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Accurate and detailed soil moisture estimates can critically shape cross-sectoral water resources decision-making. From local to regional scales, monitoring of agricultural water demands, droughts, floods, landslides, and wildfires can benefit from high-resolution soil moisture information. However, soil moisture highly varies in space and time, and as a result, it is challenging to obtain detailed information at the stakeholder-relevant spatial scales. This dissertation leverages advances in satellite remote sensing, hyper-resolution land surface modeling, high-performance computing, and machine learning to bridge this data gap. Chapter 2 introduces a novel cluster-based Bayesian merging scheme that combines NASA's SMAP satellite observations and hyper-resolution land surface modeling for obtaining satellite-based surface soil moisture retrievals at an unprecedented 30-m spatial resolution. This approach's scalability and accuracy are demonstrated in Chapter 3 by introducing SMAP-HydroBlocks, the first satellite-based surface soil moisture dataset at a 30-m resolution over the United States (2015-2019). Using this dataset, Chapter 4 assesses the multi-scale properties of soil moisture spatial variability and the persistence of this variability across spatial scales. This analysis maps where detailed information is critical for solving water, energy, and carbon scale-dependent processes and how much variability is lost when data is only available at coarse spatial scales. Using machine learning, Chapter 5 demonstrates the value of high-resolution soil moisture for drought monitoring and crop yield prediction at farmer's field scales (250-m resolution). This dissertation provides a novel pathway towards global monitoring of water resources' dynamics at locally relevant spatial scales.
This book is a collection of overview articles showing how space-based observations, combined with hydrological modeling, have considerably improved our knowledge of the continental water cycle and its sensitivity to climate change. Two main issues are highlighted: (1) the use in combination of space observations for monitoring water storage changes in river basins worldwide, and (2) the use of space data in hydrological modeling either through data assimilation or as external constraints. The water resources aspect is also addressed, as well as the impacts of direct anthropogenic forcing on land hydrology (e.g. ground water depletion, dam building on rivers, crop irrigation, changes in land use and agricultural practices, etc.). Remote sensing observations offer important new information on this important topic as well, which is highly useful for achieving water management objectives.Over the past 15 years, remote sensing techniques have increasingly demonstrated their capability to monitor components of the water balance of large river basins on time scales ranging from months to decades: satellite altimetry routinely monitors water level changes in large rivers, lakes and floodplains. When combined with satellite imagery, this technique can also measure surface water volume variations. Passive and active microwave sensors offer important information on soil moisture (e.g. the SMOS mission) as well as wetlands and snowpack. The GRACE space gravity mission offers, for the first time, the possibility of directly measuring spatio-temporal variations in the total vertically integrated terrestrial water storage. When combined with other space observations (e.g. from satellite altimetry and SMOS) or model estimates of surface waters and soil moisture, space gravity data can effectively measure groundwater storage variations. New satellite missions, planned for the coming years, will complement the constellation of satellites monitoring waters on land. This is particularly the case for the SWOT mission, which is expected to revolutionize land surface hydrology. Previously published in Surveys in Geophysics, Volume 37, No. 2, 2016
Water systems are building blocks for poverty alleviation, shared growth, sustainable development, and green growth strategies. They require data from in-situ observation networks. Budgetary and other constraints have taken a toll on their operation and there are many regions in the world where the data are scarce or unreliable. Increasingly, remote sensing satellite-based earth observation is becoming an alternative. This book briefly describes some key global water challenges, perspectives for remote sensing approaches, and their importance for water resources-related activities. It describes eight key types of water resources management variables, a list of sensors that can produce such information, and a description of existing data products with examples. Earth Observation for Water Resources Management provides a series of practical guidelines that can be used by project leaders to decide whether remote sensing may be useful for the problem at hand and suitable data sources to consider if so. The book concludes with a review of the literature on reliability statistics of remote-sensed estimations.
The book provides comprehensive information on possible applications of remote sensing data for hydrological monitoring and modelling as well as for water management decisions. Mathematical theory is provided only as far as it is necessary for understanding the underlying principles. The book is especially timely because of new programs and sensors that are or will be realised. ESA, NASA, NASDA as well as the Indian and the Brazilian Space Agency have recently launched satellites or developed plans for new sensor systems that will be especially pertinent to hydrology and water management. New techniques are presented whose structure differ from conventional hydrological models due to the nature of remotely sensed data.
