Remote sensing and apparent electrical conductivity to characterize soil water content

Abstract

Improvement in water use efficiency of crops is a key component in addressing the increasing global water demand. The time and depth of the soil water monitoring are essential when defining the amount of water to be applied to irrigated crops. Precision irrigation (PI) is a relatively new concept in agriculture, and it provides a vast potential for enhancing water use efficiency while maintaining or increasing grain yield. As part of site-specific farming, PI needs to be explored, tested, and evaluated which continues to be a research issue. Neutron probes (NPs) have consistently been used for studies as a robust and accurate method to estimate soil water content (SWC). Remote sensing derived vegetation indices have been successfully used to estimate variability of Leaf Area Index and biomass, which are related with root water uptake. Crop yield has not been evaluated on a basis of SWC as explained by NPs in time and at different depths. One among many challenges in implementing PI is the reliable characterization of the soil water content (SWC) across spatially variable fields. For this purpose, commercial retailers are employing apparent soil electrical conductivity (ECa) to create irrigation prescription maps. However, the accuracy of this method has not been properly studied at the field scale. The objectives of this study were (1) to determine the optimal time and depth of SWC and its relationship to maize grain yield (2) to determine if satellite-derived vegetation indices coupled with SWC could further improve the relationship between maize grain yield and SWC (3) to assess the potential of ECa measurement to characterize spatial distribution of SWC at field scale, and (4) to determine whether soil properties coupled with ECa could further improve the characterization of the SWC. For objectives 1 and 2, the study was conducted on maize (Zea Mays L.) irrigated in two fields in northern Colorado. Soil water data was collected at five soil depths (30, 60, 90, 120 and 150 cm), 21 and 12 times at Site I and II, respectively. Three vegetation indices were calculated on seven dates (Emergence to R3). Maize grain yield was harvested at the physiological maturity at each NPs location. Automated model selection of SWC readings to assess maize yield consistently selected three dates spread around reproductive growth stages for most depths (p value < 0.05). For objectives 3 and 4, the study was conducted on two fields located in northeastern Colorado. In-field SWC was measured using neutron probes at 41 and 31 locations for Site I and II respectively. Soil ECa measurements were acquired using Geonics EM38-MK2 unit. In addition, cation exchange capacity, clay, organic matter and salt content were coupled with soil ECa to estimate SWC. Data analysis was performed using the statistical software R. Statistical correlations and multiple linear regressions were obtained from the properties that were statistically significant (p value < 0.05). Results of the study showed that the SWC readings at the 90 cm depth had the highest correlations with maize yield, followed closely by the 120 cm. When coupled with remote sensing data, models improved by adding vegetation indices representing the crop health status right before the reproductive growth stage (V9). Thus, SWC monitoring at reproductive stages combined with vegetation indices could be a tool for improving maize irrigation management. Likewise, the SWC was found to be statistically different across ECa derived zones, indicating that ECa was able to accurately characterize average differences in SWC across management zones. Organic matter and salt content significantly improved the SWC assessment when combined with the ECa. The development of prescription maps for variable rate irrigation should be tailor made depending on the specific field characteristics influencing SWC.