Remote Sens. 2012, 4(1), 303-326; doi:10.3390/rs4010303
Article

Exploring Simple Algorithms for Estimating Gross Primary Production in Forested Areas from Satellite Data

1 Division of Science & Environmental Policy, California State University, Monterey Bay, Seaside, CA 93955, USA 2 NASA Ames Research Center, Moffett Field, CA 94040, USA 3 Department of Watershed Sciences, Utah State University, Logan, UT 84322, USA 4 Bay Area Environmental Research Institute, Sonoma, CA 95476, USA 5 National Institute of Advanced Industrial Science and Technology, Tsukuba, Ibaraki 305-8569, Japan 6 Graduate School of Agriculture, Hokkaido University, Sapporo, Hokkaido 060-8589, Japan 7 Department of Geography and Environment, Boston University, Boston, MA 02215, USA
* Author to whom correspondence should be addressed.
Received: 15 December 2011; in revised form: 12 January 2012 / Accepted: 12 January 2012 / Published: 23 January 2012
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Abstract: Algorithms that use remotely-sensed vegetation indices to estimate gross primary production (GPP), a key component of the global carbon cycle, have gained a lot of popularity in the past decade. Yet despite the amount of research on the topic, the most appropriate approach is still under debate. As an attempt to address this question, we compared the performance of different vegetation indices from the Moderate Resolution Imaging Spectroradiometer (MODIS) in capturing the seasonal and the annual variability of GPP estimates from an optimal network of 21 FLUXNET forest towers sites. The tested indices include the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Fraction of Photosynthetically Active Radiation absorbed by plant canopies (FPAR). Our results indicated that single vegetation indices captured 50–80% of the variability of tower-estimated GPP, but no one index performed universally well in all situations. In particular, EVI outperformed the other MODIS products in tracking seasonal variations in tower-estimated GPP, but annual mean MODIS LAI was the best estimator of the spatial distribution of annual flux-tower GPP (GPP = 615 × LAI − 376, where GPP is in g C/m2/year). This simple algorithm rehabilitated earlier approaches linking ground measurements of LAI to flux-tower estimates of GPP and produced annual GPP estimates comparable to the MODIS 17 GPP product. As such, remote sensing-based estimates of GPP continue to offer a useful alternative to estimates from biophysical models, and the choice of the most appropriate approach depends on whether the estimates are required at annual or sub-annual temporal resolution.
Keywords: GPP; LAI; EVI; NDVI; MODIS

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MDPI and ACS Style

Hashimoto, H.; Wang, W.; Milesi, C.; White, M.A.; Ganguly, S.; Gamo, M.; Hirata, R.; Myneni, R.B.; Nemani, R.R. Exploring Simple Algorithms for Estimating Gross Primary Production in Forested Areas from Satellite Data. Remote Sens. 2012, 4, 303-326.

AMA Style

Hashimoto H, Wang W, Milesi C, White MA, Ganguly S, Gamo M, Hirata R, Myneni RB, Nemani RR. Exploring Simple Algorithms for Estimating Gross Primary Production in Forested Areas from Satellite Data. Remote Sensing. 2012; 4(1):303-326.

Chicago/Turabian Style

Hashimoto, Hirofumi; Wang, Weile; Milesi, Cristina; White, Michael A.; Ganguly, Sangram; Gamo, Minoru; Hirata, Ryuichi; Myneni, Ranga B.; Nemani, Ramakrishna R. 2012. "Exploring Simple Algorithms for Estimating Gross Primary Production in Forested Areas from Satellite Data." Remote Sens. 4, no. 1: 303-326.

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