Remote Sensing of Forest Biomass Using GNSS Reflectometry

Santi, Emanuele; Paloscia, S.; Pettinato, S.; Fontanelli, Giacomo; Clarizia, Maria Paola; Comite, Davide; Dente, Laura; Guerriero, L.; Pierdicca, Nazzareno; Floury, Nicolas

doi:10.1109/jstars.2020.2982993

Cited by 47 publications

(35 citation statements)

References 49 publications

(63 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…because Artificial Neural Networks can learn the complex relationship between GNSS-R observables and surface geophysical parameters [54], ANN is applied in this study. Some studies have successfully used ANN for GNSS-R applications [41,[58][59][60][61]. In the application of forest biomass, the traditional method uses the traditional Reflectivity and location as input features.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

“…The implementation of low earth orbit (LEO) satellite missions for GNSS-R, e.g., the United Kingdom-Disaster Monitoring Constellation (UK-DMC), TechDemoSat-1 (TDS-1), and Cyclone Global Navigation Satellite System (CYGNSS), has demonstrated the potential of spaceborne GNSS-R for global coverage. Recently, the focus has been on AGB/CH retrieval using TDS-1 and CYGNSS data [40,41]. In general, the algorithms of these studies are based on GNSS-R observables, such as signal-to-noise ratio (SNR) and trailing edge (TE) derived from delay doppler maps (DDMs).…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, to estimate AGB/CH, the information of other parameters, e.g., surface roughness or soil moisture, is needed. For AGB/CH retrievals, Santi et al successfully applied ANN to forest biomass and achieved encouraging results with a RMSE of 76.4 Mg/ha for AGB estimation and a RMSE of 6.5 m for CH estimation [41]. However, the traditional observable method is heavily influenced by other parameters, especially soil moisture.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Improved Method for Pan-Tropical Above-Ground Biomass and Canopy Height Retrieval Using CYGNSS

et al. 2021

View full text Add to dashboard Cite

An improved method for retrieving Above-ground Biomass (AGB) and Canopy Height (CH) based on an observable from Cyclone Global Navigation Satellite System (CYGNSS), soil moisture from Soil Moisture Active Passive (SMAP) and location is proposed. The observable derived from CYGNSS is more sensitive to vegetation. The CYGNSS observable, soil moisture and the location are used as the input features of an Artificial Neural Network (ANN) to retrieve AGB and CH. The sensitivity analysis of the CYGNSS observable to target parameters shows that the proposed observable is more sensitive to AGB/CH than the conventional observable. The AGB/CH retrievals of the improved method show that it has better performance than that of the traditional method, especially in the areas with AGB in the range of 0 to100 Mg/ha and CH in the range of 0 to10 m. For AGB retrievals, the root mean square error (RMSE) and correlation coefficient are 64.84 Mg/ha and 0.80 in the range of 0 to 550 Mg/ha. Compared with the traditional method, the RMSE is decreased by 11.63%, while the correlation coefficient is increased by 5.26%. For CH retrievals, the RMSE and correlation coefficient are 5.97 m and 0.83 in the range of 0 to 45 m. The RMSE is decreased by 12.59%, while the correlation coefficient is increased by 5.06%. The analysis of the improved method in different areas shows that the performance of the improved method over the area with high vegetation is better than the area with low vegetation. The results obtained here further strengthens the capability of GNSS-R for global AGB/CH retrievals as well as different land cover areas.

show abstract

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Improved Method for Pan-Tropical Above-Ground Biomass and Canopy Height Retrieval Using CYGNSS

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Recently, some researchers have employed TDS-1 and CYGNSS data to study the potential of GNSS-R for evaluating forest biomass (Eroglu et al 2019;Carrenoluengo et al 2020). However, most of the existing works only focus on experimental analyses (such as ground-based and space-borne data), and less attention has been paid to the scattering mechanisms (Shah et al 2019;Kurum et al 2019;Eroglu et al 2019;Carrenoluengo et al 2020;Santi et al 2020;Ferrazzoli et al 2011). The experimental research without a physical basis is difficult to promote, and it greatly hinders the applications of SoOp-R over land surfaces.…”

Section: Introductionmentioning

confidence: 99%

Forest canopy scattering properties with signal of opportunity reflectometry: theoretical simulations

Calabia

et al. 2021

Geosci. Lett.

