Comparison of Three Active Microwave Models of Forest Growing Stock Volume Based on the Idea of the Water Cloud Model

Abstract

Forest growing stock volume (GSV) is an essential aspect of ecological carbon stock monitoring. The successive launches of spaceborne microwave satellites have provided a broader way to use microwave remote sensing to monitor forest accumulation. Currently, the inversion parameterization models of active microwave remote sensing stock volume mainly include the interferometric water cloud (IWCM), BIOMASAR, and Siberia. Among them, the IWCM introduces backscattering and coherence, the BIOMASAR model only introduces backscattering, and the Siberia model only introduces coherence. Although these three models combine the backscatter coefficient and coherence of SAR to estimate volume accumulation, the performance of the models has not been evaluated at the same time in the same area. Therefore, this article starts from the perspective of the three combinations of coherence and backscattering, relies on three models that do not require measured data, and evaluates the accuracy of the models’ overall inversion of GSV. In addition, we combine precipitation meteorological information, vegetation types, and seasonal variation to separately explore model performance. The comparison results show that the IWCM model is relatively stable in the process of stock volume inversion and is more sensitive to the vegetation types of coniferous and deciduous forests. The influence of seasons and precipitation on the model is weak, and the accuracy of the multi-time-series model is slightly improved. The Siberia model has a good storage volume inversion effect in this study area, but the multiple time series did not improve the model accuracy. The BIOMASAR model is simple, and its performance was slightly inferior in this study area. Precipitation can negatively affect BIOMASAR. The model results for multiple time series outperform those for single time. In summary, the stability of IWCM is more suitable for research with unknown information. The BIOMASAR model is simple, does not require coherence calculations, and is ideal for the estimation of large-scale national or world-level storage distributions. The Siberian model performs better in small regions and smaller spatiotemporal baselines.

Backscatter/FVC Space: A Method for Estimating Forest Growing Stock Volume Combining SAR and Optical Remote Sensing

“…For the evaluation of experimental results, refer to the relevant literature [5], [12]. We set three evaluation indicators, including Residual, Root Mean Square Error (RMSE) and Relative Root Mean Square Error (RRMSE).…”

“…The application of remote sensing in forests can solve this problem. Remote sensing of GSV can be divided into active remote sensing method and passive remote sensing method [1], [2], [3], [4], [5]. Passive remote sensing method is represented by optical remote sensing, which usually includes the processes of obtaining the spectral reflectance bands of ground objects, calculating various spectral indexes through band calculation, and establishing the relationship between in-situ measured GSV and spectral reflectance/indexes to estimate GSV [6], [7].…”