Download or read book Soil Moisture Mapping in South Central United States by Blending In situ Modeled and Remote Sensing Data written by Ning Zhang (Ph. D. in geography) and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Overall, this doctoral research advances physical understanding of climate processes and environmental systems. The new soil moisture products generated by this study will be made freely available for the scientific community and state and federal agencies. The new soil moisture products will facilitate the validation of other soil moisture products, and they will contribute to the agricultural, climatological, hydrological, and biological communities.

Download or read book Satellite Soil Moisture Retrieval written by Prashant K. Srivastava and published by Elsevier. This book was released on 2016-04-29 with total page 441 pages. Available in PDF, EPUB and Kindle. Book excerpt: Satellite Soil Moisture Retrieval: Techniques and Applications offers readers a better understanding of the scientific underpinnings, development, and application of soil moisture retrieval techniques and their applications for environmental modeling and management, bringing together a collection of recent developments and rigorous applications of soil moisture retrieval techniques from optical and infrared datasets, such as the universal triangle method, vegetation indices based approaches, empirical models, and microwave techniques, particularly by utilizing earth observation datasets such as IRS III, MODIS, Landsat7, Landsat8, SMOS, AMSR-e, AMSR2 and the upcoming SMAP. Through its coverage of a wide variety of soil moisture retrieval applications, including drought, flood, irrigation scheduling, weather forecasting, climate change, precipitation forecasting, and several others, this is the first book to promote synergistic and multidisciplinary activities among scientists and users working in the hydrometeorological sciences. - Demystifies soil moisture retrieval and prediction - Links soil moisture retrieval techniques with new satellite missions for earth and environmental science oriented problems - Written to be accessible to a wider range of professionals with a common interest in geo-spatial techniques, remote sensing, sustainable water resource development, and earth and environmental issues

Download or read book Mapping Soil Moisture from Remotely Sensed and In situ Data with Statistical Methods written by Yaping Xu and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Estimation of Soil Moisture in the Southern United States in 2003 Using Multi satellite Remote Sensing Measurements written by Melissa Soriano and published by . This book was released on 2008 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Soil moisture is a critical parameter for predicting and detecting floods and droughts, as well as indicating crop and vegetation health. Current indicators utilize surrogate or modeled measures of soil moisture. Actual observed soil moisture measurements have the potential to improve understanding of floods, droughts, and crop health. In this study, ground soil moisture daily average values were compared to estimates obtained from two microwave sensors, the EOS Aqua Advanced Microwave Scanning Radiometer (AMSR-E) and the Tropical Rainfall Measurement Mission Microwave Scanning Radiometer (TMI), as well as one optical sensor, the EOS Aqua Moderate Resolution Imaging Spectroradiometer (MODIS). The study areas were the Little Washita River Experimental Watershed in Oklahoma and the Little River Experimental Watershed in Georgia. This research compared AMSR-E, TMI, and MODIS data to ground data from the Little Washita Berg station and also compared AMSR-E and TMI data to ground data from the Little River Soil Climate Analysis Network station. AMSR-E and TMI performed better in Little Washita than in Little River during the crop-covered season. This may be due to the vegetation type, distribution, and density at Little River. AMSR-E exhibited a smaller range of variability than the TMI or in-situ measurements at both study sites for all time periods. In the crop-covered season of June, July, and August of 2003, MODIS soil moisture retrieval at the Little Washita site correlated better (R^2 = 0.772) with the in-situ measurements than AMSR-E or TMI soil moisture retrievals. The spatial resolution of MODIS (1 km) is finer than the spatial resolution of AMSR-E (~25 km) or TMI. Spatial resolution is an important factor because topography, soil properties, and vegetation cover may vary significantly over satellite footprints. Both microwave sensors are limited by their coarse spatial resolution. However, optical measurements are limited to cloud-free conditions. Future work includes research on algorithms which combine optical and microwave measurements to provide the advantages of each.

Download or read book Predicting the Runoff from Storm Rainfall written by Max Adam Kohler and published by . This book was released on 1951 with total page 12 pages. Available in PDF, EPUB and Kindle. Book excerpt: The estimation of the volume of runoff to be expected from a given volume of rainfall is a fundamental problem in flood forecasting. Such estimates are necessary before the unit hydrograph or other techniques can be used to predict the streamflow hydrograph. The authors describe the technique now used at the River Forecast Centers of the U.S. Weather Bureau for evaluating the effect of season, antecedent conditions, duration of rainfall and rainfall amount in determining the portion of the rainfall contributing to storm runoff. Special problems encountered in flood forecasting are emphasized. The technique, developed and tested over several years, yields a high degree of accuracy in estimated runoff. Although prepared by empirical procedures, the close agreement between relations for basins of similar hydrologic characteristics suggests that rational parameter have been adopted. The similarity between relations also simplifies the work required for their preparation.

