Soil Moisture and Forest Biomass retrieval on a global scale by using CyGNSS data and Artificial Neural Networks

被引：9

作者：

Santi, E. ^{[1
]}

Pettinato, S. ^{[1
]}

Paloscia, S. ^{[1
]}

Clarizia, M. P. ^{[2
]}

Dente, L. ^{[3
]}

Guerriero, L. ^{[3
]}

Comite, D. ^{[4
]}

Pierdicca, N. ^{[4
]}

机构：

[1] CNR IFAC, Via Madonna del Piano 10, I-50019 Florence, Italy

[2] Deimos Space UK, Harwell, Berks, England

[3] Univ Roma Tor Vergata, Rome, Italy

[4] Univ Roma La Sapienza, DIET, Rome, Italy

来源：

IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2020年

关键词：

GNSS-R; CyGNSS; Soil Moisture; Vegetation Opacity; Artificial Neural Networks;

D O I：

10.1109/IGARSS39084.2020.9323896

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This study aims at assessing the potential of the NASA's Cyclone GNSS (CyGNSS) data for observing SM and forest biomass. As reference values for the comparison, global datasets of Vegetation Optical Depth (VOD) and SM derived from NASA's Soil Moisture Active and Passive mission SMAP have been considered. The results of the sensitivity analysis suggested exploiting the CyGNSS capabilities in estimating VOD and SM by setting-up prototype retrieval algorithms based on Artificial Neural Networks (ANN).

引用

页码：5905 / 5908

页数：4

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