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Hybrid Optical Fibers – An Innovative Platform for In‐Fiber Photonic Devices
The field of hybrid optical fibers is one of the most active research areas in current fiber optics and has the vision of integrating sophisticated materials inside fibers, which are not traditionally used in fiber optics. Novel in-fiber devices with unique properties have been developed, opening up new directions for fiber optics in fields of critical interest in modern research, such as biophotonics, environmental science, optoelectronics, metamaterials, remote sensing, medicine, or quantum optics. Here the recent progress in the field of hybrid optical fibers is reviewed from an application perspective, focusing on fiber-integrated devices enabled by including novel materials inside polymer and glass fibers. The topics discussed range from nanowire-based plasmonics and hyperlenses, to integrated semiconductor devices such as optoelectronic detectors, and intense light generation unlocked by highly nonlinear hybrid waveguides.
Three perceptions of the evapotranspiration landscape: Comparing spatial patterns from a distributed hydrological model, remotely sensed surface temperatures, and sub-basin water balances
A problem encountered by many distributed hydrological modelling studies is high simulation errors at interior gauges when the model is only globally calibrated at the outlet. We simulated river runoff in the Elbe River basin in central Europe (148 268 km2) with the semi-distributed eco-hydrological model SWIM (Soil and Water Integrated Model). While global parameter optimisation led to Nash-Sutcliffe efficiencies of 0.9 at the main outlet gauge, comparisons with measured runoff series at interior points revealed large deviations. Therefore, we compared three different strategies for deriving sub-basin evapotranspiration: (1) modelled by SWIM without any spatial calibration, (2) derived from remotely sensed surface temperatures, and (3) calculated from long-term precipitation and discharge data. The results show certain consistencies between the modelled and the remote sensing based evapotranspiration rates, but there seems to be no correlation between remote sensing and water balance based estimations. Subsequent analyses for single sub-basins identify amongst others input weather data and systematic error amplification in inter-gauge discharge calculations as sources of uncertainty. The results encourage careful utilisation of different data sources for enhancements in distributed hydrological modelling.
Monitoring Agronomic Parameters of Winter Wheat Crops with Low-Cost UAV Imagery
Monitoring the dynamics in wheat crops requires near-term observations with high spatial resolution due to the complex factors influencing wheat growth variability. We studied the prospects for monitoring the biophysical parameters and nitrogen status in wheat crops with low-cost imagery acquired from unmanned aerial vehicles (UAV) over an 11 ha field. Flight missions were conducted at approximately 50 m in altitude with a commercial copter and camera system—three missions were performed between booting and maturing of the wheat plants and one mission after tillage. Ultra-high resolution orthoimages of 1.2 cm·px−1 and surface models were generated for each mission from the standard red, green and blue (RGB) aerial images. The image variables were extracted from image tone and surface models, e.g., RGB ratios, crop coverage and plant height. During each mission, 20 plots within the wheat canopy with 1 × 1 m2 sample support were selected in the field, and the leaf area index, plant height, fresh and dry biomass and nitrogen concentrations were measured. From the generated UAV imagery, we were able to follow the changes in early senescence at the individual plant level in the wheat crops. Changes in the pattern of the wheat canopy varied drastically from one mission to the next, which supported the need for instantaneous observations, as delivered by UAV imagery. The correlations between the biophysical parameters and image variables were highly significant during each mission, and the regression models calculated with the principal components of the image variables yielded R2 values between 0.70 and 0.97. In contrast, the models of the nitrogen concentrations yielded low R2 values with the best model obtained at flowering (R2 = 0.65). The nitrogen nutrition index was calculated with an accuracy of 0.10 to 0.11 NNI for each mission. For all models, information about the surface models and image tone was important. We conclude that low-cost RGB UAV imagery will strongly aid farmers in observing biophysical characteristics, but it is limited for observing the nitrogen status within wheat crops.
Lidar/radar approach to quantify the dust impact on ice nucleation in mid and high level clouds
We present the first attempt of a closure experiment regarding the relationship between ice nucleating particle concentration (INPC) and ice crystal number concentration (ICNC), solely based on active remote sensing. The approach combines aerosol and cloud observations with polarization lidar, Doppler lidar, and cloud radar. Several field campaigns were conducted on the island of Cyprus in the Eastern Mediterranean from 2015-2018 to study heterogeneous ice formation in altocumulus and cirrus layers embedded in Saharan dust. A case study observed on 10 April 2017 is discussed in this contribution. © 2019 The Authors, published by EDP Sciences.
