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 The Need for Regular Remote Sensing Observations of Global Soil Moisture written by M. Owe and published by . This book was released on 2003 with total page 15 pages. Available in PDF, EPUB and Kindle. Book excerpt: Soil moisture is an important component of the water and energy balance of the Earth's surface, and as such, is essential to many Earth science disciplines. Regular and accurate estimates of soil moisture are an important input to the study of climate change, numerical weather prediction models, drought forecasts, and agricultural predictions. Soil moisture has been identified as a parameter of significant potential for improving the accuracy of large-scale land surface-atmosphere interaction models. However, accurate estimates of surface soil moisture are often difficult to make, especially at large spatial scales. Soil moisture is a highly variable land surface parameter, and while point measurements are usually accurate, they are typically representative only of the immediate sample location, and simple averaging of point values, in order to obtain large-area spatial means, often leads to substantial errors. In addition, ground sampling is labor intensive and costly, and consequently highly impractical for long-term and/or large-scale monitoring. Since satellite remote sensing observations are already a spatially averaged value, they are ideally suited for easuring land surface parameters such as soil moisture. Passive microwave remote sensing presents the greatest potential for providing regular spatially representative estimates of surface soil moisture at global scales. But, while the optimum wavelength for soil moisture sensing is in the L-band (1.4 GHz or ? = 21 cm), such a sensor has yet to be deployed operationally. However, new and highly improved microwave retrieval techniques are being developed that maximize the information that can be obtained from less optimum sensors, such as C-band and even X-band. Progress from one such study is presented, along with preliminary results of some validation studies and plans to develop a 20-year retrospective global database of surface soil moisture. This data product will be made available to the general public through the Goddard Space Flight Center Distributed Active Archive Center (DAAC). Improved real-time estimates of surface soil moisture should greatly improve the performance of real-time models, while the development of historical data sets will provide necessary information for simulation and validation of long-term climate and global change studies.

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 Soil Moisture Estimation Using Satellite Remote Sensing and Numerical Weather Prediction Model for Hydrological Applications written by Deleen Mohammed Saleh Al-Shrafany and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Soil Moisture Estimation by Microwave Remote Sensing for Assimilation Into WATClass written by Damian Chi-Ho Kwok and published by . This book was released on 2007 with total page 83 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis examines the feasibility of assimilating space borne remotely-sensed microwave data into WATClass using the ensemble Kalman filter. WATClass is a meso-scale gridded hydrological model used to track water and energy budgets of watersheds by way of real-time remotely sensed data. By incorporating remotely-sensed soil moisture estimates into the model, the model's soil moisture estimates can be improved, thus increasing the accuracy of the entire model. Due to the differences in scale between the remotely sensed data and WATClass, and the need of ground calibration for accurate soil moisture estimation from current satellite-borne active microwave remote sensing platforms, the spatial variability of soil moisture must be determined in order to characterise the dependency between the remotely-sensed estimates and the model data and subsequently to assimilate the remotely-sensed data into the model. Two sets of data - 1996-1997 Grand River watershed data and 2002-2003 Roseau River watershed data - are used to determine the spatial variability. The results of this spatial analysis however are found to contain too much error due to the small sample size. It is therefore recommended that a larger set of data with more samples both spatially and temporally be taken. The proposed algorithm is tested with simulated data in a simulation of WATClass. Using nominal values for the estimated errors and other model parameters, the assimilation of remotely sensed data is found to reduce the absolute RMS error in soil moisture from 0.095 to approximately 0.071. The sensitivities of the improvement in soil moisture estimates by using the proposed algorithm to several different parameters are examined.

Download or read book Variational Assimilation of Remote Sensing Data for Land Surface Hydrologic Applications written by Rolf Helmut Reichle and published by . This book was released on 2000 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Observation and Measurement of Ecohydrological Processes written by Xin Li and published by Springer. This book was released on 2019-03-07 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume will discuss the state of the art of different observation and measurement techniques useful for ecohydrological studies. The techniques cover the entire spectrum of the water-soil-plant-atmosphere continuum. And the other volumes are "Water and Ecosystems", "Water-Limited Environments" and "Integrated Ecohydrological Modeling" etc.

Download or read book Algorithms to Estimate Soil Moisture Storage from Microwave Measurements written by Bruce J. Blanchard and published by . This book was released on 1979 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Microwave Remote Sensing of Soil Moisture written by Jiangyuan Zeng and published by Mdpi AG. This book was released on 2023-11-06 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This reprint focuses on the most advanced theories, models, algorithms, and products related to microwave remote sensing of soil moisture. Over the past few decades, significant efforts have been made to develop models, retrieval algorithms, downscaling methods, and validation strategies related to microwave remote sensing of soil moisture. Following the turn of the century, a series of microwave-based satellites/sensors have been successfully launched, and satellite soil moisture products have become increasingly abundant, greatly promoting the various applications of satellite soil moisture datasets. Despite numerous studies and achievements in this field, great challenges remain, such as the spatial resolution, retrieval accuracy, and validation strategies related to satellite soil moisture datasets. This reprint covers research progress on the following topics: (1) downscaling passive microwave-based soil moisture products, (2) estimating soil moisture from active microwave observations, (3) presenting some new algorithms (freeze-thaw state detection algorithm) and models (soil dielectric models) related to microwave remote sensing of soil moisture, (4) evaluating microwave-based soil moisture products, and (5) reviewing the state-of-the-art techniques and algorithms used to estimate and improve the quality of soil moisture estimations.

