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Remote Sensing of Leaf Area Index (LAI) and Other Vegetation Parameters

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ISBN: 9783039212392 / 9783039212408 Year: Pages: 334 DOI: 10.3390/books978-3-03921-240-8 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General)
Added to DOAB on : 2019-12-09 11:49:15
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Monitoring of vegetation structure and functioning is critical to modeling terrestrial ecosystems and energy cycles. In particular, leaf area index (LAI) is an important structural property of vegetation used in many land surface vegetation, climate, and crop production models. Canopy structure (LAI, fCover, plant height, and biomass) and biochemical parameters (leaf pigmentation and water content) directly influence the radiative transfer process of sunlight in vegetation, determining the amount of radiation measured by passive sensors in the visible and infrared portions of the electromagnetic spectrum. Optical remote sensing (RS) methods build relationships exploiting in situ measurements and/or as outputs of physical canopy radiative transfer models. The increased availability of passive (radar and LiDAR) RS data has fostered their use in many applications for the analysis of land surface properties and processes, thanks also to their insensitivity to weather conditions and the capability to exploit rich structural and textural information. Data fusion and multi-sensor integration techniques are pressing topics to fully exploit the information conveyed by both optical and microwave bands.

Keywords

conifer forest --- leaf area index --- smartphone-based method --- canopy gap fraction --- terrestrial laser scanning --- forest inventory --- density-based clustering --- forest aboveground biomass --- root biomass --- tree heights --- GLAS --- artificial neural network --- allometric scaling and resource limitation --- structure from motion (SfM) --- 3D point cloud --- remote sensing --- local maxima --- fixed tree window size --- managed temperate coniferous forests --- point cloud --- spectral information --- structure from motion (SfM) --- unmanned aerial vehicle (UAV) --- chlorophyll fluorescence (ChlF) --- drought --- Mediterranean --- photochemical reflectance index (PRI) --- photosynthesis --- R690/R630 --- recovery --- BAAPA --- remote sensing --- household survey --- forest --- farm types --- automated classification --- sampling design --- adaptive threshold --- over and understory cover --- LAI --- leaf area index --- EPIC --- simulation --- satellite --- MODIS --- biomass --- evaluation --- southern U.S. forests --- VIIRS --- leaf area index (LAI) --- Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) --- MODIS --- consistency --- uncertainty --- evaluation --- downscaling --- Pléiades imagery --- unmanned aerial vehicle --- stem volume estimation --- remote sensing --- clumping index --- leaf area index --- trunk --- terrestrial LiDAR --- HemiView --- forest above ground biomass (AGB) --- polarization coherence tomography (PCT) --- P-band PolInSAR --- tomographic profiles --- canopy closure --- global positioning system --- hemispherical sky-oriented photo --- signal attenuation --- geographic information system --- digital aerial photograph --- aboveground biomass --- leaf area index --- photogrammetric point cloud --- recursive feature elimination --- machine-learning --- forest degradation --- multisource remote sensing --- modelling aboveground biomass --- random forest --- Brazilian Amazon --- validation --- phenology --- NDVI --- LAI --- spectral analyses --- European beech --- altitude --- forests biomass --- remote sensing --- REDD+ --- random forest --- Tanzania --- RapidEye

Afforestation and Reforestation: Drivers, Dynamics, and Impacts

Authors: --- --- --- --- et al.
ISBN: 9783039214471 / 9783039214488 Year: Pages: 194 DOI: 10.3390/books978-3-03921-448-8 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Biology --- Forestry
Added to DOAB on : 2019-12-09 11:49:15
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Afforestation/reforestation (or forestation) has been implemented worldwide as an effective measure towards sustainable ecosystem services and addresses global environmental problems such as climate change. The conversion of grasslands, croplands, shrublands, or bare lands to forests can dramatically alter forest water, energy, and carbon cycles and, thus, ecosystem services (e.g., carbon sequestration, soil erosion control, and water quality improvement). Large-scale afforestation/reforestation is typically driven by policies and, in turn, can also have substantial socioeconomic impacts. To enable success, forestation endeavors require novel approaches that involve a series of complex processes and interdisciplinary sciences. For example, exotic or fast-growing tree species are often used to improve soil conditions of degraded lands or maximize productivity, and it often takes a long time to understand and quantify the consequences of such practices at watershed or regional scales. Maintaining the sustainability of man-made forests is becoming increasingly challenging under a changing environment and disturbance regime changes such as wildland fires, urbanization, drought, air pollution, climate change, and socioeconomic change. Therefore, this Special Issue focuses on case studies of the drivers, dynamics, and impacts of afforestation/reforestation at regional, national, or global scales. These new studies provide an update on the scientific advances related to forestation. This information is urgently needed by land managers and policy makers to better manage forest resources in today’s rapidly changing environments.

Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing

Authors: ---
ISBN: 9783039212156 / 9783039212163 Year: Pages: 438 DOI: 10.3390/books978-3-03921-216-3 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering --- Mechanical Engineering
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As computer and space technologies have been developed, geoscience information systems (GIS) and remote sensing (RS) technologies, which deal with the geospatial information, have been rapidly maturing. Moreover, over the last few decades, machine learning techniques including artificial neural network (ANN), deep learning, decision tree, and support vector machine (SVM) have been successfully applied to geospatial science and engineering research fields. The machine learning techniques have been widely applied to GIS and RS research fields and have recently produced valuable results in the areas of geoscience, environment, natural hazards, and natural resources. This book is a collection representing novel contributions detailing machine learning techniques as applied to geoscience information systems and remote sensing.

Keywords

landslide --- bagging ensemble --- Logistic Model Trees --- GIS --- Vietnam --- colorization --- random forest regression --- grayscale aerial image --- change detection --- gully erosion --- environmental variables --- data mining techniques --- SCAI --- GIS --- mapping --- single-class data descriptors --- materia medica resource --- Panax notoginseng --- one-class classifiers --- geoherb --- change detection --- convolutional network --- deep learning --- panchromatic --- remote sensing --- remote sensing image segmentation --- convolutional neural networks --- Gaofen-2 --- hybrid structure convolutional neural networks --- winter wheat spatial distribution --- classification-based learning --- real-time precise point positioning --- convergence time --- ionospheric delay constraints --- precise weighting --- landslide --- weights of evidence --- logistic regression --- random forest --- hybrid model --- traffic CO --- traffic CO prediction --- neural networks --- GIS --- land use/land cover (LULC) --- unmanned aerial vehicle --- texture --- gray-level co-occurrence matrix --- machine learning --- crop --- landslide susceptibility --- random forest --- boosted regression tree --- information gain --- landslide susceptibility map --- ALS point cloud --- multi-scale --- classification --- large scene --- coarse particle --- particulate matter 10 (PM10) --- landsat image --- machine learning --- support vector machine --- high-resolution --- optical remote sensing --- object detection --- deep learning --- transfer learning --- land subsidence --- Bayes net --- naïve Bayes --- logistic --- multilayer perceptron --- logit boost --- change detection --- convolutional network --- deep learning --- panchromatic --- remote sensing --- leaf area index (LAI) --- machine learning --- Sentinel-2 --- sensitivity analysis --- training sample size --- spectral bands --- spatial sparse recovery --- constrained spatial smoothing --- spatial spline regression --- alternating direction method of multipliers --- landslide prediction --- machine learning --- neural networks --- model switching --- spatial predictive models --- predictive accuracy --- model assessment --- variable selection --- feature selection --- model validation --- spatial predictions --- reproducible research --- Qaidam Basin --- remote sensing --- TRMM --- artificial neural network --- n/a

Advances in Quantitative Remote Sensing in China – In Memory of Prof. Xiaowen Li

Authors: --- ---
ISBN: 9783038972709 Year: Volume: 1 Pages: 404 DOI: 10.3390/books978-3-03897-271-6 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Geography
Added to DOAB on : 2019-03-08 11:42:05
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Quantitative land remote sensing has recently advanced dramatically, particularly in China. It has been largely driven by vast governmental investment, the availability of a huge amount of Chinese satellite data, geospatial information requirements for addressing pressing environmental issues and other societal benefits. Many individuals have also fostered and made great contributions to its development, and Prof. Xiaowen Li was one of these leading figures. This book is published in memory of Prof. Li. The papers collected in this book cover topics from surface reflectance simulation, inversion algorithm and estimation of variables, to applications in optical, thermal, Lidar and microwave remote sensing. The wide range of variables include directional reflectance, chlorophyll fluorescence, aerosol optical depth, incident solar radiation, albedo, surface temperature, upward longwave radiation, leaf area index, fractional vegetation cover, forest biomass, precipitation, evapotranspiration, freeze/thaw snow cover, vegetation productivity, phenology and biodiversity indicators. They clearly reflect the current level of research in this area. This book constitutes an excellent reference suitable for upper-level undergraduate students, graduate students and professionals in remote sensing.

