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Satellite Soil Moisture Retrieval

Satellite Soil Moisture Retrieval Book
Author : Prashant K Srivastava,George Petropoulos,Y.H. Kerr
Publisher : Elsevier
Release : 2016-04-29
ISBN : 0128033894
Language : En, Es, Fr & De

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Book Description :

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

Evaluation of Satellite Soil Moisture Retrieval Algorithms Using AMSR E Data

Evaluation of Satellite Soil Moisture Retrieval Algorithms Using AMSR E Data Book
Author : R. T. W. L. Hurkmans
Publisher : Unknown
Release : 2004
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Evaluation of Satellite Soil Moisture Retrieval Algorithms Using AMSR E Data book written by R. T. W. L. Hurkmans, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Soil Moisture Retrieval from Multi instrument Satellite Observations

Soil Moisture Retrieval from Multi instrument Satellite Observations Book
Author : Jana Kolassa
Publisher : Unknown
Release : 2013
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Soil Moisture Retrieval from Multi instrument Satellite Observations book written by Jana Kolassa, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Near surface Soil Moisture Retrieval at Field and Regional Scales in UK

Near surface Soil Moisture Retrieval at Field and Regional Scales in UK Book
Author : Xin Kong
Publisher : Unknown
Release : 2006
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Near surface Soil Moisture Retrieval at Field and Regional Scales in UK book written by Xin Kong, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Soil Moisture

Soil Moisture Book
Author : Gabriela Civeira
Publisher : BoD – Books on Demand
Release : 2019-02-27
ISBN : 1789851033
Language : En, Es, Fr & De

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Book Description :

This book is aimed at the majority of audiences who need to rapidly obtain a concise overview of soil moisture measurement and management. Many existing soil moisture textbooks cater for a traditional market where readers rely on years of study presented in a slender discipline. The evolution of segmental schemes has meant that soil moisture is now often included as a part of broad-based soil science programs. For those opting to specialise in soil moisture, this is a good book to choose. This book will be very useful to students, researchers and other readers who do not hold a traditional scientific background, such as those studying geography, environment science, ecology and agriculture. This book provides a concise overview of soil moisture knowledge.

Assimilation of Remotely Sensed Soil Moisture in the MESH Model

Assimilation of Remotely Sensed Soil Moisture in the MESH Model Book
Author : Xiaoyong Xu
Publisher : Unknown
Release : 2015
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

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.

Towards Medium resolution Soil Moisture Retrieval from Active and Passive Microwave Observations

Towards Medium resolution Soil Moisture Retrieval from Active and Passive Microwave Observations Book
Author : Xiaoling Wu
Publisher : Unknown
Release : 2014
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Surface soil moisture is essential to global water cycle monitoring, weather forecasting, prediction of drought and flood, and modelling of evaporation. The European Space Agency (ESA) launched the Soil Moisture and Ocean Salinity (SMOS) satellite in 2009, as the first-ever soil moisture dedicated satellite. It uses the passive microwave (radiometer) remote sensing technology due to the direct relationship with soil moisture, but due to technical limitations the spatial resolution is approximately 40 km. This places limitations on hydro-meteorological applications such as regional weather forecasting, flood prediction, and agricultural activities that have a resolution requirement of better than 10 km. Active microwave (radar) remote sensing provides a much higher spatial resolution capability (better than 3 km), but it is less sensitive to changes in soil moisture due to the confounding effects of vegetation and surface roughness. Consequently, NASA has developed the Soil Moisture Active Passive (SMAP) mission, scheduled for launch in January 2015, which will merge passive and active observations to overcome their individual limitations, thus providing a soil moisture product with resolution better than 10 km at a target accuracy of 0.04 cm3/cm3. The rationale behind this mission is to use fine resolution (3 km) radar observations to disaggregate the coarse resolution (36 km) radiometer observations into a medium-resolution (9 km) product. The downscaling algorithms for this purpose have so far undergone only limited testing with experimental data sets, and have therefore been tested mostly using synthetic data and a limited number of suitable experimental data sets mostly in the continental United States. Consequently, this thesis presents an extensive evaluation of soil moisture downscaling algorithms with an experimental data set collected from the Soil Moisture Active Passive Experiment (SMAPEx) field campaigns in south-eastern Australia. This research affords a unique opportunity to undertake a comprehensive assessment of the various downscaling approaches proposed, having applicability to the forthcoming SMAP mission. In particular, each approach is comprehensively assessed using a consistent data set collected over a diverse landscape exhibiting a range of conditions, and then inter-compared with the results from the others. A particular focus is placed on the SMAP baseline algorithm as this is currently the preferred algorithm and scheduled for implementation by NASA immediately upon launch. A preliminary study on the SMAP baseline algorithm was conducted by using existing satellite data; results from which suggested that a better representation of the SMAP data stream characteristics was required. Consequently, a study was undertaken on how to prepare the simulated SMAP data stream from the airborne data set collected from the SMAPEx field campaigns in Australia. These data were processed in terms of spatial aggregation, incidence angle normalization and azimuth effect analysis so as to be in line with the characteristics of the SMAP observations. Results indicated that data from SMAPEx could be reliably processed to represent the characteristics of the SMAP observations. The baseline algorithm was then tested using the simulated SMAP data set. Results showed that the baseline downscaling algorithm had the ability to fulfil the error requirement of medium resolution (9 km) brightness temperature product of SMAP over relatively homogenous area, but it had greater error than the requirement over the heterogeneous cropping area. Consequently, the baseline algorithm was assessed at higher resolutions in order to study the effect of land cover type and surface heterogeneity on the resulting downscaling accuracy. The medium resolution (9 km) brightness temperatures obtained from the baseline algorithm were then converted to a medium resolution soil moisture product, and results compared with other linear methods including the optional downscaling algorithm and a change detection method, and with a non-linear Bayesian merging method. The comparison of these different soil moisture downscaling algorithms suggested that the optional algorithm and the Bayesian merging method had a similar performance in retrieving medium resolution soil moisture products, with the lowest error and highest correlation between downscaled and reference soil moisture, amongst the downscaling algorithms tested. However, unless further improvements can be achieved with the Bayesian merging method the optional algorithm is recommended for application in SMAP due to its simplicity of approach and low computational requirement, thus making it simpler to apply in an operational context.