Download or read book Microwave Remote Sensing of Water in the Soil Plant System written by Alexandra Georges Konings and published by . This book was released on 2015 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: Remotely sensed measurements made by radars or radiometers in the low microwave frequency range are sensitive to soil moisture, soil roughness, and vegetation water content. Measurements made at multiple polarizations can be used to determine additional ancillary parameters alongside the primary variable of interest. However, if an attempt is made to retrieve too many parameters from too few measurements, the resulting retrievals will contain high levels of noise. In this thesis, I introduce a framework to determine an upper bound on the number of geophysical parameters that can be retrieved from remotely sensed measurements such as those made by microwave instruments. The principles behind this framework, as well as the framework itself, are then applied to derive two new ecohydrological variables: a) soil moisture profiles across much of the root-zone and b) vegetation optical depth, which is proportional to vegetation water content. For P-band observations, it is shown that soil moisture variations with depth must be accounted for to prevent large forward modeling - and thus retrieval - errors. A Tikhonov regularization approach is then introduced to allow retrieval of soil moisture in several profile layers by using statistics on the expected co-variation between soil moisture at different depths. The algorithm is tested using observations from the NASA Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) Mission over the Harvard Forest in Western Massachusetts. Additionally, at L-band, a multi-temporal algorithm is introduced to determine vegetation optical depth (VOD) alongside soil moisture. The multi-temporal approach used reduces the chance of compensating errors between the two retrieved parameters (soil moisture and vegetation optical depth), caused by small amounts of measurement noise. In several dry tropical ecosystems, the resulting VOD dataset is shown to have opposite temporal behavior to coincident cross-polarized backscattering coefficients, an active microwave indicator of vegetation water content and scattering. This possibly shows dry season bud-break or enduring litter presence in these regions. Lastly, cross-polarized backscattering coefficients are used to test the hypothesis that vegetation water refilling slows down under drought even at the ecosystem scale. Evidence for this hypothesis is only found in the driest location tested.

Download or read book Global scale Evaluation of a Hydrological Variable Measured from Space written by Amen Mohammed Al-Yaari and published by . This book was released on 2014 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Soil moisture (SM) plays a key role in meteorology, hydrology, and ecology as it controls the evolution of various hydrological and energy balance processes. The community of scientists involved in the field of microwave remote sensing has made considerable efforts to build accurate estimates of surface SM (SSM), and global SSM datasets derived from active and passive microwave instruments have recently become available. Among them, SMOS (Soil Moisture and Ocean Salinity), launched in 2009, was the first ever passive satellite specifically designed to measure the SSM, at L-band (1.4 GHz), at the global scale. Validation of the SMOS SSM datasets over different climatic regions and environmental conditions is extremely important and a necessary step before they can be used. A better knowledge of the skill and uncertainties of the SSM retrievals will help not only to improve the individual products, but also to optimize the fusion schemes required to create long-term multi-sensor products, like the essential climate variable (ECV) SSM product generated within the European Space Agency's (ESA's) Climate Change Initiative (CCI) program. After the introductory Chapters I to III, this dissertation consists of three main parts. Chap. IV of the dissertation evaluates the passive SMOS level 3 (SMOSL3) SSM products at L-band against the passive AMSR-E SSM at C-band by comparing them with a Land Data Assimilation System estimates (SM-DAS-2) produced by the European Centre for Medium Range Weather Forecasts (ECMWF). This was achieved over the common period 2010-2011 between SMOS and AMSR-E, using classical metrics (Correlation, RMSD, and Bias). In parallel, Chap. V of the dissertation evaluates the passive SMOSL3 products against the active ASCAT SSM at C-band by comparing them with land surface model simulations (MERRA-Land) using classical metrics, advanced statistical methods (triple collocation), and the Hovmöller diagram over the period 2010-2012. These two evaluations indicated that vegetation density (parameterized here by the leaf area index LAI) is a key factor to interpret the consistency between SMOS and the other remotely sensed products. This effect of the vegetation has been quantified for the first time at the global scale for the three microwave sensors. These two chapters also showed that both SMOS and ASCAT (AMSR-E) had complementary performances and, thus, have a potential for datasets fusion into long-term SSM records. In Chap. VI of the dissertation, with the general purpose to extend back the SMOSL3 SSM time series and to produce an homogeneous SM product over 2003-2014 based on SMOS and AMSR-E, we investigated the use of a multiple linear regression model based on bi-polarization (horizontal and vertical) brightness temperatures (TB) observations obtained from AMSR-E (2003 - 2011). The regression coefficients were calibrated using SMOSL3 SSM as a reference over the 2010-2011 period. The resulting merged SSM dataset was evaluated against an AMSR-E SSM retrievals and modelled SSM products (MERRA-Land) over 2007-2009. These first results show that the multi-linear regression method is a robust and simple approach to produce a realistic SSM product in terms of temporal variation and absolute values. In conclusion, this PhD showed that the potential synergy between the passive (AMSR-E and SMOS) and active (ASCAT) microwave systems at global scale is very promising for the development of improved, long-term SSM time series at global scale, such as those pursued by the ESA's CCI program. It also provides new ideas on the way to merge the different SSM datasets with the aim of producing the CCI (phase 2) long-term series (a coherent "SMOS-AMSR-E" SSM time series for the period 2003 -2014), that will be evaluated further in the framework of on-going ESA projects.

