Using mobile RE tools to give end-users their own voice

Researchers highlight end-user involvement in system design as an important concept for developing useful and usable solutions. However, end-user involvement in software engineering is still an open-ended topic. Novel paradigms such as service-oriented computing strengthen the need for more active end-user involvement in order to provide systems that are tailored to individual end-user needs. Our work is based on the fact that the majority of end-users are familiar with mobile devices and use an increasing number of mobile applications. A mobile tool enabling end-user led requirements elicitation could be just one of many applications installed on end-users' mobile devices. In this paper, we present a framework of end-user involvement in requirements elicitation which motivates our research. The main contribution of our research is a tool-supported requirements elicitation approach allowing end-users to document needs in situ. Furthermore, we present first evaluation results to highlight the feasibility of on-site end-user led requirements elicitation

Comparing surface-soil moisture from the SMOS mission and the ORCHIDEE land-surface model over the Iberian Peninsula

The aim of this study is to compare the surface soil moisture (SSM) retrieved from ESA's Soil Moisture and Ocean Salinity mission (SMOS) with the output of the ORCHIDEE (ORganising Carbon and Hydrology In Dynamic EcosystEm) land surface model forced with two distinct atmospheric data sets for the period 2010 to 2012. The comparison methodology is first established over the REMEDHUS (Red de Estaciones de MEDición de la Humedad def Suelo) soil moisture measurement network, a 30 by 40. km catchment located in the central part of the Duero basin, then extended to the whole Iberian Peninsula (IP). The temporal correlation between the in-situ, remotely sensed and modelled SSM are satisfactory (r. >. 0.8). The correlation between remotely sensed and modelled SSM also holds when computed over the IP. Still, by using spectral analysis techniques, important disagreements in the effective inertia of the corresponding moisture reservoir are found. This is reflected in the spatial correlation over the IP between SMOS and ORCHIDEE SSM estimates, which is poor (¿. ~. 0.3). A single value decomposition (SVD) analysis of rainfall and SSM shows that the co-varying patterns of these variables are in reasonable agreement between both products. Moreover the first three SVD soil moisture patterns explain over 80% of the SSM variance simulated by the model while the explained fraction is only 52% of the remotely sensed values. These results suggest that the rainfall-driven soil moisture variability may not account for the poor spatial correlation between SMOS and ORCHIDEE products.Peer ReviewedPostprint (published version

Towards Exascale Scientific Metadata Management

Advances in technology and computing hardware are enabling scientists from all areas of science to produce massive amounts of data using large-scale simulations or observational facilities. In this era of data deluge, effective coordination between the data production and the analysis phases hinges on the availability of metadata that describe the scientific datasets. Existing workflow engines have been capturing a limited form of metadata to provide provenance information about the identity and lineage of the data. However, much of the data produced by simulations, experiments, and analyses still need to be annotated manually in an ad hoc manner by domain scientists. Systematic and transparent acquisition of rich metadata becomes a crucial prerequisite to sustain and accelerate the pace of scientific innovation. Yet, ubiquitous and domain-agnostic metadata management infrastructure that can meet the demands of extreme-scale science is notable by its absence. To address this gap in scientific data management research and practice, we present our vision for an integrated approach that (1) automatically captures and manipulates information-rich metadata while the data is being produced or analyzed and (2) stores metadata within each dataset to permeate metadata-oblivious processes and to query metadata through established and standardized data access interfaces. We motivate the need for the proposed integrated approach using applications from plasma physics, climate modeling and neuroscience, and then discuss research challenges and possible solutions

Spatiotemporal analyses of soil moisture from point to footprint scale in two different hydroclimatic regions

