**1. Introduction**

Forests occupy almost one third of the Earth's land area [1], playing a major role in sustaining global material and energy cycles [2]. Forests provide a variety of ecosystem services, which are important for human well-being and the overall health of the planet Earth [3,4]. Forest condition is an essential component of both forest managemen<sup>t</sup> and ecological evaluations. It reflects the stability, resilience, and capability of carbon sequestration, timber production, as well as other services [5,6]. Current forest condition assessments are mainly based on the structure and function investigated in the field, which is costly and spatially limited [7]. It is essential to assess forest condition based on modeling structural and functional parameters. The condition assessment based on remote sensing

usually contains indicators of community structure and productivity [6–8]. The sub-compartment measurements of the National Forest Inventory in China contain the information about structure, including canopy closure, stand density and forest age, and function, including stand volume and soil condition [9,10].

The explicit mapping of spatial variations of forest structure and function parameters has been an essential e ffort in ecological analysis [11–14]. Remote sensing modeling combined sample plot data has become a well adopted method to generate spatially explicit estimates of forest parameters [15,16]. The selection of predictor variables from various sensors and algorithms can a ffect the results considerably [17,18]. Variables from optical sensors are commonly applied to predict horizontal forest structure such as canopy closure and density [19,20]. This is due to the close relationship between horizontal forest structure and aggregate spectral signatures, i.e., reflectance or vegetation indices, with global coverage, repetitiveness, and cost-e ffectiveness [21,22]. However, synthetic aperture radar (SAR) and light detection and ranging (LiDAR) sensors are capable of penetrating cloud and canopies and are suitable for mapping vertical forest parameters such as tree height and stand volume [23–25]. Whereas, complex forest parameters such as biomass, soil fertility, and forest age are generally estimated by multi-sensor data [26–29].

Modeling vegetation parameters based on remote sensing can be divided into physically based models and empirical regression algorithms [18,21,30]. Physically based models depend on numerous factors to simulate canopy reflectance, such as leaf geometry, chlorophyll concentration, water and matter contents, soil reflectance, and bidirectional reflectance distribution function, which may not be readily available [31,32]. Those are built conventionally as semi-physical models by simplifying factors based on prerequisite assumptions and using machine learning or regression methods trained with radiative transfer, which achieve robust performance [33,34]. The biophysical products, such as leaf area index (LAI) and fraction of vegetation cover (FVC) from Sentinel-2, are generated by a physically based model, which has been implemented to Moderate Resolution Imaging Spectrometer (MODIS) and Landsat sensors [35,36]. Empirical regressions require support from abundant ground measurements, and depend on the modeling relationship between remote sensing-derived predictors and field-measured samples, including parametric and non-parametric algorithms [37,38]. The former refers to statistical regression methods, by which the expression relating to the dependent variable, i.e., forest parameters, and the independent variables are estimated [39,40]. These regressions are suitable to model explicit relationships and are easily applied to a large scale [12,41,42]. As for the complex forest parameters such as stand volume, forest age, and soil fertility, it is a challenge to formulate their relationships with remote sensing data because of many a ffecting factors [43–45] which require non-parametric algorithms. Among the various non-parametric techniques, random forests (RF) has been recognized to be e fficient and accurate in modeling complex relationships between remote sensing data and forest parameters [7,17,46,47].

Forest parameter modeling based on satellite data has advantages such as repetition rate enabling long-term monitoring [48,49]. Sentinel-1 C band SAR and Sentinel-2 multispectral instrument (MSI) have the global coverage [50]. Those publicly accessible data have been applied in vegetation studies and provided capabilities for forest parameter modeling using both active and passive remote sensing techniques [51,52]. The Advanced Land Observing Satellite (ALOS/ALOS-2) Phased Array type L band SAR (PALSAR/PALSAR-2) images from L band SAR contain comprehensive information on the orientation and structure of tree canopy and stems within the pixel [53–55]. It makes the yearly mosaic ALOS/ALOS-2 images with global and free-access observations particularly useful for forest parameter mapping [56,57]. The digital surface model (DSM) from ALOS L band interferometric SAR (InSAR) had greater accuracy and can provide useful topographic indices to estimate forest parameters [58,59]. Although estimates of forest parameters from moderate resolution satellite images and abovementioned algorithms have achieved varying success [12,13,49], inventory and application of efficient algorithms and predictors from open-access remote sensing data on forest structure, function, and condition assessment continuously deserve exploration.

The Changbai Mountain National Nature Reserve (CMNNR) in Northeast China is covered with large areas of old-growth forests, which are under strict protection [60,61]. It has been regarded as the most typical natural composite body on the Eurasia Continent with a complex biota composition and abundant flora and fauna [62,63]. Due to its ecological importance, substantial researches on landscape structure, function, and productivity in the Changbai Mountain region have been reported since the early 1980s [64–69]. However, there is a lack of systematic maps of forest parameters and conditions in this vital ecosystem site.

In this study, we developed an e ffective methodology for evaluating forest conditions by mapping canopy closure, stand density, volume, age, and soil fertility in the CMNNR. The specific objectives were to: (1) model forest structure and function parameters by determining their relationships with predictors from satellite data, including L and C band SAR, topographical indices from L band InSAR, and Sentinel-2 MSI variables; (2) map five parameters using e fficient algorithms with remote sensing data; and (3) assess forest conditions based on structural and functional parameters, which can provide baseline information for forest management.

#### **2. Materials and Methods**
