Vegetation canopy height retrieval using L1 and L5 airborne GNSS-R

J. F. Munoz-Martin, D. Pascual, R. Onrubia, H. Park, A. Camps, C. Rudiger, J. P. Walker, A. Monerris

Research output: Contribution to journalArticleResearchpeer-review

5 Citations (Scopus)

Abstract

Vegetation canopy height is one of the important remote sensing parameters related to forests'; structure, and it can be related to the biomass and the carbon stock. Global Navigation Satellite System - Reflectometry (GNSS-R) has proven capable to retrieve vegetation information at a moderate resolution from space (20-65 km) using L1 C/A signals. In this study, data retrieved by the airborne Microwave Interferometric Reflectometer (MIR) GNSS-R instrument at L1 and L5 are compared to the Global Forest Canopy Height product, with a spatial resolution of 30 m. This work analyzes the waveforms measured at both bands, and the correlation of the waveform width and the reflectivity values to the canopy height product. A neural network algorithm is used for the retrieval, showing that the combination of the reflectivity and the waveform width allows to estimate the canopy height information at a very high resolution, with a root-mean-square error of 4.25 m and 4.07 m at L1 and L5, respectively, which is an error about 14% of the actual canopy height.

Original languageEnglish
Article number2502405
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Volume19
DOIs
Publication statusPublished - 29 Nov 2021

Keywords

  • Airborne
  • Artificial Neural Network
  • Canopy Height
  • Correlation
  • Global navigation satellite system
  • Global Positioning System
  • GNSS-R
  • L1 and L5
  • Scattering
  • Sea measurements
  • Spatial resolution
  • Vegetation
  • Vegetation mapping

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