Remote Sensing of Forest Biomass Using GNSS Reflectometry
| Autor: | Giacomo Fontanelli, Emanuele Santi, Leila Guerriero, Nicolas Floury, Nazzareno Pierdicca, Maria Paola Clarizia, Laura Dente, Davide Comite, Simone Pettinato, Simonetta Paloscia |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Settore ING-INF/02
Atmospheric Science Global Navigation Satellite System (GNSS) Reflectometry 010504 meteorology & atmospheric sciences Geophysics. Cosmic physics Artificial neural networks (ANNs) 0211 other engineering and technologies Satellite system 02 engineering and technology Reflectivity 01 natural sciences TechDemoSat-1 (TDS-1) Soil symbols.namesake Sensitivity forest biomass Biomass Altimeter Computers in Earth Sciences Reflectometry TC1501-1800 021101 geological & geomatics engineering 0105 earth and related environmental sciences Remote sensing Vegetation mapping Radiometer QC801-809 Cyclone Satellite System (CyGNSS) artificial neural networks (anns) cyclone satellite system (cygnss) global navigation satellite system (gnss) reflectometry soil moisture active passive (smap) techdemosat-1 (tds-1) Forestry Inversion (meteorology) Global Map Global navigation satellite system GNSS reflectometry Ocean engineering Soil Moisture Active Passive (SMAP) symbols Environmental science Doppler effect |
| Zdroj: | IEEE journal of selected topics in applied earth observations and remote sensing 13 (2020): 2351–2368. doi:10.1109/JSTARS.2020.2982993 info:cnr-pdr/source/autori:Santi, Emanuele; Paloscia, Simonetta; Pettinato, Simone; Fontanelli, Giacomo; Clarizia, Maria Paola; Comite, Davide; Dente, Laura; Guerriero, Leila; Pierdicca, Nazzareno; Floury, Nicolas/titolo:Remote Sensing of Forest Biomass Using GNSS Reflectometry/doi:10.1109%2FJSTARS.2020.2982993/rivista:IEEE journal of selected topics in applied earth observations and remote sensing (Print)/anno:2020/pagina_da:2351/pagina_a:2368/intervallo_pagine:2351–2368/volume:13 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 13, Pp 2351-2368 (2020) |
| ISSN: | 2151-1535 1939-1404 |
| DOI: | 10.1109/jstars.2020.2982993 |
| Popis: | In this study, the capability of Global Navigation Satellite System Reflectometry in evaluating forest biomass from space has been investigated by using data coming from the TechDemoSat-1 (TDS-1) mission of Surrey Satellite Technology Ltd. and from the Cyclone Satellite System (CyGNSS) mission of NASA. The analysis has been first conducted using TDS-1 data on a local scale, by selecting five test areas located in different parts of the Earth's surface. The areas were chosen as examples of various forest coverages, including equatorial and boreal forests. Then, the analysis has been extended by using CyGNSS to a global scale, including any type of forest coverage. The peak of the Delay Doppler Map calibrated to retrieve an “equivalent” reflectivity has been exploited for this investigation and its sensitivity to forest parameters has been evaluated by a direct comparison with vegetation optical depth (VOD) derived from the Soil Moisture Active Passive L-band radiometer, with a pantropical aboveground biomass (AGB) map and then with a tree height (H) global map derived from the Geoscience Laser Altimeter System installed on-board the ICEsat satellite. The sensitivity analysis confirmed the decreasing trend of the observed equivalent reflectivity for increasing biomass, with correlation coefficients 0.31 ≤ R ≤ 0.54 depending on the target parameter (VOD, AGB, or H) and on the considered dataset (local or global). These correlations were not sufficient to retrieve the target parameters by simple inversion of the direct relationships. The retrieval has been therefore based on Artificial Neural Networks making it possible to add other inputs (e.g., the incidence angle, the signal to noise ratio, and the lat/lon information in case of global maps) to the algorithm. Although not directly correlated to the biomass, these inputs helped in improving the retrieval accuracy. The algorithm was tested on both the selected areas and globally, showing a promising ability to retrieve the target parameter, either AGB or H, with correlation coefficients R ≃ 0.8. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|