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Climate change, increasing population and changes in land use are all rapidly driving the need to be able to better understand surface water dynamics. The targets set by the United Nations under Sustainable Development Goal 6 in relation to freshwater ecosystems also make accurate surface water monitoring increasingly vital. However, the last decades have seen a steady decline in in situ hydrological monitoring and the availability of the growing volume of environmental data from free and open satellite systems is increasingly being recognized as an essential tool for largescale monitoring of water resources. The scientific literature holds many promising studies on satellite-based surface-water mapping, but a systematic evaluation has been lacking. Therefore, a round robin exercise was organized to conduct an intercomparison of 14 different satellite-based approaches for monitoring inland surface dynamics with Sentinel-1, Sentinel-2, and Landsat 8 imagery. The objective was to achieve a better understanding of the pros and cons of different sensors and models for surface water detection and monitoring. Results indicate that, while using a single sensor approach (applying either optical or radar satellite data) can provide comprehensive results for very specific localities, a dual sensor approach (combining data from both optical and radar satellites) is the most effective way to undertake largescale national and regional surface water mapping across bioclimatic gradients.

Single satellite synthetic aperture radar (SAR) data are now regularly used to estimate hydraulic model parameters such as channel roughness, depth and water slope. However, despite channel geometry being critical to the application of hydraulic models and poorly known a priori, it is not frequently the object of calibration. This paper presents a unique method to simultaneously calibrate the bankfull channel depth and channel roughness parameters within a 2-D LISFLOOD-FP hydraulic model using an archive of moderate-resolution (150 m) ENVISAT satellite SAR-derived flood extent maps and a binary performance measure for a 30 × 50 km domain covering the confluence of the rivers Severn and Avon in the UK. The unknown channel parameters are located by a novel technique utilising the information content and dynamic identifiability analysis (DYNIA) (Wagener et al., 2003) of single and combinations of SAR flood extent maps to find the optimum satellite images for model calibration. Highest information content is found in those SAR flood maps acquired near the peak of the flood hydrograph, and improves when more images are combined. We found that model sensitivity to variation in channel depth is greater than for channel roughness and a successful calibration for depth could only be obtained when channel roughness values were confined to a plausible range. The calibrated reach-average channel depth was within 0.9 m (16 % error) of the equivalent value determined from river cross-section survey data, demonstrating that a series of moderate-resolution SAR data can be used to successfully calibrate the depth parameters of a 2-D hydraulic model.

Analyzing the impact of agro-environmental conditions on the dynamics of hazelnut yield in Chile. 9. International Congress on Hazelnut

Floods are hydrological extremes that have enormous environmental, social and economic consequences.The objective of this thesis was a contribution to the implementation of a processing chain that integrates remote sensing information into hydraulic models. Specifically, the aim was to improve water elevation and discharge simulations by assimilating microwave remote sensing-derived flood information into hydraulic models. The first component of the proposed processing chain is represented by a fully automated flood mapping algorithm that enables the automated, objective, and reliable flood extent extraction from Synthetic Aperture Radar images, providing accurate results in both rural and urban regions. The method operates with minimum data requirements and is efficient in terms of computational time. The map obtained with the developed algorithm is still subject to uncertainties, both introduced by the flood mapping algorithm and inherent in the image itself. In this work, particular attention was given to image uncertainty deriving from speckle. By bootstrapping the original satellite image pixels, several synthetic images were generated and provided as input to the developed flood mapping algorithm. From the analysis performed on the mapping products, speckle uncertainty can be considered as a negligible component of the total uncertainty. In the final step of the proposed processing chain real event water elevations, obtained from satellite observations, were assimilated in a hydraulic model with an adapted version of the Particle Filter, modified to work with non-Gaussian distribution of observations. To deal with model structure error and possibly biased observations, a global and a local weight variant of the Particle Filter were tested. The variant to be preferred depends on the level of confidence that is attributed to the observations or to the model. This study also highlighted the complementarity of remote sensing derived and in-situ data sets. An accurate binary flood map represents an invaluable product for different end users. However, deriving from this binary map additional hydraulic information, such as water elevations, is a way of enhancing the value of the product itself. The derived data can be assimilated into hydraulic models that will fill the gaps where, for technical reasons, Earth Observation data cannot provide information, also enabling a more accurate and reliable prediction of flooded areas., Hochwasser sind gekennzeichnet durch ihre oft erheblichen ökologischen, sozialen und wirtschaftlichen Folgen. Zielsetzung dieser Dissertation war es, einen Beitrag zur Integration von satellitengestützten Erdbeobachtungsdaten und hydraulischen Modellen zu leisten. Im vorliegenden Fall sollten Vorhersagen von Wasserständen und Abflüssen mittels Assimilation hydrologischer Parameter, die aus Mikrowellenfernerkundungsdaten abgeleitet wurden, in hydraulische Modelle verbessert werden. Die erste Komponente der vorgeschlagenen Prozessierungsskette besteht in der automatisierten Kartierung von Überschwemmungsausdehnungen anhand hochaufgelöster Radardaten. Der neu entwickelte Algorithmus ermöglicht eine Verbesserung der Genauigkeit und Zuverlässigket der kartierten Überflutungsflächen sowohl in städtischen als auch ländlichen Gebieten. Das vorgestellte Verfahren erfordert nur ein Minimum an Zusatzdaten und ist weiterhin effizient im Bezug auf die benötigte Rechenzeit. Ein weiterer Schwerpunkt der Doktorarbeit war es, die Unsicherheiten der aus Radardaten abgeleiteten Überflutungsflächen zu quantifizieren. Diese werden sowohl durch die Qualität der Rohdaten als auch durch die angewandte Prozessierungskette beeinflusst. In dieser Arbeit wurde ein besonderes Augenmerk auf die Unsicherheiten der verwendeten Radardaten gelegt, welche durch das sog. "Speckle"-Phänomen hervorgerufen werden. Anhand des sog. Bootstrapping-Verfahrens wurden zunächst mehrere synthetische Radardatensätze generiert und als Eingangsdaten in dem vorher entwickelten Kartierungsalgorithmus verwendet. Aus der Analyse der Hochwasserkarten ergab sich, dass der Beitrag des Speckle nur einen verhältnismäßig geringen Anteil an der gesamten Unsicherheit ausmacht.Abschließend wurden die Satellitendaten in ein hydraulisches Modell assimiliert. Dazu wurde eine neu entwickelte Version des sogenannten Partikelfilters benutzt. Dieser Filter ermöglicht es, Daten zu assimilieren, deren Fehlerstruktur nicht einer Gauß-Verteilung folgt. Um der Möglichkeit einer systematischen Messabweichung sowie von Fehlern in der Modellstruktur Rechnung zu tragen wurden verschiedene Experimente basierend auf einer lokalen bzw. globalen Gewichtung der Partikel duchgeführt. Welche Variante im Endeffekt zu bevorzugen ist, hängt von der jeweiligen Fehlerstruktur sowohl des Modells als auch der Fernerkundungsdaten ab. Die vorliegende Studie hebt weiterhin den komplementären Wert von Fernerkundungs- und in-situ-Daten hervor. Eine präzise Kartierung von Hochwasserflächen ist von hohem Wert für verschiedene Nutzergruppen. Dieser Wert kann noch gesteigert werden, indem zusätzlich zu der Ausdehnung weiterführende Informationen, wie z.B. Wasserstände, aus den entsprechenden Satellitendaten abgeleitet werden können. Die Integration dieser Informationen in ein hydraulisches Modell ermöglicht es einerseits, die Ausdehnung der Überschwemmung auch in solchen Gebieten zu kartieren, in denen satellitengestützte Sensoren aus technischen Gründen dazu nicht in der Lage sind. Andererseits können Satellitendaten dazu beitragen, die modellbasierten Vorhersagen der überschwemmten Gebiete genauer und zuverlässiger zu machen.

