Salt pans are unique wetland ecosystems. In the Austrian Seewinkel region, salt pans are in an increasingly vulnerable state due to groundwater drainage and heightened climatic pressures. It is crucial to model how seasonal and long-term hydrological and climatological variations affect the salt pan dynamics in Seewinkel, yet a comprehensive understanding of the driving processes is lacking. The goal of this study is to develop random forest machine learning models driven by hydrological and meteorological data that allow us to predict in early spring (March) of each year the inundation state in the subsequent summer and fall. We utilize Earth observation data from Landsat 5 (L5), 8 (L8), and 9 (L9) to derive the time series of the inundation state for 34 salt pans for the period 1984–2022. Furthermore, we demonstrate that the groundwater level observed in March is the strongest predictor of the salt pan inundation state in summer and fall. Utilizing local groundwater data yields a Matthews correlation coefficient of 0.59. Models using globally available meteorological data, either instead of or in addition to groundwater data, provide comparable results. This allows the global transfer of the approach to comparable ecosystems where no in situ data are available.

Wildfires and land use play a central role in the long‐term carbon (C) dynamics of forested ecosystems of the United States. Understanding their linkages with changes in biomass, resource use, and consumption in the context of climate change mitigation is crucial. We reconstruct a long‐term C balance of forests in the contiguous U.S. using historical reports, satellite data, and other sources at multiple scales (national scale 1926–2017, regional level 1941–2017) to disentangle the drivers of biomass C stock change. The balance includes removals of forest biomass by fire, by extraction of woody biomass, by forest grazing, and by biomass stock change, their sum representing the net ecosystem productivity (NEP). Nationally, the total forest NEP increased for most of the 20th century, while fire, harvest and grazing reduced total forest stocks on average by 14%, 51%, and 6%, respectively, resulting in a net increase in C stock density of nearly 40%. Recovery from past land‐use, plus reductions in wildfires and forest grazing coincide with consistent forest regrowth in the eastern U.S. but associated C stock increases were offset by increased wood harvest. C stock changes across the western U.S. fluctuated, with fire, harvest, and other disturbances (e.g., insects, droughts) reducing stocks on average by 14%, 81%, and 7%, respectively, resulting in a net growth in C stock density of 14%. Although wildfire activities increased in recent decades, harvest was the key driver in the forest C balance in all regions for most of the observed timeframe.

Fires are a pervasive feature of the terrestrial biosphere and contribute large carbon emissions within the earth system. Humans are responsible for the majority of fire ignitions. Physical and empirical models are used to estimate the future effects of fires on vegetation dynamics and the Earth's system. However, there is no consensus on how human-caused fire ignitions should be represented in such models. This study aimed to identify which globally available predictors of human activity explain global fire ignitions as observed by satellites. We applied a random forest machine learning framework to state-of-the-art global climate, vegetation, and land cover datasets to establish a baseline against which influences of socioeconomic data (cropland fraction, gross domestic product (GDP), road density, livestock density, grazed lands) on fire ignition occurrence were evaluated. Our results showed that a baseline random forest without human predictors captured the spatial patterns of fire ignitions globally, with hotspots over Sub-Saharan Africa and South East Asia. Adding single human predictors to the baseline model revealed that human variables vary in their effects on fire ignitions and that of the variables considered GDP is the most vital driver of fire ignitions. A combined model with all human predictors showed that the human variables improve the ignition predictions in most regions of the world, with some regions exhibiting worse predictions than the baseline model. We concluded that an ensemble of human predictors can add value to physical and empirical models. There are complex relationships between the variables, as evidenced by the improvement in bias in the combined model compared to the individual models. Furthermore, the variables tested have complex relationships that random forests may struggle to disentangle. Further work is required to detangle the complex regional relationships between these variables. These variables, e.g., population density, are well documented to have substantial effects on fire at local and regional scales; we determined that these variables may provide more insight at more continental scales.

