Abstract

Advances in sensor technologies provide the potential for real-time monitoring of soil moisture status. Little work has been conducted to study the spatial and temporal variations of soil water content at the field scale and the interactions between soil water content and environmental controlling factors in cropped fields during the growing season. In this study, we assessed the spatial and temporal variations of surface soil water content (0–0.1 m) using seven in-situ soil moisture probes and Sentinel-1 satellite data over the 2016 and 2017 cropping seasons in irrigated fields of the Central Sands Plains, Wisconsin, USA. An empirical multiple linear regression model was developed between the soil water content with Sentinel-1 backscatter data. The model was evaluated with leave-one-group-out cross-validation, which showed an R2 of 0.60, mean error of –0.00 m3 m−3, root mean square error of 0.02 m3 m−3, and Lin’s concordance correlation coefficient of 0.75. Soil volumetric water content (VWC) and soil water deficit (SWD) were mapped at 30-m resolution every 6–12 days across one field (DU field at the Wallendal farm). Using two statistical metrics (“first metric” and “last metric”) and a bootstrapping approach, the location-specific and time-dependent interactions between estimated soil water content and environmental controlling factors across the DU field during the cropping season were evaluated, and it was found that such interactions were affected by initial soil water content and plant stress status. The robustness of the model needs to be improved in the future by collecting more VWC measurements in different soil and crop conditions. The method has the potential to provide a baseline soil moisture map and can be coupled with mechanistic models and subsoil VWC measurements for improved irrigation management and water use efficiency under a changing climate.

Full Text
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