Remote Sensing of Biophysical Parameters

• Format: eBook
• Language: English
• Published: Basel 2022
• Subjects: Research & information: general; 6SV; active learning; agriculture; airborne laser scanning (ALS); artificial neural networks; ASD Field Spec; biophysical parameters (LAI; burn severity; canopy chlorophyll content; canopy loss; canopy water content; CCC; climate data records (CDR); clumping index (CI); Discrete Anisotropic Radiative Transfer (DART) model; EnMAP; equivalent water thickness; FAPAR; FAPAR); fluorescence; forest; fraction of photosynthetically active radiation absorbed by vegetation (FPAR); FVC; GPR; hyperspectral; in vivo; INFORM; invasive vegetation; LAI; Landsat 8; LaSRC; LCC; lead ions; leaf area index; leaf area index (LAI); LEDAPS; machine learning; meteosat second generation (MSG); Moderate Resolution Imaging Spectroradiometer (MODIS); MODIS; multispectral sensor; NDVI; PROSAIL; rapeseed crop; remote sensing indices; riparian; SAIL; Satellite Application Facility for Land Surface Analysis (LSA SAF); Sentinel-2; SEVIRI; site-specific farming; soil albedo; spaceborne laser scanning (SLS); spectrometry; spectroscopy; SREM; stochastic spectral mixture model (SSMM); surface reflectance; terrestrial laser scanning (TLS); the fraction of radiation absorbed by photosynthetic components (FAPARgreen); three-dimensional radiative transfer model (3D RTM); triple-source; uncertainty assessment; unmanned aircraft vehicle; vegetation indices; vegetation radiative transfer model; vertical foliage profile (VFP); wildfire; woody area index (WAI)
• Online Access: Open Access: DOAB: description of the publication; Open Access: DOAB, download the publication

 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).