Estimating leaf area index from MODIS and surface meteorological data using a dynamic Bayesian network

Remotely sensed data is the main source of vegetation leaf area index (LAI) information on the regional to global scale. Many validation results have revealed that the accuracy of the retrieved LAI is often affected by the cloud cover of imagery, instrument problems, and inversion algorithms. Ground meteorological station data, characterized by relatively high accuracy and time continuity compared with remote sensing data, can provide complementary information to remote sensing observations. In this paper, we combine the potential advantages of both types of data in order to improve LAI retrievals in the Heihe River Basin, an arid and semi-arid area in northwest China where Moderate Resolution Imaging Spectroradiometer (MODIS) LAI values are significantly underestimated. A dynamic Bayesian network (DBN) is used to integrate these two data types for time series LAI estimation. Results show that the square of correlation coefficient between LAI values estimated by our DBN method (referred to as DBN LAI) and field measured LAI values is 0.76, with a root mean square error of 0.78. The DBN LAI are closer to field measurements than the MODIS LAI standard product values. Moreover, by introducing ground meteorological station data using a dynamic process model, DBN LAI show better temporal consistency than the MODIS LAI. It is concluded that the quality of LAI retrievals can be improved by combining remote sensing data and ground meteorological station data using a filtering inference algorithm in a DBN framework. More importantly, the study provides a basis and method for utilizing ground meteorological station network data to estimate land surface parameters on a regional scale.