Keywords

evapotranspiration --- Northeast China --- MS–PT algorithm --- spatial-temporal variations --- controlling factors --- potential evapotranspiration --- vegetation remote sensing --- reflectance model --- spectra --- leaf --- copper --- PROSPECT --- leaf area density --- terrestrial LiDAR --- tree canopy --- vertical structure --- voxel --- spatial representativeness --- heterogeneity --- validation --- land-surface temperature products (LSTs) --- observations --- HiWATER --- remote sensing --- spatiotemporal representative --- cost-efficient, sampling design --- heterogeneity --- validation --- FY-3C/MERSI --- GLASS --- Land surface temperature --- Land surface emissivity --- GPP --- SIF --- MuSyQ-GPP algorithm --- BEPS --- vegetation phenology --- Tibetan Plateau --- MODIS --- NDVI --- start of growing season (SOS) --- end of growing season (EOS) --- GLASS LAI time series --- forest disturbance --- disturbance index --- latent heat --- machine learning algorithms --- plant functional type --- high-resolution freeze/thaw --- AMSR2 --- MODIS --- LAI --- ZY-3 MUX --- GF-1 WFV --- HJ-1 CCD --- maize --- PROSPECT-5B+SAILH (PROSAIL) model --- spatial heterogeneity --- variability --- evapotranspiration --- land surface variables --- probability density function --- HiWATER --- spectral --- albedometer --- interference filter --- photoelectric detector --- validation --- land surface albedo --- multi-scale validation --- rugged terrain --- MRT-based model --- MCD43A3 C6 --- precipitation --- statistics methods --- China --- Tibetan Plateau --- South China’s --- drought --- SPI --- TMI data --- crop-growing regions --- downward shortwave radiation --- machine learning --- gradient boosting regression tree --- AVHRR --- CMA --- BRDF --- aerosol --- MODIS --- sunphotometer --- arid/semiarid --- solar-induced chlorophyll fluorescence --- fluorescence quantum efficiency in dark-adapted conditions (FQE) --- SCOPE --- Fraunhofer Line Discrimination (FLD) --- gross primary productivity (GPP) --- longwave upwelling radiation (LWUP) --- Visible Infrared Imaging Radiometer Suite (VIIRS) --- surface radiation budget --- hybrid method --- remote sensing --- leaf age --- leaf spectral properties --- leaf area index --- Cunninghamia --- Chinese fir --- canopy reflectance --- NIR --- EVI2 --- geometric optical radiative transfer (GORT) model --- land surface albedo --- snow-free albedo --- rugged terrain --- topographic effects --- black-sky albedo (BSA) --- GPP --- NPP --- MODIS --- validation --- phenology --- RADARSAT-2 --- rice --- Synthetic Aperture Radar (SAR) --- decision tree --- forest canopy height --- aboveground biomass --- ICESat GLAS --- Landsat --- random forest model --- anisotropic reflectance --- BRDF --- rugged terrain --- solo slope --- composite slope --- surface solar irradiance --- geostationary satellite --- polar orbiting satellite --- LUT method --- SURFRAD --- downward shortwave radiation --- daily average value --- Antarctica --- sinusoidal method --- cloud fraction --- interpolation --- boreal forest --- GPP --- spatiotemporal distribution and variation --- meteorological factors --- phenological parameters --- multisource data fusion --- aerosol retrieval --- urban scale --- vegetation dust-retention --- multiple ecological factors --- geographical detector model --- snow cover --- passive microwave --- FY-3C/MWRI --- algorithmic assessment --- China --- land surface temperature --- satellite observations --- flux measurements --- latitudinal pattern --- land cover change --- fractional vegetation cover (FVC) --- multi-data set --- northern China --- spatio-temporal --- inter-annual variation --- uncertainty --- standard error of the mean --- downscaling --- GPP --- spatial heterogeneity --- remote sensing --- subpixel information --- LiDAR --- point cloud --- leaf --- gap fraction --- 3D reconstruction --- biodiversity --- remote sensing --- species richness --- metric comparison --- metric integration --- leaf area index --- MODIS products --- Landsat --- high resolution --- homogeneous and pure pixel filter --- pixel unmixing --- vertical vegetation stratification --- gross primary production (GPP) --- light use efficiency --- dense forest --- MODIS --- VPM --- temperature profiles --- humidity profiles --- n/a --- geometric-optical model --- thermal radiation directionality --- quantitative remote sensing inversion --- scale effects --- comprehensive field experiment