Information dealing with hydrologic cycle, precipitation, snow hydrology, evapotranspiration, runoff, soil moisture, groundwater, water quality, and water resources management and monitoring
Remote Sensing of Drought: Innovative Monitoring Approaches presents emerging remote sensing-based tools and techniques that can be applied to operational drought monitoring and early warning around the world. The first book to focus on remote sensing and drought monitoring, it brings together a wealth of information that has been scattered throughout the literature and across many disciplines. Featuring contributions by leading scientists, it assembles a cross-section of globally applicable techniques that are currently operational or have potential to be operational in the near future. The book explores a range of applications for monitoring four critical components of the hydrological cycle related to drought: vegetation health, evapotranspiration, soil moisture and groundwater, and precipitation. These applications use remotely sensed optical, thermal, microwave, radar, and gravity data from instruments such as AMSR-E, GOES, GRACE, MERIS, MODIS, and Landsat and implement several advanced modeling and data assimilation techniques. Examples show how to integrate this information into routine drought products. The book also examines the role of satellite remote sensing within traditional drought monitoring, as well as current challenges and future prospects. Improving drought monitoring is becoming increasingly important in addressing a wide range of societal issues, from food security and water scarcity to human health, ecosystem services, and energy production. This unique book surveys innovative remote sensing approaches to provide you with new perspectives on large-area drought monitoring and early warning.
Requests for regional soil moisture observations are increasing to parameterize complex hydrological models, to assess the impact of land-use changes, and to develop climate adaption strategies in the agricultural sector. Spatial land-use patterns have an impact on the soil water balance and groundwater recharge. Soil moisture is therefore a key parameter for the long-term monitoring and development of sustainable land-management and landscape design strategies that mitigate regional water scarcity and droughts. For example, the spatial organization of hedges or tree rows related to open land and wind direction avoids soil erosion, limits local evaporation, and increases local soil water storage. Since the early 1980s, satellite missions have been designed to monitor proxies for soil moisture, mainly at the national and global scale, with a relatively coarse pixel resolution and low accuracy. The local effects of weather and climate are very dynamic in space and time. Thus, a strong need exists for more accurate, regional-scale remote sensing products for soil moisture. The transfer of existing, proof-of-concept algorithms to region-specific monitoring frameworks is urgent. This Special Issue provides an overview of current developments on remote sensing-based soil moisture observations that are applicable at a regional scale. The compendium of research papers demonstrates the benefits of concurrently utilizing multi-source remote sensing data and in situ measurements through: - Using additional data and site-specific knowledge; - Combining empirical and physical approaches; - Developing concepts to deal with mixed pixels.
Environmental remote sensing plays a critical role in observing key hydrological components such as precipitation, soil moisture, evapotranspiration and total water storage on a global scale. As water security is one of the most critical issues in the world, satellite remote sensing techniques are of particular importance for emerging regions which have inadequate in-situ gauge observations. This book reviews multiple remote sensing observations, the application of remote sensing in hydrological modeling, data assimilation and hydrological capacity building in emerging regions.
Remote Sensing of Soils: Mapping, Monitoring and Measurement covers the basic, theoretical and scientific concepts of multidisciplinary subjects, including sections that relate to soil sciences, remote sensing, geoinformatics, geomatics, civil and water resource engineering, geography, agriculture, disaster management and the earth and environmental sciences. The book consists of defined elements to help guide the reader, including an abstract, introductions, a literature review, methodology, results and discussions, findings, recommendations and conclusions. Each chapter includes theoretical information that is illustrated with flow charts, tables, figures, diagrams and other related illustrations. Site-specific research and case studies are described throughout with geographical and demographical data, current scientific issues, impacts, solutions and societal benefits, thus providing readers from multi-disciplinary backgrounds the tools they need to successful map, analyze and monitor soils. Covers multispectral, hyperspectral and SAR remote sensing analysis of soil properties, soil moisture, soil salinity, and soil organic matters, etc., in spatio-temporal scale Includes a section on digital soil mapping, including integrated RS, GIS and insitu surveyed data analysis for digital soil mapping using widely accepted models and approaches Ideal for readers in the soil sciences, remote sensing, geoinformatics, geomatics, civil and water resource engineering, geography, agriculture, disaster management, and earth and environmental sciences
Remote Sensing of Soil and Land Surface Processes: Monitoring, Mapping, and Modeling couples artificial intelligence and remote sensing for mapping and modeling natural resources, thus expanding the applicability of AI and machine learning for soils and landscape studies and providing a hybridized approach that also increases the accuracy of image analysis. The book covers topics including digital soil mapping, satellite land surface imagery, assessment of land degradation, and deep learning networks and their applicability to land surface processes and natural hazards, including case studies and real life examples where appropriate. This book offers postgraduates, researchers and academics the latest techniques in remote sensing and geoinformation technologies to monitor soil and surface processes. ? Introduces object-based concepts and applications, enhancing monitoring capabilities and increasing the accuracy of mapping ? Couples artificial intelligence and remote sensing for mapping and modeling natural resources, expanding the applicability of AI and machine learning for soils and sediment studies ? Includes the use of new sensors and their applications to soils and sediment characterization ???????? Includes case studies from a variety of geographical areas