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Understory vegetation contributes considerably to biodiversity and total aboveground biomass of forest ecosystems. Whereas field inventories and LiDAR data are generally used to estimate understory vegetation density, methods for large-scale and spatially continuous estimation of understory vegetation density are still lacking. For an evergreen coniferous forest area in southern China, we developed and tested an effective and practical remote sensing-driven approach for mapping understory vegetation, based on phenological differences between over and understory vegetation. Specifically, we used plant area volume density (PAVD) calculations based on GEDI data to train a support vector regression model and subsequently estimated the understory vegetation density from Sentinel-2 derived metrics. We produced maps of PAVD for the growing and non-growing season respectively, both performing well compared against independent GEDI samples (R2 = 0.89 and 0.93, p < 0.01). Understory vegetation density was derived from the differences in PAVD between the growing and non-growing season. The understory vegetation density map was validated against field samples from 86 plots showing an overall R2 of 0.52 (p < 0.01), rRMSE = 21%. Our study developed a tangible approach to map spatially continuous understory vegetation density with the combination of GEDI LiDAR data and Sentinel-2 imagery, showing the potential to improve the estimation of terrestrial carbon storage and better understand forest ecosystem processes across larger areas.

We show an improvement in extraction of remotely sensed phenology in the Qinghai-Tibet Plateau (QTP). The improvement includes multiple preprocessing, newly proposed absolute growth rate and relative growth rate models based on botany and phenology, and selection of the appropriate growth rate model at different growing stages. We refer to this model as the tempo-differentially selected growth rate model (TDSGM). The newly developed TDSGM is a comprehensive and accurate remote sensing phenological extraction model without manual intervention, which is better than the current mainstream methods. Results show that: (1) influence of elevation and longitude on the QTP phenology has been more effective than that of latitude and vegetation types; (2) length of the growing season (LOS) was in a shortening trend in the long term, even it showed a slight extension in recent years in the short term; (3) the shortest LOS was identified in the area with an altitude of 3000 to 5500 m, which accounts for 90% of the total area of QTP. High instability of LOS was concentrated in meadows at 4000 to 5000 m; and (4) most previous studies on remotely sensed phenology showed that LOS was more related to the start of the growing season. However, we show that the LOSs of four out of seven QTP vegetation types were closely related to the end of the growth season, consistent with 1455 ground observations.