Reconstructed Solar-Induced Fluorescence: A Machine Learning Vegetation Product Based on MODIS Surface Reflectance to Reproduce GOME-2 Solar-Induced Fluorescence.
Reconstructed Solar-Induced Fluorescence: A Machine Learning Vegetation Product Based on MODIS Surface Reflectance to Reproduce GOME-2 Solar-Induced Fluorescence.
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DOI：
10.1002/2017gl076294
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发表时间：
2018-04-16
影响因子：
5.2
通讯作者：
Alemohammad SH
中科院分区：
文献类型：
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作者：
Gentine P;Alemohammad SH
空间太阳诱导荧光(SIF)观测在估算总初级生产力(GPP)方面取得了重大进展。然而，目前的SIF观测仍然在空间上粗糙、不频繁和嘈杂。在这里，我们开发了一种机器学习方法，使用中分辨率成像光谱仪(MODIS)通道的表面反射率来再现全球臭氧监测实验2(GOME-2)的晴空表面辐照度归一化的SIF。由此产生的产品是由叶绿素吸收的生态系统光合作用有效辐射(FAPARCH)的替代品。将这一新乘积与MODIS对光合作用有效辐射的估计相乘，得到了一个新的仅由MODIS重建的SIF，称为重建的SIF(Rsif)。与GPP和两个参考全球GPP产品的涡动协方差估计相比，RSIF表现出比原始SIF更高的季节和年际相关性，特别是在干旱和寒冷地区。与典型的植被指数相反，SRIF还复制了美国玉米带等生产力高的地区，与SIF类似。摘要利用可见光和近红外MODIS通道，开发了一种新的基于机器学习的植被产品？？重建太阳诱导荧光(Rsif)。与光学植被指数不同，重建的太阳诱导荧光(Rsif)改善了重建的太阳诱导荧光，与现场涡旋协方差和基于遥感的光合作用具有更好的相关性，并且噪声更低，具有高空间分辨率和长记录且不饱和。
Solar‐induced fluorescence (SIF) observations from space have resulted in major advancements in estimating gross primary productivity (GPP). However, current SIF observations remain spatially coarse, infrequent, and noisy. Here we develop a machine learning approach using surface reflectances from Moderate Resolution Imaging Spectroradiometer (MODIS) channels to reproduce SIF normalized by clear sky surface irradiance from the Global Ozone Monitoring Experiment‐2 (GOME‐2). The resulting product is a proxy for ecosystem photosynthetically active radiation absorbed by chlorophyll (fAPARCh). Multiplying this new product with a MODIS estimate of photosynthetically active radiation provides a new MODIS‐only reconstruction of SIF called Reconstructed SIF (RSIF). RSIF exhibits much higher seasonal and interannual correlation than the original SIF when compared with eddy covariance estimates of GPP and two reference global GPP products, especially in dry and cold regions. RSIF also reproduces intense productivity regions such as the U.S. Corn Belt contrary to typical vegetation indices and similarly to SIF. A new machine learning‐based vegetation product, Reconstructed Solar‐Induced Fluorescence (RSIF), is developed using visible and near‐infrared MODIS channels RSIF improves SIF with better correlation with in situ eddy covariance and remote sensing‐based photosynthesis and lower noise RSIF has high spatial resolution and a long record and does not saturate, unlike optical vegetation indices