Retrieval of Boreal Forest Heights Using an Improved Random Volume over Ground (RVoG) Model Based on Repeat-Pass Spaceborne Polarimetric SAR Interferometry: The Case Study of Saihanba, China

Mao, Yu; Michel, Opelele Omeno; Yu, Ying; Wang, Fan; Sui, Ao; Liu, Zhihui; Wu, Guoming

doi:10.3390/rs13214306

Cited by 11 publications

(6 citation statements)

References 67 publications

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“…These factors are the sole remaining coherence components associated with the vertical structure and forest height [37]. In the ALOS-2 data, we modified the multiple iteration algorithm of [18]. Also, we introduced the CRITIC method to improve the ground phase estimation accuracy, resulting in the modified CRITIC-ITDRvoG algorithm as in Equation (20).…”

Section: Iterative Process Of Temporal Decorrelationmentioning

confidence: 99%

“…A geometric parameter known as the "narrowness ratio" is introduced to quantify this observation. This ratio represents the relationship between the long and short axes of the coherence ellipse and was initially introduced in the literature of [18]. The ground phase estimation error decreases as the narrowness ratio decreases, bringing the results closer to the actual forest.…”

Section: Limitations Of the Critic-wls Ground Phase Improvement Algor...mentioning

confidence: 99%

“…Yongxin Zhang et al showed that datasets with a long spatial baseline and long wavelength have a positive effect on the inversion of forest height [17]. Mao et al [18]. suggested a multiple iteration temporal decoherence approach for the spaceborne ALOS-2 data due to its short baseline and tiny vertical wavenumber.…”

Section: Introductionmentioning

confidence: 99%

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Inversion of Boreal Forest Height Using the CRITIC Weighted Least Squares Three-Stage Temporal Decorrelation Iterative Algorithm

Sui,

Fan

2024

Remote Sensing

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The inversion of forest height using the RVoG (Random Volume over Ground) model is susceptible to overestimation or underestimation due to three error sources, propagating inaccuracies to the estimated forest height. Furthermore, variations in the height and density of the scenario could impact how well the RVoG three-stage inversion performs. This work utilizes the L-band single-baseline full polarization interferometric dataset as its basis. It optimally applies the CRITIC (Criteria Importance Through Intercriteria Correlation) method to the first stage of a three-stage process. This approach aims to overcome the issues mentioned above and enhance the accuracy of forest parameter estimation. A CRITIC weighted least squares temporal decoherence iterative algorithm is also proposed for the characteristics of the spaceborne data, in combination with the temporal decoherence algorithm of previous research. The proposed approach is tested and applied to both simulated and actual data. The optimization approach is first assessed using four simulated datasets that simulate coniferous forests with different densities and heights. The preliminary findings suggest that optimizing the complex coherence fitting process through the weighted least squares method enhances the accuracy of ground phase estimation and, consequently, improves the accuracy of the three-stage approach for inverting forest height. The ground phase estimation results for low forest height consistently remained within 0.02 rad, with a root mean square error (RMSE) below 0.05 rad, and no saturation occurred with increasing forest density. The enhanced algorithm outperforms the traditional technique in terms of accuracy in ground phase estimation. Subsequently, the optimized approach is applied to ALOS-2 spaceborne data, proving more successful than the conventional algorithm in reducing the RMSE of forest height. The findings illustrate the method’s superior inversion performance, obtaining an accuracy exceeding 80% in both the test and validation sets. The validation set’s RMSE is approximately 2.5 m, and the mean absolute error (MAE) is within 2 m. Moreover, it is observed that to counteract the uncertainty in temporal decoherence induced by climate change, a larger temporal baseline necessitates a larger random motion compensation term and phase offset term.

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Section: Iterative Process Of Temporal Decorrelationmentioning

confidence: 99%

Section: Limitations Of the Critic-wls Ground Phase Improvement Algor...mentioning

confidence: 99%

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Inversion of Boreal Forest Height Using the CRITIC Weighted Least Squares Three-Stage Temporal Decorrelation Iterative Algorithm

