The research, led by Piyapon Kankong of the University of Tokyo with colleagues including Toshiaki Owari, Takuya Hiroshima, and Nyo Me Htun, was conducted at the University of Tokyo Hokkaido Forest (UTHF) in Furano, Japan. This site, established in 1899, hosts an arboretum containing roughly 270 tree species and has maintained meticulous phenology records since 1930, including budburst, flowering, leaf coloration, and natural defoliation. That long observational tradition made it an ideal proving ground for testing whether satellite data could match the accuracy of trained observers standing beneath the canopy with binoculars.

The satellite at the center of the study is PlanetScope, a constellation operated by Planet Labs that delivers images at 3-meter spatial resolution with near-daily revisit times. That combination is critical. Workhorse environmental satellites such as MODIS (500 m), GCOM-C (250 m), Landsat (30 m), and even Sentinel-2 (10 m) simply cannot separate individual tree crowns, which are typically smaller than 10 meters across. PlanetScope’s 3-meter pixels, by contrast, can isolate the crowns of larger trees, and its daily coverage means researchers rarely miss the rapid transitions that mark autumn senescence. Over the three study years from 2022 to 2024, the team assembled 67, 73, and 103 cloud-free images of the site, respectively.

To validate the satellite data, the researchers selected 49 canopy trees belonging to 31 deciduous species, each with a fully exposed crown visible from above. Ground staff recorded leaf coloration as a percentage of the canopy, from 0 to 100, once or twice per week from late August through early December. These observations were classified into stages that defined three key metrics: the start of autumn phenology (SOA, roughly 20 percent coloration), the middle (MOA, about 50 percent), and the end (EOA, around 80 percent). The team also flew a DJI Mavic 3 Multispectral drone at 80 meters above the canopy to create centimeter-scale orthomosaics used to delineate each tree crown precisely; crown diameters in the sample ranged from 3.75 meters in Styrax obassia to 15.57 meters in Tilia japonica.

With crowns mapped, the researchers extracted six vegetation indices from the PlanetScope time series: NDVI, kNDVI, EVI, EVI2, GRVI, and the newer near-infrared reflectance of vegetation (NIRv). Time series were gap-filled, smoothed with a Savitzky-Golay filter, and fitted with double logistic models using the R package phenofit, with the Beck model providing the best fit. Two extraction methods were then compared: a threshold-based approach defining SOA, MOA, and EOA at 80, 50, and 20 percent of the seasonal amplitude, and a third-order derivative method that locates phenological dates from curvature changes in the fitted curves. The results were evaluated against ground observations using correlation, RMSE, and bias statistics.

The spectral story was clear. Among the raw surface reflectance bands, near-infrared performed best, correlating at r = -0.767 with the progression of leaf coloration across 2,257 observations. That makes physical sense: as leaves senesce, their internal cellular structure breaks down, reducing the scattering of near-infrared light, while chlorophyll degradation unmasks carotenoids and anthocyanins that raise reflectance in visible bands. Vegetation indices that incorporate the NIR band outperformed any single band, with EVI showing the strongest relationship (r = -0.791), followed closely by EVI2 and NIRv. GRVI, which excludes the near-infrared, tracked coloration least effectively.

When it came to extracting specific phenological dates, the threshold-based method generally outperformed the derivative approach, producing lower and more stable errors across species. The middle of autumn phenology emerged as the most reliably captured stage. For the ten best-performing species, which included Larix kaempferi, Carpinus cordata, Ginkgo biloba, and Quercus crispula, NIRv achieved the highest precision for MOA (r = 0.774 to 0.780, RMSE of 9.2 to 10.8 days), while EVI2 delivered the best overall accuracy with an RMSE of just 4.8 days. The authors describe this as a trade-off between precision and accuracy: NIRv is the most consistent index over time, but EVI2 lands closest to the true dates.

Species identity mattered enormously. Species with gradual, visually distinct coloration, such as larch, hornbeam, and maple, showed strong agreement between satellite and ground metrics, while Populus suaveolens, Prunus sargentii, and Tilia japonica, which color weakly, defoliate rapidly, or suffer understory interference, performed poorly. Average RMSE across all species ranged from 19.3 days in Larix kaempferi to 36.8 days in Populus suaveolens. Drone imagery helped explain the discrepancies: high-agreement species displayed clear intra-crown color gradients that PlanetScope pixels captured faithfully, whereas low-agreement species showed weak coloration or rapid leaf loss that blurred the satellite signal. Systematic biases also appeared, with satellite-derived start dates tending to run early, likely because indices like NIRv detect declines in photosynthesis before color becomes visible, and end dates tending to run late, likely because green understory vegetation contaminates the spectral signal after the canopy has finished turning.

Why does tree-by-tree autumn monitoring matter? Individual trees are the basic units of forest communities, and their phenological timing shapes photosynthetic seasonality, hydrological regulation, and nutrient cycling. Studies have documented large phenological variation both within and among species, and autumn phenology in particular responds to warming in complex, poorly understood ways, unlike the more predictable advance of spring budburst. Because the growing season length, set by the interval between spring and autumn events, is tightly linked to annual carbon dioxide sequestration, uncertainty about autumn timing translates directly into uncertainty about the forest carbon sink. Fine-scale satellite monitoring could help close that gap.

The study is not without limitations, which the authors acknowledge candidly. Each 3-meter pixel can mix signals from understory plants, cloud cover reduces image availability, and radiometric inconsistencies among the many PlanetScope sensors introduce noise that demands careful filtering. The 3-meter resolution also restricts monitoring to crowns larger than 3 meters, and the analysis covered a single site over three years with deciduous species only. Future work should compare PlanetScope against drone imagery, explore the constellation’s underused red-edge band, which is sensitive to pigment change, and extend validation across multiple forest types and longer periods. Still, the demonstration that a commercial small-satellite constellation can time the middle of autumn coloration to within about five days for well-behaved species marks a genuine advance. As climate change continues to shuffle the calendar of leaf fall, scientists now have a tool that can watch it happen, one tree at a time.

Subject of Research: Satellite monitoring of autumn leaf phenology at the individual tree scale

Article Title: Monitoring autumn leaf phenology at the individual tree scale using PlanetScope satellite imagery and ground-based observations

Article References: Kankong, P., Owari, T., Hiroshima, T., & Htun, N. M. (2026). Monitoring autumn leaf phenology at the individual tree scale using PlanetScope satellite imagery and ground-based observations. Discover Ecology, 2(1), Article 1. https://doi.org/10.1007/s44396-025-00018-5

Image Credits: AI Generated

DOI: 10.1007/s44396-025-00018-5

Keywords: autumn phenology, PlanetScope, remote sensing, leaf coloration, vegetation indices, NIRv, EVI2, individual tree crowns, UAV imagery, forest carbon, climate change, Hokkaido