Leaf area index (LAI) is an important vegetation leaf structure parameter in forest and agricultural ecosystems. Remote sensing techniques can provide an effective alternative to field-based observation of LAI. Differences in canopy structure result in different sensor types (active or passive), platforms (terrestrial, airborne, or satellite), and models being appropriate for the LAI estimation of forest and agricultural systems. This study reviews the application of remote sensing-based approaches across different system configurations (passive, active, and multisource sensors on different collection platforms) that are used to estimate forest and crop LAI and explores uncertainty analysis in LAI estimation. A comparison of the difference in LAI estimation for forest and agricultural applications given the different structure of these ecosystems is presented, particularly as this relates to spatial scale. The ease of use of empirical models supports these as the preferred choice for forest and crop LAI estimation. However, performance variation among different empirical models for forest and crop LAI estimation limits the broad application of specific models. The development of models that facilitate the strategic incorporation of local physiology and biochemistry parameters for specific forests and crop growth stages from various temperature zones could improve the accuracy of LAI estimation models and help develop models that can be applied more broadly. In terms of scale issues, both spectral and spatial scales impact the estimation of LAI. Exploration of the quantitative relationship between scales of data from different sensors could help forest and crop managers more appropriately and effectively apply different data sources. Uncertainty coming from various sources results in reduced accuracy in estimating LAI. While Bayesian approaches have proven effective to quantify LAI estimation uncertainty based on the uncertainty of model inputs, there is still a need to quantify uncertainty from remote sensing data source, ground measurements and related environmental factors to mitigate the impacts of model uncertainty and improve LAI estimation.

Accurately quantifying global tree biodiversity is critical for enhancing forest ecosystem management and forest biodiversity conservation. With the launch of NASA’s Global Ecosystem Dynamics Investigation (GEDI), we evaluated the efficacy of space-borne lidar metrics in predicting tree species richness globally and explored whether integrating spectral vegetation metrics with space-borne lidar data could improve model performances. Using Forest Global Earth Observatory (ForestGEO) data, we developed three models using the random forest algorithm to predict global tree species richness across climate zones, including a dynamic habitat index (DHI)-only model, a GEDI-only model, and a combined GEDI-DHI model. We also developed four new canopy indices for our model and determined the optimal extent for aggregating GEDI metrics. Applying the optimal pixel size (5600 m), we found that the GEDI-only model predicted tree species richness across climate zones well (R² = 0.55). One of our new GEDI metrics, representing canopy structure complexity, was among the top five most important features. The GEDI-DHI model performed similarly to the GEDI-only model using the ForestGEO dataset (R² = 0.55). Our study provides an efficient and innovative method for using GEDI data to predict global tree species richness. However, the integration of GEDI metrics with DHIs did not significantly improve the model’s performance compared to the GEDI-only model. Considering the substantial variation in tree species richness across different climate zones, we recommend modeling tree species richness for each climate zone rather than using a global model. Additionally, incorporating open-source ground-measured tree species richness data can improve predictions and inform decision-making in forest conservation management.