Near-Surface and High-Resolution Satellite Time Series for Detecting Crop Phenology
Near-Surface and High-Resolution Satellite Time Series for Detecting Crop Phenology
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DOI：
10.3390/rs14091957
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发表时间：
2022-04
期刊：
影响因子：
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通讯作者：
C. Diao;Geyang Li
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文献类型：
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作者：
C. Diao;Geyang Li
利用卫星时间序列监测作物物候对表征农业生态系统能量-水-碳通量、管理耕作措施和预测作物产量具有重要意义。尽管基于卫星的作物物候检索取得了进展，但在地面作物物候事件的背景下解释这些检索特征仍然是一个长期存在的障碍。近年来，近地表物候相机的出现（例如，PhenoCams），沿着高空间和时间分辨率的卫星图像（例如，PlanetScope图像），在很大程度上促进了检索的特征直观观察作物阶段的物候解释和验证的直接比较。本研究的目标是系统地评估近地表PhenoCams和高分辨率PlanetScope时间序列在协调传感器和地面作物物候特征。有两个关键的作物阶段（即，作物出苗和成熟阶段）为例，我们检索了不同的物候特征，从PhenoCam和PlanetScope图像的范围内的农业网站在美国。结果表明，基于曲率的Greenup和Gu-Upturn估计值与目视观测的作物出苗期具有良好的一致性（RMSE约为1周，偏差约为0-9天，R平方约为0.65-0.75）。基于阈值和导数的绿色度下降季节的结束（即，EOS）估计值与目视作物成熟度观测值吻合良好（RMSE约为5-10天，偏差约为0-8天，R平方约为0.6-0.75）。PlanetScope、PhenoCam和可视化物候学之间的一致性表明，在生理学特征良好的作物物候事件的背景下，有可能解释精细尺度传感器衍生的物候特征，这为制定连接地面-卫星物候特征的正式协议铺平了道路。
Detecting crop phenology with satellite time series is important to characterize agroecosystem energy-water-carbon fluxes, manage farming practices, and predict crop yields. Despite the advances in satellite-based crop phenological retrievals, interpreting those retrieval characteristics in the context of on-the-ground crop phenological events remains a long-standing hurdle. Over the recent years, the emergence of near-surface phenology cameras (e.g., PhenoCams), along with the satellite imagery of both high spatial and temporal resolutions (e.g., PlanetScope imagery), has largely facilitated direct comparisons of retrieved characteristics to visually observed crop stages for phenological interpretation and validation. The goal of this study is to systematically assess near-surface PhenoCams and high-resolution PlanetScope time series in reconciling sensor- and ground-based crop phenological characterizations. With two critical crop stages (i.e., crop emergence and maturity stages) as an example, we retrieved diverse phenological characteristics from both PhenoCam and PlanetScope imagery for a range of agricultural sites across the United States. The results showed that the curvature-based Greenup and Gu-based Upturn estimates showed good congruence with the visually observed crop emergence stage (RMSE about 1 week, bias about 0–9 days, and R square about 0.65–0.75). The threshold- and derivative-based End of greenness falling Season (i.e., EOS) estimates reconciled well with visual crop maturity observations (RMSE about 5–10 days, bias about 0–8 days, and R square about 0.6–0.75). The concordance among PlanetScope, PhenoCam, and visual phenology demonstrated the potential to interpret the fine-scale sensor-derived phenological characteristics in the context of physiologically well-characterized crop phenological events, which paved the way to develop formal protocols for bridging ground-satellite phenological characterization.