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

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2016-07, Vol.8 (7), p.597-597
• Main Authors: Yanghui, Kang, Ozdogan, Mutlu, Zipper, Samuel C., Roman, Miguel O., Walker, Jeff, Hong, Suk Young, Marshall, Michael, Magliulo, Vincenzo, Moreno, Jose, Alonso, Luis, Miyata, Akira, Kimball, Bruce, Loheide, Steven P., II
• Format: Article
• Language: English
• Subjects: agriculture; agroecosystem modeling; Earth Resources And Remote Sensing; LAI; Landsat; Vegetation Index
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs8070597

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 (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.