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Open AccessArticle

A Comparison between the MODIS Product (MOD17A2) and a Tide-Robust Empirical GPP Model Evaluated in a Georgia Wetland

1
Department of Geography, University of Georgia, Athens, GA 30609, USA
2
Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province/College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China
3
Atmospheric Biogeosciences Group, University of Georgia, Griffin, GA 30223, USA
4
Faculty of Resource Science and Technology, Universiti Malaysia Sarawak, Kota Samarahan 94300, Malaysia
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(11), 1831; https://doi.org/10.3390/rs10111831
Received: 28 August 2018 / Revised: 30 October 2018 / Accepted: 14 November 2018 / Published: 19 November 2018
(This article belongs to the Special Issue Satellite-Based Wetland Observation)
Despite the importance of tidal ecosystems in the global carbon budget, the relationships between environmental drivers and carbon dynamics in these wetlands remain poorly understood. This limited understanding results from the challenges associated with in situ flux studies and their correlation with satellite imagery which can be affected by periodic tidal flooding. Carbon dioxide eddy covariance (EC) towers are installed in only a few wetlands worldwide, and the longest eddy-covariance record from Georgia (GA) wetlands contains only two continuous years of observations. The goals of the present study were to evaluate the performance of existing MODIS Gross Primary Production (GPP) products (MOD17A2) against EC derived GPP and develop a tide-robust Normalized Difference Moisture Index (NDMI) based model to predict GPP within a Spartina alterniflora salt marsh on Sapelo Island, GA. These EC tower-based observations represent a basis to associate CO2 fluxes with canopy reflectance and thus provide the means to use satellite-based reflectance data for broader scale investigations. We demonstrate that Light Use Efficiency (LUE)-based MOD17A2 does not accurately reflect tidal wetland GPP compared to a simple empirical vegetation index-based model where tidal influence was accounted for. The NDMI-based GPP model was capable of predicting changes in wetland CO2 fluxes and explained 46% of the variation in flux-estimated GPP within the training data, and a root mean square error of 6.96 g C m−2 in the validation data. Our investigation is the first to create a MODIS-based wetland GPP estimation procedure that demonstrates the importance of filtering tidal observations from satellite surface reflectance data. View Full-Text
Keywords: MODIS GPP Calibration; MOD17A2; Normalized Distribution Moisture Index; Tide Adjusted Wetland Index; flux GPP; salt marsh; tidal wetlands MODIS GPP Calibration; MOD17A2; Normalized Distribution Moisture Index; Tide Adjusted Wetland Index; flux GPP; salt marsh; tidal wetlands
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MDPI and ACS Style

Tao, J.; Mishra, D.R.; Cotten, D.L.; O’Connell, J.; Leclerc, M.; Nahrawi, H.B.; Zhang, G.; Pahari, R. A Comparison between the MODIS Product (MOD17A2) and a Tide-Robust Empirical GPP Model Evaluated in a Georgia Wetland. Remote Sens. 2018, 10, 1831.

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