ABSTRACT
We present a novel data set for drought in the continental US (CONUS) built to enable computationally efficient spatio-temporal statistical and probabilistic models of drought. We converted drought data obtained from the widely-used US Drought Monitor (USDM) from its native geo-referenced polygon format to a 0.5 degree regular grid. We merged known environmental drivers of drought, including those obtained from the North American Land Data Assimilation System (NLDAS-2), US Geological Survey (USGS) streamflow data, and National Oceanic and Atmospheric Administration (NOAA) teleconnections data. The resulting data set permits statistical and probabilistic modeling of drought with explicit spatial and/or temporal dependence. Such models could be used to forecast drought at short-range, seasonal to sub-seasonal, and inter-annual timescales with uncertainty, extending the reach and value of the current US Drought Outlook from the National Weather Service Climate Prediction Center. This novel data product provides the first common gridded dataset that includes critical variables used to inform hydrological and meteorological drought.
ABSTRACT
High spatial and temporal resolution data is crucial to comprehend the dynamics of water quality fully, support informed decision-making, and allow efficient management and protection of water resources. Traditional in situ water quality measurement techniques are both time-consuming and labor-intensive, resulting in databases with limited spatial and temporal frequency. To address these challenges, satellite-driven water quality assessment has emerged as an efficient and effective solution, offering comprehensive data on larger-scale water bodies. Numerous studies have utilized multispectral and hyperspectral remote sensing data from various sensors to assess water quality, yielding promising results. However, the recent popularity of unmanned aerial vehicle (UAV) remote sensing can be attributed to its high spatial and temporal resolution, flexibility, ability to capture data at different times of day, and relatively low cost compared to traditional platforms. This study presents a comprehensive review of the current state of the art in monitoring water quality in small inland water bodies using satellite and UAV remote sensing data. It encompasses an overview of atmospheric correction algorithms and the assessment of different water quality parameters. Furthermore, the review addresses the challenges associated with monitoring water quality in these bodies of water and emphasizes the potential of UAVs to overcome these challenges by providing accurate and reliable data.
Subject(s)
Remote Sensing Technology , Water Quality , Remote Sensing Technology/methods , Unmanned Aerial Devices , Environmental Monitoring/methods , AlgorithmsABSTRACT
The development of algorithms for remote sensing of water quality (RSWQ) requires a large amount of in situ data to account for the bio-geo-optical diversity of inland and coastal waters. The GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments (GLORIA) includes 7,572 curated hyperspectral remote sensing reflectance measurements at 1 nm intervals within the 350 to 900 nm wavelength range. In addition, at least one co-located water quality measurement of chlorophyll a, total suspended solids, absorption by dissolved substances, and Secchi depth, is provided. The data were contributed by researchers affiliated with 59 institutions worldwide and come from 450 different water bodies, making GLORIA the de-facto state of knowledge of in situ coastal and inland aquatic optical diversity. Each measurement is documented with comprehensive methodological details, allowing users to evaluate fitness-for-purpose, and providing a reference for practitioners planning similar measurements. We provide open and free access to this dataset with the goal of enabling scientific and technological advancement towards operational regional and global RSWQ monitoring.
