A Gaussian Kernel-Based Spatiotemporal Fusion Model for Agricultural Remote Sensing Monitoring

Abstract

Time series normalized difference vegetation index (NDVI) is the primary data for agricultural remote sensing monitoring. Due to the tradeoff between a single sensor's spatial and temporal resolutions and the impacts of cloud coverage, the time series NDVI data cannot serve well for precision agriculture. In this study, a Gaussian kernel-based spatiotemporal fusion model (GKSFM) was developed to fuse high-resolution NDVI (Landsat) and low-resolution NDVI (MODIS) to produce a daily NDVI product at a 30-m spatial resolution. Considering that the NDVI curve of crop in each growing season can be characterized by Gaussian function, GKSFM used the Gaussian kernel to fit the nonlinear relationship between the high-resolution NDVI and the low-resolution NDVI, to obtain a more reasonable temporal increment. The experimental results show that GKSFM outperformed the comparative models in different proportions of cropland/noncropland and different crop phenology. In addition, GKSFM was also applied for crop mapping of Mishan County by fusing the NDVI images during the crop growing season. This study demonstrates that the accuracy of the proposed method can be improved in the midseason of crops.

Keywords

• Gaussian kernel
• normalized difference vegetation index (NDVI)
• spatiotemporal fusion
• time series