Abstract In tropical forests, leaf phenology—particularly the pronounced dry-season green-up—strongly regulates biogeochemical cycles of carbon and water fluxes. However, uncertainties remain in the understanding of tropical forest leaf phenology at different spatial scales. Phenocams accurately characterize leaf phenology at the crown and ecosystem scales but are limited to a few sites and time spans of a few years. Time-series satellite observations might fill this gap, but the commonly used satellites (e.g. MODIS, Landsat and Sentinel-2) have resolutions too coarse to characterize single crowns. To resolve this observational challenge, we used the PlanetScope constellation with a 3 m resolution and near daily nadir-view coverage. We first developed a rigorous method to cross-calibrate PlanetScope surface reflectance using daily BRDF-adjusted MODIS as the reference. We then used linear spectral unmixing of calibrated PlanetScope to obtain dry-season change in the fractional cover of green vegetation (GV) and non-photosynthetic vegetation (NPV) at the PlanetScope pixel level. We used the Central Amazon Tapajos National Forest k67 site, as all necessary data (from field to phenocam and satellite observations) was available. For this proof of concept, we chose a set of 22 dates of PlanetScope measurements in 2018 and 16 in 2019, all from the six drier months of the year to provide the highest possible cloud-free temporal resolution. Our results show that MODIS-calibrated dry-season PlanetScope data (1) accurately assessed seasonal changes in ecosystem-scale and crown-scale spectral reflectance; (2) detected an increase in ecosystem-scale GV fraction (and a decrease in NPV fraction) from June to November of both years, consistent with local phenocam observations with R2 around 0.8; and (3) monitored large seasonal trend variability in crown-scale NPV fraction. Our results highlight the potential of integrating multi-scale satellite observations to extend fine-scale leaf phenology monitoring beyond the spatial limits of phenocams.

中文翻译：

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卫星遥感的多尺度集成改善了亚马逊热带常绿林旱季绿化的特征

摘要 在热带森林中，叶片物候——尤其是明显的旱季绿化——强烈调节碳和水通量的生物地球化学循环。然而，在不同空间尺度上对热带森林叶物候的理解仍然存在不确定性。Phenocams 在树冠和生态系统尺度上准确地表征了叶片物候，但仅限于少数地点和几年的时间跨度。时间序列卫星观测可能会填补这一空白，但常用卫星（例如 MODIS、Landsat 和 Sentinel-2）的分辨率太粗糙而无法表征单个冠。为了解决这一观测挑战，我们使用了具有 3 m 分辨率和接近每日天底视图覆盖范围的 PlanetScope 星座。我们首先开发了一种严格的方法来交叉校准 PlanetScope 表面反射率，使用每日 BRDF 调整的 MODIS 作为参考。然后，我们使用校准的 PlanetScope 的线性光谱分解来获得 PlanetScope 像素级别的绿色植被 (GV) 和非光合植被 (NPV) 的部分覆盖率的旱季变化。我们使用了亚马逊中部塔帕霍斯国家森林 k67 站点，因为所有必要的数据（从野外到 phenocam 和卫星观测）都是可用的。对于这个概念验证，我们选择了 2018 年的 22 个 PlanetScope 测量日期和 2019 年的 16 个日期，全部来自一年中的六个干燥月份，以提供尽可能高的无云时间分辨率。我们的结果表明，MODIS 校准的旱季 PlanetScope 数据 (1) 准确评估了生态系统尺度和冠尺度光谱反射率的季节性变化；(2) 检测到两年的 6 月至 11 月生态系统规模 GV 分数增加（和 NPV 分数减少），与当地 phenocam 观测结果一致，R2 约为 0.8；(3) 监测冠级 NPV 比例的大季节性趋势变化。我们的研究结果突出了整合多尺度卫星观测以将精细尺度叶片物候监测扩展到现象的空间限制之外的潜力。(3) 监测冠级 NPV 比例的大季节性趋势变化。我们的研究结果突出了整合多尺度卫星观测以将精细尺度叶片物候监测扩展到现象的空间限制之外的潜力。(3) 监测冠级 NPV 比例的大季节性趋势变化。我们的研究结果突出了整合多尺度卫星观测以将精细尺度叶片物候监测扩展到现象的空间限制之外的潜力。