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Terrain-induced variations of radar backscatter represent an important limiting factor of many Synthetic Aperture Radar (SAR)-based applications. Radiometric terrain flattening (RTF) is a well-established method that minimizes these variations in SAR imagery. To fully understand the implications of SAR RTF, validation of its impact on the derived products is needed. In this study, we quantified the influence of the RTF on a forest mapping and classification algorithm over Austria, and compared the classification results for the conventional sigma naught and radiometrically terrain-corrected gamma backscatter. The overall accuracy for forest/non-forest mapping and forest type classification improved by 2% and 4%, respectively, over the whole of Austria, with improvements of up to 16% and 20%, respectively, in regions with strong topography. [ABSTRACT FROM AUTHOR]

The recent advances in remote sensing provide opportunities for estimating the parameters of conceptual hydrologic models more reliably. However, the question of whether and to what extent the use of satellite data in model calibration may assist in transferring model parameters to ungauged catchments has not been fully resolved. The aim of this study is to evaluate the efficiency of different methods for transferring model parameters obtained by multiple-objective calibrations to ungauged sites and to assess the model performance in terms of runoff, soil moisture, and snow cover predictions relative to existing regionalization approaches. The model parameters are calibrated to daily runoff, satellite soil moisture (Advanced Scatterometer – ASCAT), and snow cover (Moderate Resolution Imaging Spectroradiometer – MODIS) data. The assessment is based on 213 catchments situated in different physiographic and climate zones of Austria. For the transfer of model parameters, eight methods (global and local variants of arithmetic mean, regression, spatial proximity, and similarity) are examined in two periods, i.e., the period in which the model is calibrated (2000–2010) and an independent validation period (2010–2014). The predictive accuracy is evaluated by the leave-one-out cross-validation. The results show that the method by which the model is calibrated in the gauged catchment has a larger impact on runoff prediction accuracy in the ungauged catchments than the choice of the parameter transfer method. The best transfer methods are global and local similarity and the kriging approach. The performance of the transfer methods differs between lowland and alpine catchments. While the soil moisture and snow cover prediction efficiencies are higher in lowland catchments, the runoff prediction efficiency is higher in alpine catchments. A comparison of the model transfer methods, based on parameters calibrated to runoff, snow cover, and soil moisture with those based on parameters calibrated to runoff, only indicates that the former outperforms the latter in terms of simulating soil moisture and snow cover. The performance of simulating runoff is similar, and the accuracy depends mainly on the weight given to the runoff objective in the multiple-objective calibrations. [ABSTRACT FROM AUTHOR]

Recent advances in soil moisture remote sensing have produced satellite data sets with improved soil moisture mapping under vegetation and with higher spatial and temporal resolutions. In this study, we evaluate the potential of a new, experimental version of the Advanced Scatterometer (ASCAT) soil water index data set for multiple objective calibrations of a conceptual hydrologic model. The analysis is performed in 213 catchments in Austria for the period 2000–2014. An HBV (Hydrologiska Byråns Vattenbalansavdelning)-type hydrologic model is calibrated based on runoff data, ASCAT soil moisture data, and Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover data for various calibration variants. Results show that the inclusion of soil moisture data in the calibration mainly improves the soil moisture simulations, the inclusion of snow data mainly improves the snow simulations, and the inclusion of both of them improves both soil moisture and snow simulations to almost the same extent. The snow data are more efficient at improving snow simulations than the soil moisture data are at improving soil moisture simulations. The improvements of both runoff and soil moisture model efficiencies are larger in low elevation and agricultural catchments than in others. The calibrated snow-related parameters are strongly affected by including snow data and, to a lesser extent, by soil moisture data. In contrast, the soil-related parameters are only affected by the inclusion of soil moisture data. The results indicate that the use of multiple remote sensing products in hydrological modeling can improve the representation of hydrological fluxes and prediction of runoff hydrographs at the catchment scale. [ABSTRACT FROM AUTHOR]

