Remote Sensing of Forest Biomass Using GNSS Reflectometry

被引:38
|
作者
Santi, Emanuele [1 ]
Paloscia, Simonetta [1 ]
Pettinato, Simone [1 ]
Fontanelli, Giacomo [1 ]
Clarizia, Maria Paola [2 ]
Comite, Davide [3 ]
Dente, Laura [4 ]
Guerriero, Leila [4 ]
Pierdicca, Nazzareno [3 ]
Floury, Nicolas [5 ]
机构
[1] CNR, Inst Appl Phys, I-50019 Florence, Italy
[2] Deimos Space UK, Didcot OX11 0QR, Oxon, England
[3] Univ Roma La Sapienza, Dept Informat Engn Elect & Telecommun, I-00184 Rome, Italy
[4] Univ Roma Tor Vergata, Dipartimento Ingn Civile & Ingn Informat, I-00133 Rome, Italy
[5] European Space Agcy, European Space Res & Technol Ctr, NL-2201 AZ Noordwijk, Netherlands
关键词
Biomass; Forestry; Vegetation mapping; Sensitivity; Global navigation satellite system; Soil; Reflectivity; Artificial neural networks (ANNs); Cyclone Satellite System (CyGNSS); forest biomass; Global Navigation Satellite System (GNSS) Reflectometry; Soil Moisture Active Passive (SMAP); TechDemoSat-1 (TDS-1); ARTIFICIAL NEURAL-NETWORKS; VEGETATION OPTICAL DEPTH; SOIL-MOISTURE; L-BAND; SCATTERING; REFLECTIVITY; RADAR; SIGNALS; MODEL; PARIS;
D O I
10.1109/JSTARS.2020.2982993
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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 similar or equal to 0.8.
引用
收藏
页码:2351 / 2368
页数:18
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