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.

Keywords

Main Subjects

References

Aksoy, S., Yildirim, A., Gorji, T., Hamzehpour, N., Tanik, A., and Sertel, E. (2022). Assessing the performance of machine learning algorithms for soil salinity mapping in Google Earth Engine platform using Sentinel-2A and Landsat-8 OLI data. Advances in Space Research, 69(2), 1072-1086.
Allbed, A., and Kumar, L. (2013). Soil salinity mapping and monitoring in arid and semi-arid regions using remote sensing technology: a review. Advances in remote sensing. 2(4), 373–385.
Brunner, P. H. T. L., Li, H. T., Kinzelbach, W., and Li, W. P. (2007). Generating soil electrical conductivity maps at regional level by integrating measurements on the ground and remote sensing data. International Journal of Remote Sensing, 28(15), 3341-3361.
Corwin, D. L., and Scudiero, E. (2019). Review of soil salinity assessment for agriculture across multiple scales using proximal and/or remote sensors. Advances in agronomy, 158, 1-130.
Delavar, M. A., Naderi, A., Ghorbani, Y., Mehrpouyan, A., and Bakhshi, A. (2020). Soil salinity mapping by remote sensing south of Urmia Lake, Iran. Geoderma Regional, 22, e00317.
Dong, W., Wu, T., Luo, J., Sun, Y., and Xia, L. (2019). Land parcel-based digital soil mapping of soil nutrient properties in an alluvial-diluvia plain agricultural area in China. Geoderma, 340, 234-248.
Fourati, T.H., Bouaziz, M., Benzina, M., and Bouaziz, S. (2017). Detection of terrain indices related to soil salinity and mapping salt-affected soils using remote sensing and geostatistical techniques. Environmental monitoring and assessment, 189, 1-11.
Gao, J., Zhao, Q., Chang, D., Ndayisenga, F., and Yu, Z. (2022). Assessing the effect of physicochemical properties of saline and sodic soil on soil microbial communities. Agriculture, 12(6), 782.
Ghassemi, F., Jakeman, A. J., and Nix, H. A. (1995). Salinisation of land and water resources: human causes, extent, management and case studies: CAB international.
Haq, Y. U., Shahbaz, M., Asif, H. S., Al-Laith, A., and Alsabban, W. H. (2023). Spatial mapping of soil salinity using machine learning and remote sensing in Kot Addu, Pakistan. Sustainability, 15(17), 12943.
Hassani, A., Azapagic, A., and Shokri, N. (2020). Predicting long-term dynamics of soil salinity and sodicity on a global scale. Proceedings of the National Academy of Sciences, 117(52), 33017-33027.
Iyer, G. R. S., Wang, J., Wells, G., Guruvenket, S., Payne, S., Bradley, M., and Borondics, F. (2014). Large-area, freestanding, single-layer graphene–gold: a hybrid plasmonic nanostructure. ACS nano, 8(6), 6353-6362.
Khorsandi, F., J. Vaziri and A. Azizi Zohan. (2010). Haloculture, sustainable use of saline water and soils in Agriculture. Iranian National Committee of Irrigation and Drainage. 320 pp. (in Persian).
Lagacherie, P., Arrouays, D., Bourennane, H., Gomez, C., and Nkuba-Kasanda, L. (2020). Analysing the impact of soil spatial sampling on the performances of Digital Soil Mapping models and their evaluation: A numerical experiment on Quantile Random Forest using clay contents obtained from Vis-NIR-SWIR hyperspectral imagery. Geoderma, 375, 114503.
Marino, G., Zaccaria D., Snyder RL., Lagos O., Lampinen BD., Ferguson L., Grattan SR., Little C., Shapiro K., Maskey ML., Corwin DL. (2019). Actual evapotranspiration and tree performance of mature micro-irrigated pistachio orchards grown on saline-sodic soils in the San Joaquin Valley of California. Agriculture, 9(4), 76.
Moameni, A. (2010). Geographical distribution and extent of saline soils in Iran. Soil Research. 24(3): 203-216.
Muhetaer, N., Nurmemet, I., Abulaiti, A., Xiao, S., and Zhao, J. (2022). A quantifying approach to soil salinity based on a radar feature space model using ALOS PALSAR-2 data. Remote Sensing, 14(2), 363.
Peng, J., Biswas, A., Jiang, Q., Zhao, R., Hu, J., Hu, B., and Shi, Z. (2019). Estimating soil salinity from remote sensing and terrain data in southern Xinjiang Province, China. Geoderma, 337, 1309-1319.