Advances in Quantitative Remote Sensing in China – In Memory of Prof. Xiaowen Li

Authors: --- ---
ISBN: 9783038972761 Year: Volume: 2 Pages: 404 DOI: 10.3390/books978-3-03897-277-8 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Geography
Added to DOAB on : 2019-03-08 11:42:05
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Abstract

Quantitative land remote sensing has recently advanced dramatically, particularly in China. It has been largely driven by vast governmental investment, the availability of a huge amount of Chinese satellite data, geospatial information requirements for addressing pressing environmental issues and other societal benefits. Many individuals have also fostered and made great contributions to its development, and Prof. Xiaowen Li was one of these leading figures. This book is published in memory of Prof. Li. The papers collected in this book cover topics from surface reflectance simulation, inversion algorithm and estimation of variables, to applications in optical, thermal, Lidar and microwave remote sensing. The wide range of variables include directional reflectance, chlorophyll fluorescence, aerosol optical depth, incident solar radiation, albedo, surface temperature, upward longwave radiation, leaf area index, fractional vegetation cover, forest biomass, precipitation, evapotranspiration, freeze/thaw snow cover, vegetation productivity, phenology and biodiversity indicators. They clearly reflect the current level of research in this area. This book constitutes an excellent reference suitable for upper-level undergraduate students, graduate students and professionals in remote sensing.

