Validation of the ANOCOVA model for regional‐scale ECa to ECe calibration

Two approaches have emerged as the preferred means for assessing salinity at regional scale: (i) vegetative indices from satellite imagery (e.g., MODIS enhanced vegetative index, NDVI) and (ii) analysis of covariance (ANOCOVA) calibration of apparent soil electrical conductivity (EC_a) to salinity. The later approach is most recent and least extensively validated. It is the objective of this study to provide extensive validation of the ANOCOVA approach. The validation comprised 77 fields in California's Coachella Valley, ranging from 1.25 to 30.0 ha in size with an average size of 12.8 ha. Mobile electromagnetic induction (EMI) equipment surveyed the fields obtaining geospatial measurements of EC_a. Soil sample sites selected following EC_a‐directed soil sampling protocols characterized the range and spatial variation in EC_a across the field. From the data, a regional ANOCOVA model was developed. The regional ANOCOVA model successfully reduced cross‐validated, average log salinity prediction error (variance) estimate by more than 30% across the 77 fields and improved the depth‐averaged prediction accuracy in 58 of the 77 fields. The results show that the ANOCOVA modelling approach improves soil salinity predictions from EMI signal data in most of the surveys conducted, particularly fields where only a limited number of calibration sampling locations were available. The establishment of ANOCOVA models at each depth increment for a representative set of fields within a regional‐scale study area provides slope coefficients applicable to all future fields within the region, significantly reducing ground‐truth soil samples at future fields.