Download or read book Developing a Virtual Sensor VS for Mapping Soil Moisture at High Spatial and Temporal Resolution written by A. K. M. Azad Hossain and published by . This book was released on 2008 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: Mapping soil moisture at both high spatial and temporal resolution has not been possible due to lack of sensors with these combined capabilities. We transformed the Moderate Resolution Imaging Spectroradiometer (MODIS) into a virtual sensor (VS) for quantitative soil moisture mapping and monitoring at 1 km and 250 m resolution daily. The Vegetation Index (VI) - Land Surface Temperature (LST) triangle model was used as the governing algorithm for VS. We used a time series of 13 data sets from August 01, 2006 to November 06, 2006 of MODIS reflective and thermal imagery and AMSR-E Level 3 soil moisture imagery to develop the VS in the semi-arid environment of southeastern New Mexico. We used Synthetic Aperture Radar (SAR) derived soil moisture imagery for five corresponding dates of the MODIS/AMSR-E imagery to evaluate the performance of VS for soil moisture estimation along with near real time in situ soil moisture measurements. In situ soil moisture measurements, vegetation density/distribution maps, digital elevation model (DEM), soil type map and soil salinity measurements were used in both linear and non-linear numerical models with the Radarsat 1 SAR fine imagery. The numerical models based on multiple linear regressions improved soil moisture estimation for the entire study site. We found, however, that vegetation, soil type and elevation have stronger combined effect on microwave soil moisture remote sensing by non-linear regressions (neural networks). The accuracy of the soil moisture data was evaluated using Kappa statistics. A soil moisture prediction surface prepared by kriging the in situ soil moisture 2 measurements was used as the reference. We obtained the overall accuracy of 75.67% and 77.67% with a Kappa coefficient of 0.43 and 0.61 for the August 02 and November 06 data sets of 2006, respectively. We evaluated the application of VS generated soil moisture data in mapping the spatio-temporal variation in soil moisture in southeastern New Mexico. The virtual sensor developed in this research has made the AMSR-E 25 km soil moisture information suitable for more local and watershed level applications by disaggregating it to 1 km and 250 m soil moisture data using MODIS reflective and thermal imagery.

Download or read book Proximal Soil Sensing written by Raphael A. Viscarra Rossel and published by Springer Science & Business Media. This book was released on 2010-07-25 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reports on developments in Proximal Soil Sensing (PSS) and high resolution digital soil mapping. PSS has become a multidisciplinary area of study that aims to develop field-based techniques for collecting information on the soil from close by, or within, the soil. Amongst others, PSS involves the use of optical, geophysical, electrochemical, mathematical and statistical methods. This volume, suitable for undergraduate course material and postgraduate research, brings together ideas and examples from those developing and using proximal sensors and high resolution digital soil maps for applications such as precision agriculture, soil contamination, archaeology, peri-urban design and high land-value applications, where there is a particular need for high spatial resolution information. The book in particular covers soil sensor sampling, proximal soil sensor development and use, sensor calibrations, prediction methods for large data sets, applications of proximal soil sensing, and high-resolution digital soil mapping. Key themes: soil sensor sampling – soil sensor calibrations – spatial prediction methods – reflectance spectroscopy – electromagnetic induction and electrical resistivity – radar and gamma radiometrics – multi-sensor platforms – high resolution digital soil mapping - applications Raphael A. Viscarra Rossel is a scientist at the Commonwealth Scientific and Industrial Research Organisation (CSIRO) of Australia. Alex McBratney is Pro-Dean and Professor of Soil Science in the Faculty of Agriculture Food & Natural Resources at the University of Sydney in Australia. Budiman Minasny is a Senior Research Fellow in the Faculty of Agriculture Food & Natural Resources at the University of Sydney in Australia.