Keywords

evapotranspiration --- Northeast China --- MS–PT algorithm --- spatial-temporal variations --- controlling factors --- potential evapotranspiration --- vegetation remote sensing --- reflectance model --- spectra --- leaf --- copper --- PROSPECT --- leaf area density --- terrestrial LiDAR --- tree canopy --- vertical structure --- voxel --- spatial representativeness --- heterogeneity --- validation --- land-surface temperature products (LSTs) --- observations --- HiWATER --- remote sensing --- spatiotemporal representative --- cost-efficient, sampling design --- heterogeneity --- validation --- FY-3C/MERSI --- GLASS --- Land surface temperature --- Land surface emissivity --- GPP --- SIF --- MuSyQ-GPP algorithm --- BEPS --- vegetation phenology --- Tibetan Plateau --- MODIS --- NDVI --- start of growing season (SOS) --- end of growing season (EOS) --- GLASS LAI time series --- forest disturbance --- disturbance index --- latent heat --- machine learning algorithms --- plant functional type --- high-resolution freeze/thaw --- AMSR2 --- MODIS --- LAI --- ZY-3 MUX --- GF-1 WFV --- HJ-1 CCD --- maize --- PROSPECT-5B+SAILH (PROSAIL) model --- spatial heterogeneity --- variability --- evapotranspiration --- land surface variables --- probability density function --- HiWATER --- spectral --- albedometer --- interference filter --- photoelectric detector --- validation --- land surface albedo --- multi-scale validation --- rugged terrain --- MRT-based model --- MCD43A3 C6 --- precipitation --- statistics methods --- China --- Tibetan Plateau --- South China’s --- drought --- SPI --- TMI data --- crop-growing regions --- downward shortwave radiation --- machine learning --- gradient boosting regression tree --- AVHRR --- CMA --- BRDF --- aerosol --- MODIS --- sunphotometer --- arid/semiarid --- solar-induced chlorophyll fluorescence --- fluorescence quantum efficiency in dark-adapted conditions (FQE) --- SCOPE --- Fraunhofer Line Discrimination (FLD) --- gross primary productivity (GPP) --- longwave upwelling radiation (LWUP) --- Visible Infrared Imaging Radiometer Suite (VIIRS) --- surface radiation budget --- hybrid method --- remote sensing --- leaf age --- leaf spectral properties --- leaf area index --- Cunninghamia --- Chinese fir --- canopy reflectance --- NIR --- EVI2 --- geometric optical radiative transfer (GORT) model --- land surface albedo --- snow-free albedo --- rugged terrain --- topographic effects --- black-sky albedo (BSA) --- GPP --- NPP --- MODIS --- validation --- phenology --- RADARSAT-2 --- rice --- Synthetic Aperture Radar (SAR) --- decision tree --- forest canopy height --- aboveground biomass --- ICESat GLAS --- Landsat --- random forest model --- anisotropic reflectance --- BRDF --- rugged terrain --- solo slope --- composite slope --- surface solar irradiance --- geostationary satellite --- polar orbiting satellite --- LUT method --- SURFRAD --- downward shortwave radiation --- daily average value --- Antarctica --- sinusoidal method --- cloud fraction --- interpolation --- boreal forest --- GPP --- spatiotemporal distribution and variation --- meteorological factors --- phenological parameters --- multisource data fusion --- aerosol retrieval --- urban scale --- vegetation dust-retention --- multiple ecological factors --- geographical detector model --- snow cover --- passive microwave --- FY-3C/MWRI --- algorithmic assessment --- China --- land surface temperature --- satellite observations --- flux measurements --- latitudinal pattern --- land cover change --- fractional vegetation cover (FVC) --- multi-data set --- northern China --- spatio-temporal --- inter-annual variation --- uncertainty --- standard error of the mean --- downscaling --- GPP --- spatial heterogeneity --- remote sensing --- subpixel information --- LiDAR --- point cloud --- leaf --- gap fraction --- 3D reconstruction --- biodiversity --- remote sensing --- species richness --- metric comparison --- metric integration --- leaf area index --- MODIS products --- Landsat --- high resolution --- homogeneous and pure pixel filter --- pixel unmixing --- vertical vegetation stratification --- gross primary production (GPP) --- light use efficiency --- dense forest --- MODIS --- VPM --- temperature profiles --- humidity profiles --- n/a --- geometric-optical model --- thermal radiation directionality --- quantitative remote sensing inversion --- scale effects --- comprehensive field experiment

Google Earth Engine Applications

Authors: ---
ISBN: 9783038978848 9783038978855 Year: Pages: 420 DOI: 10.3390/books978-3-03897-885-5 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- Environmental Technology
Added to DOAB on : 2019-04-25 16:37:17
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In a rapidly changing world, there is an ever-increasing need to monitor the Earth’s resources and manage it sustainably for future generations. Earth observation from satellites is critical to provide information required for informed and timely decision making in this regard. Satellite-based earth observation has advanced rapidly over the last 50 years, and there is a plethora of satellite sensors imaging the Earth at finer spatial and spectral resolutions as well as high temporal resolutions. The amount of data available for any single location on the Earth is now at the petabyte-scale. An ever-increasing capacity and computing power is needed to handle such large datasets. The Google Earth Engine (GEE) is a cloud-based computing platform that was established by Google to support such data processing. This facility allows for the storage, processing and analysis of spatial data using centralized high-power computing resources, allowing scientists, researchers, hobbyists and anyone else interested in such fields to mine this data and understand the changes occurring on the Earth’s surface. This book presents research that applies the Google Earth Engine in mining, storing, retrieving and processing spatial data for a variety of applications that include vegetation monitoring, cropland mapping, ecosystem assessment, and gross primary productivity, among others. Datasets used range from coarse spatial resolution data, such as MODIS, to medium resolution datasets (Worldview -2), and the studies cover the entire globe at varying spatial and temporal scales.

