The BIOMASS Level 2 Prototype Processor: Design and Experimental Results of Above-Ground Biomass Estimation

Banda, Francesco; Giudici, Davide; Toan, Thuy Le; d’Alessandro, Mauro Mariotti; Papathanassiou, Konstantinos; Quegan, S.; Riembauer, Guido; Scipal, Klaus; Soja, Maciej J.; Tebaldini, Stefano; Ulander, Lars M. H.; Villard, Ludovic

doi:10.3390/rs12060985

Cited by 18 publications

(11 citation statements)

References 61 publications

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“…AGB model parameterization and validation. Previous studies 25,28,40,41,46 have validated the potential of the backscattered power at the 30 m layer to estimate AGB using the BP estimator. However, this paper focuses on the AGB estimation from three variables: backscattered power at the 30 m layer, FH and Q (shown in Fig.…”

Section: Airborne Lidar Datamentioning

confidence: 98%

“…Another solution is to decompose the TomoSAR backscatter contribution into ground and volume components 45 and then correlate the volume contribution to AGB. Further, recent studies based on removing ground-level contribution from interferometric pairs [46][47][48][49] have been shown to improve the results but fail to achieve the same level of accuracy as the 30 m TomoSAR backscatter intensity.…”

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confidence: 99%

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Mapping tropical forest aboveground biomass using airborne SAR tomography

Ramachandran

Saatchi

Tebaldini

et al. 2023

Sci Rep

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Mapping tropical forest aboveground biomass (AGB) is important for quantifying emissions from land use change and evaluating climate mitigation strategies but remains a challenging problem for remote sensing observations. Here, we evaluate the capability of mapping AGB across a dense tropical forest using tomographic Synthetic Aperture Radar (TomoSAR) measurements at P-band frequency that will be available from the European Space Agency’s BIOMASS mission in 2024. To retrieve AGB, we compare three different TomoSAR reconstruction algorithms, back-projection (BP), Capon, and MUltiple SIgnal Classification (MUSIC), and validate AGB estimation from models using TomoSAR variables: backscattered power at 30 m height, forest height (FH), backscatter power metric (Q), and their combination. TropiSAR airborne campaign data in French Guiana, inventory plots, and airborne LiDAR measurements are used as reference data to develop models and calculate the AGB estimation uncertainty. We used univariate and multivariate regression models to estimate AGB at 4-ha grid cells, the nominal resolution of the BIOMASS mission. Our results show that the BP-based variables produced better AGB estimates compared to their counterparts, suggesting a more straightforward TomoSAR processing for the mission. The tomographic FH and AGB estimation have an average relative uncertainty of less than 10% with negligible systematic error across the entire biomass range (~ 200–500 Mg ha−1). We show that the backscattered power at 30 m height at HV polarization is the best single measurement to estimate AGB with significantly better accuracy than the LiDAR height metrics, and combining it with FH improved the accuracy of AGB estimation to less than 7% of the mean. Our study implies that using multiple information from P-band TomoSAR data from the BIOMASS mission provides a new capability to map tropical forest biomass and its changes accurately.

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Section: Airborne Lidar Datamentioning

confidence: 98%

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confidence: 99%

Mapping tropical forest aboveground biomass using airborne SAR tomography

Ramachandran

Saatchi

Tebaldini

et al. 2023

Sci Rep

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“…For the wavelength of the longest simulated harmonic, λ max = 2π/k min in table 2, we have F/λ max ≈ 5, so this is a case of mid-scale perturbations that have the strongest effect on the image, see section 2.1. Taking into account that the perturbation amplitude exceeds one, see (47), it is not surprising to see that the SAR image is strongly affected by these perturbations, see the blue curves in figure 3. Note that for the Earth's ionosphere, the maximum expectation of the fluctuation magnitude may reach one, see [4, table 1.2], but since the turbulence is stochastic, we may encounter realizations with even higher lever of perturbations.…”

