HS6.4

Tropical floodplain forests are among the most complex ecosystem on earth, featured by vegetation adapted to survive in seasonal flood environments. Although their ability to resist the periodic water level oscillations, recent studies have shown that riparian forests are extremely sensitive to long-term hydrological changes caused by both anthropogenic and natural disturbances. During the recent decades fragmentation and regulation of rivers induced severe alterations of natural “flood pulse” and sediment supply along the whole watercourse, causing massive tree mortality and compromising seeds spreading. The hydroclimatic anomalies of El Nino/Southern Oscillation (ENSO) and climate change impact on riparian environments, aggravating forest stress and vulnerability to fires, in cases of prolonged drought, while inducing tree mortality for anoxia, when a multi-year uninterrupted flood occurred.

In order to develop future solutions to mitigate the consequences of these disturbances and to enable a sustainable and effective management of riparian forests in the aquatic-terrestrial transitional zone (ATTZ), large-scale monitoring of these areas is necessary. Mapping and monitoring of floodplain vegetation are extremely important not only to assess vegetation status but also because vegetation represents an indicator for early signs of any physical or chemical environmental degradation. Remote sensing offers practical and efficient techniques to estimate biochemical and biophysical parameters and analyse their evolution over time even for very remote and poor accessible areas such as tropical floodplains. Nevertheless, as the main vegetation dynamics are in the narrow area at the interface terrestrial and aquatic systems, a high spatial and temporal resolution of the data is needed for their analysis. Furthermore, the extreme cloudiness of tropical regions contaminates the land surface observation causing gap in the data.

In the present study, we combine Landsat (30m spatial resolution and 16 day revisit cycle) and the MODIS missions, both from Terra and Aqua platforms (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 and the associated estimation of uncertainties. Subsequently, high resolution maps of normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were derived from the high-resolution gap free reflectance data. Furthermore, estimation of NDVI and EVI uncertainties was calculated through an error propagation analyses from uncertainties of reflectance estimates.

The framework we developed has been used to derive high resolution mapping of floodplain vegetation in the large tropical rivers that during the last decades experimented a hydrological regime transition. In a first-phase, vegetation dynamic analysis focused of the tropical large rivers in Amazonia and preliminary results of the temporal series will be presented.

The coupling of hydro-geomorphological and vegetation data enables the monitoring of riparian vegetation dynamics and a better understanding of the impact that the human footprint and climate change have on them.

How to cite: Salerno, L., Moreno-Martínez, Á., Izquierdo-Verdiguier, E., Clinton, N., Siviglia, A., and Camporeale, C.: High-resolution mapping of floodplain vegetation changes in large tropical rivers, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12994, https://doi.org/10.5194/egusphere-egu21-12994, 2021.

Terrestrial vegetation couples the global water, energy and carbon exchange between the atmosphere and the land surface. Thereby, vegetation productivity is determined by a multitude of energy- and water-related variables. While the emergent sensitivity of productivity to these variables has been inferred from Earth observations, its temporal evolution during the last decades is unclear, as well as potential changes in response to trends in hydro-climatic conditions. In this study, we analyze the changing sensitivity of global vegetation productivity to hydro-climate conditions by using satellite-observed vegetation indices (i.e. NDVI) at the monthly timescale from 1982–2015. Further, we repeat the analysis with simulated leaf area index and gross primary productivity from the TRENDY vegetation models, and contrast the findings with the observation-based results. We train a random forest model to predict anomalies of productivity from a comprehensive set of hydro-meteorological variables (temperature, solar radiation, vapor pressure deficit, surface and root-zone soil moisture and precipitation), and to infer the sensitivity to each of these variables. By training models from temporal independent subsets of the data we detect the evolution of sensitivity across time. Results based on observations show that vegetation sensitivity to energy- and water-related variables has significantly changed in many regions across the globe. In particular we find decreased (increased) sensitivity to temperature in very warm (cold) regions. Thereby, the magnitude of the sensitivity tends to differ between the early and late growing seasons. Likewise, we find changing sensitivity to root-zone soil moisture with increases predominantly in the early growing season and decreases in the late growing season. For better understanding the mechanisms behind the sensitivity changes, we analyse land-cover changes, hydro-climatic trends, and abrupt disturbances (e.g. drought, heatwave events or fires could result in breaking points of sensitivity evolution in the local interpretation). In summary, this study sheds light on how and where vegetation productivity changes its response to the drivers under climate change, which can help to understand possibly resulting changes in spatial and temporal patterns of land carbon uptake.

How to cite: Li, W., Forkel, M., Migliavacca, M., Reichstein, M., Walther, S., Denissen, J., and Orth, R.: Changing sensitivity of global vegetation productivity to hydro-climate drivers, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1783, https://doi.org/10.5194/egusphere-egu21-1783, 2021.

