Regional-scale soil salinity assessment using Landsat ETM+ canopy reflectance

• Published in: Remote sensing of environment Vol. 169; pp. 335 - 343
• Main Authors: Scudiero, Elia, Skaggs, Todd H., Corwin, Dennis L.
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.11.2015
• Subjects: California; Canopies; canopy; case studies; data collection; electrical conductivity; environmental health; fallow; issues and policy; Landsat; Landsat 7; linear models; Mathematical models; meteorological data; prediction; Productivity; rain; Reflectance; Remote sensing; researchers; Saline; Salinity; Soil (material); Soil mapping; Soil salinity; soil salinization; soil surveys; soil types; Spatial cross-validation; temperature; vegetation index; INE, USA, California; USA, California, San Joaquin Valley; California; Spatial cross-validation; Landsat 7; Soil salinity; Remote sensing; Soil mapping
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2015.08.026

Soil salinization is widely recognized to be a major threat to worldwide agriculture. Despite decades of research in soil mapping, no reliable and up-to-date salinity maps are available for large geographical regions, especially for the salinity ranges that are most relevant to agricultural productivity (i.e., salinities less than 20dSm−1, when measured as the electrical conductivity of the soil saturation extract). This paper explores the potentials and limitations of assessing and mapping soil salinity via linear modeling of remote sensing vegetation indices. A case study is presented for western San Joaquin Valley, California, USA using multi-year Landsat 7 ETM+ canopy reflectance and the Canopy Response Salinity Index (CRSI). Highly detailed salinity maps for 22 fields comprising 542ha were used for ground-truthing. Re-gridded to 30×30m, the ground-truth data totaled over 5000pixels with salinity values in the range 0 to 35.2dSm−1. Multi-year maximum values of CRSI were used to model soil salinity. Soil type, meteorological data, and crop type were evaluated as covariates. All considered models were evaluated for their fit to the whole data set as well as their performance in a leave-one-field-out spatial cross-validation. The best performing model was a function of CRSI, crop type (i.e., cropped or fallow), rainfall, and average minimum temperature, with R2=0.728 when evaluated against all data and R2=0.611 for the cross-validation predictions. Broken out by salinity classes, the mean absolute errors (MAE) for the cross-validation predictions were (all units dSm−1): 2.94 for the 0–2 interval (non-saline), 2.12 for 2–4 (slightly saline), 2.35 for 4–8 (moderately saline), 3.23 for 8–16 (strongly saline), and 5.64 for >16 (extremely saline). On a per-field basis, the validation predictions had good agreement with the field average (R2=0.79, MAE=2.46dSm−1), minimum (R2=0.76, MAE=2.25dSm−1), and maximum (R2=0.76, MAE=3.09dSm−1) observed salinity. Overall, reasonably accurate and precise high resolution, regional-scale remote sensing of soil salinity is possible, even over the critical range of 0 to 20dSm−1, where researchers and policy makers must focus to prevent loss of agricultural productivity and ecosystem health. Regional scale soil salinity assessment can successfully be carried out using multi-year Landsat ETM+ canopy reflectance and information on crop cover and meteorological settings. [Display omitted] •Multi-year maxima of Landsat ETM+ vegetation indices correlates with soil salinity.•Linear regressions provide reliable salinity estimates at the regional scale.•Crop and meteorological covariates increase accuracy of soil salinity predictions.•Salinity assessment models are validated through a spatial cross-validation.