Download or read book Improving Estimates of Root Zone Soil Moisture to Understand Ecosystem Drought Sensitivity written by Douglas Baldwin and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Extreme droughts can drastically inhibit the growth of terrestrial forest ecosystems, limiting their ability to assimilate and sequester atmospheric carbon dioxide, an important ecosystem service. Temperate forests in particular are an important component of Earths terrestrial carbon sink, but ecosystem carbon (C) flux monitoring since the 1990s shows that C uptake in temperate forests is more vulnerable to drought than previously estimated. Climate models typically project that drought events will become more frequent and intense across temperate regions in coming decades. As a result, soil moisture is an increasingly crucial variable to monitor for diagnosing and forecasting drought occurrence and severity. Advancements in satellite technology now facilitate more precise and accurate daily retrievals of global near-surface soil moisture content but satellite data must be downscaled to finer resolutions before it can be used in hydrologic models to estimate subsurface root zone soil moisture content over broad spatial extents. This dissertation explores how a combination of ground based measurements, satellite data, and hydrologic modeling can be used to generate high-resolution predictions of root zone soil moisture content. Root zone soil moisture observations are also used to evaluate the sensitivity of temperate forest growth, estimated by eddy flux towers, to drought in multiple locations in northern temperate forests in the United States. The opening chapter reviews research that connects root zone soil moisture to plant water stress and the development of hydrologic models that estimate root zone soil moisture across large areas using satellite soil moisture data. Chapter two demonstrates how an ensemble Kalman filter (EnKF) data assimilation algorithm can be integrated with a physically based hydrologic model that predicts root zone soil moisture with satellite data. This model system can produce root zone soil moisture predictions close to observed in situ measurements. The model is also rigorously tested in a perfect model test experiment before its application with in situ data. The spatial extension of this model from point locations to a watershed-scale gridded surface was developed and tested with field observations at the Shale Hills Critical Zone Observatory (Chapter 3). The integration of a distance decay function with distance metrics calculated with high resolution LiDAR data into the algorithm approximated a spatial pattern of root zone soil moisture content that was comparable with the geostastical regression kriging approach. Ecosystem C flux modeling in Chapter 4 identifies potential critical points where temperate forests may become net carbon sources in high growth conditions under a range of temperate and root zone soil moisture states. There is evidence that soil physical properties and hydric regime (i.e., percentage of wetlands) influence these critical points.This dissertation uncovers new research challenges associated with large-scale root zone soil moisture mapping and drought limitations on temperate forest growth. Soil survey and terrain data are crucial for high precision estimation of satellite soil moisture, but the classification of soil-landscape units and subsequent root zone soil moisture mapping at other watersheds and regions should be investigated. The analysis in Chapter 4 shows that relatively old temperate forests with coarse textured soils can be sensitive to drought, but growth rates can also drop exponentially in relatively young forests beyond a temperature and root zone soil moisture threshold. This analysis should be extended to other temperate forests to gain a greater understanding of their drought limitations, which can then be used to improve C sequestration processes in global carbon cycling models.

Download or read book Towards Locally Relevant Global Soil Moisture Monitoring Leveraging Remote Sensing and Modeling for Water Resources Applications written by Noemi Vergopolan da Rocha and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book MAPPING SURFACE SOIL MOISTURE AND ROUGHNESS BY RADAR REMOTE SENSING IN THE SEMI ARID ENVIRONMENT written by and published by . This book was released on 2005 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: Information about the distribution of surface soil moisture can greatly benefit the management of agriculture and natural resource. However, direct measurement of soil moisture over larger areas can be impractical and expensive, which has led scientists to develop satellite based remote sensing techniques for soil moisture assessments. Retrieving soil moisture from radar satellite imagery often associated with the collection and use of ancillary field data on surface roughness. However, field data that is meant to characterize surface roughness is often unreliable, is expensive to collect and is nearly impossible to acquire for large scale applications. These issues represent barriers to the adoption and of radar data for mapping soil moisture over large areas. The research presented in the dissertation is aimed at the development of an operational soil moisture assessment system based solely on radar satellite data and a radar model, eliminating the field data requirements altogether. The research is directed towards a so-called equation-based solution of the problem as an alternative to the approach that requires the use of extensive field-data sets on surface roughness. This approach is based on the concept that if the number of equations are equal to the number of unknowns, then explicit solutions of all unknowns are possible. My research derived the necessary equations to solve for soil moisture and surface roughness. The derivation of the equations and how to use them to estimate soil moisture without using ancillary field data was demonstrated by my research. Validation results showed that the equation-based method that was developed is capable of providing more precise estimates of surface soil moisture than that of ancillary field-data supported method.

Download or read book Scientific and Technical Aerospace Reports written by and published by . This book was released on 1995 with total page 488 pages. Available in PDF, EPUB and Kindle. Book excerpt: Lists citations with abstracts for aerospace related reports obtained from world wide sources and announces documents that have recently been entered into the NASA Scientific and Technical Information Database.