Quantification of vegetation parameters such as Vegetation Optical Depth (VOD) and Vegetation Water Content (VWC) can be used for better irrigation management, yield forecasting, and soil moisture estimation. Since VOD is directly related to vegetation water content and canopy structure, it can be used as an indicator for VWC. Over the past few decades, optical and passive microwave satellite data have mostly been used to monitor VWC. However, recent research is using active data to monitor VOD and VWC benefitting from their high spatial and temporal resolution.

Attenuation of the microwave signal through the vegetation layer is parametrized by the VOD. VOD is assumed to be linearly related to VWC with the proportionality constant being an empirical parameter b. For a given wavelength and polarization, b is assumed static and only parametrized as a function of vegetation type. The hypothesis of this study is that the VOD is not similar for dry and wet vegetation and the static linear relationship between attenuation and vegetation water content is a simplification of reality.

The aim of this research is to understand the effect of surface canopy water on VOD estimation and the relationship between VOD and vegetation water content during the growing season of a corn canopy. In addition to studying the dependence of VOD on bulk VWC for dry and wet vegetation, the effect of different factors, such as different growth stages and internal vegetation water content is investigated using time series analysis.

A field experiment was conducted in Florida, USA, for a full growing season of sweet corn. The corn field was scanned every 30 minutes with a truck-mounted, fully polarimetric, L-band radar. Pre-dawn vegetation water content was measured using destructive sampling three times a week for a full growing season. VWC could therefore be analyzed by constituent (leaf, stem, ear) or by height. Meteorological data, surface canopy water (dew or interception), and soil moisture were measured every 15 minutes for the entire growing season.

The methodology of Vreugdenhil et al.  [1], developed by TU Wien for ASCAT data, was adapted to present a new technique to estimate VOD from single-incidence angle backscatter data in each polarization. The results showed that the effect of surface canopy water on the VOD estimation increased by vegetation biomass accumulation and the effect was higher in the VOD estimated from the co-pol compared with the VOD estimated from the cross-pol. Moreover, the surface canopy water considerably affected the regression coefficient values (b-factor) of the linear relationship between VOD and VWC from dry and wet vegetation. This finding suggests that considering a similar b-factor for the dry and the wet vegetation will introduce errors in soil moisture retrievals. Furthermore, it highlights the importance of considering canopy wetness conditions when using tau-omega.

• [1] Vreugdenhil,W. A. Dorigo,W.Wagner, R. A. De Jeu, S. Hahn, andM. J. VanMarle, “Analyzing the vegetation parameterization in the TU-Wien ASCAT soil moisture retrieval,” IEEE Transactions on Geoscience and Remote Sensing, vol. 54, pp. 3513–3531, 2016

How to cite: Khabbazan, S., Vermunt, P. C., Steele Dunne, S. C., Gao, G., Vreugdenhil, M., and Judge, J.: Inferring the effects of surface canopy water on VOD estimation from L-band backscatter, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6461, https://doi.org/10.5194/egusphere-egu21-6461, 2021.

Vegetation optical depth (VOD) is a remotely sensed indicator characterizing the attenuation of the Earth's thermal emission at microwave wavelengths by the vegetation layer. At L-band, VOD can be used to estimate and monitor aboveground biomass (AGB), a key component of the Earth's surface and of the carbon cycle. We observed a strong anti-correlation between SMOS (Soil Moisture and Ocean Salinity) L-band VOD (L-VOD) and soil moisture (SM) anomalies over seasonally inundated areas, confirming previous observations of an unexpected decline in K-band VOD during flooding (Jones et al., 2011). These results could be, at least partially, due to artefacts affecting the retrieval and could lead to uncertainties on the derived L-VOD during flooding. To study the behaviour of SMOS satellite L-VOD retrieval algorithm over seasonally inundated areas, the passive microwave L-MEB (L-band Microwave Emission of the Biosphere) model was used to simulate the signal emitted by a mixed scene composed of soil and standing water. The retrieval over this inundated area shows an overestimation of SM and an underestimation of L-VOD. This underestimation increases non-linearly with the surface water fraction. The phenomenon is more pronounced over grasslands than over forests. The retrieved L-VOD is typically underestimated by ~10% over flooded forests and up to 100% over flooded grasslands. This is mainly due to the fact that i) low vegetation is mostly submerged under water and becomes invisible to the sensor; and ii) more standing water is seen by the sensor. Such effects can distort the analysis of aboveground biomass (AGB) and aboveground carbon (AGC) estimates and dynamics based on L-VOD. Using the L-VOD/AGB relationship from Rodriguez-Fernandez et al. (2018), we evaluated that AGB can be underestimated by 15/20Mg ha^-1 in the largest wetlands, and up to higher values during exceptional meteorological years. Such values are more significant over herbaceous wetlands, where AGB is ~30 Mg ha^-1, than over flooded forests, which have typical AGB values of 150-300 Mg ha^-1. Consequently, to better estimate the global biomass, surface water seasonality has to be taken into account in passive microwave retrieval algorithms.

