Evaluation and Application of Remotely Sensed Soil Moisture Products

Whereas in-situ measurements of soil moisture are very accurate, achieving accurate regional soil moisture estimates derived solely from point measurements is difficult because of the dependence upon the density of the gauge network and the proper upkeep of these instruments, which can be costly. Microwave remote sensing is the only technology capable of providing timely direct measurements of regional soil moisture in areas that are lacking in-situ networks. Soil moisture remote sensing technology is well established has been successfully applied in many fashions to Earth Science applications. Since the microwave emission from the soil surface has such a high dependency upon the moisture content within the soil, we can take advantage of this relationship and combined with physically-based models of the land surface, derive accurate regional estimates of the soil column water content from the microwave brightness temperature observed from satellite-based remote sensing instruments. However, there still remain many questions regarding the most efficient methodology for evaluating and applying satellite-based soil moisture estimates. As discussed below, we to use satellite-based estimates of soil moisture dynamics to improve the predictive capability of an optimized hydrologic model giving more accurate root-zone soil moisture estimates

Satellite-Based Assessment of Grassland Conversion and Related Fire Disturbance in the Kenai Peninsula, Alaska

Spruce beetle-induced (Dendroctonus rufipennis (Kirby)) mortality on the Kenai Peninsula has been hypothesized by local ecologists to result in the conversion of forest to grassland and subsequent increased fire danger. This hypothesis stands in contrast to empirical studies in the continental US which suggested that beetle mortality has only a negligible effect on fire danger. In response, we conducted a study using Landsat data and modeling techniques to map land cover change in the Kenai Peninsula and to integrate change maps with other geospatial data to predictively map fire danger for the same region. We collected Landsat imagery to map land cover change at roughly five-year intervals following a severe, mid-1990s beetle infestation to the present. Land cover classification was performed at each time step and used to quantify grassland encroachment patterns over time. The maps of land cover change along with digital elevation models (DEMs), temperature, and historical fire data were used to map and assess wildfire danger across the study area. Results indicate the highest wildfire danger tended to occur in herbaceous and black spruce land cover types, suggesting that the relationship between spruce beetle damage and wildfire danger in costal Alaskan forested ecosystems differs from the relationship between the two in the forests of the coterminous United States. These change detection analyses and fire danger predictions provide the Kenai National Wildlife Refuge (KENWR) ecologists and other forest managers a better understanding of the extent and magnitude of grassland conversion and subsequent change in fire danger following the 1990s spruce beetle outbreak

Hydrologic and Agricultural Earth Observations and Modeling for the Water-Food Nexus

In a globalizing and rapidly-developing world, reliable, sustainable access to water and food are inextricably linked to each other and basic human rights. Achieving security and sustainability in both requires recognition of these linkages, as well as continued innovations in both science and policy. We present case studies of how Earth observations are being used in applications at the nexus of water and food security: crop monitoring in support of G20 global market assessments, water stress early warning for USAID, soil moisture monitoring for USDA's Foreign Agricultural Service, and identifying food security vulnerabilities for climate change assessments for the UN and the UK international development agency. These case studies demonstrate that Earth observations are essential for providing the data and scalability to monitor relevant indicators across space and time, as well as understanding agriculture, the hydrological cycle, and the water-food nexus. The described projects follow the guidelines for co-developing useable knowledge for sustainable development policy. We show how working closely with stakeholders is essential for transforming NASA Earth observations into accurate, timely, and relevant information for water-food nexus decision support. We conclude with recommendations for continued efforts in using Earth observations for addressing the water-food nexus and the need to incorporate the role of energy for improved food and water security assessment

Effect of planned place of birth on obstetric interventions and maternal outcomes among low-risk women : a cohort study in the Netherlands

