Scale effects of leaf area index inversion based on environmental and disaster monitoring satellite data

The spatial distribution of sub-pixel components has an impact on retrieval accuracy, and should be accounted for when inverting a three-dimensional adiative transfer model to retrieve leaf area index (LAI). To investigate this effect, we constructed three realistic scenarios with the same LAI values and other properties, except that the simulated plants had different distributions. We implemented the radiosity method to subsequently produce synthetic bidirectional reflectance factor (BRF) datasets based upon these simulated scenes. The inversion was conducted using these data, which showed that spatial distribution affects retrieval accuracy. The inversion was also conducted for LAI based on charge-coupled device (CCD) data from the Environment and Disaster Monitor Satellite (HJ-1), which depicted both forest and drought-resistant crop land cover. This showed that heterogeneity in coarse-resolution remote sensing data is the main error source in LAI inversion. The spatial distribution of global fractal dimension index, which can be used to describe the area of sub-pixel components and their spatial distribution modes, shows good consistency with the coarse resolution LAI inversion error.