View full text Add to dashboard Cite

In recent years, signal of opportunity reflectometry (SoOp-R) has become a promising remote sensing technique. This emerging technique employs the reflected signals from existing Global Navigation Satellite System (GNSS) or communication satellites to estimate geophysical parameters for Earth observation, such as wind speed, altimetry, significant wave height, soil moisture, etc. While its application for forest canopy monitoring is still in the initial stage, there are still many unknown relations between vegetation parameters and actual observations, and a proper theoretical basis needs to be established for simulation and analysis of the different observation geometries. In this paper, we develop a bistatic scattering model with various polarizations at different frequency bands. Our improved model is based on the first-order radiative transfer equation, and is developed based on the wave synthesis technique, after which it can be used for circular polarization signals in bistatic radar systems, i.e. the typical configuration of SoOp-R. We analyze the simulations of the P (0.25–0.5 GHz), L (0.5–1.5 GHz), C (4–8 GHz), and X (8–12 GHz) bands at the backscattering, specular cone, bistatic scattering, and perpendicular planes. The contributions of the different components to the total scattering are also analyzed. The results show that the coherent scattering at the specular cone is larger than the non-coherent scattering, while trunk-dominated forest canopy has strong scattering at the aforementioned different directions. Variations of canopy parameters such as trunk and branch diameters, tree density, and vegetation water content are also simulated at the specular cone plane, showing strong dependence on the final bistatic scattering observation. The simulation results show that the SoOp-R technique has a great potential for monitoring of canopy parameters.

show abstract

“…Neural networks have a good performance in both fitting and prediction. Several studies use the combination of neural networks and empirical models to map soil moisture [17][18][19]. Mirsoleimani et al [20] proposed to simulate backscattering dataset with IEM and WCM models to train neural networks, and then used the Sentinel-1 data to retrieve moisture.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of a Neural Network Model and Two Empirical Models for Estimating Soil Moisture Based on Sentinel-1 Sar Data

Li¹,

Yan²,

Chen³

et al. 2020

PIER C

View full text Add to dashboard Cite

The objective of this paper is to propose an inversion model of soil moisture using a neural network and compare the performance of this method with two empirical models in soil moisture inversion. A wide dataset of backscattering coefficients extracted from Sentinel-1 images and in-situ soil surface parameter measurements (moisture content and roughness) are used. Since the available backscattering models have limited performances of describing the nonlinear relationship between soil parameters and backscatter coefficient, the retrieval of soil parameters from radar backscattering coefficient remains challenging. The proposed inversion method of a neural network is used for establishing this relationship. At the same time, two empirical models are employed to estimate the soil moisture for comparison. The results show that for most of the six measuring stations the inverted soil moisture with the neural network model has higher correlation coefficient with the in-situs oil moisture than those by the empirical models. Moreover, the neural network model inversion results under multipolarization input conditions are discussed in this paper. The results of stations 2, 4, and 5 show that R 2 of multi-polarization inputs are increased by 0.1928, 0.4821, and 0.2758, respectively, compared with those of single-polarization inputs.

show abstract

Remote Sensing of Forest Biomass Using GNSS Reflectometry

Cited by 47 publications

References 49 publications

An Improved Method for Pan-Tropical Above-Ground Biomass and Canopy Height Retrieval Using CYGNSS

An Improved Method for Pan-Tropical Above-Ground Biomass and Canopy Height Retrieval Using CYGNSS

Forest canopy scattering properties with signal of opportunity reflectometry: theoretical simulations

Performance Evaluation of a Neural Network Model and Two Empirical Models for Estimating Soil Moisture Based on Sentinel-1 Sar Data

Contact Info

Product

Resources

About