Reprint

Remote Sensing of Biophysical Parameters

Edited by
August 2022
274 pages
  • ISBN978-3-0365-4901-9 (Hardback)
  • ISBN978-3-0365-4902-6 (PDF)

This book is a reprint of the Special Issue Remote Sensing of Biophysical Parameters that was published in

Engineering
Environmental & Earth Sciences
Summary

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).

Format
  • Hardback
License and Copyright
© 2022 by the authors; CC BY-NC-ND license
Keywords
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; FVC; 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; leaf area index (LAI); Sentinel-2; forest; machine learning; vegetation radiative transfer model; Discrete Anisotropic Radiative Transfer (DART) model; MODIS; leaf area index (LAI); fraction of photosynthetically active radiation absorbed by vegetation (FPAR); three-dimensional radiative transfer model (3D RTM); uncertainty assessment; leaf area index (LAI); vertical foliage profile (VFP); terrestrial laser scanning (TLS); airborne laser scanning (ALS); spaceborne laser scanning (SLS); riparian; invasive vegetation; burn severity; canopy loss; wildfire

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