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Water losses from water distribution means have a high environmental impact in terms of natural resource depletion (water, energy, ecosystems). This work aims to develop an optical airborne surveillance service for the detection of water leaks (WADI—Water-tightness Airborne Detection Implementation) to provide water utilities with adequate and timely information on leaks in water transportation mains outside urban areas. Firstly, a series of measurement campaigns were performed with two hyperspectral cameras and a thermal infrared camera in order to select the most appropriate wavelengths and combinations thereof for best revealing high moisture areas, which are taken as a proxy for water leakage. The Temperature-Vegetation-Index method (T-VI, also known as Triangle/Trapezoid method) was found to provide the highest contrast-to-noise ratio. This preliminary work helped select the most appropriate onboard instrumentation for two types of aerial platforms, manned (MAV) and unmanned (UAV). Afterwards, a series of measurement campaigns were performed from 2017 to 2019 in an operational environment over two water distribution networks in France and Portugal. Artificial leaks were introduced and both remote sensing platforms successfully detected them when excluding the unfavorable situations of a recent rain event or high vegetation presence. With the most recent equipment configuration, known and unknown real leaks in the overflown part of a water transportation network in Portugal have been detected. A significant number of false alarms were also observed which were due either to natural water flows (groundwater exfiltration, irrigation runoff and ponds) or to vegetation-cover variability nearby water-distribution nodes. Close interaction with the water utilities, and ancillary information like topographic factors (e.g., slope orientation), are expected to reduce the false alarm rates and improve WADI’s methodology performance.

Surface soil moisture (SM) plays a fundamental role in energy and water partitioning in the soil-plant-atmosphere continuum. A reliable and operational algorithm is much needed to retrieve regional surface SM at high spatial and temporal resolutions. Here, we provide an operational framework of estimating surface SM at fine spatial resolutions (using visible/thermal infrared images and concurrent meteorological data) based on a trapezoidal space defined by remotely sensed vegetation cover (Fc) and land surface temperature (LST). Theoretical solutions of the wet and dry edges were derived to achieve a more accurate and effective determination of the Fc/LST space. Subjectivity and uncertainty arising from visual examination of extreme boundaries can consequently be largely reduced. In addition, theoretical derivation of the extreme boundaries allows a per-pixel determination of the VI/LST space such that the assumption of uniform atmospheric forcing over the entire domain is no longer required. The developed approach was tested at the Tibetan Plateau Soil Moisture/Temperature Monitoring Network (SMTMN) site in central Tibet, China, from August 2010 to August 2011 using Moderate Resolution Imaging Spectroradiometer (MODIS) Terra images. Results indicate that the developed trapezoid model reproduced the spatial and temporal patterns of observed surface SM reasonably well, with showing a root-mean-square error of 0.06 m³·m-3 at the site level and 0.03 m³·m-3 at the regional scale. In addition, a case study on 2 September 2010 highlighted the importance of the theoretically calculated wet and dry edges, as they can effectively obviate subjectivity and uncertainties in determining the Fc/LST space arising from visual interpretation of satellite images. Compared with Land Surface Models (LSMs) in Global Land Data Assimilation System-1, the remote sensing-based trapezoid approach gave generally better surface SM estimates, whereas the LSMs showed systematic underestimation. Sensitivity analyses suggested that the trapezoid method is most sensitive to field capacity and temperature but less sensitive to other meteorological observations and parameters. [ABSTRACT FROM AUTHOR]

Water losses from water distribution means have a high environmental impact in terms of natural resource depletion (water, energy, ecosystems). This work aims to develop an optical airborne surveillance service for the detection of water leaks (WADI—Water-tightness Airborne Detection Implementation) to provide water utilities with adequate and timely information on leaks in water transportation mains outside urban areas. Firstly, a series of measurement campaigns were performed with two hyperspectral cameras and a thermal infrared camera in order to select the most appropriate wavelengths and combinations thereof for best revealing high moisture areas, which are taken as a proxy for water leakage. The Temperature-Vegetation-Index method (T-VI, also known as Triangle/Trapezoid method) was found to provide the highest contrast-to-noise ratio. This preliminary work helped select the most appropriate onboard instrumentation for two types of aerial platforms, manned (MAV) and unmanned (UAV). Afterwards, a series of measurement campaigns were performed from 2017 to 2019 in an operational environment over two water distribution networks in France and Portugal. Artificial leaks were introduced and both remote sensing platforms successfully detected them when excluding the unfavorable situations of a recent rain event or high vegetation presence. With the most recent equipment configuration, known and unknown real leaks in the overflown part of a water transportation network in Portugal have been detected. A significant number of false alarms were also observed which were due either to natural water flows (groundwater exfiltration, irrigation runoff and ponds) or to vegetation-cover variability nearby water-distribution nodes. Close interaction with the water utilities, and ancillary information like topographic factors (e.g., slope orientation), are expected to reduce the false alarm rates and improve WADI's methodology performance. [ABSTRACT FROM AUTHOR]