El Niño/Southern Oscillation (ENSO) consistently modulates climate patterns in terrestrial ecosystems, triggering vegetation greening or browning. Although vegetation dynamics in the tropics during ENSO has been widely reported, the response of vegetation greening in the near-tropics to ENSO remains uncertain. Here, we explored vegetation greening near the Tropic of Cancer in China (TCC) during three sustained ENSO events during 2001–2018 based on long-term MODIS satellite Leaf Area Index (LAI) products (i.e., MOD15A2H). The results revealed a pronounced asymmetry in vegetation greening responses to ENSO near the TCC. Specifically, vegetation browning during strong La Niña (i.e., LAI anomalies about −0.15) is twice as high as vegetation greening during strong El Niño (i.e., LAI anomalies about +0.05). In La Niña, vegetation browning was accompanied by negative surface air temperature and precipitation anomalies, while in El Niño, vegetation greening was dominated by a positive anomaly in precipitation. This study emphasizes the distinct impact of ENSO on vegetation greening in the near-tropics, providing important insights into the response of vegetation dynamics to climate extremes under a warming world.

The spatial heterogeneity of leaf traits within canopies is an important source of uncertainty in leaf parameter estimation from unmanned aerial vehicle (UAV) imagery, especially in structurally complex orchards. In this study, we combined three-dimensional (3D) radiative transfer simulations with field measurements from litchi orchards to quantify bidirectional reflectance factor (BRF) uncertainty under four leaf trait distribution patterns within the canopy. Whole-canopy leaf traits were represented using: (1) a homogeneous canopy (HC), (2) vertically divided canopy (VDC), (3) horizontally divided canopy (HDC), and (4) a canopy divided into nine sections (CD9s). Among the simplified schemes, HDC produced BRF values most consistent with the CD9s configuration, while the largest deviation between CD9s and HC was observed at 570 nm with a maximum BRF normalized difference of 65.29%. Relative contribution rate analysis based on the symmetric relative difference (SRD, %) showed that leaf trait distribution pattern dominated the variability of several VIs, including NDVI, NDRE, CCI, SIPI, LICI, and PVI. Meanwhile, other VIs (e.g., NIRv, SAVI, OSAVI and EVI) were more strongly influenced by illumination–viewing geometry. Using multiangle UAV multispectral data improved the estimation of proxy leaf chlorophyll content (LCC, max R²cv = 0.52), while nadir-only data yielded the best results for leaf nitrogen mass-based content (LNC, max R²cv = 0.41). These results emphasize that reliable UAV-based leaf trait retrieval is closely related to leaf trait distribution pattern within the canopy and its interaction with other factors (e.g., illumination–viewing geometry).