<p>Remote sensing-derived phenology is a powerful tool for monitoring vegetation dynamics across large landscapes and offers critical insights into ecological responses to climate change. However, most studies focus on broad-scale assessments, often overlooking community-level phenology, which is essential for conservation planning and adaptive forest management. This study investigated land surface phenology (LSP) in the Nandhaur Landscape, a tropical forest ecosystem situated in the Himalayan foothills of India, using MODIS, Sentinel-2, and PlanetScope satellite datasets. By evaluating the effectiveness of these sensors in capturing phenological dynamics across tree communities, this study highlights the trade-offs between spatial resolution, temporal frequency, and statistical reliability.</p><p>The findings revealed significant phenological variability among tree communities, with deciduous forests exhibiting greater fluctuations in the start (SOS) and end (EOS) of the growing season than evergreen forests. Sentinel-2 emerged as the most suitable sensor for capturing community-level variations owing to its balance between spatial detail and reliability, whereas MODIS remains advantageous for broad-scale monitoring. Despite its high spatial resolution, PlanetScope introduces increased variability. By analysing Sentinel-2 NDVI time series data from 2018 to 2024, this study identified interannual variations in phenology driven by topography and climate dynamics. These findings emphasise the need for long-term phenological monitoring to assess the resilience of different tree communities. Future research should integrate ground-based observations with remote sensing and explore sensor fusion techniques to improve the accuracy of phenological assessments, ultimately supporting evidence-based conservation and forest-management strategies.</p>