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

Bora Lee,Hyojung Kwon,John Tenhunen,Akira Miyata,Steve Lindner

doi:10.3390/rs9010020

Abstract

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.

Highlights

Leaf area index (LAI) is one of the key parameters in estimating ecosystem productivity of various process-based models and is strongly related to plant phenology and vegetation dynamics [1,2,3].leaf area index (LAI) influences many biological and physical processes driving the exchange of matter and energy flow [4]
Our objectives were (1) to estimate the seasonal course of LAI based on the traditional approach by partitioning the entire growing season into two growth phases; and (2) to develop an alternative method using a consistent development curve to estimate seasonal LAI by identifying the time at which the maximum Normalized Difference VI (NDVI) (NDVImax ) and Enhanced VI (EVI) (EVImax ) occur
LAI was positively correlated with NDVI and EVI when the regressions were applied to the entire growing season (Figure 2)

Summary

Introduction

Leaf area index (LAI) is one of the key parameters in estimating ecosystem productivity of various process-based models and is strongly related to plant phenology and vegetation dynamics [1,2,3]. LAI influences many biological and physical processes driving the exchange of matter and energy flow [4]. LAI serves as a useful indicator to characterize the condition of vegetation owing to its rapid response to different stress factors and changes in climatic conditions [5]. Estimation of LAI is an essential step in most of the process-based models for carbon and water fluxes in vegetative ecosystems [6,7,8]. Use of inaccurately estimated LAI as an input variable for process-based models will propagate errors in estimating CO2 and H2 O exchange in vegetative ecosystems.

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Conclusion