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.

Download or read book Stochastic Characterization of Remotely Sensed Near surface Soil Moisture with Applications to Downscaling and Data Assimilation written by Laura Maria Parada Limpias and published by . This book was released on 2005 with total page 536 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Estimating Soil Moisture and Energy Fluxes Using Assimilation of Remotely Sensed Land Surface State Variables written by Srinivas Chintalapati and published by . This book was released on 2006 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Remote sensing Soil Moisture Using Four dimensional Data Assimilation PHD written by Paul Raymond Houser and published by . This book was released on 1996 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Assimilating Remotely Sensed Soil Moisture and Latent Heat Fluxes in a Distributed Hydrological Model written by J. M. Schuurmans and published by . This book was released on 2001 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Geospatial Technology written by Hassane Jarar Oulidi and published by Springer Nature. This book was released on 2019-08-29 with total page 111 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aims to exchange and share the experiences and research results on the geospatial technology applied in water resources management. It will present the most recent innovations, trends, challenges encountered and the solutions adopted in the area of geospatial technology. It will be beneficial for academicians, scientists, meteorologists, and consultants working in the field of water resources management.

Download or read book Estimating Root zone Soil Moisture by Assimilating Remotely Sensed Biophysical States Into Modelling written by Mahboobeh Sadat Hashemian Rahaghi and published by . This book was released on 2016 with total page 137 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Estimation and Evaluation of Soil Moisture Profile with Surrogate Remote Sensing Measurements written by Jikang Li and published by . This book was released on 1999 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Remote Sensing and Hydrology 2000 written by Manfred Owe and published by . This book was released on 2001 with total page 632 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 Data Assimilation for Atmospheric Oceanic and Hydrologic Applications written by SEON KI PARK and published by Springer Science & Business Media. This book was released on 2009-02-08 with total page 481 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data assimilation (DA) has been recognized as one of the core techniques for modern forecasting in various earth science disciplines including meteorology, oceanography, and hydrology. Since early 1990s DA has been an important s- sion topic in many academic meetings organized by leading societies such as the American Meteorological Society, American Geophysical Union, European G- physical Union, World Meteorological Organization, etc. nd Recently, the 2 Annual Meeting of the Asia Oceania Geosciences Society (AOGS), held in Singapore in June 2005, conducted a session on DA under the - tle of “Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications.” nd This rst DA session in the 2 AOGS was a great success with more than 30 papers presented and many great ideas exchanged among scientists from the three different disciplines. The scientists who participated in the meeting suggested making the DA session a biennial event. th Two years later, at the 4 AOGS Annual Meeting, Bangkok, Thailand, the DA session was of cially named “Sasaki Symposium on Data Assimilation for At- spheric, Oceanic and Hydrologic Applications,” to honor Prof. Yoshi K. Sasaki of the University of Oklahoma for his life-long contributions to DA in geosciences.

Download or read book Soil Moisture Modeling and Scaling Using Passive Microwave Remote Sensing written by Narendra N. Das and published by . This book was released on 2007 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Soil moisture in the shallow subsurface is a primary hydrologic state governing land-atmosphere interaction at various scales. The primary objectives of this study are to model soil moisture in the root zone in a distributed manner and determine scaling properties of surface soil moisture using passive microwave remote sensing. The study was divided into two parts. For the first study, a root zone soil moisture assessment tool (SMAT) was developed in the ArcGIS platform by fully integrating a one-dimensional vadose zone hydrology model (HYDRUS-ET) with an ensemble Kalman filter (EnKF) data assimilation capability. The tool was tested with data set from the Southern Great Plain 1997 (SGP97) hydrology remote sensing experiment. Results demonstrated that SMAT displayed a reasonable capability to generate soil moisture distribution at the desired resolution at various depths of the root zone in Little Washita watershed during the SGP97 hydrology remote sensing experiment. To improve the model performance, several outstanding issues need to be addressed in the future by: including "effective" hydraulic parameters across spatial scales; implementing subsurface soil properties data bases using direct and indirect methods; incorporating appropriate hydrologic processes across spatial scales; accounting uncertainties in forcing data; and preserving interactions for spatially correlated pixels. The second study focused on spatial scaling properties of the Polarimetric Scanning Radiometer (PSR)-based remotely sensed surface soil moisture fields in a region with high row crop agriculture. A wavelet based multi-resolution technique was used to decompose the soil moisture fields into larger-scale average soil moisture fields and fluctuations in horizontal, diagonal and vertical directions at various resolutions. The specific objective was to relate soil moisture variability at the scale of the PSR footprint (800 m X 800 m) to larger scale average soil moisture field variability. We alsoinvestigated the scaling characteristics of fluctuation fields among various resolutions. The spatial structure of soil moisture exhibited linearity in the log-log dependency of the variance versus scale-factor, up to a scale factor of -2.6 (6100 m X 6100 m) irrespective of wet and dry conditions, whereas dry fields reflect nonlinear (multi-scaling) behavior at larger scale-factors.

Download or read book Hillslope scale Soil Moisture Estimation with a Physically based Ecohydrology Model and L band Microwave Remote Sensing Observations from Space written by Alejandro Nicolas Flores and published by . This book was released on 2009 with total page 488 pages. Available in PDF, EPUB and Kindle. Book excerpt: (cont.) Experiments in which true soil moisture conditions were simulated by the model and used to produce synthetic observations at spatial scales significantly coarser than the model resolution reveal that sequential assimilation of observations improves the hillslope-scale near-surface moisture estimate. Results suggest that the data assimilation framework is an effective means of disaggregating coarse-scale observations according to the model physics represented by the ecohydrology model. The thesis concludes with a discussion of contributions, implications, and future directions of this work.

Download or read book Spatial Characterization of Remotely Sensed Soil Moisture written by Deeptha Thattai and published by . This book was released on 1998 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 Assimilation of Near surface Soil Moisture Using Extended Kalman Filter written by Mingshi Chen and published by . This book was released on 2000 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Spatial Modelling of the Terrestrial Environment written by Richard E.J. Kelly and published by John Wiley & Sons. This book was released on 2004-10-22 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding and predicting the behaviour of natural and human environmental systems is crucial for the effective management of the Earth’s limited resources. Recently, great advances have been made through spatial modelling. This book provides a snapshot of the latest research in modelling technologies and methodologies within five environmental fields; the cryosphere, hydrology, geomorphology, vegetation interfaces and urban environments. Spatial Modelling of the Terrestrial Environment deals with the use of remote sensing, numerical models and GIS in addressing important natural and human environmental sciences issues, focusing on the theory and application of modelling remotely sensed data within the context of environmental processes. Extensive case material exemplifies the latest research and modelling paradigms presented in the book.