This paper presents time stability analyses of soil moisture at different spatial measurement support scales (point scale and airborne remote sensing (RS) footprint scale 800 m × 800 m) in two different hydroclimatic regions. The data used in the analyses consist of in situ and passive microwave remotely sensed soil moisture data from the Southern Great Plains Hydrology Experiments 1997 and 1999 (SGP97 and SGP99) conducted in the Little Washita (LW) watershed, Oklahoma, and the Soil Moisture Experiments 2002 and 2005 (SMEX02 and SMEX05) in the Walnut Creek (WC) watershed, Iowa. Results show that in both the regions soil properties (i.e., percent silt, percent sand, and soil texture) and topography (elevation and slope) are significant physical controls jointly affecting the spatiotemporal evolution and time stability of soil moisture at both point and footprint scales. In Iowa, using point‐scale soil moisture measurements, the WC11 field was found to be more time stable (TS) than the WC12 field. The common TS points using data across the 3 year period (2002–2005) were mostly located at moderate to high elevations in both the fields. Furthermore, the soil texture at these locations consists of either loam or clay loam soil. Drainage features and cropping practices also affected the field‐scale soil moisture variability in the WC fields. In Oklahoma, the field having a flat topography (LW21) showed the worst TS features compared to the fields having gently rolling topography (LW03 and LW13). The LW13 field (silt loam) exhibited better time stability than the LW03 field (sandy loam) and the LW21 field (silt loam). At the RS footprint scale, in Iowa, the analysis of variance (ANOVA) tests show that the percent clay and percent sand are better able to discern the TS features of the footprints compared to the soil texture. The best soil indicator of soil moisture time stability is the loam soil texture. Furthermore, the hilltops (slope ∼0%–0.45%) exhibited the best TS characteristics in Iowa. On the other hand, in Oklahoma, ANOVA results show that the footprints with sandy loam and loam soil texture are better indicators of the time stability phenomena. In terms of the hillslope position, footprints with mild slope (0.93%–1.85%) are the best indicators of TS footprints. Also, at both point and footprint scales in both the regions, land use–land cover type does not influence soil moisture time stability

Multi-temporal evaluation of soil moisture and land surface temperature dynamics using in situ and satellite observations

Soil moisture (SM) is an important component of the Earth’s surface water balance and by extension the energy balance, regulating the land surface temperature (LST) and evapotranspiration (ET). Nowadays, there are two missions dedicated to monitoring the Earth’s surface SM using L-band radiometers: ESA’s Soil Moisture and Ocean Salinity (SMOS) and NASA’s Soil Moisture Active Passive (SMAP). LST is remotely sensed using thermal infrared (TIR) sensors on-board satellites, such as NASA’s Terra/Aqua MODIS or ESA & EUMETSAT’s MSG SEVIRI. This study provides an assessment of SM and LST dynamics at daily and seasonal scales, using 4 years (2011–2014) of in situ and satellite observations over the central part of the river Duero basin in Spain. Specifically, the agreement of instantaneous SM with a variety of LST-derived parameters is analyzed to better understand the fundamental link of the SM–LST relationship through ET and thermal inertia. Ground-based SM and LST measurements from the REMEDHUS network are compared to SMOS SM and MODIS LST spaceborne observations. ET is obtained from the HidroMORE regional hydrological model. At the daily scale, a strong anticorrelation is observed between in situ SM and maximum LST (R ˜ -0.6 to -0.8), and between SMOS SM and MODIS LST Terra/Aqua day (R ˜ - 0.7). At the seasonal scale, results show a stronger anticorrelation in autumn, spring and summer (in situ R ˜ -0.5 to -0.7; satellite R ˜ -0.4 to -0.7) indicating SM–LST coupling, than in winter (in situ R ˜ +0.3; satellite R ˜ -0.3) indicating SM–LST decoupling. These different behaviors evidence changes from water-limited to energy-limited moisture flux across seasons, which are confirmed by the observed ET evolution. In water-limited periods, SM is extracted from the soil through ET until critical SM is reached. A method to estimate the soil critical SM is proposed. For REMEDHUS, the critical SM is estimated to be ~0.12 m3/m3 , stable over the study period and consistent between in situ and satellite observations. A better understanding of the SM–LST link could not only help improving the representation of LST in current hydrological and climate prediction models, but also refining SM retrieval or microwave-optical disaggregation algorithms, related to ET and vegetation status.Peer ReviewedPostprint (published version

Understanding Mobile Search Task Relevance and User Behaviour in Context

Improvements in mobile technologies have led to a dramatic change in how and when people access and use information, and is having a profound impact on how users address their daily information needs. Smart phones are rapidly becoming our main method of accessing information and are frequently used to perform `on-the-go' search tasks. As research into information retrieval continues to evolve, evaluating search behaviour in context is relatively new. Previous research has studied the effects of context through either self-reported diary studies or quantitative log analysis; however, neither approach is able to accurately capture context of use at the time of searching. In this study, we aim to gain a better understanding of task relevance and search behaviour via a task-based user study (n=31) employing a bespoke Android app. The app allowed us to accurately capture the user's context when completing tasks at different times of the day over the period of a week. Through analysis of the collected data, we gain a better understanding of how using smart phones on the go impacts search behaviour, search performance and task relevance and whether or not the actual context is an important factor.Comment: To appear in CHIIR 2019 in Glasgow, U

