The relation between microwave backscatter and incidence angle estimated from observations of the Advanced Scatterometer (ASCAT) onboard the Metop satellites contains valuable information on the dynamics of vegetation water content and structure. The relation between backscatter and incidence angle (parameterized using so-called slope and curvature parameter) has been related to vegetation water dynamics in studies on the North American Grasslands and the Cerrado Savannah. The current approach to estimate time series of the slope and curvature parameters involves a kernel smoother, weighing observations according to their temporal distance to the day of interest. While this approach provides a robust representation of backscatter-incidence angle relation over longer time scales, it does not accurately capture the timing of short-term changes. To further improve the correspondence between backscatter-incidence angle relation and vegetation water dynamics, the timing of short-term changes should be preserved in the estimation of slope and curvature. This would allow slope and curvature to be reconciled with independent estimates of biogeophysical variables, and allow us to isolate high-frequency variations due to, for example, intercepted precipitation or soil moisture. Here, an alternative method is introduced to estimate the ASCAT backscatter-incidence angle relation using temporally constrained least squares. While the proposed method yields similar performance to the kernel smoother in aggregated statistics, this method retains the timing of short-term changes.

ASCAT normalized backscatter (σ₄₀^o) and slope (σ^′) contain valuable information about soil moisture and vegetation. While σ₄₀^o has been assimilated to constrain soil moisture, sometimes together with Leaf Area Index (LAI), this study is the first to assimilate σ^′ directly to constrain vegetation states. Here, we assimilate σ₄₀^o and slope σ^′ into the ISBA-A-gs LSM using the Simplified Extended Kalman Filter (SEKF) using a Deep Neural Network (DNN) as the observation operator. The performances of the data assimilation (DA) and open loop (OL) are evaluated against in-situ soil moisture observations from the International Soil Moisture Network (ISMN), and LAI observations from the Copernicus Global Land Service (CGLS). Given an accurate and physically plausible observation operator, along with well-defined model and observation errors, the data assimilation system should yield improved estimates of the model states. However, results show that the DA performance is neutral compared to the OL in terms of the median unbiased root mean square error (ubRMSE) and Pearson correlation coefficient (ρ) across all validation sites. In addition, an analysis of the residuals and innovations confirms that DA had limited or no impact. This poor performance is perplexing. Furthermore, given the growing interest in the use of machine-learning-based observation operators, it is essential to understand the role that the use of the DNN may be playing in this poor performance. While representativeness errors and error specification play some part, it is demonstrated that the key factor constraining the efficacy of the SEKF is the correct estimation of the Jacobians that control the degree to which the observations update the states in the SEKF. It is argued that the DNN relating model states to satellite observations must have physically-plausible and robust Jacobians for the DNN to be effective in a data assimilation framework.

Agricultural droughts are extreme events which are often a result of interplays between multiple hydro-meteorological processes. Therefore, assessing drought occurrence, extent, duration and intensity is complex and requires the combined use of multiple variables, such as temperature, rainfall, soil moisture (SM) and vegetation state. The benefit of using information on SM and vegetation state is that they integrate information on precipitation, temperature and evapotranspiration, making them direct indicators of plant available water and vegetation productivity. Microwave remote sensing enables the retrieval of both SM and vegetation information, and satellite-based SM and vegetation products are available operationally and free of charge on a regional or global scale and daily basis. As a result, microwave remote sensing products play an increasingly important role in drought monitoring applications. Here, we provide an overview of recent developments in using microwave remote sensing for large-scale agricultural drought monitoring. We focus on the intricacy of monitoring the complex process of drought development using multiple variables. First, we give a brief introduction on fundamental concepts of microwave remote sensing together with an overview of recent research, development and applications of drought indicators derived from microwave-based satellite SM and vegetation observations. This is followed by a more detailed overview of the current research gaps and challenges in combining microwave-based SM and vegetation measurements with hydro-meteorological data sets. The potential of using microwave remote sensing for drought monitoring is demonstrated through a case study over Senegal using multiple satellite- and model-based data sets on rainfall, SM, vegetation and combinations thereof. The case study demonstrates the added-value of microwave-based SM and vegetation observations for drought monitoring applications. Finally, we provide an outlook on potential developments and opportunities.