The frequency and severity of wildfires in the Alpine region will likely increase due to climate change. Most fire danger forecasts currently adopted in this region are based on meteorological data, such as the Canadian Fire Weather Index (FWI). They are typically only available at relatively coarse spatial resolutions (up to ca. 1 km) and, therefore, are of limited use in mountain regions with complex topography. Other factors, such as vegetation type and structure and the role of humans causing ignitions, are typically not considered. We address this gap by presenting a novel, high-resolution, satellite-supported integrated forest fire danger system (IFDS) for Austria. For this purpose, we use radar and optical satellite data from the Copernicus Sentinel-1 and Sentinel-2 missions, airborne laser scanning (ALS), socio-economic data, and topographic properties next to meteorological data. Two independent methods were investigated: (i) an expert-based approach that allows combining various data layers with different weightings assigned by experts and (ii) a machine-learning approach. Here, we focus on the results of the machine learning approach for a study area covering the federal state of Styria in Austria (ca. 16 400 km²). We use several data layers computed within our study as predictors in random forest models. Moisture indicators and tree species maps were derived from satellite data from the Copernicus Earth observation programme. Vegetation structure parameters, solar potential and a digital surface model (DSM) were derived from ALS data. In addition to the remote sensing data, we used meteorological variables, fire weather indices (FWI) and socio-economic data. We trained the model using forest fire events from the Austrian fire database. The cross-validation showed that the best-performing model predicts high fire danger for most fire events (87%). By integrating all the information layers compared to a baseline model using only FWI, the overall accuracy improved from 68% to 87%. The feature importance showed that the vegetation structure parameters, tree species, socio-economic parameters and DSM are essential for the model in addition to the meteorological predictors. Using this data-driven approach allowed us to learn from past fire occurrences and improved the spatial representation of fire ignition drivers, their importance and interactions. Also, this method permitted the identification of areas with higher danger risk, typically located in the vicinity of densely populated settlements. This study has been performed within the CONFIRM project with funding from the Austrian Research Promotion Agency (FFG).

<p>The European Space Agency (ESA) Climate Change Initiative (CCI) provides long-term surface soil moisture (SM) records with daily temporal resolution. However, the coarse spatial resolution of approximately 25 km limits their use in many hydrological applications, such as agricultural water management, drought monitoring, and rainfall-runoff response.  </p> <p>To address this constraint, we downscaled the CCI SM product to 0.01° (~ 1 km) using machine learning and a set of static and dynamic variables affecting the spatial organization of SM. In particular, datasets describing the vegetation status throughout time, as well as land cover class and soil and topographic attributes were fed into a Random Forest model. </p> <p>Here, we will first present in detail the methodological framework that allowed us to generate the high-resolution dataset. Then, we will thouroughly evaluate its accuracy against in-situ measurements from across Europe, and further compare it to other SM products (e.g., from Sentinel-1). Finally, we will highlight the strengths and limitations of the downscaled SM dataset and discuss possible improvements.</p>

Seewinkel salt pans are a unique wetland ecosystem in eastern Austria that serves as habitat for a diverse range of e.g. birds and halophilic species. Due to groundwater drainage by channels and wells, the salt pans are in an increasingly vulnerable state as they are decisively conditioned by duration and timing of water abundance. However, water gauge data are merely given for three salt pans. The dynamics of salt pans in Seewinkel, locally referred to as Salzlacken, remain insufficiently understood in the context of continuously changing seasonal and long-term hydrological, meteorological, and climatological patterns. Based on previous results on salt pan mapping and monitoring, this work advances inundation state prediction for 34 salt pans by using high-resolution remote sensing data and machine learning methods. The random forest classification models build on hydrological and meteorological predictors in 12-monthly temporal resolution, as, e.g., reduced precipitation sums during the preceding winter season affect the recharge rates of salt pans and groundwater and, as a result, drying state in summer. Four models predict summer drying state at respective four points in time, namely in March, April, May, and June of each year between 1984 and 2022. We first show that remotely sensed water extent products, retrieved from Landsat data can serve as a target variable for data-driven modelling of small-scale salt pan water-dynamics. Secondly, we show that the applied models can successfully predict summer drying state and inundation periods of individual salt pans achieving a maximum F1-score of 0.81. Finally, it is demonstrated that very similar model results can be attained without in-situ groundwater measurements. Research based on water gauge measurements with similar model-designs has been done in the context of lakes, whereas the combination of satellite-derived water extent and salt pans, especially for ecosystems of small size, remains underrepresented. As the data retrieval in this work is based on global and freely available remote sensing data, this method is transferable to comparable salt pan ecosystems in other parts of the world.