Keywords

Google Earth Engine --- NDVI --- vegetation index --- Landsat --- remote sensing --- phenology --- surface reflectance --- cropland mapping --- cropland areas --- 30-m --- Landsat-8 --- Sentinel-2 --- Random Forest --- Support Vector Machines --- segmentation --- RHSeg --- Google Earth Engine --- Africa --- remote sensing --- semi-arid --- ecosystem assessment --- land use change --- image classification --- seasonal vegetation --- carbon cycle --- Google Earth Engine --- crop yield --- gross primary productivity (GPP) --- data fusion --- Landsat --- MODIS --- MODIS --- Random Forest --- pasture mapping --- Brazilian pasturelands dynamics --- Google Earth Engine --- crop classification --- multi-classifier --- cloud computing --- time series --- high spatial resolution --- BACI --- Enhanced Vegetation Index --- Google Earth Engine --- cloud-based geo-processing --- satellite-derived bathymetry --- image composition --- pseudo-invariant features --- sun glint correction --- empirical --- spatial error --- Google Earth Engine --- low cost in situ --- Sentinel-2 --- Mediterranean --- burn severity --- change detection --- Landsat --- dNBR --- RdNBR --- RBR --- composite burn index (CBI) --- MTBS --- lower mekong basin --- landsat collection --- suspended sediment concentration --- online application --- google earth engine --- Landsat --- Google Earth Engine --- protected area --- forest and land use mapping --- machine learning classification --- China --- temporal compositing --- image time series --- multitemporal analysis --- change detection --- cloud masking --- Landsat-8 --- Google Earth Engine (GEE) --- Google Earth Engine --- LAI --- FVC --- FAPAR --- CWC --- plant traits --- random forests --- PROSAIL --- small-scale mining --- industrial mining --- google engine --- image classification --- land-use cover change --- seagrass --- habitat mapping --- image composition --- machine learning --- support vector machines --- Google Earth Engine --- Sentinel-2 --- Aegean --- Ionian --- global scale --- soil moisture --- Soil Moisture Ocean Salinity --- Soil Moisture Active Passive --- Google Earth Engine --- drought --- cloud computing --- remote sensing --- snow hydrology --- water resources --- Google Earth Engine --- user assessment --- MODIS --- snow cover --- flood --- disaster prevention --- emergency response --- decision making --- Google Earth Engine --- land cover --- deforestation --- Brazilian Amazon --- Bayesian statistics --- BULC-U --- Mato Grosso --- spatial resolution --- Landsat --- GlobCover --- SDG --- surface urban heat island --- Geo Big Data --- Google Earth Engine --- global monitoring service --- Google Earth Engine --- web portal --- satellite imagery --- trends --- earth observation --- wetland --- Google Earth Engine --- Sentinel-1 --- Sentinel-2 --- random forest --- cloud computing --- geo-big data --- cloud computing --- big data analytics --- long term monitoring --- data archival --- early warning systems

Remote Sensing of Precipitation: Volume 1

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ISBN: 9783039212859 / 9783039212866 Year: Pages: 480 DOI: 10.3390/books978-3-03921-286-6 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering --- Environmental Engineering
Added to DOAB on : 2019-08-28 11:21:27
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Precipitation is a well-recognized pillar in global water and energy balances. An accurate and timely understanding of its characteristics at the global, regional, and local scales is indispensable for a clearer understanding of the mechanisms underlying the Earth’s atmosphere–ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises a primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne.

