Tracking crop phenology in a highly dynamic landscape with knowledge-based Landsat–MODIS data fusion

Earth observation image data are regularly used to capture surface conditions over large areas, but there is a trade-off between high (or low) spatial and low (or high) temporal resolution. The Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) overcomes this trade-off by fusing high spatial and temporal resolution multisource image data. However, ESTARFM requires additional modifications in order to provide reliable estimates of surface conditions showing large spectral differences in highly dynamic and fragmented agricultural systems. We modified ESTARFM by taking a knowledge-based approach to track maize and rice phenology in a highly dynamic and fragmented agricultural landscape in Ethiopia in 2019. The two major improvements included: (i) Selection of Landsat-MODIS imageries based on crop sowing and harvesting information and (ii) generation and use of a land cover map to select similar pixels. We assessed model performance with the enhanced vegetation index (EVI) derived from independent Landsat image data and in-situ leaf area index (LAI) data. The improved ESTARFM workflow resulted in reliable Landsat-MODIS prediction (R2 = 0.67, RMSE = 0.07) compared to the standard ESTARFM workflow (R2 = 0.54 RMSE = 0.01) during the rapid growth stage. Our modifications outperformed the standard implementation of ESTARFM according to LAI magnitude (R2 = 0.73–0.84 versus R2 = 0.58–0.64) and phenological timing (RMSE = 8 days verses RMSE = 12 days). Our modified application of ESTARFM serves as a basis for monitoring crop growth and development in highly dynamic and fragmented agricultural systems.