Human-fire interactions have always played an important role in the United States of America. Important processes include land clearing with fires in the course of agricultural expansion and development of the West during the 19th century, large-scale fire suppression in the first half of the 20th century and recent “mega-fire” events in California. Strong regional divergences occurred: Fire regimes in the Eastern U.S. were significantly altered due to settlement and land-use changes over the past 100 years, resulting in reduced severity of fire events. In the West the area extent and severity of wildfires has increased, especially in recent decades, arguably due to more frequent climatic extreme events. Although the historical fire narrative in the U.S. has been studied in numerous publications, the links between these developments and changes in the socio-metabolic system i.e., changes in resource use, and consumption, are to our knowledge less well understood. In this study we investigate the influence of anthropogenic alteration of fire regimes on forest biomass Carbon stocks in comparison to forest uses, i.e., the extraction of woody biomass and forest grazing on multiple spatial scales. We develop a long-term reconstruction of biomass burned in forests on the national, regional, and state level based on statistical and remote-sensing data. We describe and examine historical differences between fire regimes in the Eastern and Western United States in connection with human use of forest for the period 1940-2017. Using panel data analysis, we investigate the diverse connection between forest change, socio-metabolic processes, natural disturbances (i.e., wildfires), and associated human fire control on various spatial and temporal scales. With this study we aim to contribute to a better understanding of the underlying socio-metabolic drivers and accompanying processes of altered forest fire regimes.

While ensuring food security worldwide, irrigation is altering the water cycle and generating numerous environmental side effects. As detailed knowledge about the timing and the amounts of water used for irrigation over large areas is still lacking, remotely sensed soil moisture has proved potential to fill this gap. However, the spatial resolution and revisit time of current satellite products represent a major limitation to accurately estimating irrigation. This work aims to systematically quantify their impact on the retrieved irrigation information, hence assessing the value of satellite soil moisture for estimating irrigation timing and water amounts. In a real-world experiment, we modeled soil moisture using actual irrigation and meteorological data, obtained from farmers and weather stations, respectively. Modeled soil moisture was compared against various remotely sensed products differing in terms of spatio-temporal resolution to test the hypothesis that high-resolution observations can disclose the irrigation signal from individual fields while coarse-scale satellite products cannot. Then, in a synthetic experiment, we systematically investigated the effect of soil moisture spatial and temporal resolution on the accuracy of irrigation estimates. The analysis was further elaborated by considering different irrigation scenarios and by adding realistic amounts of random errors in the soil moisture time series. We show that coarse-scale remotely sensed soil moisture products achieve higher correlations with rainfed simulations, while high-resolution satellite observations agree significantly better with irrigated simulations, suggesting that high-resolution satellite soil moisture can inform on field-scale (∼40 ha) irrigation. A thorough analysis of the synthetic dataset showed that satisfactory results, both in terms of detection (F-score > 0.8) and quantification (Pearson’s correlation > 0.8), are found for noise-free soil moisture observations either with a temporal sampling up to 3 days or if at least one-third of the pixel covers the irrigated field(s). However, irrigation water amounts are systematically underestimated for temporal samplings of more than one day, and decrease proportionally to the spatial resolution, i.e., coarsening the pixel size leads to larger irrigation underestimations. Although lower spatial and temporal resolutions decrease the detection and quantification accuracies (e.g., R between 0.6 and 1 depending on the irrigation rate and spatio-temporal resolution), random errors in the soil moisture time series have a stronger negative impact (Pearson R always smaller than 0.85). As expected, better performances are found for higher irrigation rates, i.e. when more water is supplied during an irrigation event. Despite the potentially large underestimations, our results suggest that high-resolution satellite soil moisture has the potential to track and quantify irrigation, especially over regions where large volumes of irrigation water are applied to the fields, and given that low errors affect the soil moisture observations.

