Leaf area index inversion combining with HJ-1/CCD and Landsat 8/OLI data in the middle reach of the Heihe River basin

Abstract

The main restriction on surface parameter inversion from remote sensing data with 30 m resolution is the limited number of observations. Nevertheless, a network or multiple-sensor method can efficiently increase the number of observations. In this study, a multi-sensor database was generated from HJ-1/CCD and Landsat 8/OLI from June 2013 to August in 2013 in the middle reach of Heihe River Basin. Characteristics, including proportion of valid observations, distribution of observation angles, bidirectional reflectance distribution function, and data consistency among sensors after preprocessing, of the multi-sensor dataset were analyzed. Difference in observation quality from different sensors is a major issue regarding Leaf Area Index (LAI) inversion from a multi-sensor dataset. Therefore, an observation quality control criterion was initially designed. Multi-sensor observations that satisfied the quality control requirements were used to inverse LAI based on a look-up table built by the unified model. The synthesis LAI over 10 days was set as the mean of LAI inversion from each sensor observation because of limited observation number. Analysis and validation were performed based on LAI products produced in the middle reach of the Heihe River Basin. Results show that the percentage of valid LAI inversion significantly increased from 6.4% to 49.7% of the single-sensor inversion to 75.9% of the multi-sensor inversion. Validated results show that the average RMSE between field measurements and LAI inversion was 0.71.The network of HJ-1/CCD and Landsat 8/OLI sensors with 30 m spatial resolution can generate LAI products with reasonable accuracy and continuous temporal resolution.