Impact of day/night time land surface temperature in soil moisture disaggregation algorithms

Since its launch in 2009, the ESA’s SMOS mission is providing global soil moisture (SM) maps at ~40 km, using the first L-band microwave radiometer on space. Its spatial resolution meets the needs of global applications, but prevents the use of the data in regional or local applications, which require higher spatial resolutions (~1-10 km). SM disaggregation algorithms based generally on the land surface temperature (LST) and vegetation indices have been developed to bridge this gap. This study analyzes the SM-LST relationship at a variety of LST acquisition times and its influence on SM disaggregation algorithms. Two years of in situ and satellite data over the central part of the river Duero basin and the Iberian Peninsula are used. In situ results show a strong anticorrelation of SM to daily maximum LST (R˜-0.5 to -0.8). This is confirmed with SMOS SM and MODIS LST Terra/Aqua at day time-overpasses (R˜-0.4 to -0.7). Better statistics are obtained when using MODIS LST day (R˜0.55 to 0.85; ubRMSD˜0.04 to 0.06 m3 /m3 ) than LST night (R˜0.45 to 0.80; ubRMSD˜0.04 to 0.07 m3 /m3 ) in the SM disaggregation. An averaged ensemble of day and night MODIS LST Terra/Aqua disaggregated SM estimates also leads to robust statistics (R˜0.55 to 0.85; ubRMSD˜0.04 to 0.07 m3 /m3 ) with a coverage improvement of ~10-20 %.Peer ReviewedPostprint (published version

Observing and modelling phytoplankton community structure in the North Sea

© Author(s) 2017. CC Attribution 3.0 License. Phytoplankton form the base of the marine food chain, and knowledge of phytoplankton community structure is fundamental when assessing marine biodiversity. Policy makers and other users require information on marine biodiversity and other aspects of the marine environment for the North Sea, a highly productive European shelf sea. This information must come from a combination of observations and models, but currently the coastal ocean is greatly under-sampled for phytoplankton data, and outputs of phytoplankton community structure from models are therefore not yet frequently validated. This study presents a novel set of in situ observations of phytoplankton community structure for the North Sea using accessory pigment analysis. The observations allow a good understanding of the patterns of surface phytoplankton biomass and community structure in the North Sea for the observed months of August 2010 and 2011. Two physical-biogeochemical ocean models, the biogeochemical components of which are different variants of the widely used European Regional Seas Ecosystem Model (ERSEM), were then validated against these and other observations. Both models were a good match for sea surface temperature observations, and a reasonable match for remotely sensed ocean colour observations. However, the two models displayed very different phytoplankton community structures, with one better matching the in situ observations than the other. Nonetheless, both models shared some similarities with the observations in terms of spatial features and inter-annual variability. An initial comparison of the formulations and parameterizations of the two models suggests that diversity between the parameter settings of model phytoplankton functional types, along with formulations which promote a greater sensitivity to changes in light and nutrients, is key to capturing the observed phytoplankton community structure. These findings will help inform future model development, which should be coupled with detailed validation studies, in order to help facilitate the wider application of marine biogeochemical modelling to user and policy needs

An Empirical Study Comparing Unobtrusive Physiological Sensors for Stress Detection in Computer Work.

Several unobtrusive sensors have been tested in studies to capture physiological reactions to stress in workplace settings. Lab studies tend to focus on assessing sensors during a specific computer task, while in situ studies tend to offer a generalized view of sensors' efficacy for workplace stress monitoring, without discriminating different tasks. Given the variation in workplace computer activities, this study investigates the efficacy of unobtrusive sensors for stress measurement across a variety of tasks. We present a comparison of five physiological measurements obtained in a lab experiment, where participants completed six different computer tasks, while we measured their stress levels using a chest-band (ECG, respiration), a wristband (PPG and EDA), and an emerging thermal imaging method (perinasal perspiration). We found that thermal imaging can detect increased stress for most participants across all tasks, while wrist and chest sensors were less generalizable across tasks and participants. We summarize the costs and benefits of each sensor stream, and show how some computer use scenarios present usability and reliability challenges for stress monitoring with certain physiological sensors. We provide recommendations for researchers and system builders for measuring stress with physiological sensors during workplace computer use