Identifying Yield Predictors Behaving as a Geotag: A Time-Varying Analysis of a Nationwide Cotton Data

Crop yield estimation at the national scale is on the rise with a clear increase in freely available satellite images; providing field-level crop masks and their corresponding Earth Observation (EO) data-based predictors. In line with increasing EO data, the yield estimation problem is moved from a univariate to a multivariate time series analysis problem. Indeed, most of the state of the art methods applied to yield estimation are enabled to increase the accuracy of the yield estimation model using these multisource EO data. However, one of the major drawbacks of these methods is the lack of explainability of the yield. In this study, we will try to understand what EO-based time series data says about the agricultural practices differences among the geographically distributed fields and which kinds of dissimilarities exist among these multi-source EO data, and what regional/global factors cause these dissimilarities. In order to understand this, the study will go through the shape based and feature based time series similarity metrics that often highlight different characteristics of the time series data. While the temperature's parameters (i.e., the incident solar radiation and 2 m dewpoint temperature), not the temperature itself, highlight geographical variation in yield data and behave as a geotag, these climate variables are not the only cause, but are the contributing ones, driving the yield variation distribution patterns of nationwide data.