Citizen science, scientific work and data collection conducted by or with non-experts, is rapidly growing. Although the potential of citizen science activities to generate enormous amounts of data otherwise not feasible is widely recognized, the obtained data are often treated with caution and scepticism. Their quality and reliability is not fully trusted since they are obtained by non-experts using low-cost instruments or scientifically non-verified methods. In this study, we evaluate the performance of Parrot's Flower Power soil moisture sensor used within the European citizen science project the GROW Observatory (GROW; https://growobservatory.org , last access: 30 March 2020). The aim of GROW is to enable scientists to validate satellite-based soil moisture products at an unprecedented high spatial resolution through crowdsourced data. To this end, it has mobilized thousands of citizens across Europe in science and climate actions, including hundreds who have been empowered to monitor soil moisture and other environmental variables within 24 high-density clusters around Europe covering different climate and soil conditions. Clearly, to serve as reference dataset, the quality of ground observations is crucial, especially if obtained from low-cost sensors. To investigate the accuracy of such measurements, the Flower Power sensors were evaluated in the lab and field. For the field trials, they were installed alongside professional soil moisture probes in the Hydrological Open Air Laboratory (HOAL) in Petzenkirchen, Austria. We assessed the skill of the low-cost sensors against the professional probes using various methods. Apart from common statistical metrics like correlation, bias, and root-mean-square difference, we investigated and compared the temporal stability, soil moisture memory, and the flagging statistics based on the International Soil Moisture Network (ISMN) quality indicators. We found a low intersensor variation in the lab and a high temporal agreement with the professional sensors in the field. The results of soil moisture memory and the ISMN quality flags analysis are in a comparable range for the low-cost and professional probes; only the temporal stability analysis shows a contrasting outcome. We demonstrate that low-cost sensors can be used to generate a dataset valuable for environmental monitoring and satellite validation and thus provide the basis for citizen-based soil moisture science. [ABSTRACT FROM AUTHOR]

Citizen science, scientific work and data collection conducted by or with non-experts, is rapidly growing. Although the potential of citizen science activities to generate enormous amounts of data otherwise not feasible is widely recognized, the obtained data are often treated with caution and skepticism. Their quality and reliability is not fully trusted since they are obtained by non-experts using low-cost instruments or scientifically non-verified methods. In this study, we evaluate the performance of Parrot's Flower Power soil moisture sensor used within the European citizen science project, the GROW Observatory (GROW; https://growobservatory.org). The aim of GROW is to enable scientists to validate satellite-based soil moisture products at an unprecedented high spatial resolution through crowdsourced data. To this end, it has mobilized thousands of citizens across Europe in science and climate actions, including hundreds who have been empowered to monitor soil moisture and other environmental variables within twenty four high-density clusters around Europe covering different climate and soil conditions. Clearly, to serve as reference dataset, the quality of ground observations is crucial, especially if obtained from low-cost sensors. To investigate the accuracy of such measurements, the Flower Power sensors were evaluated in the lab and field. For the field trials, they were installed alongside professional soil moisture probes in the Hydrological Open Air Laboratory (HOAL) in Petzenkirchen, Austria. We assessed the skill of the low cost sensors against the professional probes using various methods. Apart from common statistical metrics like correlation, bias and root-mean-square difference, we investigated and compared the temporal stability, soil moisture memory, and the flagging statistics based on the International Soil Moisture Network (ISMN) quality indicators. We found a low inter-sensor variation in the lab and a high temporal agreement with the professional sensors in the field. The results of soil moisture memory and the ISMN quality flags analysis are in a comparable range for the low-cost and professional probes, only the temporal stability analysis shows a contrasting outcome. We demonstrate that low-cost sensors can be used to generate a dataset valuable for environmental monitoring and satellite validation and thus provide the basis for citizen-based soil moisture science. [ABSTRACT FROM AUTHOR]

