Abstract
This paper aims to retrieve temporal high-resolution LAI derived by fusing MOD15 products (1 km resolution), field-measured LAI and ASTER reflectance (15-m resolution). Though the inversion of a physically based canopy reflectance model using high-resolution satellite data can produce high-resolution LAI products, the obstacle to producing temporal products is obvious due to the low temporal resolution of high resolution satellite data. A feasible method is to combine different source data, taking advantage of the spatial and temporal resolution of different sensors. In this paper, a high-resolution LAI retrieval method was implemented using a dynamic Bayesian network (DBN) inversion framework. MODIS LAI data with higher temporal resolution were used to fit the temporal background information, which is then updated by new, higher resolution data, herein ASTER data. The interactions between the different resolution data were analyzed from a Bayesian perspective. The proposed method was evaluated using a dataset collected in the HiWater (Heihe Watershed Allied Telemetry Experimental Research) experiment. The determination coefficient and RMSE between the estimated and measured LAI are 0.80 and 0.43, respectively. The research results suggest that even though the coarse-resolution background information differs from the high-resolution satellite observations, a satisfactory estimation result for the temporal high-resolution LAI can be produced using the accumulated information from both the new observations and background information.
Highlights
The leaf area index (LAI), defined as half of the leaf surface area on a unit ground surface area, is an important structural parameter of vegetation [1]
The quantitative products from high-resolution remote-sensing observation data yield a discontinuous time series that suffers from bad weather conditions
The framework for estimating temporal, high-resolution LAIs is based on a dynamic Bayesian network (DBN), which combines the dynamic-change information from coarse-resolution LAI products with observation information from high-resolution remote-sensing data
Summary
The leaf area index (LAI), defined as half of the leaf surface area on a unit ground surface area, is an important structural parameter of vegetation [1]. Retrieving LAIs from remotely sensed data is an efficient method compared to ground-based measurement [2]. The above LAI products yield a coarse resolution from 500 to 3000 m. In the context of land and resource monitoring in a small regional area, temporal high-resolution LAI products are more appreciated [8,9]. One straightforward method to produce high resolution LAIs is using high resolution satellite data, such as Landsat TM/ETM+ [8] and ASTER VNIR [10,11] imagery with 30 m and 15 m resolution, respectively. The quantitative products from high-resolution remote-sensing observation data yield a discontinuous time series that suffers from bad weather conditions. To obtain high-quality LAI products, researchers have begun to synthetically calculate LAIs using multi-source remote-sensing data using dynamic change information from existing remote-sensing products [12,13]
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