Download or read book Spatial and Temporal Modeling of Soil Moisture Using Remote Sensing written by Scott D. Lindsey and published by . This book was released on 1991 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Remote Sensing of Drought written by Brian D. Wardlow and published by CRC Press. This book was released on 2012-04-24 with total page 487 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Aircraft Remote Sensing of Soil Moisture and Hydrologic Parameters Chickasha Okla and Riesel Tex 1978 Data Report written by T. J. Jackson and published by . This book was released on 1980 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Temperature and Soil Moisture Studies Using In situ and Remotely Sensed Data in Little Washita Oklahoma and Little River Georgia Watersheds written by Diane Carolyn Zehrfuhs and published by . This book was released on 2001 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Creating a Bridge Between Remote Sensing and Hydrologic Models written by and published by . This book was released on 1983 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Assimilation of Remotely Sensed Soil Moisture in the MESH Model written by Xiaoyong Xu and published by . This book was released on 2015 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: Soil moisture information is critically important to weather, climate, and hydrology forecasts since the wetness of the land strongly affects the partitioning of energy and water at the land surface. Spatially distributed soil moisture information, especially at regional, continental, and global scales, is difficult to obtain from ground-based (in situ) measurements, which are typically based upon sparse point sources in practice. Satellite microwave remote sensing can provide large-scale monitoring of surface soil moisture because microwave measurements respond to changes in the surface soil's dielectric properties, which are strongly controlled by soil water content. With recent advances in satellite microwave soil moisture estimation, in particular the launch of the Soil Moisture and Ocean Salinity (SMOS) satellite and the Soil Moisture Active Passive (SMAP) mission, there is an increased demand for exploiting the potential of satellite microwave soil moisture observations to improve the predictive capability of hydrologic and land surface models. In this work, an Ensemble Kalman Filter (EnKF) scheme is designed for assimilating satellite soil moisture into a land surface-hydrological model, Environment Canada's standalone MESH to improve simulations of soil moisture. After validating the established assimilation scheme through an observing system simulation experiment (synthetic experiment), this study explores for the first time the assimilation of soil moisture retrievals, derived from SMOS, the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and the Advanced Microwave Scanning Radiometer 2 (AMSR2), in the MESH model over the Great Lakes basin. A priori rescaling on satellite retrievals (separately for each sensor) is performed by matching their cumulative distribution function (CDF) to the model surface soil moisture's CDF, in order to reduce the satellite-model bias (systematic error) in the assimilation system that is based upon the hypothesis of unbiased errors in model and observation. The satellite retrievals, the open-loop model soil moisture (no assimilation) and the assimilation estimates are, respectively, validated against point-scale in situ soil moisture measurements in terms of the daily-spaced time series correlation coefficient (skill R). Results show that assimilating either L-band retrievals (SMOS) or X-band retrievals (AMSR-E/AMSR2) can favorably influence the model soil moisture skill for both surface and root zone soil layers except for the cases with a small observation (retrieval) skill and a large open-loop skill. The skill improvement [Delta]RA-M, defined as the skill for the assimilation soil moisture product minus the skill for the open-loop estimates, typically increases with the retrieval skill and decreases with increased open-loop skill, showing a strong dependence upon [Delta]RS-M, defined as the retrieval skill minus the model (open-loop) surface soil moisture skill. The SMOS assimilation reveals that the cropped areas typically experience large [Delta]RA-M, consistent with a high satellite observation skill and a low open-loop skill, while [Delta]RA-M is usually weak or even negative for the forest-dominated grids due to the presence of a low retrieval skill and a high open-loop skill. The assimilation of L-band retrievals (SMOS) typically results in greater [Delta]RA-M than the assimilation of X-band products (AMSR-E/AMSR2), although the sensitivity of the assimilation to the satellite retrieval capability may become progressively weaker as the open-loop skill increases. The joint assimilation of L-band and X-band retrievals does not necessarily yield the best skill improvement. As compared to previous studies, the primary contributions of this thesis are as follows. (i) This work examined the potential of latest satellite soil moisture products (SMOS and AMSR2), through data assimilation, to improve soil moisture model estimates. (ii) This work, by taking advantage of the ability of SMOS to estimate surface soil moisture underneath different vegetation types, revealed the vegetation cover modulation of satellite soil moisture assimilation. (iii) The assimilation of L-band retrievals (SMOS) was compared with the assimilation of X-band retrievals (AMSR-E/AMSR2), providing new insight into the dependence of the assimilation upon satellite retrieval capability. (iv) The influence of satellite-model skill difference [Delta]RS-M on skill improvement [Delta]RA-M was consistently demonstrated through assimilating soil moisture retrievals derived from radiometers operating at different microwave frequencies, different vegetation cover types, and different retrieval algorithms.