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Title: Comparative regional-scale soil salinity assessment with near-ground apparent electrical conductivity and remote sensing canopy reflectance

Author
item SCUDIERO, ELIA - University Of California
item Skaggs, Todd
item Corwin, Dennis

Submitted to: Ecological Indicators
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/12/2016
Publication Date: 6/27/2016
Citation: Scudiero, E., Skaggs, T.H., Corwin, D.L. 2016. Comparative regional-scale soil salinity assessment with near-ground apparent electrical conductivity and remote sensing canopy reflectance. Ecological Indicators. 70:276-284. doi: 10.1016/j.ecolind.2016.06.015.

Interpretive Summary: In arid and semi-arid regions with intensively managed irrigated agriculture, soil salinization is a well-known hazard that can over time reduce the productivity of farmland. Left unchecked, soil salinity may increase to the point that lands become unsuitable for farming. Efforts to combat and mitigate salinity damage are hindered be a lack of data on the extent and variability of soil salinity at both farm and regional scales. In this study, we compare two approaches to mapping soil salinity in large agricultural regions, one using near-ground soil measurements and the other using satellite-based remote sensing measurements. The results show that accurate, high-resolution soil salinity mapping can be done using the near-ground technique at moderately large scales (up to around 250,000 acres). The remote sensing approach is slightly less accurate, but it is preferable for mapping larger areas (>250,000 acres) because of reduced costs. This work has implications for managing agricultural lands and irrigation waters, and will be of interest to land resource managers, agricultural consultants, extension specialists, farmers, scientists and researchers working on remote sensing and land management, and the Natural Resource Conservation Service.

Technical Abstract: Soil salinity is recognized worldwide as a major threat to agriculture, particularly in arid and semi-arid regions. Farmers and decision makers need updated and accurate maps of salinity in agronomically and environmentally relevant ranges (i.e., <20 dS m/1, when salinity is measured as electrical conductivity of the saturation extract, ECe). State-of-the-art approaches for creating accurate field-scale ECe maps with sufficient sub-field detail for potential application to site-specific management practices are achievable using (i) analysis of covariance (ANOCOVA) of near-ground measurements of apparent soil electrical conductivity (ECa) and (ii) regression modeling of multi-year remote sensing canopy reflectance and other co-variates (e.g., crop type, annual rainfall). This study presents a comparison of the two approaches to establish their viability and utility. The approaches were tested using 22 fields (total 542 ha) located in California’s western San Joaquin Valley. In 2013 ECa-directed soil sampling resulted in the collection of 267 soil samples across the 22 fields, which were analyzed for ECe, ranging from 0-38.6 dS m/1. The ANOCOVA ECa-ECe model returned R2=0.87 and root mean square error (RMSE) of 3.05 dS m/1. For the remote sensing approach seven years (2007-2013) of Landsat 7 reflectance were considered. The remote sensing salinity model had R2=0.73 and RMSE=3.63 dS m/1. The robustness of the models was tested with a leave-one-field-out (lofo) cross-validation to assure maximum independence between training and validation datasets. For the ANOCOVA model, lofo cross-validation provided a range of scenarios in terms of RMSE. The worst, median, and best fit scenarios provided global cross-validation R2 of 0.52, 0.80, and 0.81, respectively. The lofo cross-validation for the remote sensing approach returned a R2 of 0.65. The ANOCOVA approach performs particularly well at ECe values < 10 dS m/1, but requires extensive field work. Field work is reduced considerably with the remote sensing approach, but due to the larger errors at low ECe values, the methodology is less suitable for crop selection, and other practices that require accurate knowledge of salinity variation within a field, making it more useful for assessing trends in salinity across a regional scale. The two models proved to be viable solutions at large spatial scales, with the ANOCOVA approach more appropriate for multiple-field to landscape scales (1-10 km2) and the remote sensing approach best for landscape to regional scales (>10 km2).