Keywords

GPM --- IMERG --- satellite precipitation adjustment --- numerical weather prediction --- heavy precipitation --- flood-inducing storm --- complex terrain --- precipitation --- geostationary microwave sensors --- polar systems --- synoptic weather types --- drop size distribution (DSD) --- microstructure of rain --- disdrometer --- radar reflectivity–rain rate relationship --- CHIRPS --- CMORPH --- TMPA --- MSWEP --- statistical evaluation --- VIC model --- hydrological simulation --- precipitation --- satellite --- GPM --- TRMM --- CFSR --- PERSIANN --- MSWEP --- streamflow simulation --- lumped models --- Peninsular Spain --- GPM IMERG v5 --- TRMM 3B42 v7 --- precipitation --- evaluation --- Huaihe River basin --- precipitation --- radar --- radiometer --- T-Matrix --- microwave scattering --- quantitative precipitation estimates --- validation --- PERSIANN-CCS --- meteorological radar --- satellite rainfall estimates --- satellite precipitation retrieval --- neural networks --- GPM --- GMI --- remote sensing --- hurricane Harvey --- GPM satellite --- IMERG --- tropical storm rainfall --- gridded radar precipitation --- precipitation --- satellites --- climate models --- regional climate models --- X-band radar --- dual-polarization --- precipitation --- complex terrain --- runoff simulations --- snowfall detection --- snow water path retrieval --- supercooled droplets detection --- GPM Microwave Imager --- Satellite Precipitation Estimates --- GPM --- TRMM --- IMERG --- GSMaP --- TMPA --- CMORPH --- assessment --- Pakistan --- heavy rainfall prediction --- satellite radiance --- data assimilation --- RMAPS --- harmonie model --- radar data assimilation --- pre-processing --- mesoscale precipitation patterns --- GNSS meteorology --- GPS --- Zenith Tropospheric Delay --- precipitable water vapor --- SEID --- single frequency GNSS --- Precise Point Positioning --- low-cost receivers --- goGPS --- GPM --- IMERG --- TRMM --- precipitation --- Cyprus --- satellite precipitation product --- Tianshan Mountains --- GPM --- TRMM --- CMORPH --- heavy precipitation --- rainfall retrieval techniques --- forecast model --- Red–Thai Binh River Basin --- TMPA 3B42V7 --- TMPA 3B42RT --- rainfall --- bias correction --- linear-scaling approach --- climatology --- topography --- precipitation --- remote sensing --- CloudSat --- CMIP --- high latitude --- mineral dust --- wet deposition --- cloud scavenging --- dust washout process --- Saharan dust transportation --- precipitation rate --- precipitating hydrometeor --- hydrometeor classification --- cloud radar --- Ka-band --- thunderstorm --- thundercloud --- vertical air velocity --- terminal velocity --- Milešovka observatory --- rain gauges --- radar --- quality indexes --- satellite rainfall retrievals --- validation --- surface rain intensity --- kriging with external drift --- PEMW --- MSG --- SEVIRI --- downscaling --- tropical cyclone --- rain rate --- precipitation --- remote sensing --- radiometer --- retrieval algorithm --- GPM --- DPR --- validation network --- volume matching --- reflectivity --- rainfall rate --- TRMM-era TMPA --- GPM-era IMERG --- satellite rainfall estimate --- Mainland China --- satellite precipitation --- Global Precipitation Measurement (GPM) --- IMERG --- TRMM-TMPA --- Ensemble Precipitation (EP) algorithm --- topographical and seasonal evaluation --- daily rainfall estimations --- TRMM 3B42 v7 --- rain gauges --- Amazon Basin --- regional rainfall regimes --- regional rainfall sub-regimes --- TRMM 3B42 V7 --- CMORPH_CRT --- PERSIANN_CDR --- GR models --- hydrological simulation --- Red River Basin --- satellite precipitation --- Tibetan Plateau --- GPM --- IMERG --- GSMaP --- precipitation --- weather --- radar --- GPM --- RADOLAN --- QPE --- TRMM --- TMPA --- 3B42 --- validation --- rainfall --- telemetric rain gauge --- Lai Nullah --- Pakistan --- XPOL radar --- GPM/IMERG --- WRF-Hydro --- CHAOS --- hydrometeorology --- flash flood --- Mandra --- typhoon --- IMERG --- GSMaP --- Southern China --- precipitation --- satellite remote sensing --- error analysis --- triple collocation --- precipitation --- TRMM --- GPM --- IMERG --- weather radar --- precipitable water vapor --- precipitation retrieval --- rain rate --- QPE

Remote Sensing of Precipitation: Volume 2

Author:
ISBN: 9783039212873 / 9783039212880 Year: Pages: 318 DOI: 10.3390/books978-3-03921-288-0 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering --- Environmental Engineering
Added to DOAB on : 2019-08-28 11:21:27
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Abstract

Precipitation is a well-recognized pillar in global water and energy balances. An accurate and timely understanding of its characteristics at the global, regional, and local scales is indispensable for a clearer understanding of the mechanisms underlying the Earth’s atmosphere–ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises a primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne.

