An Empirical Assessment of the MODIS Land Cover Dynamics and TIMESAT Land Surface Phenology Algorithms

Stanimirova, Radost; Cai, Zhanzhang; Melaas, Eli K.; Gray, Josh M.; Eklundh, Lars; Jönsson, Per; Friedl, Mark A.

doi:10.3390/rs11192201

Title: An Empirical Assessment of the MODIS Land Cover Dynamics and TIMESAT Land Surface Phenology Algorithms

Journal Article · Fri Sep 20 00:00:00 EDT 2019 · Remote Sensing

DOI:https://doi.org/10.3390/rs11192201· OSTI ID:1802812

Stanimirova, Radost ^[1]; Cai, Zhanzhang ^[2]; Melaas, Eli K. ^[1]; Gray, Josh M. ^[3]; Eklundh, Lars ^[2]; Jönsson, Per ^[4]; Friedl, Mark A. ^[1]

Boston Univ., MA (United States). Dept. of Earth and Environment
Lund Univ. (Sweden). Dept. of Physical Geography and Ecosystems Analysis
North Carolina State Univ., Raleigh, NC (United States). Dept. of Forestry and Environmental Resources; North Carolina State Univ., Raleigh, NC (United States). Center for Geospatial Analytics
Malmö Univ., Malmö (Sweden). Dept. of Materials Science and Applied Mathematics

Observations of vegetation phenology at regional-to-global scales provide important information regarding seasonal variation in the fluxes of energy, carbon, and water between the biosphere and the atmosphere. Numerous algorithms have been developed to estimate phenological transition dates using time series of remotely sensed spectral vegetation indices. A key challenge, however, is that different algorithms provide inconsistent results. This study provides a comprehensive comparison of start of season (SOS) and end of season (EOS) phenological transition dates estimated from 500 m MODIS data based on two widely used sources of such data: the TIMESAT program and the MODIS Global Land Cover Dynamics (MLCD) product. Specifically, we evaluate the impact of land cover class, criteria used to identify SOS and EOS, and fitting algorithm (local versus global) on the transition dates estimated from time series of MODIS enhanced vegetation index (EVI). Satellite-derived transition dates from each source are compared against each other and against SOS and EOS dates estimated from PhenoCams distributed across the Northeastern United States and Canada. Our results show that TIMESAT and MLCD SOS transition dates are generally highly correlated (r = 0.51-0.97), except in Central Canada where correlation coefficients are as low as 0.25. Relative to SOS, EOS comparison shows lower agreement and higher magnitude of deviations. SOS and EOS dates are impacted by noise arising from snow and cloud contamination, and there is low agreement among results from TIMESAT, the MLCD product, and PhenoCams in vegetation types with low seasonal EVI amplitude or with irregular EVI time series. In deciduous forests, SOS dates from the MLCD product and TIMESAT agree closely with SOS dates from PhenoCams, with correlations as high as 0.76. Overall, our results suggest that TIMESAT is well-suited for local-to-regional scale studies because of its ability to tune algorithm parameters, which makes it more flexible than the MLCD product. At large spatial scales, where local tuning is not feasible, the MLCD product provides a readily available data set based on a globally consistent approach that provides SOS and EOS dates that are comparable to results from TIMESAT.

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Research Organization:: Princeton Univ., NJ (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

Grant/Contract Number:: SC0016011

OSTI ID:: 1802812

Journal Information:: Remote Sensing, Vol. 11, Issue 19; ISSN 2072-4292

Publisher:: MDPICopyright Statement

Country of Publication:: United States

Language:: English

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