Next Article in Journal
Multisource Remote Sensing Imagery Fusion Scheme Based on Bidimensional Empirical Mode Decomposition (BEMD) and Its Application to the Extraction of Bamboo Forest
Next Article in Special Issue
Spatiotemporal Variability of Land Surface Phenology in China from 2001–2014
Previous Article in Journal
Potential of ALOS2 and NDVI to Estimate Forest Above-Ground Biomass, and Comparison with Lidar-Derived Estimates
Previous Article in Special Issue
Characterizing Cropland Phenology in Major Grain Production Areas of Russia, Ukraine, and Kazakhstan by the Synergistic Use of Passive Microwave and Visible to Near Infrared Data
Open AccessArticle

Evaluation of a Phenology-Dependent Response Method for Estimating Leaf Area Index of Rice Across Climate Gradients

Plant Ecology, Bayreuth Center of Ecology and Environmental Research (BayCEER), Universitätsstrasse 30, University of Bayreuth, 95447 Bayreuth, Germany
Department of Forest Ecosystems and Society, Oregon State University, 321 Richardson Hall, Corvallis, OR 97331, USA
Institute for Agro-Environmental Sciences, NARO, Tsukuba 305-8604, Japan
Author to whom correspondence should be addressed.
Academic Editors: Geoffrey M. Henebry, Forrest M. Hoffman, Jitendra Kumar, Xiaoyang Zhang, Ioannis Gitas and Prasad S. Thenkabail
Remote Sens. 2017, 9(1), 20;
Received: 9 August 2016 / Revised: 30 November 2016 / Accepted: 21 December 2016 / Published: 29 December 2016
Accurate estimate of the seasonal leaf area index (LAI) in croplands is required for understanding not only intra- and inter-annual crop development, but also crop management. Lack of consideration in different growth phases in the relationship between LAI and vegetation indices (VI) often results in unsatisfactory estimation in the seasonal course of LAI. In this study, we partitioned the growing season into two phases separated by maximum VI ( VI max ) and applied the general regression model to the data gained from two phases. As an alternative method to capture the influence of seasonal phenological development on the LAI-VI relationship, we developed a consistent development curve method and compared its performance with the general regression approaches. We used the Normalized Difference VI (NDVI) and the Enhanced VI (EVI) from the rice paddy sites in Asia (South Korea and Japan) and Europe (Spain) to examine its applicability across different climate conditions and management cycles. When the general regression method was used, separating the season into two phases resulted in no better estimation than the estimation obtained with the entire season observation due to an abrupt change in seasonal LAI occurring during the transition between the before and after VI max . The consistent development curve method reproduced the seasonal patterns of LAI from both NDVI and EVI across all sites better than the general regression method. Despite less than satisfactory estimation of a local LAI max , the consistent development curve method demonstrates improvement in estimating the seasonal course of LAI. The method can aid in providing accurate seasonal LAI as an input into ecological process-based models. View Full-Text
Keywords: leaf area index; rice paddy; NDVI; EVI; consistent development curve leaf area index; rice paddy; NDVI; EVI; consistent development curve
Show Figures

Graphical abstract

MDPI and ACS Style

Lee, B.; Kwon, H.; Miyata, A.; Lindner, S.; Tenhunen, J. Evaluation of a Phenology-Dependent Response Method for Estimating Leaf Area Index of Rice Across Climate Gradients. Remote Sens. 2017, 9, 20.

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.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop