Monitoring time-series crop leaf area index from higher resolution remotely sensed data

Abstract

The leaf area index (LAI) is a characteristic parameter of vegetation canopies. It is significant for research on global climate change and ecological environment. China HJ-1 satellite has a revisit cycle of four days, providing HJ-1 CCD with a resolution of 30 m. However, the HJ-1 is incapable of obtaining observations at multiple angles for it has only one near-nadir view angle. This is problematic because single angle observations provide insufficient data for determining the LAI. This article proposes a new method for determining LAI using HJ-1 data. The proposed method uses background knowledge of dynamic land surface processes that are extracted from MODerate resolution Imaging Spectroradiometer (MODIS) LAI 1 km resolution data. To process the uncertainties that arise from using two data sources with different spatial resolutions, the proposed method is implemented in a dynamic Bayesian network scheme by integrating a LAI dynamic process model and a canopy reflectance model with remotely sensed data. Validation was conducted using field LAI data collected in the Guantao County, Hebei Province, China. The results showed that the determination coefficient between estimated and measured LAI was 0.791, and the RMSE was 0.61. This method can enhance the accuracy of the retrieval results while retaining the time series variation characteristics of the vegetation LAI. The results suggest that this algorithm can be widely applied to determining high-resolution leaf area indices using data from China HJ-1 satellite even if information from single angle observations are insufficient for quantitative application.