Scalable Functionalization of Optical Fibers Using Atomically Thin Semiconductors
Atomically thin transition metal dichalcogenides are highly promising for integrated optoelectronic and photonic systems due to their exciton-driven linear and nonlinear interactions with light. Integrating them into optical fibers yields novel opportunities in optical communication, remote sensing, and all-fiber optoelectronics. However, the scalable and reproducible deposition of high-quality monolayers on optical fibers is a challenge. Here, the chemical vapor deposition of monolayer MoS2 and WS2 crystals on the core of microstructured exposed-core optical fibers and their interaction with the fibers’ guided modes are reported. Two distinct application possibilities of 2D-functionalized waveguides to exemplify their potential are demonstrated. First, the excitonic 2D material photoluminescence is simultaneously excited and collected with the fiber modes, opening a novel route to remote sensing. Then it is shown that third-harmonic generation is modified by the highly localized nonlinear polarization of the monolayers, yielding a new avenue to tailor nonlinear optical processes in fibers. It is anticipated that the results may lead to significant advances in optical-fiber-based technologies. © 2020 The Authors. Published by Wiley-VCH GmbH
Identification of dust sources in a Saharan dust hot-spot and their implementation in a dust-emission model
Although mineral dust plays a key role in the Earth’s climate system and in climate and weather prediction, models still have difficulties in predicting the amount and distribution of mineral dust in the atmosphere. One reason for this is the limited understanding of the distribution of dust sources and their behavior with respect to their spatiotemporal variability in activity. For a better estimation of the atmospheric dust load, this paper presents an approach to localize dust sources and thereby estimate the sediment supply for a study area centered on the Aïr Massif in Niger with a north–south extent of 16 ∘ –22 ∘ N and an east–west extent of 4 ∘ –12 ∘ E. This approach uses optical Sentinel-2 data at visible and near infrared wavelengths together with HydroSHEDS flow accumulation data to localize ephemeral riverbeds. Visible channels from Sentinel-2 data are used to detect sand sheets and dunes. The identified sediment supply map was compared to the dust source activation frequency derived from the analysis of Desert-Dust-RGB imagery from the Meteosat Second Generation series of satellites. This comparison reveals the strong connection between dust activity, prevailing meteorology and sediment supply. In a second step, the sediment supply information was implemented in a dust-emission model. The simulated emission flux shows how much the model results benefit from the updated sediment supply information in localizing the main dust sources and in retrieving the seasonality of dust activity from these sources. The described approach to characterize dust sources can be implemented in other regional model studies, or even globally, and can thereby help to get a more accurate picture of dust source distribution and a more realistic estimation of the atmospheric dust load.
Modelling mineral dust in the Central Asian region
In Central Asia, climate and air quality are largely affected by local and long-travelled mineral dust. For the last century, the area has experienced severe land-use changes and water exploitation producing new dust sources. Today global warming causes rapid shrinking of mountain glaciers with yet unknow consequences for dust and its climate effects. Despite the importance for a growing population, only little is known about sources, transport pathways and properties of Central Asian dust. A transport study with a global aerosol-climate model is undertaken to investigate the life cycle of mineral dust in Central Asia for the period of a remote-sensing campaign in Tajikistan in 2015-2016. An initial evaluation with sun photometer measurements shows reasonable agreement for the average amount of dust, but a significant weakness of the model in reproducing the seasonality of local dust with maximum activity in summer. Source apportionment reveals a major contribution from Arabia throughout the year in accordance with observations. In the model, local sources mainly contribute in spring and autumn while summer-time dust production is underestimated. The results underline the importance of considering long-range transport and, locally, a detailed representation of atmospheric dynamics and surface characteristics for modelling dust in Central Asia. © 2019 The Authors, published by EDP Sciences.
Deriving intensity–duration–frequency (IDF) curves using downscaled in situ rainfall assimilated with remote sensing data
The rainfall intensity–duration–frequency (IDF) curves play an important role in water resources engineering and management. The applications of IDF curves range from assessing rainfall events, classifying climatic regimes, to deriving design storms and assisting in designing urban drainage systems, etc. The deriving procedure of IDF curves, however, requires long-term historical rainfall observations, whereas lack of fine-timescale rainfall records (e.g. sub-daily) often results in less reliable IDF curves. This paper presents the utilization of remote sensing sub-daily rainfall, i.e. Global Satellite Mapping of Precipitation (GSMaP), integrated with the Bartlett-Lewis rectangular pulses (BLRP) model, to disaggregate the daily in situ rainfall, which is then further used to derive more reliable IDF curves. Application of the proposed method in Singapore indicates that the disaggregated hourly rainfall, preserving both the hourly and daily statistic characteristics, produces IDF curves with significantly improved accuracy; on average over 70% of RMSE is reduced as compared to the IDF curves derived from daily rainfall observations. © 2019, The Author(s).
Recurrence Analysis of Vegetation Indices for Highlighting the Ecosystem Response to Drought Events: An Application to the Amazon Forest
Forests are important in sequestering CO2 and therefore play a significant role in climate change. However, the CO2 cycle is conditioned by drought events that alter the rate of photosynthesis, which is the principal physiological action of plants in transforming CO2 into biological energy. This study applied recurrence quantification analysis (RQA) to describe the evolution of photosynthesis-related indices to highlight disturbance alterations produced by the Atlantic Multidecadal Oscillation (AMO, years 2005 and 2010) and the El Niño-Southern Oscillation (ENSO, year 2015) in the Amazon forest. The analysis was carried out using Moderate Resolution Imaging Spectroradiometer (MODIS) images to build time series of the enhanced vegetation index (EVI), the normalized difference water index (NDWI), and the land surface temperature (LST) covering the period 2001–2018. The results did not show significant variations produced by AMO throughout the study area, while a disruption due to the global warming phase linked to the extreme ENSO event occurred, and the forest was able to recover. In addition, spatial differences in the response of the forest to the ENSO event were found. These findings show that the application of RQA to the time series of vegetation indices supports the evaluation of the forest ecosystem response to disruptive events. This approach provides information on the capacity of the forest to recover after a disruptive event and, therefore is useful to estimate the resilience of this particular ecosystem.