Optimal timing of satellite data acquisition for estimating and modeling soil salinity in cotton fields of the Mingbulak District, Uzbekistan

Abstract

Agriculture is frequently hampered by soil salinity, which has a negative impact on crop growth and yield. This study aims to identify the optimal timing of satellite data acquisition to predict soil salinity levels indirectly using satellite images in cotton growth fields as a basis. Data was collected in the Mingbulak district of Uzbekistan, where soil electrical conductivity (EC) was measured in a laboratory using soil samples collected from various fields with similar management practices. In this research, we present a linear regression model that uses satellite data and the Normalized Difference Salinity Index (NDSI) to forecast soil salinity levels indirectly. The results of the linear regression analysis showed a positive correlation between the soil electrical conductivity values and the NDSI values for each month, with August having the highest correlation (R2 = 0.70). The study found that the cotton growth stages and the process of soil salinity formation in the study area were the main factors affecting the correlation between electrical conductivity and NDSI. The model developed in this study has R2 value of 0.70. This suggests a moderate to strong relationship between the two variables, which is promising for the indirect assessment of soil salinity using the NDSI index. The study discovered a positive relationship between soil electrical conductivity and NDSI values, which were highest in pre-flowering and flowering stages of cotton. Our findings show that satellite-based estimation and modeling with NDSI can be used to indirectly assess cotton field soil salinity, especially during the pre-flowering and flowering stages. This study contributes to the development of optimal satellite data acquisition timing, which can improve soil salinity predictions and agricultural productivity.

Keywords

• Cotton
• index
• soil
• salinity
• satellite image

References

1. Allbed, A., 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.
2. Asfaw, E., Suryabhagavan, K.V., Argaw, M., 2018. Soil salinity modeling and mapping using remote sensing and GIS: The case of Wonji sugar cane irrigation farm, Ethiopia. Journal of the Saudi Society of Agricultural Sciences 17: 250–258.
3. Aslanov, I., Kholdorov, S., Ochilov, S., Jumanov, A., Jabbarov, Z., Jumaniyazov, I., Namozov, N., 2021. Evaluation of soil salinity level through using Landsat-8 OLI in Central Fergana valley, Uzbekistan. E3S Web of Conferences 258, 03012.
4. Chen, P., Xu, W., Zhan, Y., Yang, W., Wang, J., Lan, Y., 2022. Evaluation of cotton defoliation rate and establishment of spray prescription map using remote sensing imagery. Remote Sensing 14(17): 4206.
5. Corwin, D.L., 2021. Climate change impacts on soil salinity in agricultural areas. European Journal of Soil Science 72(2): 842-862.
6. Elhag, M., Bahrawi, J.A., 2017. Soil salinity mapping and hydrological drought indices assessment in arid environments based on remote sensing techniques. Geoscientific Instrumentation Methods and Data Systems 6: 149–158.
7. FAO, 2021. Standard operating procedure for soil electrical conductivity, soil/water, 1:5. FAO, Rome, Italy. 15p. Available at Access date: 18.05.2023: https://www.fao.org/3/cb3354en/cb3354en.pdf
8. Guo, W., Maas, S.J., Bronson, K.F., 2012. Relationship between cotton yield and soil electrical conductivity, topography, and Landsat imagery. Precision Agriculture 13: 678–692.

