Next Article in Journal
Kernel Supervised Ensemble Classifier for the Classification of Hyperspectral Data Using Few Labeled Samples
Previous Article in Journal
Quantifying the Impacts of Environmental Factors on Vegetation Dynamics over Climatic and Management Gradients of Central Asia
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2016, 8(7), 597; doi:10.3390/rs8070597

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

1
Nelson Institute Center for Sustainability and the Global Environment, University of Wisconsin-Madison, 1710 University Avenue, Madison, WI 53726, USA
2
Department of Geography, University of Wisconsin-Madison, Madison, WI 53706, USA
3
Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
4
Terrestrial Information Systems Laboratory, NASA Goddard Space Flight Center, Code 619 Bld-32 S-036F, Greenbelt, MD 20771, USA
5
Department of Civil Engineering, Monash University, Clayton, Victoria 3800, Australia
6
Department of Agricultural Environment, National Institute of Agricultural Sciences (NAS), RDA, Wanju 55365, Korea
7
Climate Research Unit, World Agroforestry Centre, United Nations Ave, Gigiri, P.O. Box 30677, Nairobi 00100, Kenya
8
CNR–ISAFOM, Institute for Mediterranean Agricultural and Forest Systems, National Research Council, via Patacca 85, 80040 Ercolano (Napoli), Italy
9
Institute of Biometeorology of the National Research Council (IBIMET-CNR), Firenze 8-50145, Italy
10
Laboratory for Earth Observation, Department of Earth Physics and Thermodynamics, University of Valencia, Burjassot, Valencia 46100, Spain
11
Institute for Agro-Environmental Sciences, NARO, Tsukuba 305-8604, Japan
12
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
View Full-Text   |   Download PDF [3852 KB, uploaded 15 July 2016]   |  

Abstract

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
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Supplementary material

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top