One limitation of the use of remote sensing is the implementation of systematized workflows for large scale monitoring of crops performance. This information is particularly relevant for Kansas due to a marked regional variability of annual precipitations, soil types, and yield potential across the state. We propose a workflow for the systematic extraction of in seasonal phenological metrics and the detection of anomalies using MODIS MOD13Q1 product during the period 2000-2016. The methodology utilizes geolocated fields across for the: 1) extraction of NDVI time series. 2) interpretation of the time series profile for the estimation of phenological metrics, and 3) identification of in seasonal anomalies. The metrics include: date of emergence, critical phases of the vegetative period, and, maturity. The methodology disaggregates the information of the time series in the following components for in seasonal anomalies detection: A) seasonal, B) general trend, and C) residual signal, key for the identification of anomalous temporal segments in the seasons. Finally, the detected temporal anomalies are then evaluated as extreme in-seasonal weather event footprints penalizing yield.