Abstract
Agricultural production and food security highly depend on crop growth and condition throughout the growing season. Timely and spatially explicit information on crop phenology can assist in informed decision making and agricultural land management. Remote sensing can be a powerful tool for agricultural assessment. Remotely sensed data is ideally suited for both large-scale and field-level analyses due to the wide variability of datasets with diverse spatiotemporal resolution. To derive crop-specific phenometrics, we fused time series from Landsat 8 and Sentinel 2 with Moderate-resolution Imaging Spectroradiometer (MODIS) data. Using a linear regression approach, synthetic Landsat 8 and Sentinel 2 data were created based on MODIS imagery. This fusion-process resulted in synthetic imagery with radiometric characteristics of original Landsat 8 and Sentinel 2 data. We created four different time series using synthetic data as well as a mix of original and synthetic data. The extracted time series of phenometrics consisting of both synthetic and original data showed high detail in the final phenomaps which allowed intra-field level assessment of crops. In-situ field reports were used for validation. Our phenometrics showed only a few days of deviation for most crops and datasets. The proposed data integration method can be applied in areas where data from a single high-resolution source is scarce.