A Reconstructed Global Daily Seamless SIF Product at 0.05 Degree Resolution Based on TROPOMI, MODIS and ERA5 Data
A Reconstructed Global Daily Seamless SIF Product at 0.05 Degree Resolution Based on TROPOMI, MODIS and ERA5 Data
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DOI：
10.3390/rs14061504
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发表时间：
2022-03
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通讯作者：
Jiaochan Hu;J. Jia;Yan Ma;Liangyun Liu;Haoyang Yu
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作者：
Jiaochan Hu;J. Jia;Yan Ma;Liangyun Liu;Haoyang Yu
卫星获取的太阳诱导叶绿素荧光（SIF）已被证明是在区域或全球范围内监测植被光合作用活动的一个有价值的工具。然而，粗糙的时空分辨率或离散的空间覆盖的大多数卫星SIF数据集阻碍了他们的潜力，研究碳循环和生态过程在更精细的尺度。虽然最近的对流层监测仪器（TROPOMI）部分地解决了这个问题，但是当以高时空分辨率网格化时，SIF仍然具有空间不足和时空不连续性的缺点（例如，0.05°，1天或2天），由于其不均匀的采样大小，条带间隙，云污染。在这里，我们使用随机森林（RF）方法以及TROPOMI SIF，MODIS反射率和气象数据集，在2018-2020年期间生成了一个新的全球SIF产品，具有每日和0.05°分辨率（SDSIF）的无缝时空覆盖。我们研究了解释变量和模型约束条件的选择如何影响模型的准确性。最后，鉴于SIF对更近空间和时间内的环境变量的类似反应，对特定大陆和月份的模型进行了训练和应用。对于测试样品，该策略实现了比一个通用模型（R2 = 0.913，RMSE = 0.0653 mW/m2/nm/sr）更好的精度（R2 = 0.928，RMSE = 0.0597 mW/m2/nm/sr）。SDSIF产品可以很好地保留原始TROPOMI SIF的时间和空间特征，在大多数区域（80%的全局像素），两者之间具有高时间相关性（平均R2约为0.750）和低空间残差（小于±0.081 mW/m2/nm/Sr）。与5个通量站点的原始SIF相比，SDSIF填补了时间空白，并且在日尺度上与塔基SIF具有更好的一致性（平均R2从0.467增加到0.744）。因此，它提供了比来自稀疏每日观测的原始SIF平均值更可靠的4天SIF平均值（例如，在达曼站点的R2从0.614提高到0.837），这导致与基于4天塔的GPP的更好的相关性。此外，重建的无缝SIF也提高了全局覆盖率和局部空间细节。与原有的TROPOMI SIF相比，该产品在时空连续性和细节方面具有优势，这将有利于卫星SIF在更精细的时空尺度上理解碳循环和生态过程。
Satellite-derived solar-induced chlorophyll fluorescence (SIF) has been proven to be a valuable tool for monitoring vegetation’s photosynthetic activity at regional or global scales. However, the coarse spatiotemporal resolution or discrete space coverage of most satellite SIF datasets hinders their full potential for studying carbon cycle and ecological processes at finer scales. Although the recent TROPOspheric Monitoring Instrument (TROPOMI) partially addresses this issue, the SIF still has drawbacks in spatial insufficiency and spatiotemporal discontinuities when gridded at high spatiotemporal resolutions (e.g., 0.05°, 1-day or 2-day) due to its nonuniform sampling sizes, swath gaps, and clouds contaminations. Here, we generated a new global SIF product with Seamless spatiotemporal coverage at Daily and 0.05° resolutions (SDSIF) during 2018–2020, using the random forest (RF) approach together with TROPOMI SIF, MODIS reflectance and meteorological datasets. We investigated how the model accuracy was affected by selection of explanatory variables and model constraints. Eventually, models were trained and applied for specific continents and months given the similar response of SIF to environmental variables within closer space and time. This strategy achieved better accuracy (R2 = 0.928, RMSE = 0.0597 mW/m2/nm/sr) than one universal model (R2 = 0.913, RMSE = 0.0653 mW/m2/nm/sr) for testing samples. The SDSIF product can well preserve the temporal and spatial characteristics in original TROPOMI SIF with high temporal correlations (mean R2 around 0.750) and low spatial residuals (less than ±0.081 mW/m2/nm/sr) between them two at most regions (80% of global pixels). Compared with the original SIF at five flux sites, SDSIF filled the temporal gaps and was better consistent with tower-based SIF at the daily scale (the mean R2 increased from 0.467 to 0.744. Consequently, it provided more reliable 4-day SIF averages than the original ones from sparse daily observations (e.g., the R2 at Daman site was raised from 0.614 to 0.837), which resulted in a better correlation with 4-day tower-based GPP. Additionally, the global coverage ratio and local spatial details had also been improved by the reconstructed seamless SIF. Our product has advantages in spatiotemporal continuities and details over the original TROPOMI SIF, which will benefit the application of satellite SIF for understanding carbon cycle and ecological processes at finer spatial and temporal scales.