气候变化领域集成服务门户(CCPortal): Performance stability of the MODIS and VIIRS LAI algorithms inferred from analysis of long time series of products

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DOI	10.1016/j.rse.2021.112438
	Performance stability of the MODIS and VIIRS LAI algorithms inferred from analysis of long time series of products
	Yan K.; Pu J.; Park T.; Xu B.; Zeng Y.; Yan G.; Weiss M.; Knyazikhin Y.; Myneni R.B.
发表日期	2021
ISSN	00344257
卷号	260
英文摘要	The science teams of Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imager Radiometer Suite (VIIRS) have been supporting various global climate, biogeochemistry, and energy flux research efforts by producing valuable long-term Leaf Area Index (LAI) products. Although intensive LAI validation studies have been carried out globally, retrieval accuracy has been assessed only over short time spans and product quality is reported as a constant based on the assumption that it is stationary. However, a preliminary evaluation found a time-dependent signal degradation of the MODIS sensors which may cause the uncertainty of the Bidirectional Reflectance Factor (BRF) product to exceed the preset uncertainty buffer in the retrieval algorithm and result in MODIS LAI quality variation and time-series inconsistency with VIIRS LAI. Therefore, to ensure the reliability of trend-detection studies and to answer the critical question of whether the algorithm configuration is still suitable for the uncertainty level of present BRF product, there is a need for a more comprehensive investigation regarding the performance stability of LAI retrieval algorithm due to model uncertainty and input uncertainties. This paper reports analyses of inter-annual stability and trends of LAI uncertainties (inferred from product quality flag rather than ground-based validation) as well as LAI magnitudes using multi-year (MODIS: 2001–2019, VIIRS: 2013–2019) and multi-site (445 sites) datasets. We found that the quality metrics of the two products are consistent across different biome types and LAI values (R2 ranged from 0.88 to 0.99). In the 19-year MODIS period, we found a significant increasing trend in LAI magnitudes (4.5%/decade) which agrees with previous reports, while there was no significant trend in product quality. Moreover, quality metrics showed much smaller inter-annual variations than LAI values, which confirms the performance stability of retrieval algorithms and suggests that the configuration of MODIS algorithm is still compatible with the uncertainty of algorithm inputs. Additionally, this study provides support to the “Greening Asia” studies by eliminating the possibility that the “greening” could be attributed to artificial trends caused by sensor degradation. We also found very small inter-annual variations in atmospheric conditions (e.g., cloud and aerosols), which further assures that the performance stability of retrieval algorithm. Overall, our results demonstrate the robustness of the retrieval algorithm in the presence of changes in input uncertainties. The temporal stability of the algorithm performance indirectly strengthen the confidence in the continuous use of MODIS LAI products either independently or in combination with its successor - VIIRS LAI - to study global and regional vegetation dynamics © 2021 Elsevier Inc.
英文关键词	Leaf area index (LAI); MODIS; Performance stability; Product quality; Trend detection; VIIRS
语种	英语
scopus关键词	Radiometers; Stability; Time series; Time series analysis; Uncertainty analysis; Bidirectional reflectance factors; Leaf area index; Moderate-resolution imaging spectroradiometers; Performance stability; Products quality; Retrieval algorithms; Trend detection; Uncertainty; Visible-infrared imager-radiometer suites; Quality control; algorithm; energy flux; leaf area index; long-term change; MODIS; performance assessment; sensor; VIIRS; Asia
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/178851
作者单位	School of Land Science and Techniques, China University of Geosciences, Beijing, 100083, China; Department of Earth and Environment, Boston University, Boston, MA 02215, United States; State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101, China; NASA Ames Research Center, Moffett Field, CA 94035, United States; Bay Area Environmental Research Institute, Moffett Field, CA 94035, United States; Department of Global Ecology, Stanford UniversityCA 94305, United States; Institut National de la Recherche Agronomique, Université d'Avignon et des Pays du Vaucluse (INRA-UAPV), 228 Route de l'Aérodrome, Avignon, 84914, France
推荐引用方式 GB/T 7714	Yan K.,Pu J.,Park T.,et al. Performance stability of the MODIS and VIIRS LAI algorithms inferred from analysis of long time series of products[J],2021,260.
APA	Yan K..,Pu J..,Park T..,Xu B..,Zeng Y..,...&Myneni R.B..(2021).Performance stability of the MODIS and VIIRS LAI algorithms inferred from analysis of long time series of products.Remote Sensing of Environment,260.
MLA	Yan K.,et al."Performance stability of the MODIS and VIIRS LAI algorithms inferred from analysis of long time series of products".Remote Sensing of Environment 260(2021).