Operational Framework for Field-Scale Crop Sowing and Emergence Date Estimation Using Daily Synthetic Harmonized Landsat Sentinel-2 Time Series

Satellite-based crop phenology provides critical information for agricultural management; however, accurately detecting specific crop development stages remains challenging. During early stages, such as sowing and emergence, satellite imagery captures a spectral signal that is a mixture of soil and vegetation. This study developed an operational framework for estimating field-scale sowing and emergence dates using daily synthetic Harmonized Landsat Sentinel-2 (HLS) data. The assumption is that sowing and emergence dates can be estimated by using later growth stages since crop development follows a consistent pattern driven by physiological processes and environmental conditions. We first evaluated 4 gap-filling techniques to generate a daily synthetic HLS-based enhanced vegetation index (EVI) time series over 15 tiles across the USA. Then, 6 phenological metrics were retrieved using the asymmetric double sigmoid function, and the results were validated over 20 PhenoCam sites with corn and soybeans. Different predictive models were evaluated for sowing and emergence date estimation, and the optimal approach was used to predict these dates in crop fields in Iowa and Missouri. The polynomial gap-filling technique performed best in reconstructing the original EVI images, and phenological stages derived from daily EVI images and PhenoCam data showed strong agreement, with an R 2 of 0.94 and a bias of 12 d. Elastic net regression performed better in estimating sowing and emergence dates, with a root mean square error of ±10 d. The proposed framework offers a consistent pipeline to reconstruct gap-free HLS data, extract phenological stages, and estimate sowing and emergence dates for agricultural monitoring.