ECOSTRESS estimates gross primary production with fine spatial resolution for different times of day from the International Space Station
ECOSTRESS estimates gross primary production with fine spatial resolution for different times of day from the International Space Station
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DOI：
10.1016/j.rse.2021.112360
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发表时间：
2021-03-02
影响因子：
13.5
通讯作者：
Baldocchi, Dennis D.
中科院分区：
文献类型：
--
作者：
Li, Xing;Xiao, Jingfeng;Baldocchi, Dennis D.
第一性生产力（GPP）是植物通过光合作用吸收的碳量，其准确估算对于理解生态系统功能、碳循环和气候碳反馈具有重要意义。遥感已被广泛用于在区域到全球范围内量化全球初级生产力。然而，极地轨道卫星（例如，Landsat、Sentinel、Terra、Aqua、Suomi NPP、JPSS、OCO-2）缺乏检查GPP日周期的能力，因为它们观测地球？在一天的同一时间，2018年6月发射的空间站生态系统星载热辐射计实验（ECOSTRESS）以高空间分辨率（70 m？距离国际空间站（ISS）70米。在这里，我们利用ECOSTRESS数据，使用基于机器学习的数据驱动方法，以高空间分辨率预测一天中不同时间的瞬时GPP。预测GPP模型使用瞬时ECOSTRESS LST观测值沿着，以及来自中分辨率成像光谱仪（MODIS）的每日增强植被指数（EVI）、来自国家土地覆盖数据库（NCLD）的土地覆盖类型和来自ERA 5再分析数据集的瞬时气象数据。我们的模型估计瞬时GPP在56通量塔网站相当不错（R2 = 0.88，均方根误差（RMSE）= 2.42？mol CO2 m ~（-2）s ~（-1）。由ECOSTRESS LST驱动的瞬时GPP估计捕获了不同生物群落的塔式GPP的日变化。然后，我们为加州中部和北方制作了多个高分辨率ECOSTRESS GPP地图。我们发现GPP在一天中的不同时间有不同的变化（例如，上午晚些时候较高，中午左右达到峰值，黄昏时接近零），以及不同景观之间生产力的明显差异（例如，热带稀树草原、农田、草地和森林）的不同时间。ECOSTRESS GPP还捕获了光合作用日循环的季节变化。这项研究证明了使用ECOSTRESS数据产生瞬时GPP（即，ECOSTRESS数据的采集时间的GPP）。ECOSTRESS GPP可以揭示植物光合作用和水分利用在昼夜循环过程中的变化，并为农业管理和陆地生物圈/地表模型的未来改进提供信息。
Accurate estimation of gross primary production (GPP), the amount of carbon absorbed by plants via photosynthesis, is of great importance for understanding ecosystem functions, carbon cycling, and climate-carbon feedbacks. Remote sensing has been widely used to quantify GPP at regional to global scales. However, polarorbiting satellites (e.g., Landsat, Sentinel, Terra, Aqua, Suomi NPP, JPSS, OCO-2) lack the capability to examine the diurnal cycles of GPP because they observe the Earth?s surface at the same time of day. The Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS), launched in June 2018, observes the land surface temperature (LST) at different times of day with high spatial resolution (70 m ? 70 m) from the International Space Station (ISS). Here, we made use of ECOSTRESS data to predict instantaneous GPP with high spatial resolution for different times of day using a data-driven approach based on machine learning. The predictive GPP model used instantaneous ECOSTRESS LST observations along with the daily enhanced vegetation index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), land cover type from the National Land Cover Database (NCLD), and instantaneous meteorological data from the ERA5 reanalysis dataset. Our model estimated instantaneous GPP across 56 flux tower sites fairly well (R2 = 0.88, Root Mean Squared Error (RMSE) = 2.42 ?mol CO2 m- 2 s- 1). The instantaneous GPP estimates driven by ECOSTRESS LST captured the diurnal variations of tower GPP for different biomes. We then produced multiple high resolution ECOSTRESS GPP maps for the central and northern California. We found distinct changes in GPP at different times of day (e.g., higher in late morning, peak around noon, approaching zero at dusk), and clear differences in productivity across landscapes (e.g., savannas, croplands, grasslands, and forests) for different times of day. ECOSTRESS GPP also captured the seasonal variations in the diurnal cycling of photosynthesis. This study demonstrates the feasibility of using ECOSTRESS data for producing instantaneous GPP (i.e., GPP for the acquisition time of the ECOSTRESS data) for different times of day. The ECOSTRESS GPP can shed light on how plant photosynthesis and water use vary over the course of the diurnal cycle and inform agricultural management and future improvement of terrestrial biosphere/land surface models.