Remote Sensing of Energy Fluxes and Soil Moisture Content

Remote Sensing of Energy Fluxes and Soil Moisture Content Book
Author : George Petropoulos
Publisher : CRC Press
Release : 2013-10-28
ISBN : 1466505796
Language : En, Es, Fr & De

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Book Description :

Integrating decades of research conducted by leading scientists in the field, Remote Sensing of Energy Fluxes and Soil Moisture Content provides an overview of state-of-the-art methods and modeling techniques employed for deriving spatio-temporal estimates of energy fluxes and soil surface moisture from remote sensing. It also underscores the range

Simulation of Spatial and Temporal Variability of Soil Moisture Using the Simultaneous Heat And Water SHAW Model

Simulation of Spatial and Temporal Variability of Soil Moisture Using the Simultaneous Heat And Water  SHAW  Model Book
Author : Swapan Roy
Publisher : Unknown
Release : 2014
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Simulation of Spatial and Temporal Variability of Soil Moisture Using the Simultaneous Heat And Water SHAW Model book written by Swapan Roy, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Microwave Indices from Active and Passive Sensors for Remote Sensing Applications

Microwave Indices from Active and Passive Sensors for Remote Sensing Applications Book
Author : Emanuele Santi,Simonetta Paloscia
Publisher : MDPI
Release : 2019-10-21
ISBN : 3038978205
Language : En, Es, Fr & De

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Book Description :

Past research has comprehensively assessed the capabilities of satellite sensors operating at microwave frequencies, both active (SAR, scatterometers) and passive (radiometers), for the remote sensing of Earth’s surface. Besides brightness temperature and backscattering coefficient, microwave indices, defined as a combination of data collected at different frequencies and polarizations, revealed a good sensitivity to hydrological cycle parameters such as surface soil moisture, vegetation water content, and snow depth and its water equivalent. The differences between microwave backscattering and emission at more frequencies and polarizations have been well established in relation to these parameters, enabling operational retrieval algorithms based on microwave indices to be developed. This Special Issue aims at providing an overview of microwave signal capabilities in estimating the main land parameters of the hydrological cycle, e.g., soil moisture, vegetation water content, and snow water equivalent, on both local and global scales, with a particular focus on the applications of microwave indices.

Use of Satellite Remote Sensing in Hydro ecological Research

Use of Satellite Remote Sensing in Hydro ecological Research Book
Author : Dimitrios Stampoulis
Publisher : Unknown
Release : 2014
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Use of Satellite Remote Sensing in Hydro ecological Research book written by Dimitrios Stampoulis, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Soil Moisture Estimation Using Satellite Remote Sensing and Numerical Weather Prediction Model for Hydrological Applications