Background: The use of interventions in childbirth has increased the past decades. There is concern that some women might receive more interventions than they really need. For low-risk women, midwife-led birth settings may be of importance as a counterbalance towards the increasing rate of interventions. The effect of planned place of birth on interventions in the Netherlands is not yet clear. This study aims to give insight into differences in obstetric interventions and maternal outcomes for planned home versus planned hospital birth among women in midwife-led care. Methods: Women from twenty practices across the Netherlands were included in 2009 and 2010. Of these, 3495 were low-risk and in midwife-led care at the onset of labour. Information about planned place of birth and outcomes, including instrumental birth (caesarean section, vacuum or forceps birth), labour augmentation, episiotomy, oxytocin in third stage, postpartum haemorrhage >1000 ml and perineal damage, came from the national midwife-led care perinatal database, and a postpartum questionnaire. Results: Women who planned home birth more often had spontaneous birth (nulliparous women aOR 1.38, 95 % CI 1.08-1.76, parous women aOR 2.29, 95 % CI 1.21-4.36) and less often episiotomy (nulliparous women aOR 0.73, 0.58-0.91, parous women aOR 0.47, 0.33-0.68) and use of oxytocin in the third stage (nulliparous women aOR 0.58, 0.42-0.80, parous women aOR 0.47, 0.37-0.60) compared to women who planned hospital birth. Nulliparous women more often had anal sphincter damage (aOR 1.75, 1.01-3.03), but the difference was not statistically significant if women who had caesarean sections were excluded. Parous women less often had labour augmentation (aOR 0.55, 0.36-0.82) and more often an intact perineum (aOR 1.65, 1.34-2.03). There were no differences in rates of vacuum/forceps birth, unplanned caesarean section and postpartum haemorrhage >1000 ml. Conclusions: Women who planned home birth were more likely to give birth spontaneously and had fewer medical interventions. © 2016 The Author(s)

Silicon-Organic Hybrid (SOH) and Plasmonic-Organic Hybrid (POH) integration

Silicon-organic hybrid (SOH) and plasmonic-organic hybrid (POH) integration combines organic clectro-optic materials with silicon photonic and plasmonic waveguides, The concept enables fast and power-efficient modulators that support advanced modulation formats such as QPSK and 16QAM

Silicon-organic hybrid electro-optical devices

Organic materials combined with strongly guiding silicon waveguides open the route to highly efficient electro-optical devices. Modulators based on the so-called silicon-organic hybrid (SOH) platform have only recently shown frequency responses up to 100 GHz, high-speed operation beyond 112 Gbit/s with fJ/bit power consumption. In this paper, we review the SOH platform and discuss important devices such as Mach-Zehnder and IQ-modulators based on the linear electro-optic effect. We further show liquid-crystal phase-shifters with a voltage-length product as low as V pi L = 0.06 V.mm and sub-mu W power consumption as required for slow optical switching or tuning optical filters and devices

Silicon-Organic Hybrid (SOH) and Plasmonic-Organic Hybrid (POH) integration

Silicon photonics offers tremendous potential for inexpensive high-yield photonic-electronic integration. Besides conventional dielectric waveguides, plasmonic structures can also be efficiently realized on the silicon photonic platform, reducing device footprint by more than an order of magnitude. However, nei-ther silicon nor metals exhibit appreciable second-order optical nonlinearities, thereby making efficient electro-optic modulators challenging to realize. These deficiencies can be overcome by the concepts of silicon-organic hybrid (SOH) and plasmonic-organic hybrid integration, which combine SOI waveguides and plasmonic nanostructures with organic electro-optic cladding materials

Earth Observations and Integrative Models in Support of Food and Water Security

Global food production depends upon many factors that Earth observing satellites routinely measure about water, energy, weather, and ecosystems. Increasingly sophisticated, publicly-available satellite data products can improve efficiencies in resource management and provide earlier indication of environmental disruption. Satellite remote sensing provides a consistent, long-term record that can be used effectively to detect large-scale features over time, such as a developing drought. Accuracy and capabilities have increased along with the range of Earth observations and derived products that can support food security decisions with actionable information. This paper highlights major capabilities facilitated by satellite observations and physical models that have been developed and validated using remotely-sensed observations. Although we primarily focus on variables relevant to agriculture, we also include a brief description of the growing use of Earth observations in support of aquaculture and fisheries

Deep Convolutional LSTM for improved flash flood prediction

Flooding remains one of the most devastating and costly natural disasters. As flooding events grow in frequency and intensity, it has become increasingly important to improve flood monitoring, prediction, and early warning systems. Recent efforts to improve flash flood forecasts using deep learning have shown promise, yet commonly-used techniques such as long short term memory (LSTM) models are unable to extract potentially significant spatial relationships among input datasets. Here we propose a hybrid approach using a Convolutional LSTM (ConvLSTM) network to predict stream stage heights using multi-modal hydrometeorological remote sensing and in-situ inputs. Results suggest the hybrid network can more effectively capture the specific spatiotemporal landscape dynamics of a flash flood-prone catchment relative to the current state-of-the-art, leading to a roughly 26% improvement in model error when predicting elevated stream conditions. Furthermore, the methodology shows promise for improving prediction accuracy and warning times for supporting local decision making