The future of Earth observation in hydrology

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 smart-phones 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

Evaluating the Variability of Urban Land Surface Temperatures Using Drone Observations

Urbanization and climate change are driving increases in urban land surface temperatures that pose a threat to human and environmental health. To address this challenge, we must be able to observe land surface temperatures within spatially complex urban environments. However, many existing remote sensing studies are based upon satellite or aerial imagery that capture temperature at coarse resolutions that fail to capture the spatial complexities of urban land surfaces that can change at a sub-meter resolution. This study seeks to fill this gap by evaluating the spatial variability of land surface temperatures through drone thermal imagery captured at high-resolutions (13 cm). In this study, flights were conducted using a quadcopter drone and thermal camera at two case study locations in Milwaukee, Wisconsin and El Paso, Texas. Results indicate that land use types exhibit significant variability in their surface temperatures (3.9–15.8 °C) and that this variability is influenced by surface material properties, traffic, weather and urban geometry. Air temperature and solar radiation were statistically significant predictors of land surface temperature (R2 0.37–0.84) but the predictive power of the models was lower for land use types that were heavily impacted by pedestrian or vehicular traffic. The findings from this study ultimately elucidate factors that contribute to land surface temperature variability in the urban environment, which can be applied to develop better temperature mitigation practices to protect human and environmental health

High-resolution debris cover mapping using UAV-derived thermal imagery: limits and opportunities

Debris-covered glaciers are widespread in high mountain ranges on Earth. However, the dynamic evolution of debris-covered glacier surfaces is not well understood, in part due to difficulties of mapping debris cover thickness in high spatiotemporal resolution. In this study we present land surface temperatures (LST) and its diurnal variability measured from an unpiloted aerial vehicle (UAV) at high spatial resolution. We test two common approaches to derive debris thickness maps by (1) solving a surface energy balance model (SEBM) in conjunction with meteorological reanalysis data and (2) least squares regression of a rational curve using debris thickness field measurements. In addition, we take advantage of the measured diurnal temperature cycle and estimate the rate of change of heat storage within the debris cover. Both approaches resulted in debris thickness estimates with a RMSE of 6 to 8 cm between observed and modelled debris thicknesses, depending on the time of the day. The diurnal variability of the LST controls the relationship between LST and debris thickness and the non-linearity increases with increasing LST. During the warming phase of the debris cover, the LST depends strongly on the terrain aspect, rendering clear-sky morning flights that do not account for aspect-effects problematic. Our sensitivity analysis of various parameters in the SEBM highlights the relevance of the effective thermal conductivity when LST is high. Residual and variable bias of UAV-derived LSTs during a flight require calibration, which we achieve with bare ice surfaces. The model performance would benefit from more accurate LST measurements, which are difficult to achieve with uncooled sensors.</p

Development of an Unmanned Aerial Vehicle for Atmospheric Turbulence Measurement

An unmanned aerial vehicle was developed to study turbulence in the atmospheric boundary layer. The development of the aircraft, BLUECAT5, and instrumentation package culminated in a series of flight experiments conducted in two different locations near Stillwater, Oklahoma, USA. The flight experiments employed the use of two of the unmanned aerial vehicles flying simultaneously, each containing a five-hole pressure probe as part of a turbulence-measuring instrumentation package. A total of 18 flights were completed with the objective to measure atmospheric properties at five altitudes between 20 and 120 meters. Multiple flights were flown over two days in which the effects of the diurnal cycle on the boundary layer were investigated. Profiles for mean wind velocity, temperature, and humidity all follow expected boundary layer behavior throughout the day. Evolution of the boundary layer can be seen with the early morning, stable boundary layer identified and its transition to the early mid-day convective mixed boundary layer observed. The corresponding increase in turbulence intensity was found to be significant. The success of the test campaign demonstrated the ability of the developed unmanned system to measure turbulence in the atmospheric boundary layer