The North American Prairie Pothole Region (PPR) represents a large system of wetlands with great importance for biodiversity, water storage and flood management. Knowledge of seasonal and inter-annual surface water dynamics in the PPR is important for understanding the functionality of these wetland ecosystems and the changing degree of hydrologic connectivity between them. Optical sensors that are widely used for retrieving such information are often limited by their temporal resolution and cloud cover, especially in the case of flood events. Synthetic aperture radar (SAR) sensors can potentially overcome such limitations. However, water extent retrieval from SAR data is often impacted by environmental factors, such as wind on water surfaces. Hence, robust retrieval methods are required to reliably monitor water extent over longer time periods . The aim of this study was to develop a robust approach for classifying open water extent in the PPR and to analyse the obtained time series covering the entire available Sentinel-1 observation period from 2015 to 2020 in the hydrometeorological context. Open water in prairie potholes was classified by fusing dual-polarised Sentinel-1 data and high-resolution topographical information using a Bayesian framework. The approach was tested for a study area in North Dakota. The resulting surface water maps were validated using high-resolution airborne optical imagery. For the observation period, the total water area, the number of waterbodies and the median area per waterbody were computed. The validation of the retrieved water maps yielded producer's accuracies between 84 % and 95 % for calm days and between 74 % and 88 % for windy days. User's accuracies were above 98 % in all cases, indicating a very low occurrence of false positives due to the constraints introduced by topographical information. The observed dynamics of total water area displayed both intra-annual and inter-annual patterns. In addition to differences in seasonality between small (<1 ha) and large (>1 ha) waterbodies due to the effect of evaporation during summer, these size classes also responded differently to an extremely wet period from 2019 to 2020 in terms of the increase in the number of waterbodies and the total area covered. The results demonstrate the potential of Sentinel-1 data for high-resolution monitoring of prairie wetlands. Limitations of this method are related to wind inhibiting the correct water extent retrieval and to the rather long acquisition interval of 12 d over the PPR, which is a result of the observation strategy of Sentinel-1.

Topic: Wetlands provide important ecosystem services, e.g. for biodiversity and water resources. The salt pans in the Neusiedler See – Seewinkel National Park are a unique ecosystem for animal and plant species especially adapted to the extreme conditions prevailing in and around the salt pans. The conservation of the salt pans is largely based on the interplay with the water balance of the area and the influence of physical as well as anthropogenic factors. The large number of lakes, which are often only filled with water for a short time due to their shallow depth, makes monitoring by means of installed gauges more difficult. Remote sensing, on the other hand, is an important source of consistent information in space and time. In the FEMOWinkel project, which is funded within the framework of the Austrian StartClim 2021 program, long time series of multispectral satellite data are exploited for monitoring and data-driven modeling of water extent in the salt pans. The water bodies are delineated based on image time series of the Landsat satellites, which have been providing data almost continuously since the 1980s. Cloud gaps will partly be filled with radar-based information provided by European Copernicus – Programme. The derived time series of water body extent and number are validated by comparison with aerial photographs and – where available – water levels. The salt pans are characterized with respect to their seasonal variability and their reaction to longer-term changes in water availability by comparison with ancillary data, e.g., surface and groundwater levels, climate data and other remote sensing products, such as soil moisture and vegetation indices. In the third step, a data-driven modeling of the lake extent is carried out using machine learning methods. We will also address the question of whether it is possible to predict the effects of dry or wet winters and springs on water body extent in the following summer using these methods. Preliminary results of the time series analysis show a pronounced dynamic in the extent of the water bodies over the course of the study time. Periods, e.g., from 1990 to 1993 and 2001 to 2007, in which some of the lakes fell dry, alternated with wetter periods, e.g., from 1994 to 1999, in which the salt pans remained at least partially filled even in summer. These differences correlate with drought indicators such as the Standardised Precipitation-Evapotranspiration Index (SPEI). The remote sensing-based approach will make it possible to transfer the applied methods to other similar ecosystems located in steppe regions.

