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How Universal is the Relationship Between Remotely Sensed Vegetation Indices and Crop Leaf Area Index? A Global AssessmentLeaf 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 (CI(sub Green)). 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 greater than 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.
Document ID
20170002668
Acquisition Source
Goddard Space Flight Center
Document Type
Reprint (Version printed in journal)
External Source(s)
doi:10.3390/rs8070597
Authors
Yanghui, Kang ORCID(Wisconsin-Madison Univ. Madison, WI, United States)
Ozdogan, Mutlu (Wisconsin-Madison Univ. Madison, WI, United States)
Zipper, Samuel C. ORCID(Wisconsin-Madison Univ. Madison, WI, United States)
Roman, Miguel O. (NASA Goddard Space Flight Center Greenbelt, MD, United States)
Walker, Jeff (Monash Univ. Victoria, Australia)
Hong, Suk Young (National Academy of Agricultural Science (NAAS) Korea, Republic of)
Marshall, Michael (International Centre for Research in AgroForestry Nairobi, Kenya)
Magliulo, Vincenzo ORCID(Consiglio Nazionale delle Ricerche Naples, Italy)
Moreno, Jose (Valencia Univ. Burjassot, Spain)
Alonso, Luis (Valencia Univ. Burjassot, Spain)
Miyata, Akira (National Inst. for Agro-Environmental Sciences (NIAES) Tsukuba, Japan)
Kimball, Bruce (Agricultural Research Service Phoenix, AZ, United States)
Loheide, Steven P., II (Wisconsin-Madison Univ. Madison, WI, United States)
Date Acquired
March 29, 2017
Publication Date
July 15, 2016
Publication Information
Publication: Remote Sensing
Publisher: MDPI
Volume: 8
Issue: 7
e-ISSN: 2072-4292
Subject Category
Earth Resources And Remote Sensing
Report/Patent Number
GSFC-E-DAA-TN40736
Funding Number(s)
PROJECT: SCMD-EarthScienceSystem_281945
CONTRACT_GRANT: DEB-1038759
CONTRACT_GRANT: DEB-0822700
Distribution Limits
Public
Copyright
Public Use Permitted.
Technical Review
NASA Peer Committee
Keywords
Vegetation Index
LAI
Agroecosystem modeling

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