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Remote Sens. 2016, 8(7), 597;

How Universal Is the Relationship between Remotely Sensed Vegetation Indices and Crop Leaf Area Index? A Global Assessment

Nelson Institute Center for Sustainability and the Global Environment, University of Wisconsin-Madison, 1710 University Avenue, Madison, WI 53726, USA
Department of Geography, University of Wisconsin-Madison, Madison, WI 53706, USA
Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
Terrestrial Information Systems Laboratory, NASA Goddard Space Flight Center, Code 619 Bld-32 S-036F, Greenbelt, MD 20771, USA
Department of Civil Engineering, Monash University, Clayton, Victoria 3800, Australia
Department of Agricultural Environment, National Institute of Agricultural Sciences (NAS), RDA, Wanju 55365, Korea
Climate Research Unit, World Agroforestry Centre, United Nations Ave, Gigiri, P.O. Box 30677, Nairobi 00100, Kenya
CNR–ISAFOM, Institute for Mediterranean Agricultural and Forest Systems, National Research Council, via Patacca 85, 80040 Ercolano (Napoli), Italy
Institute of Biometeorology of the National Research Council (IBIMET-CNR), Firenze 8-50145, Italy
Laboratory for Earth Observation, Department of Earth Physics and Thermodynamics, University of Valencia, Burjassot, Valencia 46100, Spain
Institute for Agro-Environmental Sciences, NARO, Tsukuba 305-8604, Japan
US Arid-Land Agricultural Research Center, USDA, Agricultural Research Service, Maricopa, AZ 85138, USA
Author to whom correspondence should be addressed.
Academic Editors: James Campbell and Prasad S. Thenkabail
Received: 11 May 2016 / Revised: 28 June 2016 / Accepted: 7 July 2016 / Published: 15 July 2016
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Leaf Area Index (LAI) is a key variable that bridges remote sensing observations to the quantification of agroecosystem processes. In this study, we assessed the universality of the relationships between crop LAI and remotely sensed Vegetation Indices (VIs). We first compiled a global dataset of 1459 in situ quality-controlled crop LAI measurements and collected Landsat satellite images to derive five different VIs including Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), two versions of the Enhanced Vegetation Index (EVI and EVI2), and Green Chlorophyll Index (CIGreen). Based on this dataset, we developed global LAI-VI relationships for each crop type and VI using symbolic regression and Theil-Sen (TS) robust estimator. Results suggest that the global LAI-VI relationships are statistically significant, crop-specific, and mostly non-linear. These relationships explain more than half of the total variance in ground LAI observations (R2 > 0.5), and provide LAI estimates with RMSE below 1.2 m2/m2. Among the five VIs, EVI/EVI2 are the most effective, and the crop-specific LAI-EVI and LAI-EVI2 relationships constructed by TS, are robust when tested by three independent validation datasets of varied spatial scales. While the heterogeneity of agricultural landscapes leads to a diverse set of local LAI-VI relationships, the relationships provided here represent global universality on an average basis, allowing the generation of large-scale spatial-explicit LAI maps. This study contributes to the operationalization of large-area crop modeling and, by extension, has relevance to both fundamental and applied agroecosystem research. View Full-Text
Keywords: LAI; Vegetation Index; agriculture; Landsat; agroecosystem modeling LAI; Vegetation Index; agriculture; Landsat; agroecosystem modeling

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Kang, Y.; Özdoğan, M.; Zipper, S.C.; Román, M.O.; Walker, J.; Hong, S.Y.; Marshall, M.; Magliulo, V.; Moreno, J.; Alonso, L.; Miyata, A.; Kimball, B.; Loheide, S.P. How Universal Is the Relationship between Remotely Sensed Vegetation Indices and Crop Leaf Area Index? A Global Assessment. Remote Sens. 2016, 8, 597.

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