Green areas play an important role within urban agglomerations due to their impact on local climate and their recreation function. For detailed monitoring, frameworks like the flora fauna habitat (FFH) classification scheme of the European Union’s Habitat Directive are broadly used. By date, FFH classifications are mostly expert-based. Within this study, a data-driven approach for FFH classification is tested. For two test areas in the municipality of Vienna, ALS point cloud data are used to derive predictor variables like terrain features, vegetation structure and potential insulation as well as reflection properties from full waveform analysis on a 1 m grid. In addition, Sentinel-1 C-Band time series data are used to increase the temporal resolution of the predicting features and to add phenological characteristics. For two 1.3 × 1.3 km test tiles, random forest classifiers are trained using different combinations (ALS, SAR, ALS+SAR) of input features. For all model test runs, the combination of ALS and SAR input features lead to best prediction accuracies when applied on test data.

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.

The North American Prairie Pothole Region (PPR) consists of millions of wetlands and holds great importance for biodiversity, water storage and flood management. The wetlands cover a wide range of sizes from a few square metres to several square kilometres. Prairie hydrology is greatly influenced by the threshold behaviour of potholes leading to spilling as well as merging of adjacent wetlands. The knowledge of seasonal and inter-annual surface water dynamics in the PPR is critical for understanding this behaviour of connected and isolated wetlands. Synthetic aperture radar (SAR) sensors, e.g. used by the Copernicus Sentinel-1 mission, have great potential to provide high-accuracy wetland extent maps even when cloud cover is present. We derived water extent during the ice-free months May to October from 2015 to 2020 by fusing dual-polarised Sentinel-1 backscatter data with topographical information. The approach was applied to a prairie catchment in North Dakota. Total water area, number of water bodies and median area per water body were computed from the time series of water extent maps. Surface water dynamics showed strong seasonal dynamics especially in the case of small water bodies (< 1 ha) with a decrease in water area and number of small water bodies from spring throughout summer when evaporation rates in the PPR are typically high. Larger water bodies showed a more stable behaviour during most years. Inter-annual dynamics were strongly related to drought indices based on climate data, such as the Palmer Drought Severity Index. During the extremely wet period of late 2019 to 2020, the dynamics of both small and large water bodies changed markedly. While a larger number of small water bodies was encountered, which remained stable throughout the wet period, also the area of larger water bodies increased, partly due to merging of smaller adjacent water bodies. The results demonstrate the potential of Sentinel-1 data for long-term monitoring of prairie wetlands while limitations exist due to the rather low temporal resolution of 12 days over the PPR.

Flood masks are among the most common remote sensing products, used for rapid crisis information and as input for hydraulic and impact models. Despite the high relevance of such products, vegetated and urban areas are still unreliably mapped and are sometimes even excluded from analysis. The information content of synthetic aperture radar (SAR) images is limited in these areas due to the side-looking imaging geometry of radar sensors and complex interactions of the microwave signal with trees and urban structures. Classification from SAR data can only be optimized to reduce false positives, but cannot avoid false negatives in areas that are essentially unobservable to the sensor, for example, due to radar shadows, layover, speckle and other effects. We therefore propose to treat satellite-based flood masks as intermediate products with true positives, and unlabeled cells instead of negatives. This corresponds to the input of a positive-unlabeled (PU) learning one-class classifier (OCC). Assuming that flood extent is at least partially explainable by topography, we present a novel procedure to estimate the true extent of the flood, given the initial mask, by using the satellite-based products as input to a PU OCC algorithm learned on topographic features. Additional rainfall data and distance to buildings had only minor effect on the models in our experiments. All three of the tested initial flood masks were considerably improved by the presented procedure, with obtainable increases in the overall κ score ranging from 0.2 for a high quality initial mask to 0.7 in the best case for a standard emergency response product. An assessment of κ for vegetated and urban areas separately shows that the performance in urban areas is still better when learning from a high quality initial mask.