Rahimian, M. H., Shayannejad, M., Eslamian, S., Gheysari, M., and Jafari, R. (2019). Daily and Seasonal Pistachio Evapotranspiration in Saline Condition: Comparison of Satellite-Based and Ground-Based Results. Journal of the Indian Society of Remote Sensing, 47, 777-787.
Ren, D., Wei, B., Xu, X., Engel, B., Li, G., Huang, Q., Xiong, Y. and Huang, G. (2019). Analyzing spatiotemporal characteristics of soil salinity in arid irrigated agro-ecosystems using integrated approaches. Geoderma, 356, 113935.
Scudiero, E., Skaggs, T. H., and Corwin, D. L. (2014). Regional scale soil salinity evaluation using Landsat 7, western San Joaquin Valley, California, USA. Geoderma Regional, 2, 82-90.
Shamsi, S., Kamali, A., and Hasheminejhad, Y. (2022). An approach to predict soil salinity changes in irrigated pistachio orchards (Ardakan, Yazd Province): A case study. Journal of Soil Science Society of Iran, 1(1), 1-10.
Taghizadeh-Mehrjardi, R., Ayoubi, S., Namazi, Z., Malone, B. P., Zolfaghari, A. A., and Sadrabadi, F. R. (2016). Prediction of soil surface salinity in arid region of central Iran using auxiliary variables and genetic programming. Arid Land Research and Management, 30(1), 49-64.
Taghizadeh-Mehrjardi, R., Minasny, B., Sarmadian, F., and Malone, B. P. (2014). Digital mapping of soil salinity in Ardakan region, central Iran. Geoderma, 213, 15-28.
Taghizadeh-Mehrjardi, R., Schmidt, K., Toomanian, N., Heung, B., Behrens, T., Mosavi, A., Band, S.S., Amirian-Chakan, A., Fathabadi, A. and Scholten, T. (2021). Improving the spatial prediction of soil salinity in arid regions using wavelet transformation and support vector regression models. Geoderma, 383, 114793.
Tavares, S. R., Vasques, G. M., Oliveira, R. P., Dantas, M. M., and Rodrigues, H. M. (2024). Random Forest-Based Fusion of Proximal and Orbital Remote Sensor Data for Soil Salinity Mapping in a Brazilian Semi-arid Region. In: Pedometrics in Brazil (pp. 197-209). Cham: Springer Nature Switzerland.
Wang, F., Yang, S., Wei, Y., Shi, Q., and Ding, J. (2021). Characterizing soil salinity at multiple depth using electromagnetic induction and remote sensing data with random forests: A case study in Tarim River Basin of southern Xinjiang, China. Science of the Total Environment, 754, 142030.
Wang, J., Ding, J., Abulimiti, A., and Cai, L. (2018). Quantitative estimation of soil salinity by means of different modeling methods and visible-near infrared (VIS–NIR) spectroscopy, Ebinur Lake Wetland, Northwest China. PeerJ, 6, e4703.
Wang, N., Chen, S., Huang, J., Frappart, F., Taghizadeh, R., Zhang, X., Wigneron, J.P., Xue, J., Xiao, Y., Peng, J. and Shi, Z. (2024). Global Soil Salinity Estimation at 10 m Using Multi-Source Remote Sensing. Journal of Remote Sensing, 4, 0130.
Xiao, C., Ji, Q., Chen, J., Zhang, F., Li, Y., Fan, J., Hou, X., Yan, F. and Wang, H. (2023). Prediction of soil salinity parameters using machine learning models in an arid region of northwest China. Computers and Electronics in Agriculture, 204, 107512.
Yimer,  A. M., Sodango,  T. H., and Assefa,  S. A. (2022). Analysis and Modeling of Soil Salinity Using Sentinel-2A and LANDSAT-8 images in the Afambo Irrigated Area, Afar Region, Ethiopia. Preprints. https://doi.org/10.20944/preprints202204.0250.v1
Zare, S., Abtahi, A., Shamsi, S. R. F., and Lagacherie, P. (2021). Combining laboratory measurements and proximal soil sensing data in digital soil mapping approaches. Catena, 207, 105702.
Zhao, S., Ayoubi, S., Mousavi, S.R., Mireei, S.A., Shahpouri, F., Wu, S.X., Chen, C.B., Zhao, Z.Y. and Tian, C.Y. (2024). Integrating proximal soil sensing data and environmental variables to enhance the prediction accuracy for soil salinity and sodicity in a region of Xinjiang Province, China. Journal of Environmental Management, 364, 121311.
 
 
Dryland Soil Research (DLSR)
Volume 1, Issue 2
December 2024
Pages 43-57

Files

Share

How to cite

Statistics

ارتقاء امنیت وب با وف بومی