Regional scale soil salinity evaluation using Landsat 7, western San Joaquin Valley, California, USA

Scudiero, Elia; Skaggs, Todd H.; Corwin, Dennis L.

doi:10.1016/j.geodrs.2014.10.004

Cited by 121 publications

(77 citation statements)

References 44 publications

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“…where CRSI is the multi-year maximum value (at each 30 × 30 m location i) of the crop response salinity index (Scudiero et al, 2014); RAIN (mm) is the annual rainfall (from the year with maximum CRSI); TEMP ( • C) is the annual average minimum temperature (from the year with maximum CRSI); and ı 0 , ı crop , ı 1 , and ı 2 are the regression parameters. The ı crop coefficient has two values, one for cropped (i.e., any herbaceous crop and pasture land) and one for fallow (e.g., covered with halophyte weeds) soils.…”

Section: Remote Sensing Of Soil Salinitymentioning

confidence: 99%

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

Scudiero

Skaggs

Corwin

2016

Ecological Indicators

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a b s t r a c tSoil salinity is recognized worldwide as a major threat to agriculture, particularly in arid and semi-arid regions. Producers 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, EC e ). State-of-the-art approaches for creating accurate EC e maps beyond field scale (i.e., 1 km 2 ) include: (i) Analysis Of Covariance (ANOCOVA) of near-ground measurements of apparent soil electrical conductivity (EC a ) 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 EC a -directed soil sampling resulted in the collection of 267 soil samples across the 22 fields, which were analyzed for EC e , ranging from 0 to 38.6 dS m −1 . The ANOCOVA EC a -EC e model returned a coefficient of determination (R 2 ) of 0.87 and root mean square prediction error (RMSPE) 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 R 2 = 0.73 and RMSPE = 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 RMSPE. The worst, median, and best fit scenarios provided global cross-validation R 2 of 0.52, 0.80, and 0.81, respectively. The lofo cross-validation for the remote sensing approach returned a R 2 of 0.65. The ANOCOVA approach performs particularly well at EC e 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 EC e 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 km 2 ) and the remote sensing approach best for landscape to regional scales (>10 km 2 ).Published by Elsevier Ltd.

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Section: Remote Sensing Of Soil Salinitymentioning

confidence: 99%

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

Scudiero

Skaggs

Corwin

2016

Ecological Indicators

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View full text Add to dashboard Cite

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“…Scudiero et al (2015) developed a prediction model for WSJV root zone salinity using CRSI as a predictor variable. The CRSI is defined (Scudiero et al 2014) as Increased plant vigor corresponds to a higher CRSI value. Notice that the CRSI is not a salinity-specific vegetation index; it was selected by Scudiero et al (2015) because it provided better performance than other vegetation indices when applied to their salinity ground-truth calibration data.…”

Section: Remote Sensing Of Soil Salinitymentioning

confidence: 99%

“…The β crop parameter indicates the presence or absence of cropping and has a value of −100.76 for fallow soils and −93.40 otherwise. Scudiero et al (2015) calibrated the model using data for 5,283 Landsat 7 pixels located in 22 WSJV fields that had been extensively surveyed for salinity by Scudiero et al (2014). The model calibration produced R 2 = 0.73.…”

Section: Remote Sensing Of Soil Salinitymentioning

confidence: 99%

“…4A), the field is characterized by substantial small-scale soil heterogeneity, which is likely due to movements of the Kings River through paleohistory. The root zone salinity measured by Scudiero et al (2014) is shown in figure 4B (ground-truth data), and the remote-sensing prediction is shown in figure 4C. In figure 4B, the average EC e was 3.01 dS/m (standard deviation of 0.84 dS/m), whereas in figure 4C the average was 4.22 dS/m (standard deviation of 1.57 dS/m).…”

Section: Soils With Salt Crustsmentioning

confidence: 99%

“…In this 80-acre fallow field near Stratford (Kings County), salinity was mapped with an apparent electrical conductivity survey and soil sampling by Scudiero et al (2014). Multitemporal (Aug. 22, 2012;Sept.…”

Section: Map Spatial Resolutionmentioning

confidence: 99%

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