Detailed information about irrigation timing and water use at a high spatial resolution is critical for monitoring and improving agricultural water use efficiency. However, neither statistical surveys nor remote sensing-based approaches can currently accommodate this need. To address this gap, we propose a novel approach based on the TU Wien Sentinel-1 Surface Soil Moisture product, characterized by a spatial sampling of 500 m and a revisit time of 1.5–4 days over Europe. Spatiotemporal patterns of soil moisture are used to identify individual irrigation events and estimate irrigation water amounts. To retrieve the latter, we include formulations of evapotranspiration and drainage losses to account for vertical fluxes, which may significantly influence sub-daily soil moisture variations. The proposed approach was evaluated against field-scale irrigation data reported by farmers at three sites in Germany with heterogeneous field sizes, crop patterns, irrigation systems and management. Our results show that most field-scale irrigation events can be detected using soil moisture information (mean F-score = 0.77). Irrigation estimates, in terms of temporal dynamics as well as spatial patterns, were in agreement with reference data (mean Pearson correlation = 0.64) regardless of field-specific characteristics (e.g., crop type). Hence, the proposed approach has the potential to be applied over large regions with varying cropping systems.

The assessment of forest fire risk has recently gained interest in countries of Central Europe and the alpine region since the occurrence of forest fires is expected to increase with a changing climate. Information on forest fuel structure, which is related to forest structure, is a key component in such assessments. Forest structure information can be derived from airborne laser scanning (ALS) data, whose value for the derivation of respective metrics at a high accuracy level has been demonstrated in numerous studies over the last years. Yet, the temporal resolution of ALS data is low as flight missions are typically carried out in time intervals of five to ten years in Central Europe. ALS-derived forest structure descriptors for fire risk assessments, therefore, are often outdated. Open access earth observation data offer the potential to fill these information gaps. Data provided by synthetic aperture radar (SAR) sensors, in particular, are of interest in this context since this technology has a known sensitivity to the vegetation structure and acquires data independent of weather or daylight conditions. In our study, we investigate the potential to derive forest structure descriptors from time series of Sentinel-1 (S-1) SAR data for a deciduous forest site in the Eastern part of Austria. We focus on forest stand height and fractional cover, which is a measure for forest density, as both of these components impact forest fire propagation and ignition. The two structure metrics are estimated using a random forest (RF) model, which takes a total of 36 predictors as input, which we compute from the S-1 time series. The model is trained using ALS-derived structure metrics acquired during the same year as the S-1 data. We estimated stand height with a root mean square error (RMSE) of 4.76 m and a bias of 0.09 m at 100 m resolution, while the RMSE for the fractional cover estimation is 0.08 with a bias of zero at the same resolution. The spatial comparison of the structure predictions with the ALS reference further shows that the general structure is well reproduced. Yet, fine scale variations cannot be completely reproduced by the S1-derived structure products, and the height of tall stands and very dense canopy parts are underestimated. Due to the high correlation of the predicted values to the reference (Pearson’s R of 0.88 and 0.94 for the stand height and the fractional cover, respectively), we consider S-1 time series in combination with ALS data with low temporal resolution and machine learning techniques to be a reliable data source and workflow for regularly (e.g. < yearly) updating ALS structure information in an operational way.

