The phenomenon that organisms can distinguish genetically related individuals from strangers (i.e., kin recognition) and exhibit more cooperative behaviours towards their relatives (i.e., positive kin discrimination) has been documented in a wide variety of organisms. However, its occurrence in plants has been considered only recently. Despite the concerns about some methodologies used to document kin recognition, there is sufficient evidence to state that it exists in plants. Effects of kin recognition go well beyond reducing resource competition between related plants and involve interactions with symbionts (e.g., mycorrhizal networks). Kin recognition thus likely has important implications for evolution of plant traits, diversity of plant populations, ecological networks and community structures. Moreover, as kin selection may result in less competitive traits and thus greater population performance, it holds potential promise for crop breeding. Exploration of these evo‐ecological and agricultural implications requires adequate control and measurements of relatedness, sufficient replication at genotypic level and comprehensive measurements of performance/fitness effects of kin discrimination. The primary questions that need to be answered are: when, where and by how much positive kin discrimination improves population performance.

Rapid technological advancements and increasing data availability have improved the capacity to monitor and evaluate Earth's ecology via remote sensing. However, remote sensing is notoriously ‘blind’ to fine‐scale ecological processes such as interactions among plants, which encompass a central topic in ecology. Here, we discuss how remote sensing technologies can help infer plant–plant interactions and their roles in shaping plant‐based systems at individual, community and landscape levels. At each of these levels, we outline the key attributes of ecosystems that emerge as a product of plant–plant interactions and could possibly be detected by remote sensing data. We review the theoretical bases, approaches and prospects of how inference of plant–plant interactions can be assessed remotely. At the individual level, we illustrate how close‐range remote sensing tools can help to infer plant–plant interactions, especially in experimental settings. At the community level, we use forests to illustrate how remotely sensed community structure can be used to infer dominant interactions as a fundamental force in shaping plant communities. At the landscape level, we highlight how remotely sensed attributes of vegetation states and spatial vegetation patterns can be used to assess the role of local plant–plant interactions in shaping landscape ecological systems. Synthesis. Remote sensing extends the domain of plant ecology to broader and finer spatial scales, assisting to scale ecological patterns and search for generic rules. Robust remote sensing approaches are likely to extend our understanding of how plant–plant interactions shape ecological processes across scales—from individuals to landscapes. Combining these approaches with theories, models, experiments, data‐driven approaches and data analysis algorithms will firmly embed remote sensing techniques into ecological context and open new pathways to better understand biotic interactions.