Optical remote sensing can effectively capture 2-dimensional (2D) forest information, such as woodland area and percentage forest cover. However, accurately estimating forest vertical-structure relevant parameters such as height using optical images remains challenging, which leads to low accuracy of estimating forest stocks like biomass and carbon stocks. Thus, accurately obtaining vertical structure information of forests has become a significant bottleneck in the application of optical remote sensing to forestry. Microwave remote sensing such as synthetic aperture radar (SAR) and polarimetric SAR provides the capability to penetrate forest canopies with the L-band signal, and is particularly adept at capturing the vertical structure information of forests, which is an alternative ideal remote-sensing data source to overcome the aforementioned limitation. This paper utilizes the Citexs data analysis platform, along with the CNKI and PubMed databases, to investigate the advancements of applying L-band SAR technology to forest canopy penetration and structure-parameter estimation, and provides a comprehensive review based on 58 relevant articles from 1978 to 2024 in the PubMed database. The metrics, including annual publication numbers, countries/regions from which the publications come, institutions, and first authors, with the visualization of results, were utilized to identify development trends. The paper summarizes the state of the art and effectiveness of L-band SAR in addressing the estimation of forest height, moisture, and forest stocks, and also examines the penetration depth of the L-band in forests and highlights key influencing factors. This review identifies existing limitations and suggests research directions in the future and the potential of using L-band SAR technology for forest parameter estimation.

There is a large amount of remote sensing data available for land use and land cover (LULC) classification and thus optimizing selection of remote sensing variables is a great challenge. Although many methods such as Jeffreys–Matusita (JM) distance and random forests (RF) have been developed for this purpose, the existing methods ignore correlation and information duplication among remote sensing variables. In this study, a novel approach was proposed to improve the measures of potential class separability for the selection of remote sensing variables by taking into account correlations among the variables. The proposed method was examined with a total of thirteen spectral variables from a Gaofen-1 image, three class separability measures including JM distance, transformed divergence and B-distance and three classifiers including Bayesian discriminant (BD), Mahalanobis distance (MD) and RF for classification of six LULC types at the East Dongting Lake of Hunan, China. The results showed that (1) The proposed approach selected the first three spectral variables and resulted in statistically stable classification accuracies for three improved class separability measures. That is, the classification accuracies using three or more spectral variables statistically did not significantly differ from each other at a significant level of 0.05; (2) The statistically stable classification accuracies obtained by integrating MD and BD classifiers with the improved class separability measures were also statistically not significantly different from those by RF; (3) The numbers of the selected spectral variables using the improved class separability measures to create the statistically stable classification accuracies by MD and BD classifiers were much smaller than those from the original class separability measures and RF; and (4) Three original class separability measures and RF led to similar ranks of importance of the spectral variables, while the ranks achieved by the improved class separability measures were different due to the consideration of correlations among the variables. This indicated that the proposed method more effectively and quickly selected the spectral variables to produce the statistically stable classification accuracies compared with the original class separability measures and RF and thus improved the selection of the spectral variables for the classification.

Based on the generated database of 413 and 490 plots of biomass of Populus spp. and Betula spp. in Eurasia, statistically significant changes in the structure of forest stand biomass were found with shifts in January temperatures and average annual precipitation. When analyzing harvest data, the propeller-shaped biomass patterns in the gradients of average annual precipitation and average January temperatures are obtained, which are common for both deciduous species. Correspondingly, Populus and Betula forests show a regularity common to the biomass components: in the cold zones the precipitation increase leads to the increase of biomass, and in the warm ones to their decrease. In wet areas, the increase of temperature causes the decrease of biomass, and in dry areas, it causes their increase. In accordance with the law of the limiting factor by Liebig-Shelford, it is shown that both an decrease in temperature in dry conditions and a increase in precipitation in a warm climate lead to a decrease in the biomass of trees.

Currently, the problem of the impact of climate change on the productivity of forest ecosystems and their carbon-depositing capacity is far from being solved. Therefore, this paper presents the models for the stand biomass of the two-needled subgenus’ (Pinus spp.) and the genus Picea spp.’s trends along the trans-Eurasian hydrothermal gradients, designed for pure stands in a number of 2110- and 870-sample plots with Pinus and Picea correspondingly. It was found that in the case of an increase in mean winter temperatures by 1 °C, pine and spruce respond by increasing the biomass of most components, and in the case of an increase in the annual sum of precipitation by 100 mm, the total, aboveground, stem and root biomasses of pine and spruce react the same way, but crown biomass reacts in the opposite way. Therefore, all identified trends are species-specific.

SAR images with two polarizations show strong potential for mapping forest stock volume (FSV) combined with limited samples. However, accurately mapping FSV still presents challenges in selecting the optimal acquisition date to obtain the SAR images during specific phenological stages within the annual forest cycle (growth and dormant stages). To clarify the impacts of phenological stages within the annual cycle on FSV mapping, SAR images with various polarization models and bands (Sentinel-1(S), GaoFen-3(GF-3 (G)) and ALOS-2(A)) were acquired within the growth and dormant stages of an annual cycle in a boreal evergreen coniferous forest (Chinese pine) and a deciduous coniferous forest (Larch). Subsequently, single-band (G, S, and A) and multi-band combined alternative variable sets (A + G, A + S, S + G, and A + S + G) were extracted within the same stage, respectively. Finally, the forward selection approach was utilized in conjunction with four different models (MLR, KNN, RF, and SVR) to obtain the most suitable variable sets and generate FSV mapping. The results demonstrated a strong correlation between the intensity of backscattering coefficients and the phenological stages of the forest. Within the dormant stage, there was a significant decrease in the gaps of backscattering coefficients obtained from the same polarization compared to those within the growth stage. Furthermore, the results also revealed that more signals from inside the canopy could be detected during the dormant stage in both evergreen coniferous forests and deciduous coniferous forests. Subsequently, the accuracy in mapping FSV obtained from single-band SAR images within the dormant stage are slightly higher than that within the growth stage, and the accuracy was still significantly affected by both overestimation and underestimation. Moreover, the combined effects of different bands significantly improve the reliability of mapped FSV. The rRMSE values in four multi-band combinations ranged from 22.37% to 29.40% for Chinese pine forests and from 21.27% to 34.38% for Larch forests, and the optimal result was observed from combinations of A + S + G acquired within the dormant stage. It is confirmed that SAR signal and their sensitivity to FSV depends on the stages of forest annual growth cycle. In comparison to the growth period, dual-polarization SAR data acquired during the dormant stage is more suitable for estimating FSV in boreal forests.

Forest ecosystems play an essential role in climate stabilization, and the study of their capabilities in this aspect is of paramount importance. How climate changes affect the biomass of trees and stands in transcontinental gradients is unknown today? The objective of this study was (a) to verify the operation of the law of the limiting factor at the transcontinental level when modeling changes in the biomass of trees and stands of the genus Betula spp. of Eurasia in relation to geographically determined indicators of temperatures and precipitation, and (b) to show the possibility of using the constructed climate-conditioned models of tree and stand biomass in predicting temporal changes in tree and stand biomass based on the principle of space-for-time substitution. As a result of the implementation of the principles of the limiting factor and space-for-time substitution, a common pattern has been established on tree and stand levels: in sufficiently moisture-rich climatic zones, an increase in temperature by 1 °C with a constant amount of precipitation causes an increase in aboveground biomass, and in moisture-deficient zones, it decreases; in warm climatic zones, a decrease in precipitation by 100 mm at a constant average January temperature causes a decrease in aboveground biomass, and in cold climatic zones, it increases.