Listing 1 - 4 of 4 |
Sort by
|
Choose an application
This book covers topics ranging from a detailed error analysis of SSTs to new applications employed, for example, in the study of the El Niño–La Niña Southern Oscillation, lake temperatures, and coral bleaching. New techniques for interpolation and algorithm development are presented, including improvements for cloud detection. Analysis of the pixel-to-pixel uncertainties provides insight to applications for high spatial resolutions. New approaches for the estimation and evaluation of SSTs are presented. In addition, an overview of the Climate Change Initiative, with specific applications to SST, is presented. The book provides an excellent overview of the current technology, while also highlighting new technologies and their applications to new missions.
Infrared --- Microwave --- Sea Surface Temperature --- Climate Data Records --- Group for High Resolution Sea Surface Temperature
Choose an application
Vegetation plays an essential role in the study of the environment through plant respiration and photosynthesis. Therefore, the assessment of the current vegetation status is critical to modeling terrestrial ecosystems and energy cycles. Canopy structure (LAI, fCover, plant height, biomass, leaf angle distribution) and biochemical parameters (leaf pigmentation and water content) have been employed to assess vegetation status and its dynamics at scales ranging from kilometric to decametric spatial resolutions thanks to methods based on remote sensing (RS) data.Optical RS retrieval methods are based on the radiative transfer processes of sunlight in vegetation, determining the amount of radiation that is measured by passive sensors in the visible and infrared channels. The increased availability of active RS (radar and LiDAR) data has fostered their use in many applications for the analysis of land surface properties and processes, thanks to their insensitivity to weather conditions and the ability to exploit rich structural and texture information. Optical and radar data fusion and multi-sensor integration approaches are pressing topics, which could fully exploit the information conveyed by both the optical and microwave parts of the electromagnetic spectrum.This Special Issue reprint reviews the state of the art in biophysical parameters retrieval and its usage in a wide variety of applications (e.g., ecology, carbon cycle, agriculture, forestry and food security).
Research & information: general --- hyperspectral --- spectroscopy --- equivalent water thickness --- canopy water content --- agriculture --- EnMAP --- LAI --- LCC --- FAPAR --- FVC --- CCC --- PROSAIL --- GPR --- machine learning --- active learning --- Landsat 8 --- surface reflectance --- LEDAPS --- LaSRC --- 6SV --- SREM --- NDVI --- artificial neural networks --- canopy chlorophyll content --- INFORM --- leaf area index --- SAIL --- fluorescence --- in vivo --- spectrometry --- ASD Field Spec --- lead ions --- remote sensing indices --- meteosat second generation (MSG) --- biophysical parameters (LAI --- FAPAR) --- SEVIRI --- climate data records (CDR) --- stochastic spectral mixture model (SSMM) --- Satellite Application Facility for Land Surface Analysis (LSA SAF) --- the fraction of radiation absorbed by photosynthetic components (FAPARgreen) --- triple-source --- leaf area index (LAI) --- woody area index (WAI) --- clumping index (CI) --- Moderate Resolution Imaging Spectroradiometer (MODIS) --- soil albedo --- unmanned aircraft vehicle --- multispectral sensor --- vegetation indices --- rapeseed crop --- site-specific farming --- Sentinel-2 --- forest --- vegetation radiative transfer model --- Discrete Anisotropic Radiative Transfer (DART) model --- MODIS --- fraction of photosynthetically active radiation absorbed by vegetation (FPAR) --- three-dimensional radiative transfer model (3D RTM) --- uncertainty assessment --- vertical foliage profile (VFP) --- terrestrial laser scanning (TLS) --- airborne laser scanning (ALS) --- spaceborne laser scanning (SLS) --- riparian --- invasive vegetation --- burn severity --- canopy loss --- wildfire --- hyperspectral --- spectroscopy --- equivalent water thickness --- canopy water content --- agriculture --- EnMAP --- LAI --- LCC --- FAPAR --- FVC --- CCC --- PROSAIL --- GPR --- machine learning --- active learning --- Landsat 8 --- surface reflectance --- LEDAPS --- LaSRC --- 6SV --- SREM --- NDVI --- artificial neural networks --- canopy chlorophyll content --- INFORM --- leaf area index --- SAIL --- fluorescence --- in vivo --- spectrometry --- ASD Field Spec --- lead ions --- remote sensing indices --- meteosat second generation (MSG) --- biophysical parameters (LAI --- FAPAR) --- SEVIRI --- climate data records (CDR) --- stochastic spectral mixture model (SSMM) --- Satellite Application Facility for Land Surface Analysis (LSA SAF) --- the fraction of radiation absorbed by photosynthetic components (FAPARgreen) --- triple-source --- leaf area index (LAI) --- woody area index (WAI) --- clumping index (CI) --- Moderate Resolution Imaging Spectroradiometer (MODIS) --- soil albedo --- unmanned aircraft vehicle --- multispectral sensor --- vegetation indices --- rapeseed crop --- site-specific farming --- Sentinel-2 --- forest --- vegetation radiative transfer model --- Discrete Anisotropic Radiative Transfer (DART) model --- MODIS --- fraction of photosynthetically active radiation absorbed by vegetation (FPAR) --- three-dimensional radiative transfer model (3D RTM) --- uncertainty assessment --- vertical foliage profile (VFP) --- terrestrial laser scanning (TLS) --- airborne laser scanning (ALS) --- spaceborne laser scanning (SLS) --- riparian --- invasive vegetation --- burn severity --- canopy loss --- wildfire
Choose an application
Vegetation plays an essential role in the study of the environment through plant respiration and photosynthesis. Therefore, the assessment of the current vegetation status is critical to modeling terrestrial ecosystems and energy cycles. Canopy structure (LAI, fCover, plant height, biomass, leaf angle distribution) and biochemical parameters (leaf pigmentation and water content) have been employed to assess vegetation status and its dynamics at scales ranging from kilometric to decametric spatial resolutions thanks to methods based on remote sensing (RS) data.Optical RS retrieval methods are based on the radiative transfer processes of sunlight in vegetation, determining the amount of radiation that is measured by passive sensors in the visible and infrared channels. The increased availability of active RS (radar and LiDAR) data has fostered their use in many applications for the analysis of land surface properties and processes, thanks to their insensitivity to weather conditions and the ability to exploit rich structural and texture information. Optical and radar data fusion and multi-sensor integration approaches are pressing topics, which could fully exploit the information conveyed by both the optical and microwave parts of the electromagnetic spectrum.This Special Issue reprint reviews the state of the art in biophysical parameters retrieval and its usage in a wide variety of applications (e.g., ecology, carbon cycle, agriculture, forestry and food security).
