Ecoregion-wise fractional mapping of tree functional composition in temperate mixed forests with sentinel data: Integrating time-series spectral and radar data

Abstract

Temperate mixed forest ecosystems are composed of various tree functional types (TFTs) that differ in canopy structure, phenology, and physiological response to climate change. An accurate characterization of the composition of these TFTs is important for quantifying land surface carbon, energy, and water cycling, as well as process-based simulation of forest dynamics. However, because the pixel size of satellite imagery is usually larger than temperate tree crowns, it is challenging to untangle the significant pixel-wise signal mixture of TFT across mixed forest regions. Spectral Mixture Analysis (SMA) has been widely used to derive the sub-pixel fractional composition of TFT from satellite imagery, but accounting for the broad spectral variability within TFTs across space and time remains a challenge. Synthetic aperture radar (SAR) can indicate biomass mixture information, but it has not been fully exploited for deriving subpixel TFT composition. To improve TFT composition mapping in mixed forest regions, we developed a Fisher-transformation-based Spectral and Radar Time-series Mixture Analysis (F-SRTMA) framework on Google Earth Engine. The F-SRTMA framework aims to address the space-time TFT variability of satellite signatures based on two modified modules: (1) the use of spectral and radar data with spatial and temporal information, and (2) feature optimization based on Fisher Discriminant Analysis (FDA). We tested the F-SRTMA at three representative temperate mixed landscapes located in the northeastern United States, where time-series Sentinel-1 and -2 data were used to calibrate our F-SRTMA approach. Airborne hyperspectral and LiDAR-derived canopy height data were used to generate ground reference TFT fraction maps for validation. The results demonstrate that (1) compared to the spectral time-series model, the synergy of spectral and radar time-series features yielded higher accuracy at the local sites (r2 = 0.649 vs. 0.680); (2) optimized feature based on FDA significantly minimized the within-TFT variability while maximizing the between-TFT variability, which further improved model generalizability across different landscapes, yielding the highest accuracy with cross-site r2 increasing from 0.634 to 0.715 and RMSE decreasing from 0.207 to 0.164. Collectively, these results suggest that F-SRTMA can be an accurate and generalizable approach for sub-pixel fraction mapping across temperate mixed landscapes, with the potential to be applied to other mixed forest ecosystems.