Detailed information about the timing and the amount of water used for irrigation at a high spatial resolution is critical for monitoring and improving agricultural water use efficiency. However, neither statistical surveys nor current remote sensing-based approaches can accommodate this need. Being a natural source of information on the amount of water entering the ground, soil moisture is directly related to irrigation and precipitation. Hence, we present a novel approach based on the TU Wien Sentinel-1 Surface Soil Moisture product to fill this gap, i.e. detect and quantify irrigation water amounts at sub-kilometric scale. Theoretically, irrigation occurring in a specific field should be reflected by a local increase in soil moisture, while surrounding non-irrigated fields exhibit a different behavior. We harness the spatio-temporal patterns of soil moisture to identify individual irrigation events, and then to estimate irrigation water heights. To retrieve the latter we include formulations of evapotranspiration and drainage as such vertical fluxes have a significant impact on sub-daily soil moisture variations. The proposed approach is evaluated against field scale irrigation data reported by farmers at three sites in Germany with heterogeneous field sizes, crop patterns, irrigation systems and management. Our results show that most irrigation events occurring in a field can be detected using soil moisture information at 500 m and 1-3 days resolution, however, lower accuracy is found during the peak of the growing season. The retrieved irrigation water heights increase with the fraction of pixel under irrigation as higher water amounts are expected over largely irrigated pixels. Finally, irrigation estimates are in agreement with reference data, in terms of temporal dynamics as well as spatial patterns, regardless of field-specific characteristics (e.g. crop type, irrigation system). Unlike most approaches based on microwave soil moisture data, the proposed framework does not rely on additional datasets, which are either locally available or do not even exist at (sub-) kilometric resolution. Hence, the proposed approach has the potential to be applied over large regions with varying cropping systems (e.g. national and even continental scale).

Wildfires are becoming an increasing threat to human health, infrastructure, forestry, agriculture and biodiversity. In Alpine regions, fires are often at the start of cascading risks including avalanches, mudslides or rock fall due to the loss of forest and vegetation layers. Additionally, wildfires are expected to occur more frequently in the future as a result of a warming climate, which is estimated to affect alpine regions in particular. Fire danger forecasts, such as the commonly used Fire Weather indices, indicate the danger of forest fires based on numerical weather forecasts. Such indices are typically available at coarse spatial resolutions and, hence, have limited use in mountainous regions with their highly variable weather and other environmental conditions. Stakeholders, such as fire departments and forest managers, require more detailed forecasts in order to make robust decisions and efficiently plan their resources. The CONFIRM project, which started in December 2019 with funding from the Austrian Research Promotion Agency (FFG) under the Austrian Space Applications Programme (ASAP), addresses this gap by using high-resolution earth observation data provided by the European Copernicus programme to develop a pre-operational fire danger forecast system. Data from both optical and microwave sensors aboard satellites are known to be sensitive to changes in soil and vegetation water content. Exploiting this sensitivity, satellite data with high temporal and spatial resolutions from the Copernicus Sentinel-1 and Sentinel-2 missions will be used to estimate fuel moisture state. The estimates will be integrated with airborne Laser-scanning (LiDAR) data, high-resolution weather forecasts, socioeconomic and topographic data to develop a novel, high-resolution integrated forest fire danger system (IFDS) for Austria. The project team will apply its expertise in forest management, remote sensing, fire science and machine learning to estimate fire danger using the Austrian fire database, an extensive record of historic fire events, as a training dataset. Key stakeholders from national weather services (ZAMG, DWD), fire brigades, state forest administrations and infrastructure providers (Austrian Railways ÖBB) are continuously involved in the project to develop the IFDS according to their requirements. They will evaluate the prototype of the system during the fire season of 2021.

Fast-growing population numbers and climate change as a driving force for extreme meteorological events lead to an increasing impact of natural disasters on the environment in general and human life in particular. Concurrently, technical developments strongly enable a better spacecraft-driven earth observation resulting in an increasing demand for rapid crisis mapping products (Voigt, et al., 2016). Of all globally monitored natural disasters floods occurred with a frequency of 39 % in the period from 1980 to 2015 (Munich RE, 2016). This number emphasizes the need for a global time-independent flood monitoring followed by information about the spatial distribution of flooded areas. This information directly serves as an input for end users, e.g. rescue forces in the field, insurance companies, planners of infrastructure, etc. In this article, we demonstrate an automated processing chain automatically deriving flood information from globally acquired Sentinel-1 data. We first describe the data used and further elaborate on the methodology in more detail. The processing chain has been designed to compute global data and currently operates on different areas of interest all over the world. However, we apply the methodology to a single-use case in Kerala, India, and discuss the benefits and disadvantages of a radar-based flood detection system. We conclude this article with the further use of the results in the form of a web-based presentation as well as crisis mapping products.