How to cite: Bousquet, E., Mialon, A., Rodriguez-Fernandez, N., Prigent, C., Wagner, F., and Kerr, Y.: Influence of surface water variations on VOD and biomass estimates from passive microwave sensors, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5786, https://doi.org/10.5194/egusphere-egu21-5786, 2021.

A shift to more sustainable land cultivation practices is necessary to meet the future crop demand, which faces a vastly growing population and changing climatic conditions. To assess which management practices can be effectively applied at a regional scale, good spatial monitoring techniques are required. With a regional version of the AquaCrop model v6.1, we simulate crop biomass production and soil moisture at a 1-km resolution over Europe. Biomass productivity is compared against the Dry Matter Productivity of the Copernicus Global Land Service, derived from optical satellite sensors, while surface moisture content is evaluated with Sentinel-1 and SMAP microwave satellite retrieval products and inter-compared with in situ data. We show that the AquaCrop model can successfully be applied at a relatively fine resolution over a large scale, using global input data.

This research is part of a H2020 project, named SHui. SHui is a collaborative effort between Universities from Europe and China, with the overall aim of managing water scarcity in cropping systems for individuals as well as stakeholder organizations.

How to cite: de Roos, S., De Lannoy, G., and Raes, D.: Using a regional version of the AquaCrop model to simulate crop biomass and soil moisture: an evaluation with remote sensing data products, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2265, https://doi.org/10.5194/egusphere-egu21-2265, 2021.

Coping with the issue of water scarcity and growing competition for water among different sectors requires effective water management strategies and decision processes. ‘Getting it right’ becomes doubly important when dealing with intenational transboundary rivers. The Yarmouk tributary to the Jordan River is one highly exploited in the Middle East, and is enveloped by ambiguous treaties and decades of violent and non-violent conflict. Seeking to chart a more sustainable and equitable future, this work performs a 'water accounting plus' methodology employing readily available remotely sensed satellite-based data coupled with available measurements.  A variety of methods described herein were used to detect irrigated crops and produce maps showing the distribution throughout the basin. The framework also focuses on the classification of land use categories and the processes by which water is depleted over all land use classes that contributes to separate the beneficial from non-beneficial usage of water. The analysis was started prior to the 2011 start of the Syrian war in order to study the initial distribution of land use classes as well as the water depletion processes before any change in the basin. It shows that more than half of the exploitable water is not consumed within the basin and depleted outside. In contrast, most of the water consumed within the basin is wasted and depleted in a non-beneficial way. Roughly 35% of the cultivated area shown to be irrigated through withdrawals which exceed the capacity of the source. This result reflects the high abstraction rates from groundwater via a large number of unlicensed wells mostly located at the Syrian side. This study also detect a deficiency in the water balance of the Yarmouk River. The findings are relevant to sustainable management not only for water-dependent sectors but also for geopolitical stability among the riparian countries. In this way, open- access remote sensing derived data can provide useful information about the status of water resources especially when ground measurements are poor or absent.

Keywords: Yarmouk, Water Accounting Plus, IWM, Irrigated crops, WAPOR.

How to cite: Abdallah, C., Tarhini, G., Daher, M., Khatib, H., and Zeitoun, M.: Water Accounting Plus (WA+) through Remote Sensing in the Yarmouk Tributary Basin, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8690, https://doi.org/10.5194/egusphere-egu21-8690, 2021.

Drylands contribute strongly to global biogeochemical cycles and their variability. While precipitation is the main driver of plant water availability, secondary water resources like shallow groundwater and lateral convergence of soil moisture may play important roles in supporting ecosystems against water limitation at the local scale. Despite their strong relevance, the effects of secondary water resources are often ignored or highly uncertain in studies over large spatial domains. 

Here, we aimed to quantify the degree to which land properties control secondary water resources over water-limited regions in Africa. To do so, we first detected the seasonal decay periods of Fractional Vegetation Cover (FVC) time series from the changes in FVC over time at daily temporal resolution. FVC data is provided by the EUMETSAT from the image acquisitions from the geostationary satellite MSG. We then calculated the seasonal decay rate of FVC (λ) and used it with other climate, land and vegetation properties at 5 km spatial resolution. We hypothesized that any secondary water resource should slow down vegetation decays in drylands. We used gradient boosting machine learning to model λ and constrained the model according to the hypothesis. Finally, we used Shapley additive explanations in order to quantify the effects of land properties on spatial variation of the modelled λ.

