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

REMOTE SENSING OF ENVIRONMENT

卷:169

Article

Scudiero, Elia¹  Skaggs, Todd H.¹  Corwin, Dennis L.¹ 

[1] USDA ARS, US Salin Lab, Riverside, CA 92507 USA

关键词: Soil salinity;    Landsat 7;    Remote sensing;    Spatial cross-validation;    Soil mapping;   

DOI  :  10.1016/j.rse.2015.08.026

来源: Elsevier

【 摘 要 】

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 20 dS m(-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 542 ha were used for ground-truthing. Re-gridded to 30 x 30 m, the ground-truth data totaled over 5000 pixels with salinity values in the range 0 to 352 dS m(-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 R-2 = 0.728 when evaluated against all data and R-2 = 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 dS m(-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 (R-2 = 0.79, MAE = 2.46 dS m(-1)), minimum (R-2 = 0.76, MAE = 225 dS m(-1)), and maximum (R-2 = 0.76, MAE = 3.09 dS m(-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 20 dS m(-1), where researchers and policy makers must focus to prevent loss of agricultural productivity and ecosystem health. Published by Elsevier Inc.

【 授权许可】

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