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Exploring the contribution of crop water use to remotely sensed estimates of soil salinity in irrigated agriculture


Globally, 72% of the world's water withdrawals are used for agriculture. As the world's population continues to grow and increase its caloric intake, agricultural producers must provide more food and fiber with the same amount of water and soil, or less, due to expanding urbanization and climate change. In Colorado (CO, U.S.A.), agricultural producers in the South Platte and Arkansas River Basins, for instance, have been offered water transfer programs to temporarily or permanently transfer their water shares to municipalities and industry. Another challenge agricultural growers face is soil salinization, which needs to be monitored. In the Arkansas River basin, upflux from saline shallow groundwater tables consistently contributes to crop evapotranspiration (ET), leaving salts in the vadose zone. These salts accumulate over decades to the point where crop yields decline, threatening agricultural sustainability. Remote sensing is an economical tool to monitor salinity (e.g., soil electrical conductivity; EC, dS m-1) at large spatial scales. Existing remote sensing models that predict EC mostly utilize vegetation indices (VIs), which are arithmetic combinations of vegetation reflectances captured by discrete spectral bands. In this study, two additional explanatory variables were investigated: 1) the actual crop ET (ETa, mm d-1), and 2) the crop water stress index (CWSI). Calculations of ETa were performed using Landsat satellite multispectral imagery and a surface energy balance approach. This research was conducted over two growing seasons in commercial maize fields located within the Fairmont Drainage District near Swink, CO. Results indicate that models including ETa or CWSI with existing VIs improve the accuracy of soil EC mapping over models including VIs alone. The developed EC models are accurate within ±1 dS m-1 (Root Mean Squared Error), which is considered well within the precision required to make pragmatic field and ditch company-level management decisions.


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remote sensing


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