Remote Sensing of Leaf Area Index (LAI) and a Spatiotemporally Parameterized Model for Mixed Grasslands

Leaf area index (LAI) is an important biophysical variable used to reflect the vegetation condition in ecosystems. However, accurate estimation of LAI is highly dependent upon the spatiotemporal scales. Both direct (destructive sampling, litter fall collection and point contact sampling) and indirect methods (optical instruments) have been used to measure LAI in mixed grasslands. In particular, remote sensing technique is rapidly gaining wide interest in developing various empirical and physical models for LAI estimation. The present review compares the advantages and disadvantages of different methods in estimating LAI. It also summarizes the spatiotemporal variation of LAI and its sensitive factors. The suitability of remote sensing data in capturing the spatiotemporal variation of LAI is particularly discussed. Based on the gaps found in existing literature, this paper attempts to theoretically propose a spatiotemporally parameterized model to improve the accuracy of LAI derivation in mixed grasslands. The overall objective will be achieved by the following steps:1) Determine the sensitive factors influencing LAI spatiotemporal variation; 2) Identify appropriate remote sensing data in terms of spatial, spectral and temporal resolutions; 3) Establish the LAI parameterized model; 4) Assess the model accuracy and test it in one hydrology model.