Mapping Soil Volumetric Water Content at Multiple Depths for Variable Rate Irrigation Using <scp>UAV</scp> and Yield Monitor Data With Random Forests

ABSTRACT The Mountain West has been experiencing severe drought for &gt; 20 years. As agriculture uses the greatest amount of the limited fresh water supply, employing variable rate irrigation (VRI) can reduce agricultural water use. To effectively apply VRI, accurate maps of soil volumetric water content (VWC) for the whole root zone (0–120 cm) are essential. This research employs Random Forests (RFs) with topographic, crop reflectance data from an Unmanned Aerial Vehicle and data from a yield monitor to map VWC at four depths. The RF model generally predicted VWC well, within ~1%–3% RMSE, but its performance varied between soil depths and sampling periods. Predictions were slightly more accurate for the top two depths than for the deeper two depths. The models showed that terrain and scaling factors rather than crop attributes were the most influential in predicting VWC at different depths at the scale of an individual field. An exception where crop and yield attributes were important was Fall 2017, which followed a hotter than average Summer. Both high and low spatial resolution data were important to predictions as they relate to features of different scales in the field. A jack‐knife procedure showed that, on average, sampling effort could be reduced to 50–60 samples from &gt; 100 while maintaining errors of only 2%–3%. Since SCORPAN factors vary little within a field, large samples are still needed to calibrate dense covariates, so testing the RF approach at the farm scale may be more practical for mapping soil water content.