon cycles at the canopy level. We found that trends in ET from croplands are not as pronounced as trends in GPP. Although RZWC is quantified using the water deficit driven by ET, changes in RZWC are more correlated to changes in GPP. We show that the potential effects of vegetation responses to increasing atmospheric CO₂ concentrations on streamflow are still outweighed by impacts of land-use change on low flows in Amazon rivers. However, this might not be the case for all water cycle components, and, therefore, we highlight the importance of considering the carbon cycle in hydrological assessment studies.

How to cite: Nóbrega, R. and Prentice, I. C.: Rapid and temporary increases in low flows in the Amazon explained by changes in root-zone water storage, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1775, https://doi.org/10.5194/egusphere-egu22-1775, 2022.

The regular flood pulse of large tropical rivers is the main driver of ecological and biogeochemical process in large Amazonian floodplains. Endemic vegetation species developed adaptation to survive in seasonal flood environments and tune their vital process with periodic flood events, water levels, and sedimentary processes. The construction of hydroelectric dam causes alterations of natural hydrological regime and sediment supply, threatening downstream floodplain forest.

An assessment of impact of river regulation on floodplain vegetation is crucial to develop a modern approach to the regulated rivers management in the Neotropics and to mitigate the impact of damming on floodplain environment. Nevertheless, floodplain forest monitoring requires high resolution mapping as vegetation dynamics are in the narrow area at the interface terrestrial and aquatic systems. Most of the existing satellite images that afford land observations have severe limitations due to their coarse resolution or missing  data caused by the extreme cloudiness conditions in of tropical regions.

In the present work, we propose an innovative approach based on high-resolution mapping for the monitoring long-term evolution of vegetation in a highly impacted environment (Uatama river) due to Balbina dam regulation.  We combine Landsat (30m spatial resolution and 16 days revisit cycle) and the MODIS missions (500m spatial resolution and daily revisit cycle), using HISTARFM algorithm, to reduce noise and produce monthly gap-free high-resolution (30 m) observations over land. Areas characterized by vegetation changes are identified through the analysis of of vegetation index products derived from the high-resolution reflectance data.  Furthermore, hydrological modification within these areas are assessed by using a global water surface dataset.

We found a deep redistribution of floodplain forest concentrated in areas that experienced a hydrologic regime transition after dam construction. The vegetation changes comprise not only vegetation degradation of areas with greater hydrological stress but also with large floodplain areas not flooded afterwards, which were invaded by upland forest. Although the dam was built more than 30 years ago, its effects on the vegetation continue and the situation seems far from reaching a new environmental equilibrium.

The framework proposed offers a practical and novel tool to accurately monitor riparian vegetation dynamics over time even for very remote and poorly accessible areas such as tropical floodplains. Furthermore, the assessment of the impact that the human footprint has on tropical floodplain allows a more careful management of the watersheds.

How to cite: Salerno, L., Moreno-Martínez, A., Izquierdo-Verdiguier, E., Clinton, N., Siviglia, A., and Camporeale, C.: The long-term floodplain forest modifications of a regulated tropical river, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13063, https://doi.org/10.5194/egusphere-egu22-13063, 2022.