Soil Moisture Estimation Using Satellite Remote Sensing and Numerical Weather Prediction Model for Hydrological Applications Book
Author : Deleen Mohammed Saleh Al-Shrafany
Publisher : Unknown
Release : 2012
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Soil moisture is an important variable in hydrological modelling used for real time flood forecasting and water resources management. However, it is a very challenging task to measure soil moisture over a hydrological catchment using conventional in-situ sensors. Remote sensing is gaining popularity due to its large coverage suitable for soil moisture measurement at a catchment scale albeit there are still many knowledge gaps to be filled in. This thesis focuses on investigating soil moisture estimation from remote sensing satellite and land surface model (LSM) coupled with a Numerical Weather Prediction (NWP) model. A hydrological-based approach has been conducted to assess/evaluate the estimated soil moisture using event-based water balance and Probability Distributed Model (PDM). An Advance Microwave Scanning Radiometer (AMSR) and a physically- based Land Parameters Retrieval Model (LPRM) have been used to retrieve surface soil moisture over the sturdy area. The LPRM vegetation and roughness parameters have been empirically calibrated by a new approach proposed in this thesis. The relevant parameters are calibrated on the hydrological model through achieving the best correlation between the observation-based catchment storage and the retrieved surface soil moisture. The development of the land surface model coupled with the NWP model is used to estimate soil moisture at different combinations of soil layers. The optimal combination of the top two layers is found to have the best performance when compared to the catchment water storage. Regression-based mathematical models have been derived to predict the catchment storage from the estimated soil moisture based on both satellite remote sensing and the LSM-NWP model. Three schemes are proposed to examine the behaviour of soil moisture products over different seasons in order to find the appropriate formulas in different scenarios. Finally, weighted coefficients and arithmetic average data fusion methods are explored to integrate two independent soil moisture products from the AMSR-E satellite and the LSM-NWP. It has been found that the merged output is a significant improvement over their individual estimates. The implementation of the fusion technique has provided a new opportunity for information integration from satellite and NWP model. Keywords: Soil moisture, Satellite remote sensing, satellite, land surface model, NWP model, rainfall-runoff model, water balance, PDM model.

Estimation of Soil Moisture in the Southern United States in 2003 Using Multi satellite Remote Sensing Measurements

Estimation of Soil Moisture in the Southern United States in 2003 Using Multi satellite Remote Sensing Measurements Book
Author : Melissa Soriano
Publisher : Unknown
Release : 2008
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

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.

Extreme Hydroclimatic Events and Multivariate Hazards in a Changing Environment

Extreme Hydroclimatic Events and Multivariate Hazards in a Changing Environment Book
Author : Viviana Maggioni,Christian Massari
Publisher : Elsevier
Release : 2019-06-06
ISBN : 0128149000
Language : En, Es, Fr & De

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Book Description :

Extreme Hydroclimatic Events and Multivariate Hazards in a Changing Environment: A Remote Sensing Approach reviews multivariate hazards in a non-stationary environment, covering both short and long-term predictions from earth observations, along with long-term climate dynamics and models. The book provides a detailed overview of remotely sensed observations, current and future satellite missions useful for hydrologic studies and water resources engineering, and a review of hydroclimatic hazards. Given these tools, readers can improve their abilities to monitor, model and predict these extremes with remote sensing. In addition, the book covers multivariate hazards, like landslides, in case studies that analyze the combination of natural hazards and their impact on the natural and built environment. Finally, it ties hydroclimatic hazards into the Sendai Framework, providing another set of tools for reducing disaster impacts. Emphasizes recent and future satellite missions to study, monitor and forecast hydroclimatic hazards Provides a complete overview and differentiation of remotely sensed products that are useful for monitoring extreme hydroclimatic and related events Covers real-life examples and applications of integrating remote sensing products to study complex multi-hydroclimatic hazards

The Dynamics of Active Layer Soil Moisture Over Canadian Arctic Tundra in Trail Valley Creek NT Observed In situ and with Remote Sensing

The Dynamics of Active Layer Soil Moisture Over Canadian Arctic Tundra in Trail Valley Creek  NT  Observed In situ and with Remote Sensing Book
Author : Rachel Humphrey
Publisher : Unknown
Release : 2015
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download The Dynamics of Active Layer Soil Moisture Over Canadian Arctic Tundra in Trail Valley Creek NT Observed In situ and with Remote Sensing book written by Rachel Humphrey, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Evaluating the Spatial and Temporal Variability of Soil Moisture Within the Brightwater Creek Saskatchewan Canada

Evaluating the Spatial and Temporal Variability of Soil Moisture Within the Brightwater Creek  Saskatchewan  Canada Book
Author : Travis Burns
Publisher : Unknown
Release : 2015
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Evaluating the Spatial and Temporal Variability of Soil Moisture Within the Brightwater Creek Saskatchewan Canada book written by Travis Burns, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Mixed Pixel Retrieval of Soil Moisture from L band Passive Microwave Observations