Sui,

Fan

2024

Remote Sensing

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“…Since different AGB inversion schemes have different topographic correction processes, the inversion technology of aboveground biomass is first analyzed. At present, AGB estimation techniques based on SAR data can be divided into: (1) Statistical modeling method based on SAR characteristic parameters (including backscattering coefficient [16][17][18][19], polarization decomposition parameter [20,21], interference coherence [22][23][24], and tomographic vertical structure parameter [25]) and (2) forest canopy height extracted from interferometric SAR (InSAR) [26][27][28], polarimetric interferometric SAR (PolInSAR) [29][30][31] and tomographic SAR (TomoSAR) [32]; and then indirect AGB estimation using an allometric equation [33,34]. Among them, the accuracy of indirect inversion of forest AGB based on an allometric equation is heavily dependent on the inversion accuracy of the growth equation and forest canopy height.…”

Section: Introductionmentioning

confidence: 99%

Inversion of Forest above Ground Biomass in Mountainous Region Based on PolSAR Data after Terrain Correction: A Case Study from Saihanba, China

Nie,

Hu,

et al. 2024

Remote Sensing

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Accurate retrieval of forest above ground biomass (AGB) based on full-polarization synthetic aperture radar (PolSAR) data is still challenging for complex surface regions with fluctuating terrain. In this study, the three-step process of radiometric terrain correction (RTC), which includes polarization orientation angle correction (POAC), effective scattering area correction (ESAC), and angular variation effect correction (AVEC), is adopted as the technical framework. In the ESAC stage, a normalized correction factor is introduced based on local incidence angle and radar incidence angle to achieve accurate correction of PolSAR data information and improve the inversion accuracy of forest AGB. In order to verify the validity and robustness of this research method, the full-polarization SAR data of ALOS-2 and the ground measured AGB data collected in the Saihanba research area in 2020 were used for experiments. Our findings revealed that in the ESAC phase, the introduction of the normalized correction factor can effectively eliminate the ESA phenomenon and improve the correlation coefficients of the backscatter coefficient and AGB. Taking the data of 25 July 2020 as an example, ESAC increases the correlation coefficients between AGB and the backscattering coefficients of HH, HV, and VV polarization channels by 0.343, 0.296, and 0.382, respectively. In addition, the RTC process has strong robustness in different AGB statistical models and different date PolSAR data.

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“…The forest ecosystem is the main component of the terrestrial ecosystem [1,2]. Estimating the distribution and change of carbon storage in forest ecosystems is helpful for understanding the relationship between carbon sources and sinks [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Time-Series Forest Leaf Area Index (LAI) Based on Sentinel-2 and MODIS

Yang,

Huang,

Qing

et al. 2023

Applied Sciences

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The LAI is a key parameter used to describe the exchange of material and energy between soil, vegetation and the atmosphere. It has become an important driving datum in the study of carbon and water cycle mechanism models at many regional scales. In order to obtain high temporal resolution and high spatial resolution LAI products, this study proposed a method to combine the high temporal resolution of MODIS LAI products with the high spatial resolution of Sentinel-2 data. The method first used the LACC algorithm to smooth the LAI time-series data and extracted the normalized growth curve of the MODIS LAI of forest and used this curve to simulate the annual variation of the LAI. Secondly, it estimated the LAI at the period of full leaf spread based on the traditional remote sensing statistical model and Sentinel-2 remote sensing data as the maximum value of the forest LAI in the study area and used it to control the LAI growth curve. Finally, the time-series LAI data set was created by multiplying the maximum LAI by the normalized forest LAI growth curve. The results indicate that: (1) the remote sensing statistical estimation model of LAI was developed using the atmospherically resistant vegetation index ARVI (R2 = 0.494); (2) the MODIS LAI normalized growth curve keeps a good level of agreement with the actual variation. This study provides a simple and efficient method for obtaining effective time-series forest LAI data for the scope of small- and medium-sized areas.

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Retrieval of Boreal Forest Heights Using an Improved Random Volume over Ground (RVoG) Model Based on Repeat-Pass Spaceborne Polarimetric SAR Interferometry: The Case Study of Saihanba, China

Cited by 11 publications

References 67 publications

Inversion of Boreal Forest Height Using the CRITIC Weighted Least Squares Three-Stage Temporal Decorrelation Iterative Algorithm

Inversion of Boreal Forest Height Using the CRITIC Weighted Least Squares Three-Stage Temporal Decorrelation Iterative Algorithm

Inversion of Forest above Ground Biomass in Mountainous Region Based on PolSAR Data after Terrain Correction: A Case Study from Saihanba, China

Estimation of Time-Series Forest Leaf Area Index (LAI) Based on Sentinel-2 and MODIS

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