Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1002/2017GL076294 |
Reconstructed Solar-Induced Fluorescence: A Machine Learning Vegetation Product Based on MODIS Surface Reflectance to Reproduce GOME-2 Solar-Induced Fluorescence | |
Gentine, P.1; Alemohammad, S. H.1,2 | |
2018-04-16 | |
发表期刊 | GEOPHYSICAL RESEARCH LETTERS |
ISSN | 0094-8276 |
EISSN | 1944-8007 |
出版年 | 2018 |
卷号 | 45期号:7页码:3136-3146 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | 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. Plain Language Summary A new proxy for photosynthesis is developed using Moderate Resolution Imaging Spectroradiometer observations and a machine learning approach. The new product is able to effectively reproduce observations from eddy covariance towers and more sophisticated photosynthesis models that rely on more information (such as weather information). |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000435743400030 |
WOS关键词 | INDUCED CHLOROPHYLL FLUORESCENCE ; SOIL-MOISTURE RETRIEVAL ; GLOBAL DISTRIBUTION ; TERRESTRIAL GROSS ; CARBON ; PHOTOSYNTHESIS ; RATIO ; WATER ; SIMULATIONS ; VARIABILITY |
WOS类目 | Geosciences, Multidisciplinary |
WOS研究方向 | Geology |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/27494 |
专题 | 气候变化 |
作者单位 | 1.Columbia Univ, Dept Earth & Environm Engn, New York, NY 10027 USA; 2.Radiant Earth, Washington, DC USA |
推荐引用方式 GB/T 7714 | Gentine, P.,Alemohammad, S. H.. Reconstructed Solar-Induced Fluorescence: A Machine Learning Vegetation Product Based on MODIS Surface Reflectance to Reproduce GOME-2 Solar-Induced Fluorescence[J]. GEOPHYSICAL RESEARCH LETTERS,2018,45(7):3136-3146. |
APA | Gentine, P.,&Alemohammad, S. H..(2018).Reconstructed Solar-Induced Fluorescence: A Machine Learning Vegetation Product Based on MODIS Surface Reflectance to Reproduce GOME-2 Solar-Induced Fluorescence.GEOPHYSICAL RESEARCH LETTERS,45(7),3136-3146. |
MLA | Gentine, P.,et al."Reconstructed Solar-Induced Fluorescence: A Machine Learning Vegetation Product Based on MODIS Surface Reflectance to Reproduce GOME-2 Solar-Induced Fluorescence".GEOPHYSICAL RESEARCH LETTERS 45.7(2018):3136-3146. |
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