Timely Mapping of Crop Stage and Watering Events Through Sentinel-L Time-Series

Abstract

Reliable and timely mapping of crop growth conditions and of their water resources is considered a prioritary application in light of the abrupt climate changes. With this view, the paper presents a novel approach that makes use of dense C-Band time-series for the timely estimation of crop growth stages and for the detection of changes in crop water conditions, especially related to precipitation and irrigation events. Aided by vegetation indexes extracted from Landsat and Sentinel-2 imagery, the proposed Sentinel-l centered method exploits both temporal patterns of crop growth and spatial patterns of water anomalies to enhance its classification robustness.