9. Hamad, I.A., 2016. The use of ınverse distance weighted and fuzzy logic to estimate land suitability by geographic ınformation system in South of Iraq. Alexandria Science Exchange Journal 37: 26-35.
10. Ijaz, M., Ahmad, H.R., Bibi, S., Ayub, M.A., Khalid, S., 2020. Soil salinity detection and monitoring using Landsat data: a case study from Kot Addu, Pakistan. Arabian Journal of Geosciences 13: 510.
11. Ivushkin, K., Bartholomeus, H., Bregt, A.K., Pulatov, A., 2017. Satellite thermography for soil salinity assessment of cropped areas in Uzbekistan. Land Degradation and Development 28(3): 870–877.
12. Jahanbazi, L., Heidari, A., Mohammadi, M.H., Kuniushkova, M., 2023. Salt accumulation in soils under furrow and drip irrigation using modified waters in Central Iran. Eurasian Journal of Soil Science 12(1): 63–78.
13. Khajehzadeh, M., Afzali, S.F., Honarbakhsh, A., Ingram, B., 2022. Remote sensing and GIS-based modeling for predicting soil salinity at the watershed scale in a semi-arid region of southern Iran. Arabian Journal of Geosciences 15, 423.
14. Khan, N.M., Rastoskuev, V. V, Shalina, E. V, Sato, Y., 2001. Mapping Salt-affected Soils Using Remote Sensing Indicators-A Simple Approach With the Use of GIS IDRISI. Proceedings of the 22nd Asian Conference on Remote Sensing, 5-9 November 2001, Singapore. Center for Remote Imaging, Sensing and Processing (CRISP), National University of Singapore; Singapore Institute of Surveyors and Valuers; Asian Association on Remote Sensing, 5 p.
15. Kholdorov, Sh., Gopakumar, L., Katsura, K., Jabbarov, Z., Jobborov, O., Shamsiddinov, T., Khakimov, A., 2022. Soil salinity assessment research using remote sensing techniques: a special focus on recent research. IOP Conference Series: Earth and Environmental Science 1068: 012037.
16. Kholdorov, S., Jabbarov, Z., Shamsiddinov, T., 2023a. Soil governance: A review of the current legislative framework for managing soil resources in Uzbekistan. Soil Security 13: 100105.
17. Kholdorov, Sh, Lakshmi, G., Jabbarov, Z., Yamaguchi, T., Yamashita, M., Samatov, N., Katsura, K., 2023b. Analysis of irrigated salt-affected soils in the central Fergana Valley, Uzbekistan, Using Landsat 8 and Sentinel-2 Satellite Images, Laboratory Studies, and Spectral Index-Based Approaches. Eurasian Soil Science 56, 1178–1189.
18. Kholliyev, A., Norboyeva, U., Kholov, Y., Boltayeva, Z., 2020. Productivity of cotton varieties ın soil salinity and water deficiency. The American Journal of Applied Sciences 2(10): 7–13.
19. Li, Y., Chang, C., Wang, Z., Zhao, G., 2022. Remote sensing prediction and characteristic analysis of cultivated land salinization in different seasons and multiple soil layers in the coastal area. International Journal of Applied Earth Observation and Geoinformation 111: 102838.
20. Liu, Y., Zhang, F., Wang, C., Wu, S., Liu, J., Xu, A., Pan, K., Pan, X., 2019. Estimating the soil salinity over partially vegetated surfaces from multispectral remote sensing image using non-negative matrix factorization. Geoderma 354: 113887.
21. Metternicht, G.I., Zinck, J.A., 2003. Remote sensing of soil salinity: Potentials and constraints. Remote Sensing of Environment 85(1): 1-20.
22. Pankova E.I., Mazikov V.M., 1985. Guidelines for the use of aerial photography materials for assessing soil salinity and conducting salt surveys of irrigated areas of the cotton-growing zone on a large and medium scale. Moscow, Russia. 73p. (in Russian)
23. Pankova E.I., Mazikov V.M., 1976. Estimation of salinity of irrigated soils of cotton fields from aerial photographs (on the example of the Golodnoy Steppe). Pochvvedenie 5: 55–65. (in Russian)
24. Salcedo, F.P., Cutillas, P.P., Cabañero, J.J.A., Vivaldi, A.G., 2022. Use of remote sensing to evaluate the effects of environmental factors on soil salinity in a semi-arid area. Science of The Total Environment 815: 152524.
25. Scudiero, E., Skaggs, T.H., Corwin, D.L., 2015. Regional-scale soil salinity assessment using Landsat ETM+ canopy reflectance. Remote Sensing of Environment 169: 335–343.
26. Stavi, I., Thevs, N., Priori, S., 2021. Soil salinity and sodicity in drylands: A review of causes, effects, monitoring, and restoration measures. Frontiers in Environmental Science 9: 712831.
27. Tan, J., Ding, J., Han, L., Ge, X., Wang, X., Wang, J., Wang, R., Qin, S., Zhang, Z., Li, Y., 2023. Exploring planetscope satellite capabilities for soil salinity estimation and mapping in arid regions oases. Remote Sensing 15(4): 1066.
28. Richards, L.A., 1954. Diagnosis and improvement of saline and alkaline soils. Agriculture Handbook, Vol. 60, USDA, Washington DC, 160 p.
29. Vaughn, I., 2019. Landsat 8 (L8) Data Users Handbook. USGS Science for a Changing World. EROS, Sioux Falls, South Dakota. 106p.
30. Wiegand, C.L., Rhoades, J.D., Escobar, D.E., Everitt, J.H., 1994. Photographic and videographic observations for determining and mapping the response of cotton to soil salinity. Remote Sensing of Environment 49(3): 212-223.
31. Zafar, M.M., Shakeel, A., Haroon, M., Manan, A., Sahar, A., Shoukat, A., Mo, H., Farooq, M.A., Ren, M., 2022. Effects of salinity stress on some growth, physiological, and biochemical parameters in cotton (Gossypium hirsutum L.) germplasm. Journal of Natural Fibers 19(14): 8854–8886.
32. Zhang, J., Zhang, Z., Chen, J., Chen, H., Jin, J., Han, J., Wang, X., Song, Z., Wei, G., 2021. Estimating soil salinity with different fractional vegetation cover using remote sensing. Land Degradation and Development 32(2): 597–612.
33. Zhang, X., Shu, C., Wu, Y., Ye, P., Du, D., 2023. Advances of coupled water-heat-salt theory and test techniques for soils in cold and arid regions: A review. Geoderma 432: 116378.
34. Zhang, Y., Hou, K., Qian, H., Gao, Y., Fang, Y., Xiao, S., Tang, S., Zhang, Q., Qu, W., Ren, W., 2022. Characterization of soil salinization and its driving factors in a typical irrigation area of Northwest China. Science of the Total Environment 837: 155808.
35. Zhu, K., Sun, Z., Zhao, F., Yang, T., Tian, Z., Lai, J., Zhu, W., Long, B., 2021. Relating hyperspectral vegetation indices with soil salinity at different depths for the diagnosis ofwinter wheat salt stress. Remote Sensing 13(2): 250.
36. Zörb, C., Geilfus, C.M., Dietz, K.J., 2019. Salinity and crop yield. Plant Biology 21(S1): 31-38.