Land surface phenology (LSP) provides critical information for investigating vegetation growth and development, studying ecosystem biodiversity, modeling terrestrial carbon and surface energy budgets, detecting land cover and land use change, and monitoring climate change. Although operational 500 m LSP products have been produced from coarse resolution data observed from Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS), an LSP product is also needed at the Landsat scale (30 m) to enhance the environmental monitoring and modeling. However, temporal frequency of 30 m satellite data is always inadequate for reliable LSP detection, despite enrichment by the operational harmonized Landsat and Sentinel-2 (HLS) product. In this study, we propose a new algorithm of LSP detection for the generation of a 30 m LSP product using routinely produced HLS and VIIRS surface reflectance products. Specifically, the new algorithm compares a HLS EVI2 (two-band enhanced vegetation index) time series at a given 30 m pixel with the set of 500 m VIIRS EVI2 time series neighboring the HLS pixel and selects the most similar temporal shape of VIIRS time series even though the amplitude and/or phase between HLS and VIIRS EVI2 time series may be mismatched. The shape of the selected VIIRS EVI2 time series is then used to match to the given HLS EVI2 time series to generate a synthetic HLS-VIIRS time series. The HLS-VIIRS time series is subsequently processed using the hybrid piecewise logistic model to detect the phenological transition dates and to quantify the confidence of LSP detection. This new algorithm is evaluated by implementing 30 m LSP detection in eight HLS tiles in the northeastern (forests), central (croplands), and western (shrublands) United States. Evaluation finds that the new-algorithm-detected greenup onset (1) agrees well with the standard VIIRS LSP product without bias, (2) closely correlates to PhenoCam observations with a slope close to one, and (3) compares well with both PhenoCam and field species-specific observations with a mean absolute difference of 8 days and a difference less than 10 days in more than 70% of the validation samples. This implementation suggests that the new algorithm could be implemented for regional and global LSP product generation at a 30 m resolution.

地表物候学（LSP）提供了重要信息，可用于调查植被的生长和发育，研究生态系统生物多样性，对陆地碳和表面能预算进行建模，检测土地覆盖和土地利用的变化以及监测气候变化。尽管已从中分辨率成像光谱仪（MODIS）和可见红外成像辐射仪套件（VIIRS）观测到的粗分辨率数据中生产出了500 m的LSP产品，但在Landsat规模（30 m）上也需要LSP产品以改善环境监视和建模。然而，尽管通过统一运行的Landsat和Sentinel-2（HLS）产品进行了丰富的工作，但30 m卫星数据的时频始终不足以进行可靠的LSP检测。在这个研究中，我们提出了一种使用常规生产的HLS和VIIRS表面反射产品生成30 m LSP产品的LSP检测新算法。具体而言，新算法将给定的30 m像素处的HLS EVI2（两波段增强植被指数）时间序列与邻近HLS像素的500 m VIIRS EVI2时间序列集进行比较，并选择最相似的VIIRS时间序列的时间形状即使HLS和VIIRS EVI2时间序列之间的幅度和/或相位可能不匹配。然后，将所选VIIRS EVI2时间序列的形状用于匹配给定的HLS EVI2时间序列，以生成合成的HLS-VIIRS时间序列。随后使用混合分段逻辑模型处理HLS-VIIRS时间序列，以检测物候转换日期并量化LSP检测的置信度。通过在美国东北部（森林），中部（农田）和西部（灌木地）的8个HLS磁贴中执行30 m LSP检测来评估该新算法。评估发现，新算法检测到的Greenup发作（1）与标准VIIRS LSP产品无偏差非常吻合;（2）与PhenoCam观测值密切相关，且斜率接近1;（3）与PhenoCam和在超过70％的验证样本中，平均物种差异为8天且差异小于10天的特定领域物种观察。这种实施方式表明，可以以30 m的分辨率为区域和全球LSP产品生成实施新算法。和西部（灌木丛）美国。评估发现，新算法检测到的Greenup发作（1）与标准VIIRS LSP产品无偏差非常吻合;（2）与PhenoCam观测值密切相关，且斜率接近1;（3）与PhenoCam和在超过70％的验证样本中，平均特定绝对差异为8天且差异小于10天的特定领域物种观察。这种实施方式表明，可以以30 m的分辨率为区域和全球LSP产品生成实施新算法。和西部（灌木丛）美国。评估发现，新算法检测到的Greenup发作（1）与标准VIIRS LSP产品无偏差非常吻合;（2）与PhenoCam观测值密切相关，且斜率接近1;（3）与PhenoCam和在超过70％的验证样本中，平均特定绝对差异为8天且差异小于10天的特定领域物种观察。这种实施方式表明，可以以30 m的分辨率为区域和全球LSP产品生成实施新算法。（3）与PhenoCam和田间物种特定的观察结果很好地比较，在70％以上的验证样本中，平均绝对差为8天，而差值小于10天。这种实施方式表明，可以以30 m的分辨率为区域和全球LSP产品生成实施新算法。（3）与PhenoCam和田间物种特定的观察结果很好地比较，在70％以上的验证样本中，平均绝对差为8天，而差值小于10天。这种实施方式表明，可以以30 m的分辨率为区域和全球LSP产品生成实施新算法。