Mixed Pixel Retrieval of Soil Moisture from L band Passive Microwave Observations Book
Author : Nan Ye
Publisher : Unknown
Release : 2014
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Soil moisture plays a key role in the water, energy, and carbon exchanges at the interface between the atmosphere and earth surface. Its spatial and temporal distributions at regional and global scales are required by many disciplines, including hydrology, meteorology, and agriculture. During the last three decades, passive microwave remote sensing has been widely acknowledged as the most promising technique to measure the spatial distribution of near surface (top few centimetre) soil moisture, due to its direct relationship to the soil dielectric constant, its ability to penetrate clouds, and its reduced sensitivity to vegetation canopy and surface roughness. Therefore, the first two space missions dedicated to soil moisture, the Europe Space Agency (ESA)'s Soil Moisture and Ocean Salinity (SMOS) mission and the National Aeronautics and Space Administration (NASA)'s Soil Moisture Active Passive (SMAP) mission, are based on L-band (~1.4 GHz) passive microwave observations every two to three days. Using radiative transfer models, brightness temperature observations are used to estimate water content of the top approximately five centimetres soil with a target accuracy of ~0.04 m3/m3.Based on the current level of antenna technology, the best spatial resolution that can be achieved at L-band by both the SMOS and SMAP radiometer approaches is approximately 40 km. At such a coarse scale, non-soil targets such as surface rock, urban areas, and standing water are present within many SMOS and SMAP pixels across the world, potentially confounding the radiometric observations, and in turn degrading the soil moisture retrieval if not accounted for their contribution. Consequently, the objective of thesis is to determine the impact of land surface heterogeneity conditions on L-band passive microwave satellite footprints using airborne passive microwave brightness temperature observations collected during five Australian airborne field campaigns conduced within the past eight years.Using the Polarimetric L-band Multi-beam Radiometer (PLMR) mounted on a scientific aircraft, brightness temperature of the SMOS and SMAP sized study areas were measured at viewing angles of 7°, 21.5°, and 38.5°. Due to the strong angular dependency of brightness temperature, the multi-angular PLMR observations need to be normalised to a reference angle. The angle 38.5° was chosen to closely replicate the fixed incidence angle of SMAP. In this thesis the Cumulative Distribution Function (CDF) based method is developed for incidence angle normalisation by matching the CDF of observations for each non-reference angle. Subsequently, the effects of surface rock, urban areas, and standing water were explored using the incidence-angle-normalised airborne brightness temperature observations and coincident ground sampling data. The brightness temperature difference between that of the mixed pixel and its soil only equivalent was defined as the non-soil targets induced brightness temperature contribution that will potentially lead to a soil moisture retrieval error if not accounted for. It was found that about 13% of SMOS and SMAP pixels on the world's land mass may be adversely affected by surface rock, urban areas, or standing water. However, such pixels are not uniformly distributed or coincident, meaning that such factors may be particularly important in some parts of the world.

China Satellite Navigation Conference CSNC 2021 Proceedings

China Satellite Navigation Conference  CSNC 2021  Proceedings Book
Author : Changfeng Yang,Jun Xie
Publisher : Springer Nature
Release : 2021
ISBN : 9811631387
Language : En, Es, Fr & De

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Book Description :

China Satellite Navigation Conference (CSNC 2021) Proceedings presents selected research papers from CSNC 2021 held during 22nd-25th, 2021 May in Nanchang, China. These papers discuss the technologies and applications of the Global Navigation Satellite System (GNSS), and the latest progress made in the China BeiDou System (BDS) especially. They are divided into 10 topics to match the corresponding sessions in CSNC2021 which broadly covered key topics in GNSS. Readers can learn about the BDS and keep abreast of the latest advances in GNSS techniques and applications.

Agricultural Water Management

Agricultural Water Management Book
Author : Prashant K. Srivastava,Manika Gupta,George Tsakiris,Nevil Quinn
Publisher : Academic Press
Release : 2020-12-02
ISBN : 0128123621
Language : En, Es, Fr & De

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Book Description :

Agricultural Water Management: Theories and Practices advances the scientific understanding, development and application of agricultural water management through an integrated approach. This book presents a collection of recent developments and applications of agricultural water management from advanced sources, such as satellite, mesoscale and climate models that are integrated with conceptual modeling systems. Users will find sections on drought, irrigation scheduling, weather forecasting, climate change, precipitation forecasting, and more. By linking these systems, this book provides the first resource to promote the synergistic and multidisciplinary activities of scientists in hydro-meteorological and agricultural sciences. As agricultural water management has gained considerable momentum in recent decades among the earth and environmental science communities as they seek solutions and an understanding of the concepts integral to agricultural water management, this book is an ideal resource for study and reference. Presents translational insights into drought, irrigation scheduling, weather forecasting, climate change and precipitation forecasting Advances the scientific understanding, development and application of agricultural water management Integrates geo-spatial techniques, agriculture, remote sensing, sustainable water resource development, applications and other diverse areas within earth and environmental, meteorological and hydrological sciences