Integration of Remote and Proximal Sensing Techniques for Soil Salinity Assessment of Pistachio Orchards

Document Type : Original Article

Authors

1 Khorasan Razavi Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Mashhad, Iran

2 National Salinity Research Center, Agricultural Research, Education and Extension Organization (AREEO), Yazd, Iran

Abstract

Soil salinity is a property that varies in space and time. In pistachio orchards irrigated with saline and brackish water sources, understanding the variability of soil salinity is crucial for managing irrigation and leaching practices. Excessive leaching leads to water loss, while insufficient leaching results in salt accumulation in the soil, ultimately reducing water productivity. Traditional methods of assessing soil salinity, which rely on sampling and laboratory analysis, are both time-consuming and costly for understanding this temporal and spatial variability. Utilysing the remote  and proximal sensing tools can reduce the time and cost of monitoring soil salinity changes. This project was designed and executed to evaluate these methods in mapping soil salinity variations in the field and for local scales. The proximal sensing indicators in this study included measuring the apparent electrical conductivity of the soil using the EM38 device, as well as measuring the canopy diameter and the number of clusters on pistachio trees. Remote sensing indicators included the mean digital numbers of Sentinel-2 satellite sensors and vegetation indices derived from them. Ground measurements involved soil sampling from 25 points in a 75-hectare pistachio orchard in Khorasan Razavi Province, down to a depth of 90 cm at 30 cm intervals. Results showed that models using RS and PS indicators, both individually and in combination, could predict soil salinity with statistically significant relationships (P < 0.01). However, the determination coefficients (R²) were relatively low, ranging from 0.26 to 0.56, indicating moderate predictive power. Among the methods tested, the Partial Least Squares Regression (PLSR) model based on remote sensing variables was applied to predict salinity over a larger 4080-hectare area using a combination of Google Earth Engine and R coding media. The Support Vector Machine (SVM) model yielded the highest mapping accuracy for interpolation (R² = 0.9, RMSE = 0.75 dS/m), demonstrating its relative effectiveness in spatially predicting soil salinity.

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References

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Dryland Soil Research (DLSR)
Volume 1, Issue 2
December 2024
Pages 43-57

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