To ensure future food security, improved agricultural management approaches are required. For many of those applications, precise knowledge of the distribution of crop types is essential. Various machine and deep learning models have been used for automated crop classification using microwave remote sensing time series. However, the application of these approaches on a large spatial and temporal scale is barely investigated. In this study, the performance of two frequently used algorithms, Long Short-Term Memory (LSTM) networks and Random Forest (RF), for crop classification based on Sentinel-1 time series and meteorological data on a large spatial and temporal scale is assessed. For data from Austria, the Netherlands, and France and the years 2015–2019, scenarios with different spatial and temporal scales were defined. To quantify the complexity of these scenarios, the Fisher Discriminant measurement F1 (FDR1) was used. The results demonstrate that both classifiers achieve similar results for simple classification tasks with low FDR1 values. With increasing FDR1 values, however, LSTM networks outperform RF. This suggests that the ability of LSTM networks to learn long-term dependencies and identify the relation between radar time series and meteorological data becomes increasingly important for more complex applications. Thus, the study underlines the importance of deep learning models, including LSTM networks, for large-scale applications. [ABSTRACT FROM AUTHOR]

The role of soil moisture is widely accepted as a significant factor in the mass and energy balance of catchments as a controller in surface and subsurface runoff generation. The paper examines the potential of a new dataset based on advanced scatterometer satellite remote sensing of soil moisture (ASCAT) for multiple objective calibrations of a dual-layer, conceptual, semi-distributed hydrological model. The surface and root zone soil moisture indexes based on ASCAT data were implemented into calibration of the hydrological model. Improvements not only in the instrument specifications, i.e., better temporal and spatial sampling, but also in the higher radiometric accuracy and retrieval algorithm, were applied. The analysis was performed in 209 catchments situated in different physiographic and climate zones of Austria for the period 2007–2018. We validated the model for two validation periods. The results show that multiple objective calibrations have a substantial positive effect on constraining the model parameters. The combined use of soil moisture and discharges in the calibration improved the soil moisture simulation in more than 73% of the catchments, except for the catchments with higher forest cover percentages. Improvements also occurred in the runoff model efficiency, in more than 27% of the catchments, mostly in the watersheds with a lower mean elevation and a higher proportion of farming land use, as well as in the Alpine catchments where the runoff is not significantly influenced by snowmelt and glacier runoff. [ABSTRACT FROM AUTHOR]

Scatterometer observations over land are sensitive to the water content in soil and vegetation, but have been rarely used to study seasonal changes in the plant water status and seasonal development of deciduous trees. Here we use Advanced Scatterometer (ASCAT) observations to investigate the sensitivity of C-band backscatter to spring phenology of temperate deciduous broadleaf forests in Austria. ASCAT's multi-angle looking capability enables the observation of backscatter over a large range of incidence angles. The vegetation status affects the slope of the backscatter-incidence angle relationship. We discovered a maximum in the slope around the month April, hereafter referred to as spring peak, predominantly in regions covered by deciduous broadleaf forest. We hypothesized that the spring peak indicates the average timing of leaf emergence in the deciduous trees in the sensor footprint. The hypothesis was tested by comparing the timing of the spring peak to leaf unfolding observations from the PEP725 phenology database, to the increase of leaf area index (LAI) during spring, and to temperature. Our results demonstrate a good agreement between the ASCAT spring peaks, phenology observations and temperature conditions. The steepest increase in LAI however lags behind the ASCAT peak by several days to a few weeks, suggesting that the spring peak in fact marks the timing of maximum woody water content, which occurs right before leaf emergence. Based on these observations, we conclude that the ASCAT signal has a high sensitivity to spring reactivation and in particular water uptake of bare deciduous broadleaf trees. Our findings might provide the basis for novel developments to estimate eco-physiological changes of forests during spring at large scales. Unlabelled Image • Metop ASCAT backscatter and slope are sensitive to vegetation phenology. • In spring, a peak in the slope is observed over deciduous broadleaf forest (DBF). • This spring peak is hypothesized to be caused by leaf emergence over DBF. • Comparison to ground phenology, leaf area index, and temperature data. • Time delay to LAI suggests that the real cause is spring water uptake in DBF. [ABSTRACT FROM AUTHOR]