The TU Wien Soil Moisture Retrieval (TUW SMR) approach is used to produce several operational soil moisture products from the Advanced Scatterometer (ASCAT) on the Metop series of satellites as part of the EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF). The incidence angle dependence of backscatter is described by a second-order Taylor polynomial, the coefficients of which are used to normalize ASCAT observations to the reference incidence angle of 40° and for correcting vegetation effects. Recently, a kernel smoother was developed to estimate the coefficients dynamically, in order to account for interannual variability. In this study, we used the kernel smoother for estimating these coefficients, where we distinguished for the first time between their two uses, meaning that we used a short and fixed window width for the backscatter normalisation while we tested different window widths for optimizing the vegetation correction. In particular, we investigated the impact of using the dynamic vegetation parameters on soil moisture retrieval. We compared soil moisture retrievals based on the dynamic vegetation parameters to those estimated using the current operational approach by examining their agreement, in terms of the Pearson correlation coefficient, unbiased RMSE and bias with respect to in situ soil moisture. Data from the United States Climate Research Network were used to study the influence of climate class and land cover type on performance. The sensitivity to the kernel smoother half-width was also investigated. Results show that estimating the vegetation parameters with the kernel smoother can yield an improvement when there is interannual variability in vegetation due to a trend or a change in the amplitude or timing of the seasonal cycle. However, using the kernel smoother introduces high-frequency variability in the dynamic vegetation parameters, particularly for shorter kernel half-widths. Keywords: soil moisture; backscatter; radar remote sensing;.

This study investigates the performance of the TU Wien soil moisture retrieval (TUW-SMR) algorithm by adapting the strength of the vegetation correction. The semiempirical change detection method TUW-SMR exploits the multiangle backscatter observations from spaceborne fan-beam scatterometer systems in order to derive surface soil moisture information expressed in the degree of saturation. The vegetation parameterization of TUW-SMR is controlled by the dry and wet crossover angles that are used to determine the dry and wet backscatter reference. Backscatter observations from the Advanced Scatterometer (ASCAT) are used to produce four soil moisture data sets based on different dry and wet crossover angles describing: 1) a static, respectively, no vegetation correction; 2) the currently used seasonal vegetation correction; 3) a stronger seasonal vegetation correction; and 4) a spatially variable seasonal vegetation correction with the stronger vegetation correction over vegetated areas and no vegetation correction over bare land. All four ASCAT soil moisture data sets are evaluated against soil moisture estimates from GLDAS-2.1 Noah land surface model and the European Space Agency (ESA) climate change initiative (CCI) Passive v04.5 soil moisture product using the triple collocation method and traditional correlation analysis. The results show that the spatially variable vegetation correction overall improves soil moisture estimates in both more densely vegetated areas, e.g., in large parts of North America and Europe, and more sparsely vegetated, e.g., Western Africa. Nonetheless, the experiment also provides insight into challenging retrieval conditions where the TUW-SMR fails to take all relevant backscatter processes into account, e.g., wetlands and bare soils with subsurface scattering.

Vegetation products based on microwave remote sensing observations, such as Vegetation Optical Depth (VOD), are increasingly used in a variety of applications. One disadvantage is the often coarse spatial resolution of tens of kilometers of products retrieved from microwave observations from spaceborne radiometers and scatterometers. This can potentially be overcome by using new high-resolution Synthetic Aperture Radar (SAR) observations from Sentinel-1. However, the sensitivity of Sentinel-1 backscatter to vegetation dynamics, or its use in radiative transfer models, such as the water cloud model, has only been tested at field to regional scale. In this study, we compared the cross-polarization ratio (CR) to vegetation dynamics as observed in microwave-based Vegetation Optical Depth from coarse-scale satellites over Europe. CR was obtained from Sentinel-1 VH and VV backscatter observations at 500 m sampling and resampled to the spatial resolution of VOD from the Advanced SCATterometer (ASCAT) on-board the Metop satellite series. Spatial patterns between median CR and ASCAT VOD correspond to each other and to vegetation patterns over Europe. Analysis of temporal correlation between CR and ASCAT VOD shows that high Pearson correlation coefficients (R_p) are found over croplands and grasslands (median R_p > 0.75). Over deciduous broadleaf forests, negative correlations are found. This is attributed to the effect of structural changes in the vegetation canopy which affect CR and ASCAT VOD in different ways. Additional analysis comparing CR to passive microwave-based VOD shows similar effects in deciduous broadleaf forests and high correlations over crop-and grasslands. Though the relationship between CR and VOD over deciduous forests is unclear, results suggest that CR is useful for monitoring vegetation dynamics over crop-and grassland and a potential path to high-resolution VOD.

