High spatial-and temporal-resolution NDVI produced by the assimilation of MODIS and HJ-1 data

Abstract

The normalized difference vegetation index (NDVI) is widely used in global environmental and climatic change research. However, the 1 km Moderate Resolution Imaging Spectroradiometer (MODIS) is too coarse to quantify changes in heterogeneous landscapes. On the other hand, the 30 m charge-coupled device (CCD) sensor on the Chinese environment satellite (HJ-1) is severely affected by weather, which limits its use in studying the biophysical processes evolving rapidly during the growing season. In cloudy areas, the problem is compounded; only a few images can be obtained for the whole year. It is therefore impossible to obtain the high temporal spatial resolution NDVI required in some applications. To solve this problem, the continuous correction (CC) data assimilation method was proposed to produce high temporal spatial resolution NDVI by combining the advantages of the MODIS temporal information and the CCD spatial information. The MODIS 16 day compositing/8 day windows Nadir BRDF-Adjusted Reflectance and the CCD reflectance were used to predict 8 day/30 m NDVI for the Heihe River basin, China, in 2009. Comparison of predicted data with field data showed that the two were in good agreement. The method demonstrated feasibility, and the NDVI produced provided better vegetation information. The performance of CC depended on the acquisition time and amount of the CCD images.