Keywords

GPM --- IMERG --- satellite precipitation adjustment --- numerical weather prediction --- heavy precipitation --- flood-inducing storm --- complex terrain --- precipitation --- geostationary microwave sensors --- polar systems --- synoptic weather types --- drop size distribution (DSD) --- microstructure of rain --- disdrometer --- radar reflectivity–rain rate relationship --- CHIRPS --- CMORPH --- TMPA --- MSWEP --- statistical evaluation --- VIC model --- hydrological simulation --- precipitation --- satellite --- GPM --- TRMM --- CFSR --- PERSIANN --- MSWEP --- streamflow simulation --- lumped models --- Peninsular Spain --- GPM IMERG v5 --- TRMM 3B42 v7 --- precipitation --- evaluation --- Huaihe River basin --- precipitation --- radar --- radiometer --- T-Matrix --- microwave scattering --- quantitative precipitation estimates --- validation --- PERSIANN-CCS --- meteorological radar --- satellite rainfall estimates --- satellite precipitation retrieval --- neural networks --- GPM --- GMI --- remote sensing --- hurricane Harvey --- GPM satellite --- IMERG --- tropical storm rainfall --- gridded radar precipitation --- precipitation --- satellites --- climate models --- regional climate models --- X-band radar --- dual-polarization --- precipitation --- complex terrain --- runoff simulations --- snowfall detection --- snow water path retrieval --- supercooled droplets detection --- GPM Microwave Imager --- Satellite Precipitation Estimates --- GPM --- TRMM --- IMERG --- GSMaP --- TMPA --- CMORPH --- assessment --- Pakistan --- heavy rainfall prediction --- satellite radiance --- data assimilation --- RMAPS --- harmonie model --- radar data assimilation --- pre-processing --- mesoscale precipitation patterns --- GNSS meteorology --- GPS --- Zenith Tropospheric Delay --- precipitable water vapor --- SEID --- single frequency GNSS --- Precise Point Positioning --- low-cost receivers --- goGPS --- GPM --- IMERG --- TRMM --- precipitation --- Cyprus --- satellite precipitation product --- Tianshan Mountains --- GPM --- TRMM --- CMORPH --- heavy precipitation --- rainfall retrieval techniques --- forecast model --- Red–Thai Binh River Basin --- TMPA 3B42V7 --- TMPA 3B42RT --- rainfall --- bias correction --- linear-scaling approach --- climatology --- topography --- precipitation --- remote sensing --- CloudSat --- CMIP --- high latitude --- mineral dust --- wet deposition --- cloud scavenging --- dust washout process --- Saharan dust transportation --- precipitation rate --- precipitating hydrometeor --- hydrometeor classification --- cloud radar --- Ka-band --- thunderstorm --- thundercloud --- vertical air velocity --- terminal velocity --- Milešovka observatory --- rain gauges --- radar --- quality indexes --- satellite rainfall retrievals --- validation --- surface rain intensity --- kriging with external drift --- PEMW --- MSG --- SEVIRI --- downscaling --- tropical cyclone --- rain rate --- precipitation --- remote sensing --- radiometer --- retrieval algorithm --- GPM --- DPR --- validation network --- volume matching --- reflectivity --- rainfall rate --- TRMM-era TMPA --- GPM-era IMERG --- satellite rainfall estimate --- Mainland China --- satellite precipitation --- Global Precipitation Measurement (GPM) --- IMERG --- TRMM-TMPA --- Ensemble Precipitation (EP) algorithm --- topographical and seasonal evaluation --- daily rainfall estimations --- TRMM 3B42 v7 --- rain gauges --- Amazon Basin --- regional rainfall regimes --- regional rainfall sub-regimes --- TRMM 3B42 V7 --- CMORPH_CRT --- PERSIANN_CDR --- GR models --- hydrological simulation --- Red River Basin --- satellite precipitation --- Tibetan Plateau --- GPM --- IMERG --- GSMaP --- precipitation --- weather --- radar --- GPM --- RADOLAN --- QPE --- TRMM --- TMPA --- 3B42 --- validation --- rainfall --- telemetric rain gauge --- Lai Nullah --- Pakistan --- XPOL radar --- GPM/IMERG --- WRF-Hydro --- CHAOS --- hydrometeorology --- flash flood --- Mandra --- typhoon --- IMERG --- GSMaP --- Southern China --- precipitation --- satellite remote sensing --- error analysis --- triple collocation --- precipitation --- TRMM --- GPM --- IMERG --- weather radar --- precipitable water vapor --- precipitation retrieval --- rain rate --- QPE

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