Research & information: general --- hyperspectral --- spectroscopy --- equivalent water thickness --- canopy water content --- agriculture --- EnMAP --- LAI --- LCC --- FAPAR --- FVC --- CCC --- PROSAIL --- GPR --- machine learning --- active learning --- Landsat 8 --- surface reflectance --- LEDAPS --- LaSRC --- 6SV --- SREM --- NDVI --- artificial neural networks --- canopy chlorophyll content --- INFORM --- leaf area index --- SAIL --- fluorescence --- in vivo --- spectrometry --- ASD Field Spec --- lead ions --- remote sensing indices --- meteosat second generation (MSG) --- biophysical parameters (LAI --- FAPAR) --- SEVIRI --- climate data records (CDR) --- stochastic spectral mixture model (SSMM) --- Satellite Application Facility for Land Surface Analysis (LSA SAF) --- the fraction of radiation absorbed by photosynthetic components (FAPARgreen) --- triple-source --- leaf area index (LAI) --- woody area index (WAI) --- clumping index (CI) --- Moderate Resolution Imaging Spectroradiometer (MODIS) --- soil albedo --- unmanned aircraft vehicle --- multispectral sensor --- vegetation indices --- rapeseed crop --- site-specific farming --- Sentinel-2 --- forest --- vegetation radiative transfer model --- Discrete Anisotropic Radiative Transfer (DART) model --- MODIS --- fraction of photosynthetically active radiation absorbed by vegetation (FPAR) --- three-dimensional radiative transfer model (3D RTM) --- uncertainty assessment --- vertical foliage profile (VFP) --- terrestrial laser scanning (TLS) --- airborne laser scanning (ALS) --- spaceborne laser scanning (SLS) --- riparian --- invasive vegetation --- burn severity --- canopy loss --- wildfire
Choose an application
Vegetation plays an essential role in the study of the environment through plant respiration and photosynthesis. Therefore, the assessment of the current vegetation status is critical to modeling terrestrial ecosystems and energy cycles. Canopy structure (LAI, fCover, plant height, biomass, leaf angle distribution) and biochemical parameters (leaf pigmentation and water content) have been employed to assess vegetation status and its dynamics at scales ranging from kilometric to decametric spatial resolutions thanks to methods based on remote sensing (RS) data.Optical RS retrieval methods are based on the radiative transfer processes of sunlight in vegetation, determining the amount of radiation that is measured by passive sensors in the visible and infrared channels. The increased availability of active RS (radar and LiDAR) data has fostered their use in many applications for the analysis of land surface properties and processes, thanks to their insensitivity to weather conditions and the ability to exploit rich structural and texture information. Optical and radar data fusion and multi-sensor integration approaches are pressing topics, which could fully exploit the information conveyed by both the optical and microwave parts of the electromagnetic spectrum.This Special Issue reprint reviews the state of the art in biophysical parameters retrieval and its usage in a wide variety of applications (e.g., ecology, carbon cycle, agriculture, forestry and food security).
hyperspectral --- spectroscopy --- equivalent water thickness --- canopy water content --- agriculture --- EnMAP --- LAI --- LCC --- FAPAR --- FVC --- CCC --- PROSAIL --- GPR --- machine learning --- active learning --- Landsat 8 --- surface reflectance --- LEDAPS --- LaSRC --- 6SV --- SREM --- NDVI --- artificial neural networks --- canopy chlorophyll content --- INFORM --- leaf area index --- SAIL --- fluorescence --- in vivo --- spectrometry --- ASD Field Spec --- lead ions --- remote sensing indices --- meteosat second generation (MSG) --- biophysical parameters (LAI --- FAPAR) --- SEVIRI --- climate data records (CDR) --- stochastic spectral mixture model (SSMM) --- Satellite Application Facility for Land Surface Analysis (LSA SAF) --- the fraction of radiation absorbed by photosynthetic components (FAPARgreen) --- triple-source --- leaf area index (LAI) --- woody area index (WAI) --- clumping index (CI) --- Moderate Resolution Imaging Spectroradiometer (MODIS) --- soil albedo --- unmanned aircraft vehicle --- multispectral sensor --- vegetation indices --- rapeseed crop --- site-specific farming --- Sentinel-2 --- forest --- vegetation radiative transfer model --- Discrete Anisotropic Radiative Transfer (DART) model --- MODIS --- fraction of photosynthetically active radiation absorbed by vegetation (FPAR) --- three-dimensional radiative transfer model (3D RTM) --- uncertainty assessment --- vertical foliage profile (VFP) --- terrestrial laser scanning (TLS) --- airborne laser scanning (ALS) --- spaceborne laser scanning (SLS) --- riparian --- invasive vegetation --- burn severity --- canopy loss --- wildfire
Listing 1 - 4 of 4 |
Sort by
|