New strategies for row-crop management based on cost-effective remote sensors

Agricultural technology can be an excellent antidote to resource scarcity. Its growth has led to the extensive study of spatial and temporal in-field variability. The challenge of accurate management has been addressed in recent years through the use of accurate high-cost measurement instruments by researchers. However, low rates of technological adoption by farmers motivate the development of alternative technologies based on affordable sensors, in order to improve the sustainability of agricultural biosystems. This doctoral thesis has as main objective the development and evaluation of systems based on affordable sensors, in order to address two of the main aspects affecting the producers: the need of an accurate plant water status characterization to perform a proper irrigation management and the precise weed control. To address the first objective, two data acquisition methodologies based on aerial platforms have been developed, seeking to compare the use of infrared thermometry and thermal imaging to determine the water status of two most relevant row-crops in the region, sugar beet and super high-density olive orchards. From the data obtained, the use of an airborne low-cost infrared sensor to determine the canopy temperature has been validated. Also the reliability of sugar beet canopy temperature as an indicator its of water status has been confirmed. The empirical development of the Crop Water Stress Index (CWSI) has also been carried out from aerial thermal imaging combined with infrared temperature sensors and ground measurements of factors such as water potential or stomatal conductance, validating its usefulness as an indicator of water status in super high-density olive orchards. To contribute to the development of precise weed control systems, a system for detecting tomato plants and measuring the space between them has been developed, aiming to perform intra-row treatments in a localized and precise way. To this end, low cost optical sensors have been used and compared with a commercial LiDAR laser scanner. Correct detection results close to 95% show that the implementation of these sensors can lead to promising advances in the automation of weed control. The micro-level field data collected from the evaluated affordable sensors can help farmers to target operations precisely before plant stress sets in or weeds infestation occurs, paving the path to increase the adoption of Precision Agriculture techniques

Time to Stem Lightweight Approaches and Focus on Real Minefield Data?

While preparing for airborne IR thermography fieldwork as part of the Odyssey2025 Project between Humanity & Inclusion and Mobility Robotics in Chad, a comprehensive literature study was conducted by the authors From the literature reviewed, the authors identified a disconnect between thermography-related research projects and practical, real-world HMA operations. The importance of real fieldwork, the significance of undergoing a literature review before starting your own research, and the need for researchers to work in conjunction with HMA operators are all essential, not only to those working in HMA, but more importantly, to the post-conflict communities the sector strives to help

Maritime Advanced Geospatial Intelligence Craft for Oil Spill Response: Selected Resources and Annotations

This selection of resources highlights the utility of Unmanned Surface Vehicles (USV) for use in marine spill response. Each entry is followed by a brief summary and evaluation of the source (i.e., the annotation). Most annotations will define the scope of the source, list significant cross references, and identify relevant USV capabilities. There is no attempt to provide actual hypotheses, data, or graphics, especially concerning cited articles published in refereed journals. The purpose of the annotation is to inform the reader of the relevance, accuracy, and quality of the sources cited. Relevance relates to the citation’s presentation of capabilities that improve marine spill response operations. Significant interest involves the use of sensors that characterize the environment to support oil spill cleanup operations. The diversity of resources is especially relevant since no two oil spills are the same owing to the variation in oil types, locations, and weather conditions. The development of USVs for oil spill monitoring, cleanup, and science reduces some of the dependence on expensive ship time

Maritime Advanced Geospatial Intelligence Craft for Oil Spill Response: Selected Resources and Annotations

This selection of resources highlights the utility of Unmanned Surface Vehicles (USV) for use in marine spill response. Each entry is followed by a brief summary and evaluation of the source (i.e., the annotation). Most annotations will define the scope of the source, list significant cross references, and identify relevant USV capabilities. There is no attempt to provide actual hypotheses, data, or graphics, especially concerning cited articles published in refereed journals. The purpose of the annotation is to inform the reader of the relevance, accuracy, and quality of the sources cited. Relevance relates to the citation’s presentation of capabilities that improve marine spill response operations. Significant interest involves the use of sensors that characterize the environment to support oil spill cleanup operations. The diversity of resources is especially relevant since no two oil spills are the same owing to the variation in oil types, locations, and weather conditions. The development of USVs for oil spill monitoring, cleanup, and science reduces some of the dependence on expensive ship time