Download or read book Remote Sensing Techniques for Soil Moisture and Agricultural Drought Monitoring written by Lingli Wang and published by . This book was released on 2008 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Drought is the most complex and least understood of all natural hazards, affecting more people than any other hazard. Soil moisture is a primary indicator for agricultural drought. This dissertation is aimed at evaluating and investigating soil moisture and drought monitoring using remote sensing techniques. Recent technological advances in remote sensing have shown that soil moisture can be measured by a variety of remote sensing techniques, each with its own strengths and weaknesses. This research is designed to combine the strengths of optical/infrared as well as microwave remote sensing approaches for soil moisture estimation. A soil moisture estimation algorithm at moderate resolution was developed based on the well known "Universal Triangle" relation by using MODIS land parameters as well as ground measured soil moisture. Though lower in spatial resolution, AMSR-E microwave measurements provides daily global soil moisture of the top soil layer, which are typically less affected by clouds, making them complementary to MODIS measurements over regions of clouds. Considering that the "Universal Triangle" approach for soil moisture estimation is based on empirical relations which lack solid physical basis, a new physics based drought index, the Normalized Multi-band Drought Index (NMDI) was proposed for monitoring soil and vegetation moisture from space by using one near-infrared (NIR) and two shortwave infrared (SWIR) channels. Typical soil reflectance spectra and satellite acquired canopy reflectances are used to validate the usefulness of NMDI. Its ability for active fire detection has also been investigated using forest fires burning in southern Georgia, USA and southern Greece in 2007. Combining information from multiple NIR and SWIR channels makes NMDI a most promising indicator for drought monitoring and active fire detecting. Given the current technology, satellite remote sensing can only provide soil moisture measurements for the top soil profile, and these near-surface soil moisture must be related to the complete soil moisture profile in the unsaturated zone in order to be useful for hydrologic, climatic and agricultural studies. A new numerical method was presented to solve the governing equation for water transport in unsaturated soil by matching physical and numerical diffusion. By applying a new numerical scheme with which to discrete the kinematic wave equation on the space-time plane, this method shows the capability to simulate the physical diffusion of the diffusive wave with the numerical diffusion generated in the difference solution under certain conditions. Compared with other numerical methods with the first-order finite differences scheme, this method has enhanced the solution precision to the second order. An example application shows a good agreement with the observed data and suggests this new approach can be appropriate for soil moisture profile estimation. By combining the proposed soil moisture and drought estimation techniques, the daily soil moisture profile at high resolution can be gained, and is thus expected to be helpful not only in drought monitoring and active fire detecting, but also in agricultural applications and climate studies

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 Soil Moisture Remote Sensing Using Active Microwaves and Land Surface Modeling written by Rogier van der Velde and published by . This book was released on 2010 with total page 193 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book A Methodology for the Assessment of Surface Resistance and Soil Water Storage Variability at Mesoscale Based on Remote Sensing Measurements written by W. G. M. Bastiaanssen and published by . This book was released on 1994 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 Advances in Geoscience and Remote Sensing written by Gary Jedlovec and published by IntechOpen. This book was released on 2009-10-01 with total page 754 pages. Available in PDF, EPUB and Kindle. Book excerpt: Remote sensing is the acquisition of information of an object or phenomenon, by the use of either recording or real-time sensing device(s), that is not in physical or intimate contact with the object (such as by way of aircraft, spacecraft, satellite, buoy, or ship). In practice, remote sensing is the stand-off collection through the use of a variety of devices for gathering information on a given object or area. Human existence is dependent on our ability to understand, utilize, manage and maintain the environment we live in - Geoscience is the science that seeks to achieve these goals. This book is a collection of contributions from world-class scientists, engineers and educators engaged in the fields of geoscience and remote sensing.