Unveiling the High-Resolution Cotton Yield Variations from Low-Resolution Statistics: Lessons from a Nationwide Study in Turkey

Earth Observation (EO)-based crop yield estimation, which focuses on leveraging crop conditions at any time t has recently played a critical role in the development of nationwide crop monitoring. Following the developments in open data policy in remote sensing, the high-resolution freely available EO data provides annual crop masks and enables the understanding of more complex patterns of the agricultural practices at field level. Despite the detailed information provided by EO-imaging data at the field level, the resolution of the target variable, commune-level yield, restricts the effective usage of EO imaging data and, in turn, imposes limitations on leveraging the large amount of EO data, which could provide accurate yield estimation by a physics aware data-driven estimation models. In this paper, we explore the challenges of the uncertainties associated with data-driven yield estimation using the Turkey cotton dataset, which comprises EO-based time series from Sentinel-1 and Sentinel-2 imaging satellites, together with climate variables and soil properties. These uncertainties can arise from discrepancies in the EO-based crop mask data, an unreliable statistical dataset, and the variance in the spatio-temporal characteristics of descriptive features and low-resolution yield statistics that are used in the estimation.