Model output (NSE = 0.55) revealed that over drylands of Africa, ∼1/3 of spatial variation of λ is attributed to land properties, half of which is attributed to direct land effects while the rest is attributed to the land interactions with climate and vegetation. Though at local scales, this attribution gets much stronger over hotspots with strong secondary water resources, i.e., shallow groundwater. Spatially, land attributed variations of λ show that vegetation decays slower in regions with shallow groundwater and faster in regions where land surface is disconnected from the groundwater. Topographic complexity is another important factor, with slower vegetation decay in complex terrain, likely due to enhanced lateral moisture convergence. Moreover, these responses intensify with increasing climatological water limitation. 

We found strong effects of land parameters on seasonal vegetation decay rate, spatially structured but at local scales. This highlights the importance of local scale processes affecting water availability in drylands not only at local but also continental to global scales and shows the need of bridging processes across spatial scales in regional-to-global hydrological and vegetation models.

How to cite: Küçük, Ç., Koirala, S., Carvalhais, N., Miralles, D. G., Reichstein, M., and Jung, M.: Local-scale secondary water resources modulate seasonal water limitation across Africa, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1572, https://doi.org/10.5194/egusphere-egu21-1572, 2021.

Satellite based remote sensing plays important role in studying regional to continental scale drought. One of the unique elements of remote sensing platforms is their multi-sensor capabilities, which enhance the capacity for characterizing drought from a variety of aspects. However, multi-sensor integrated drought evaluation is in its infancy. To advocate and encourage on-going exploration and integration of multi-sensor remote sensing for drought studies, we provide an overview of the role of multi-sensor remote sensing for addressing knowledge gaps and driving advances in drought studies. We first present a comprehensive summary of large-scale drought-related remote sensing datasets that can be used for multi-sensor drought studies. Then we provide a detailed review of how the integrated multi-sensor remote sensing could enhance our analysis in multiple important drought related phenomena and mechanisms such as drought-induced tree mortality, drought-related ecosystem fires, post-drought recovery and legacy effects, flash drought, and drought trends under climate change. We also provide a summary of recent modeling advances towards developing integrated multi-sensor remote sensing drought indices. We highlight that leveraging multi-sensor remote sensing provides unique benefits for regional to global drought studies, particularly in: 1) revealing the complex drought impact mechanisms on various ecosystem components; 2) providing continuous long-term drought related information at large scales; 3) presenting real-time drought information with high spatiotemporal resolution; 4) providing multiple lines of evidence of drought monitoring to improve modeling and prediction robustness; and 5) improving the accuracy of drought monitoring and assessment efforts.

How to cite: Wang, L., Jiao, W., and McCabe, M.: The role of multi-sensor remote sensing for drought characterization: challenges and opportunities, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1849, https://doi.org/10.5194/egusphere-egu21-1849, 2021.

Teleconnections relate regional pressure patterns to local climate anomalies, influencing the variation of vegetation patterns. Over west continental Europe, droughts have been widely investigated with persistent low-frequency atmospheric circulation patterns (e.g. the North Atlantic Oscillation, NAO) with the centers over the Atlantic based on the 500mb height anomalies of the Northern Hemisphere. However, the effects of teleconnection patterns with the centers of active variability over the North and Caspian Seas is largely unexplored for droughts related to vegetation patterns. In this study, we explored the impact of the North Sea-Caspian Pattern (NCP) on regional ecohydrologic conditions in the Greater Region of Luxembourg in Western Europe. Using a Principal Component Analysis (PCA), we first decomposed the annual Normalized Difference Vegetation Index (NDVI) from the Global Inventory Monitoring and Modeling System (GIMMS) between 1981 and 2015. In the first PCA component, a distinctive greening trend of NDVI is detected since the late 1980s. However, the corresponding station observations and the ERA5 reanalysis data show that the region in west continental Europe became increasingly drier based on the difference between precipitation and evaporation. We explain the above paradoxical greening but drying patterns by the mechanism of NCP over the region. During the positive phase of NCP, the high pressure over the North Sea weakens circulation over the region and leads to warmer conditions in west continental Europe. These conditions are good for vegetation growth because the region was mainly energy-limited during the observed period at the annual scale based on a Budyko analysis. However, the positive phase of NCP also promotes divergent conditions at the lower troposphere and it reduces moisture flux over the region. In the Budyko space, the persistent positive phase of NCP would lead the energy-limited region to be water-limited. As the positive phase of NCP is expected to be more frequent along with the increasing global temperatures, the region may start to experience increasing water stress on vegetation. These results suggest that unforeseen droughts related to vegetation may be emerging in the region. New drought monitoring and management measures related to vegetation should be developed at west continental Europe, especially during the positive phase of NCP.

How to cite: Xu, B., He, Q., Chun, K. P., Klaus, J., Schoppach, R., and Yetemen, Ö.: Detecting drought conditions related to vegetation at west continental Europe based on variations from the North and Caspian Seas, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10518, https://doi.org/10.5194/egusphere-egu21-10518, 2021.