|WHITNEY, KRISTEN - Chapman University|
|SCUDIERO, ELIA - University Of California|
|EL-ASKARY, HESHAM - Chapman University|
|ALLALI, MOHAMED - Chapman University|
Submitted to: Ecological Indicators
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/25/2018
Publication Date: 6/6/2018
Citation: Whitney, K., Scudiero, E., El-Askary, H.M., Skaggs, T.H., Allali, M., Corwin, D.L. 2018. Validating the use of MODIS time series for salinity assessment over agricultural soils in California, USA. Ecological Indicators. 93:889-898. https://doi.org/10.1016/j.ecolind.2018.05.069.
Interpretive Summary: Soil salinization is a well-known hazard that can reduce the productivity of agricultural land. Soil salinity negatively impacts the economic and environmental sustainability of farmland in western San Joaquin Valley, CA. Monitoring soil salinity at regional and state levels is essential for identifying and understating drivers and trends in agricultural soil salinity, and for developing mitigation strategies and management plans. In this work we test a method for remote sensing detection of saline soils using NASA’s MODIS satellite. The methodology was originally proposed for the farmland of the Yellow River Delta, China, which is characterized by a continental rainy climate. We tested, validated, and improved the method with success to the semi-arid farmland. 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: Testing soil salinity assessment methodologies over different regions is important for future continental and global scale applications. A novel regional-scale soil salinity modeling approach using plant-performance metrics was proposed by Zhang et al. (2015) for farmland in the Yellow River Delta, China, a region with a humid continental/subtropical climate. The one-year integral of temporally interpolated MODIS Enhanced Vegetation Index (EVI) time series data was proposed as an explanatory variable for agricultural soil salinity modeling. Here, we test such a methodology in California’s Central Valley, USA, a region with a semi-arid Mediterranean climate. Time series of EVI, Normalized Difference Vegetation Index (NDVI), and Canopy Response Salinity Index (CRSI) were created for the 2007-2013 period. Seventy-three MODIS pixels surveyed for 0-1.2-m soil salinity in 2013 were used as the ground-truth dataset. Our results validate the tested approach: the 2013 integral of CRSI (best performing index) had a Pearson correlation coefficient (r) of -0.699 with salinity. Results obtained using temporally integrated data were almost always better than those obtained using individual data. Furthermore, we show that the methodology can be improved by the use of multi-year data. Further research is needed to improve spatial resolution and the selection of vegetation indices.