Estimating plant-water stress using remote sensing data
Overview
Soil moisture is an essential portion of Earth’s water balance. Understanding drivers behind and impacts of depleted soil water content in agricultural settings is key to promoting crop productivity, especially in areas where water is limited. Changes in soil water content are monitored using a variety of in field soil moisture probes and remote sensing techniques. In situ data provide detailed in field information but may be cost prohibitive on a large scale. Remotely sensed datasets provide high temporal and spatial resolution but are often limited by lack of ties with physical processes related to soil water content variability. Linking remotely sensed data to physiological processes in soils is essential to cross-validating soil moisture variability models.
As part of this research, we are studying the relationship between remote sensing data and in situ soil moisture data with the aim of informing optimal probe placement in fields and estimating soil moisture and yield at field scales for agricultural fields in Colorado, Utah, and Idaho. Additionally, we are exploring the dynamics of soil moisture seasonality with the intent of identifying underlying drivers of soil moisture variability for various regions in Colorado.
Funding
METER Group
Study areas
Idaho, Colorado, Utah
Figure Caption:
The figure to the right is taken from Smith et al. (2021) and shows (a) satellite-derived ET (eeMETRIC), (b) Normalized Difference Water Index, (c) variable rate irrigation as a percentage of normal, (d) normalized yield, and (e) satellite-based estimates of water-stressed areas.
