269-1 Spatio-Temporal Analysis of Multi-Year Landsat 7 Data for Regional Scale Soil Salinity Assessment.

Poster Number 527

See more from this Division: ASA Section: Global Agronomy
See more from this Session: Remote Sensing and Digital Soil Mapping Applications: II (includes graduate student competition)
Tuesday, November 4, 2014
Long Beach Convention Center, Exhibit Hall ABC
Share |

Elia Scudiero, USDA-ARS Salinity Laboratory, Riverside, CA, Dennis L. Corwin, USDA-ARS, U.S. Salinity Laboratory, Riverside, CA and Todd H. Skaggs, USDA-ARS, Riverside, CA
Poster Presentation
  • 87765_Scudiero_poster.pdf (1.3 MB)
  • Despite decades of research in soil mapping, characterizing the spatial variability of soil salinity across broad regions remains a crucial challenge. This work explores the potential benefits of employing reflectance data from the six spectral bands (blue, 450-520 nm; green, 520-600 nm; red, 630-690 nm; near-infrared, 770-900 nm; infrared-1; 1550-1750 nm, and infrared-2, 2090-2350 nm) of the Landsat 7 (L7) satellite sensor (30x30 m resolution) for salinity assessment. Acquisitions of L7 throughout the western San Joaquin Valley, California (ca.15000 km2) were investigated over a seven-year period. Two salinity ground truth datasets were evaluated, across 23 fields farmed with various crops: 226 direct measurements (ca. 2x2 m resolution), from the 0-1.2 m soil profile; and ca. 6000 block-kriged estimations (30x30 m resolution), derived from geospatial electromagnetic induction measurements. The multi-year average of L7 data generally provided stronger correlations (up to R2=0.41), than those observed for each single year. Slightly stronger correlations (up to R2=0.43) were observed between salinity and the multi-year temporal variability of L7 reflectance (i.e., standard deviation at each map-cell over time). The strength of the correlations between L7 data and soil salinity varied according to changing meteorological conditions through the seven-year period and according to soil texture at a field by field basis. Additionally, selected salinity ranges (i.e., 0-2, 2-4, 4-8, 8-16, and >16 dS m-1) were characterized by significantly different values of the blue, green, red, and near-infrared bands. The results suggest that data fusion of the L7 multi-year reflectance with information on meteorological conditions, crop type, and soil texture could lead to a reliable salinity prediction model for the entire western San Joaquin Valley. Land resource managers, producers, agriculture consultants, extension specialists, and Natural Resource Conservation Service field staff are the beneficiaries of regional scale maps of soil salinity.
    See more from this Division: ASA Section: Global Agronomy
    See more from this Session: Remote Sensing and Digital Soil Mapping Applications: II (includes graduate student competition)
    Previous Abstract | Next Abstract >>