The objective of this paper is to analyze the improvement in the performance of the particle filter by including a resample-move step or by using a modified Gaussian particle filter. Specifically, the standard particle filter structure is altered by the inclusion of the Markov chain Monte Carlo move step. The second choice adopted in this study uses the moments of an ensemble Kalman filter analysis to define the importance density function within the Gaussian particle filter structure. Both variants of the standard particle filter are used in the assimilation of densely sampled discharge records into a conceptual rainfall-runoff model. The results indicate that the inclusion of the resample-move step in the standard particle filter and the use of an optimal importance density function in the Gaussian particle filter improve the effectiveness of particle filters. Moreover, an optimization of the forecast ensemble used in this study allowed for a better performance of the modified Gaussian particle filter compared to the particle filter with resample-move step. © 2013. American Geophysical Union. All Rights Reserved. ispartof: Water Resources Research vol:49 issue:7 pages:4005-4021 status: published

In this chapter, we introduce the vision of the H2020 project ``Precision Farming of Hazelnut Orchards'' (PANTHEON), which is to develop the agricultural equivalent of an industrial Supervisory Control and Data Acquisition (SCADA) system to be used for precision farming of orchards. PANTHEON's objective is to design an integrated system where a relatively limited number of heterogeneous unmanned robotic components (including terrestrial and aerial robots) move within the orchard to collect data and perform typical farming operations. In addition, an Internet-of-Things (IoT) agrometeorological solar-powered network is deployed to continuously monitor the environmental conditions of the orchard. The information so collected is then stored in a central operative unit that integrates the data to perform automatic feedback actions (e.g., to regulate the irrigation system) and to support the decisions of the agronomists and farmers in charge of the orchard. The proposed SCADA system will acquire information at the resolution of the individual plant, to drastically increase, compared to current best practices, the detection of possible limiting factors at the level of the individual plant and to react accordingly. Differently from the current state of the art in precision farming for large-scale orchards, the capability of monitoring the state and the evolution of each single tree will be the enabling technology to allow more focused interventions. This will lead to a better average health of the orchard and to an increased effectiveness of integrated pest management (IPM) activities. In conclusion, the ongoing implemented architecture has the potential to increase production while, at the same time, being more cost-effective and environmentally friendly. To summarize, we believe that the proposed SCADA paradigm for Precision Agriculture may represent an attractive opportunity for the design of a novel real-time software architecture. In other words, by allowing the processing of massive amounts of datasets derived from the SCADA architecture, it will be possible to step up the current effectiveness of Precision Agriculture (PA) methodologies by providing real-time answers to the questions posed by farm managers, when in need of timely decisions.