Section: Numerical Examplesmentioning

confidence: 99%

Vertical autofocus for the phase screen in a turbulent ionosphere

Gilman

Tsynkov

2023

Inverse Problems

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The performance of spaceborne synthetic aperture radars (SAR) is affected by the Earth's ionosphere. In particular, the ionospheric turbulence causes phase perturbations of the SAR signals, which may lead to image distortions. A convenient way to model those phase perturbations is by means of a phase screen. The latter is an infinitesimally thin layer positioned at a certain elevation above the Earth's surface. The radar signal acquires an instant perturbation once its trajectory intersects the screen. The trajectory is a ray between the antenna and the target, and the magnitude of the perturbation is equal to the screen density at the intersection point. The density is a bivariate function of the coordinates along the screen. The coordinates of a specific intersection point are determined by the ray itself, as well as the screen elevation. Thus, the magnitude of the phase perturbation explicitly depends on the screen elevation. Accordingly, to compensate for the resulting image distortions one should be able to determine the elevation of the screen. In the paper, we develop an algorithm of vertical autofocus that derives this elevation from the received SAR data, given a pair of point scatterers in the target area. The proposed algorithm exploits a modification of the coherent interferometric (CINT) imaging that was previously employed to reduce the effect of phase errors due to the trajectory uncertainty. In our analysis, we highlight the differences between this case and transionospheric propagation.

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“…A consequence of the increased canopy penetration is that the ground contributes significantly to the total backscattered field, either through direct rough surface scattering or double-bounce scattering by the ground and tree trunks [10]. Variations in ground roughness, slope and soil moisture can thus introduce a significant bias in the estimated biomass [11]. The BIOMASS mission is designed for fully-polarimetric, interferometric and tomographic imaging.…”

Section: Introductionmentioning

confidence: 99%

“…Very little is known about the characteristics of such temporal variations, especially in boreal forests, making it difficult to design biomass estimation algorithms that are robust to temporal variations. The lack of a quantitative understanding of environmentally induced backscatter variations is also the main issue in developing algorithms for forest degradation detection [11].…”

Section: Introductionmentioning

confidence: 99%

Temporal Characteristics of P-Band Tomographic Radar Backscatter of a Boreal Forest

Monteith

Ulander

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Temporal variations in synthetic aperture radar (SAR) backscatter over forests are of concern for any SAR mission with the goal of estimating forest parameters from SAR data. In this article, a densely sampled, two-year long time series of P-band (420 to 450 MHz) boreal forest backscatter, acquired by a tower-based radar, is analyzed. The experiment setup provides time series data at multiple polarizations. Tomographic capabilities allow the separation of backscatter at different heights within the forest. Temporal variations of these multipolarized, tomographic radar observations are characterized and quantified. The mechanisms studied are seasonal variations, effects of freezing conditions, diurnal variations, effects of wind and the effects of rainfall on backscatter. An emphasis is placed on upper-canopy backscatter, which has been shown to be a robust proxy for forest biomass. The canopy backscatter was more stable than ground-level backscatter during non-frozen conditions, supporting forest parameter retrieval approaches based on tomography or interferometric ground notching. Large backscatter variations during frozen conditions, which may be detected using cross-polarised backscatter observations, can result in large errors in forest parameter estimates. Diurnal backscatter variations observed during hot periods were likely connected to tree water transport and storage mechanisms. Backscatter changes were also observed during strong winds. These variations were small in comparison to the variations due to freeze-thaw and soil moisture changes and should not result in significant forest parameter estimation errors. The presented results are useful for designing physically based and semiempirical scattering models that account for temporal changes in scattering characteristics.

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The BIOMASS Level 2 Prototype Processor: Design and Experimental Results of Above-Ground Biomass Estimation

Cited by 18 publications

References 61 publications

Mapping tropical forest aboveground biomass using airborne SAR tomography

Mapping tropical forest aboveground biomass using airborne SAR tomography

Vertical autofocus for the phase screen in a turbulent ionosphere

Temporal Characteristics of P-Band Tomographic Radar Backscatter of a Boreal Forest

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