In just the past 5 years, the field of Earth observation has progressed beyond the offerings of conventional space-agency-based platforms to include a plethora of sensing opportunities afforded by CubeSats, unmanned aerial vehicles (UAVs), and smartphone technologies that are being embraced by both for-profit companies and individual researchers. Over the previous decades, space agency efforts have brought forth well-known and immensely useful satellites such as the Landsat series and the Gravity Research and Climate Experiment (GRACE) system, with costs typically of the order of 1 billion dollars per satellite and with concept-to-launch timelines of the order of 2 decades (for new missions). More recently, the proliferation of smartphones has helped to miniaturize sensors and energy requirements, facilitating advances in the use of CubeSats that can be launched by the dozens, while providing ultra-high (3- 5 m) resolution sensing of the Earth on a daily basis. Startup companies that did not exist a decade ago now operate more satellites in orbit than any space agency, and at costs that are a mere fraction of traditional satellite missions. With these advances come new space-borne measurements, such as real-time high-definition video for tracking air pollution, storm-cell development, flood propagation, precipitation monitoring, or even for constructing digital surfaces using structure-from-motion techniques. Closer to the surface, measurements from small unmanned drones and tethered balloons have mapped snow depths, floods, and estimated evaporation at sub-metre resolutions, pushing back on spatio-temporal constraints and delivering new process insights. At ground level, precipitation has been measured using signal attenuation between antennae mounted on cell phone towers, while the proliferation of mobile devices has enabled citizen scientists to catalogue photos of environmental conditions, estimate daily average temperatures from battery state, and sense other hydrologically important variables such as channel depths using commercially available wireless devices. Global internet access is being pursued via highaltitude balloons, solar planes, and hundreds of planned satellite launches, providing a means to exploit the "internet of things" as an entirely new measurement domain. Such global access will enable real-time collection of data from billions of smartphones or from remote research platforms. This future will produce petabytes of data that can only be accessed via cloud storage and will require new analytical approaches to interpret. The extent to which today's hydrologic models can usefully ingest such massive data volumes is unclear. Nor is it clear whether this deluge of data will be usefully exploited, either because the measurements are superfluous, inconsistent, not accurate enough, or simply because we lack the capacity to process and analyse them. What is apparent is that the tools and techniques afforded by this array of novel and game-changing sensing platforms present our community with a unique opportunity to develop new insights that advance fundamental aspects of the hydrological sciences. To accomplish this will require more than just an application of the technology: in some cases, it will demand a radical rethink on how we utilize and exploit these new observing systems.

In just the past 5 years, the field of Earth observation has progressed beyond the offerings of conventional space-agency-based platforms to include a plethora of sensing opportunities afforded by CubeSats, unmanned aerial vehicles (UAVs), and smartphone technologies that are being embraced by both for-profit companies and individual researchers. Over the previous decades, space agency efforts have brought forth well-known and immensely useful satellites such as the Landsat series and the Gravity Research and Climate Experiment (GRACE) system, with costs typically of the order of 1 billion dollars per satellite and with concept-to-launch timelines of the order of 2 decades (for new missions). More recently, the proliferation of smartphones has helped to miniaturize sensors and energy requirements, facilitating advances in the use of CubeSats that can be launched by the dozens, while providing ultra-high (3-5 m) resolution sensing of the Earth on a daily basis. Start-up companies that did not exist a decade ago now operate more satellites in orbit than any space agency, and at costs that are a mere fraction of traditional satellite missions. With these advances come new space-borne measurements, such as real-time high-definition video for tracking air pollution, storm-cell development, flood propagation, precipitation monitoring, or even for constructing digital surfaces using structure-from-motion techniques. Closer to the surface, measurements from small unmanned drones and tethered balloons have mapped snow depths, floods, and estimated evaporation at sub-metre resolutions, pushing back on spatio-temporal constraints and delivering new process insights. At ground level, precipitation has been measured using signal attenuation between antennae mounted on cell phone towers, while the proliferation of mobile devices has enabled citizen scientists to catalogue photos of environmental conditions, estimate daily average temperatures from battery state, and sense other hydrologically important variables such as channel depths using commercially available wireless devices. Global internet access is being pursued via high-altitude balloons, solar planes, and hundreds of planned satellite launches, providing a means to exploit the “internet of things” as an entirely new measurement domain. Such global access will enable real-time collection of data from billions of smartphones or from remote research platforms. This future will produce petabytes of data that can only be accessed via cloud storage and will require new analytical approaches to interpret. The extent to which today's hydrologic models can usefully ingest such massive data volumes is unclear. Nor is it clear whether this deluge of data will be usefully exploited, either because the measurements are superfluous, inconsistent, not accurate enough, or simply because we lack the capacity to process and analyse them. What is apparent is that the tools and techniques afforded by this array of novel and game-changing sensing platforms present our community with a unique opportunity to develop new insights that advance fundamental aspects of the hydrological sciences. To accomplish this will require more than just an application of the technology: in some cases, it will demand a radical rethink on how we utilize and exploit these new observing systems.