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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]

Airborne laser scanning (ALS) data can be used for downscaling point-based forest inventory (FI) measurements to obtain spatially distributed estimates of forest parameters at a more detailed, local scale. Such downscaling algorithms usually consist of a direct coupling between selected FI parameters and ALS data collected at the field sampling locations. Thus, precise co-registration between FI and ALS data is an essential preprocessing step to obtain accurate predictive relationships. This paper presents a new, automated co-registration approach that searches iteratively for the best match between an ALS-based canopy height model and the tree positions and heights measured during the FI. While the basic principle of the algorithm applies to various types of FI sampling configurations, the co-registration approach was developed specifically to take into account the tree selection criterion posed by angle count sampling. The angle count sampling method only includes trees that at a given distance from the sample plot centre have a minimum required diameter at breast height (DBH). This tree selection criterion leads to maximum plot radii and number of inventoried trees that strongly vary from sample plot to sample plot. In the automated co-registration procedure, several criteria (e.g. the occurrence of more than one spatial cluster of minimum residuals and a predominance of deciduous trees in a sample plot) were used to detect possible uncertain solutions and to reduce post-processing efforts by an image operator. Model calibration and validation were based on national forest inventory (NFI) and ALS data from the Austrian federal state of Vorarlberg. Transferability and robustness of the approach was verified using an independent local FI. The results show that 68% of the NFI sample plots and 74% of the local FI plots could be automatically co-registered to a location at a distance of less than 5.0 m from the reference location. The maximum difference of 5.0 m used for marking a solution as correct was based on the relatively small influence that deviations of up to this value have on ALS-based predictions of biophysical forest variables at a stand level. The quality flagging criteria adopted were very successful in identifying uncertain solutions; only one out of 153 co-registered sample plots with a deviation from the reference data set greater than 5.0 m was not identified as uncertain. Applying the automatically co-registered sample plots in calibration of a growing stock model provided estimates that were clearly superior to those obtained with the original plot positions and even slightly outperformed those based on manual co-registration. As the algorithm developed will be part of an operational processing chain for Austrian NFI data, it has a high practical relevance. [ABSTRACT FROM AUTHOR]

This paper evaluates the performance of a recently developed approach for wide-area stem volume estimations based on airborne laser scanning (ALS) and national forest inventory (NFI) data in the case where data recorded under operational conditions are used as input. This entails that neither ALS data nor NFI samples were collected and optimized for the current study. The approach was tested for the Austrian state of Vorarlberg, which covers an area of 2601 km2 and encloses about 970 km2 of forest land. ALS data with point densities varying between 1 and 4 points m-2 were acquired in the framework of a commercial state-wide terrain mapping project during several winter- and summer-flight campaigns. The stem volume model was calibrated with all NFI data available for Vorarlberg, whereas additional local forest inventory data were used for independent validation. Moreover, several relevant operational issues were addressed in this study, such as the determination of the optimum area used to calculate the reference laser metrics input to the model, the effect of gridding point cloud data to speed up processing, and the stratification of input data into coniferous and deciduous sample plots. Without tree species stratification and based on the 3D laser heights model, calibration provided a maximum R2 of 0.79 and a standard deviation (SD) of residuals derived from cross-validation of 107.4 m3 ha-1 (31.5%). Calibrating the model only with coniferous samples increased the achieved R2 to 0.81 and decreased SD to 104.8 m3 ha-1 (29.7%). As only eight NFI sample plots were available for deciduous forest a robust calibration of a separate model could not be obtained. Calibrating the model with a rasterized canopy height model (CHM) instead of using the 3D laser heights just led to a slight decrease in accuracy (R2 = 0.75, SD = 120.9 m3 ha-1 (35.5%) without forest-type stratification and R2 = 0.78 and SD = 117.2 m3 ha-1 (33.1%) for the coniferous stem volume model). Finally, the stem volume model calibrated with CHM data was adopted to generate a stem volume map of the entire State of Vorarlberg. Validation of this map with the additional local forest inventory data confirmed the accuracies (R2 = 0.75; SD = 135.6 m3 ha-1 (32.3%)) that were derived during calibration of the stem volume model based on the NFI data. The models and methods presented in this study are used operationally for forest and environment policy purposes and practical applications in Austria. [ABSTRACT FROM AUTHOR]

