Accurate monitoring of vegetation phenology (e.g., the start and end of the growing season, SOS and EOS) is helpful for understanding the impacts of climate change on vegetation and the terrestrial carbon cycle. The remote sensing-based vegetation index (e.g., enhanced vegetation index, EVI) and remote sensing-based phenology index (e.g., normalized difference greenness index, NDGI) are the major data sources for phenology monitoring at regional and global scales. However, these remote sensing-based indices are vulnerable to the influences of backgrounds and their variations. As a result, it is difficult to obtain high-precision vegetation phenology by using only the remote sensing-based indices, especially for the EOS. In this study, we developed a background-free phenology index (BFPI) by coupling the remote sensing-based index and the meteorological factor-based normalized growing season index (calculated by the normalized daily minimum temperature, vapor pressure deficit, and photoperiod). The BFPIs (BFPI_EVI and BFPI_NDGI) were constructed as products of the EVI/NDGI and normalized growing season index. The performances of the BFPIs in phenology monitoring were evaluated by using the gross primary production data from 64 carbon flux towers and green chromatic coordinate data from 57 PhenoCam sites in forests and grasslands in the Northern Hemisphere. The results showed that the BFPIs performed better than the remote sensing-based indices in phenology monitoring for both forests and grasslands. The BFPI_NDGI performed better than the BFPI_EVI for SOS monitoring, and the two BFPIs had nearly equal performances for monitoring the EOS of grasslands. For forests, the BFPI_EVI performed better than the BFPI_NDGI in phenology monitoring. Although the performances of the BFPIs were limited for EOS monitoring, the phenology monitoring accuracy based on BFPIs were still obviously higher than that based on the remote sensing indices. Overall, the newly-developed BFPI that integrates biological and meteorological factors not only improved the precision of phenology monitoring, but also provided a new perspective for multisource data-based phenology monitoring.

准确监测植被物候（如生长季节的开始和结束、SOS和EOS）有助于了解气候变化对植被和陆地碳循环的影响。基于遥感的植被指数（如增强植被指数，EVI）和基于遥感的物候指数（如，归一化差异绿度指数，NDGI）是区域和全球尺度物候监测的主要数据来源。然而，这些基于遥感的指数容易受到背景及其变化的影响。因此，仅使用基于遥感的指标很难获得高精度的植被物候，尤其是对于 EOS。在这项研究中，我们通过耦合基于遥感的指数和基于气象因素的归一化生长季节指数（由归一化的每日最低温度、蒸汽压亏缺和光周期计算）开发了一个无背景物候指数（BFPI）。BFPI (BFPI_EVI和 BFPI _NDGI）被构建为 EVI/NDGI 和标准化生长季节指数的产品。BFPIs 在物候监测中的表现通过使用来自北半球森林和草原的 64 个碳通量塔的总初级生产数据和来自 57 个 PhenoCam 站点的绿色色坐标数据进行评估。结果表明，BFPI在森林和草原物候监测中的表现优于基于遥感的指标。BFPI _NDGI在 SOS 监测方面的表现优于 BFPI _EVI，两种 BFPI 在监测草地 EOS 方面的表现几乎相同。对于森林，BFPI _EVI的表现优于 BFPI _NDGI在物候监测中。尽管BFPIs对EOS监测的性能有限，但基于BFPIs的物候监测精度仍明显高于基于遥感指标的物候监测精度。总体而言，新开发的融合生物和气象因素的BFPI不仅提高了物候监测的精度，而且为基于多源数据的物候监测提供了新的视角。