Abstract
Currently, multiple leaf area index (LAI) products retrieved from remote sensing data are widely used in crop growth monitoring, land-surface process simulation and studies of climate change. However, most LAI products are only retrieved from individual satellite observations, which may result in spatial-temporal discontinuities and low accuracy in these products. In this paper, a new method was developed to simultaneously retrieve multiscale LAI data from satellite observations with different spatial resolutions based on an ensemble multiscale filter (EnMsF). The LAI average values corresponding to the date of satellite observations were calculated from the multi-year Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product and were used as a priori knowledge for LAI in order to construct an initial ensemble multiscale tree (EnMsT). Satellite observations obtained at different spatial resolutions were then applied to update the LAI values at each node of the EnMsT using a two-sweep filtering procedure. Next, the retrieved LAI values at the finest scale were used as a priori knowledge for LAI for the new round of construction and updating of the EnMsT, until the sum of the difference of LAI values at each node of the EnMsT between two adjacent updates is less than a given threshold. The method was tested using Thematic Mapper (TM) or Enhanced Thematic Mapper Plus (ETM+) surface reflectance data and MODIS surface reflectance data from five sites that have different vegetation types. The results demonstrate that the retrieved LAI values for each spatial resolution were in good agreement with the aggregated LAI reference map values for the corresponding spatial resolution. The retrieved LAI values at the coarsest scale provided better accuracy with the aggregated LAI reference map values (root mean square error (RMSE) = 0.45) compared with that obtained from the MODIS LAI values (RMSE = 1.30).
Highlights
Leaf area index (LAI) is defined as half of the total area of green leaves for a given unit of horizontal ground surface area [1]
The average and standard deviation of multi-year Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product corresponding to the date of satellite observations was used as a priori knowledge for LAI at the finest scale to construct the initial ensemble multiscale tree (EnMsT)
At the Bondville site, Thematic Mapper (TM) surface reflectance data obtained on Day 228, 2000 and MODIS surface reflectance data obtained on Day 225, 2000 were used to retrieve multiscale LAI values
Summary
Leaf area index (LAI) is defined as half of the total area of green leaves for a given unit of horizontal ground surface area [1]. LAI is an essential land surface biophysical parameter that describes the amount and change of vegetation, and is a significant input for climate and ecological simulations, including crop growth models [2], hydrological models [3], ecology models [4], weather forecasting [5]. The methods used to estimate LAI from remote sensing data can be distinguished into two types: empirical methods and physical methods. Empirical methods, which utilize statistical relationships between LAI and a variety of vegetation indices to calculate LAI values, are easy to operate and Remote Sens. Since physical methods can be adjusted for a wide range of situations, an increasing number of studies tend to use canopy radiative-transfer models in the inversion of LAI values [9,10]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