With the increasing occurrence of forest fires in the mid-latitudes and the alpine region, fire risk assessments become important in these regions. Fuel assessments involve the collection of information on forest structure as, e.g., the stand height or the stand density. The potential of airborne laser scanning (ALS) to provide accurate forest structure information has been demonstrated in several studies. Yet, flight acquisitions at the state level are carried out in intervals of typically five to ten years in Central Europe, which often makes the information outdated. The Sentinel-1 (S-1) synthetic aperture radar mission provides freely accessible earth observation (EO) data with short revisit times of 6 days. Forest structure information derived from this data source could, therefore, be used to update the respective ALS descriptors. In our study, we investigated the potential of S-1 time series to derive stand height and fractional cover, which is a measure of the stand density, over a temperate deciduous forest in Austria. A random forest (RF) model was used for this task, which was trained using ALS-derived forest structure parameters from 2018. The comparison of the estimated mean stand height from S-1 time series with the ALS derived stand height shows a root mean square error (RMSE) of 4.76 m and a bias of 0.09 m on a 100 m cell size, while fractional cover can be retrieved with an RMSE of 0.08 and a bias of 0.0. However, the predictions reveal a tendency to underestimate stand height and fractional cover for high-growing stands and dense areas, respectively. The stratified selection of the training set, which we investigated in order to achieve a more homogeneous distribution of the metrics for training, mitigates the underestimation tendency to some degree, yet, cannot fully eliminate it. We subsequently applied the trained model to S-1 time series of 2017 and 2019, respectively. The computed difference between the predictions suggests that large decreases in the forest height structure in this two-year interval become apparent from our RF-model, while inter-annual forest growth cannot be measured. The spatial patterns of the predicted forest height, however, are similar for both years (Pearson's R = 0.89). Therefore, we consider that S-1 time series in combination with machine learning techniques can be applied for the derivation of forest structure information in an operational way. [ABSTRACT FROM AUTHOR]

During the last decade, satellite-based precipitation products have been believed to be a potential source for forcing inputs in hydro-meteorological and agricultural models, which are essential especially over the mountains area or in basins where ground gauges are generally sparse or nonexistent. This study comprehensively evaluates several newly released precipitation products, i.e., MSWEP-V2.2, IMERG-V05B, IMERG-V06A, IMERG-V05-RT, ERA5, and SM2RAIN-ASCAT, at daily and monthly time-scales over Austria. We show that all the examined products are able to reproduce the spatial precipitation distribution over the country. MSWEP, followed by IMERG-V05B and -V06A, show the strongest agreement with in situ observations and perform better than other products with respect to spatial patterns and statistical metrics. Both IMERG and ERA5 products seem to have systematic precipitation overestimation at the monthly time-scale. IMERG-V06A performs slightly better than IMERG-V05B. With respect to heavy precipitation (P > 10 mm/day), MSWEP compare to other products demonstrate advantages in detecting precipitation events with a higher spatial average of probability of detection (POD) and lower false alarm ratio (FAR) scores skill (0.74 and 0.28), while SM2RAIN and ERA5 reveal lower POD (0.35) and higher FAR (0.56) in this precipitation range in comparison with other products. However, the ERA5 and MSWEP indicate robust average POD and FAR values with respect to light/moderate precipitation (10 mm > P ≥ 0.1 mm) with 0.94 and 0.11, respectively. Such robustness of MSWEP may be rooted in applying the daily rain gauges in calibration processes. Moreover, although all products accurately map the spatial precipitation distribution they still have difficulty capturing the effects of topography on precipitation. The overall performance of the precipitation products was lower in the peripheries of the study area where most stations are situated in the mountainous area and was higher over the low altitude regions. However, according to our analysis of the considered products, MSWEP-V2.2, followed by IMERG-V06S and -V05B, are the most suitable for driving hydro-meteorological, agricultural, and other models over mountainous terrain. [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 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]

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]