The emergence of leaves on deciduous broadleaf (DB) trees marks the start of the growing season in temperate forests. While the timing of this event affects the carbon, water and energy exchange between the vegetation and the atmosphere, its shift is also an indicator of climate change, which makes the monitoring of this variable highly valuable (Buermann et al., 2018). Observing vegetation phenology, including leaf-out of deciduous trees, has a long tradition in many European countries. Since 2004, two reference datasets of phenological observations have been established: COST725 and its successor, the Pan European Phenology project (PEP725), which is used in this study (Templ et al., 2018). The PEP725 database provides leaf-out dates for different DB species all over Europe, including observations dating back to the 1950s. However, a drawback of combining observations from a number of different phenological networks is the fact that the quality of the different datasets and consequently of the PEP725 database varies widely.Satellite remote sensing provides a tool to observe different variables of the Earth system globally under the same measuring conditions. In the context of monitoring vegetation dynamics, it has been shown that the Advanced Scatterometer (ASCAT) on-board the Metop-A satellite is sensitive to seasonal changes in the structure and water content of crops (Vreugdenhil et al., 2017). In this study, we show the sensitivity of ASCAT to the phenology of deciduous trees by comparing ASCAT to PEP725 observations over Austria. A comprehensive validation of leaf-out dates derived from ASCAT is carried out for a period of 10 years over regions with different climatological and topographical conditions. The median absolute difference between the leaf-out dates derived from ASCAT and the PEP725 database is less than 10 days when including all 188 sites available in Austria. Moreover, we found that the order in which different tree species leaf-out is constant: the silver birch (Betula pendula) is always the first and the common oak (Quercus robur) the last species to leaf-out, with a period of 1-2 weeks in between. The leaf-out event derived from ASCAT is found between those two species in most years. This study shows how microwave remote sensing can complement in situ observations of vegetation phenology and thus support current efforts in quantifying effects of climate change on DB forests and consequently the global carbon cycle.References:Buermann, W., Forkel, M., O'Sullivan, M., Sitch, S., Friedlingstein, P., Haverd, V.,... & Lombardozzi, D. (2018). Widespread seasonal compensation effects of spring warming on northern plant productivity. Nature, 562(7725), 110.Templ, B., Koch, E., Bolmgren, K., Ungersböck, M., Paul, A., Schiefinger, H.,... & Kaspar, F. (2018). Pan European Phenological database (PEP725): a single point of access for European data. International journal of biometeorology, 1-5.Vreugdenhil, M., Hahn, S., Melzer, T., Bauer-Marschallinger, B., Reimer, C., Dorigo, W. A., & Wagner, W. (2017). Assessing vegetation dynamics over mainland Australia with metop ASCAT. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(5), 2240-2248. [ABSTRACT FROM AUTHOR]

The involvement of non-experts in scientific work and data collection, namely citizen science, is rapidly increasing. Although the potential of citizen science activities and projects to generate huge amounts of data otherwise not feasible is widely recognized, the obtained data and observations are often treated with caution and skepticism. Their quality and reliability is not fully trusted since they are obtained by non-experts using low-cost instruments and/or simplified methods. In this study, we evaluate the performance of the low-cost soil moisture sensor used within the European citizen science project GROW Observatory (GROW; https://growobservatory.org/). The aim of GROW is to empower hundreds of motivated citizens to monitor soil moisture and other environmental variables within nine high-density clusters around Europe covering different climate and soil conditions. The citizen's contribution is laying the foundation for scientists to validate satellite-based soil moisture products at an unprecedented high spatial resolution. Clearly, to serve as reference dataset, the quality of ground observations is crucial, especially if obtained from low-cost sensors. To investigate the accuracy of such measurements, the low-cost sensors were installed alongside professional soil moisture probes in the Hydrological Open Air Laboratory (HOAL) in Petzenkirchen, Austria, where data has already been collected for more than a year. We assess the skill of the low cost sensors against the professional probes by the means of various methods. Apart from common statistical metrics like correlation, bias and rmsd, we investigate and compare the temporal stability as well as soil moisture memory. We will demonstrate that low-cost sensors can be used to generate a dataset valuable for environmental monitoring and satellite validation and thus provide the basis for citizen-based soil moisture science.This study is funded by the GROW Observatory project of the European Union's Horizon 2020 research and innovation programme (https://growobservatory.org/) and the International Soil Moisture Network under IDEAS+ of the European Space Agency (https://ismn.geo.tuwien.ac.at/). [ABSTRACT FROM AUTHOR]