Based on forest canopy height inversion theory, the height of ambiguity (HoA) PolInSAR data has an important influence on the final invers...
Based on forest canopy height inversion theory, the height of ambiguity (HoA) PolInSAR data has an important influence on the final inversion results. HoA reflects the height change caused by an interference phase change of 2Ï€, with low forests requiring smaller HoA and taller forests requiring larger HoA; in contrast, multibaseline PolInSAR can effectively solve this problem. Multibaseline PolInSAR has the advantage of more baseline combinations within the same observation unit relative to single-baseline data. Among these combinations, the best baseline needs to be selected to invert the forest height. Based on the RVoG model, the distribution area of the complex coherence within the complex plane is approximately elliptical;
accordingly, the baseline that best fits the assumptions of the RVoG model can be determined among several baseline combinations considering factors such as the coherence separation, coherence magnitude, and coherence region shape. In previous studies, we compared the differences in forest height inversion accuracy between baseline selection methods using the RVoG model and found that the results of baseline selection via the product of average coherence magnitude and separation (PROD) method were the most satisfactory [22,23,36,37]. In this study, we used the PROD method to select baselines (Equation (4)), using the product of coherence separation degree and average coherence amplitude as the judgment criterion; when the product of the coherence separation degree and coherence amplitude corresponding to the baseline reaches its maximum value, it is more consistent with the RVoG model hypothesis. PROD = absγH − γL × absγH + γL (4)
where γL denotes the complex coherence near the surface. 2.4. Error Source Analysis of Underestimation and Overestimation in the RVoG Model 2.4.1. Analysis of the Error Sources of Overestimation for Low Canopy The RVoG model relies on polarized interferometric features to estimate forest canopy height from PolInSAR data. However, temporal decorrelation effects are not considered in this model, and the contribution of temporal decorrelation has an important impact on forest parameter estimation in real situations. Therefore, two distinct temporal decorrelation processes can be introduced in the RVoG model: γTV, which denotes the temporal decorrelation coefficient associated with volume, and γTG, which denotes the temporal decorrelation coefficient of ground scatter [38,39], as shown in Equation (5): γ(ω) = e jÏ•0 γvγTV + γTGm(ω) 1 + m(ω) (5) Remote Sens. 2022, 14, 6145 6 of 27 According to Equation (5), the total temporal decorrelation depends on the groundto–volume magnitude ratio (m(ω)),
which defines the relationship between the overall temporal decorrelation and the ground–volume magnitude ratio. Thus, areas of low vegetation with low forest depression and a high ground-to-volume magnitude ratio tend to be severely affected by temporal decorrelation. In contrast, taller vegetation areas with more forest depression and lower ground–to-volume magnitude ratios tend to experience less impact from temporal decorrelation. When the temporal baseline is relatively short (less than one hour), the surface scatterers on the ground surface can be assumed to be constant, i.e., the dielectric constant does not change, and γTG = 1.
Thus, the most common temporal decorrelation contribution of forests is wind-induced leaf oscillation. The distribution of the volume coherence (γv-obv) and the ground phase (ϕ0) in the unit circle are indicated by red dots in Figure 1 when there is no temporal decorrelation. Considering the effects of temporal decorrelation, volume coherence is more severely affected by this issue, especially in the case of low forests. With an increase in the temporal decorrelation factor of volume scattering (γTV), the volume coherence (γv-obv) shifts to γvγt-obv in the direction of the center of the unit circle [20,21], causing the ground phase estimated by the RVoG model also to shift (yellow dots in Figure 2), at which point the ground phase calculated by the RVoG model is expressed by ϕ0-bias. Figure 2 shows that the volume and ground phases are misestimated as a result of the effects of temporal decorrelation, which leads to an increase in phase center height of the volume coherence and ultimately leading to an overestimation of the forest height.
No comments