Spatial and temporal information fusion for crop condition monitoring

Crop growth condition information is critical for crop management and yield estimation. In order to monitor crop conditions from space, high spatial and temporal resolution remote sensing data are required. Data fusion approach provides a way to generate such data set from multiple remote sensing data sources. In this paper, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) was used to generate daily Landsat-like surface reflectance over central Iowa from 2001 to 2014. The fused Landsat-MODIS results were compared to the actual Landsat observations. Constrains and limitations of data fusion approaches were discussed. Data fusion results will be applied to map crop condition at field scales. Crop condition results will be compared to the Crop Progress reports from the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS).