Feasibility of estimating cotton water stress based on spectral indices of Landsat and Sentinel 2 satellite images

Document Type : Complete scientific research article

Authors

1 Ph.D. Student in Irrigation and Drainage Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran.

2 Corresponding Author, Associate Prof., Dept. of Water Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran.

3 Associate Prof., Dept. of Water Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran.

4 Assistant Prof., Dept. of Arid Regions Management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran.

Abstract

Background and objectives: Water deficiency, as one of the factors of cotton crop stress, is a reaction to the changes that occur in the plant growth environment and has a negative effect on the productivity of crops, which can be well prevented with different methods of agricultural land management. Agricultural land management requires the use of sufficient data and information from different parts of agricultural land, and it is in this way that productivity can be significantly improved.
Materials and methods: The studied area is within the cotton lands of Shir Ali Abad and Sistani villages, a neighborhood of the functions of Engirab Agricultural Services Department, Gorgan city, in the geographical coordinates of 36o52’22” to 36o52’52” north latitude and 54o21’55” to 54o20’50” east longitude. Accurate and continuous monitoring of soil moisture content, as a representative of soil moisture stress, was done with field measurements of soil moisture and other environmental parameters (air temperature, leaf surface temperature, leaf surface index and also salinity), during the growing season (late May to late October) for 5 months. After extracting spectral bands from Landsat and Sentinel 2 satellite images, spectral indices were calculated. Using the methods of multivariate linear regression (MLR) and M5 tree regression, the relationship between spectral indices as an independent variable and soil surface moisture as a dependent variable, search and finally the optimal model by examining error evaluation criteria with the highest accuracy and the lowest resulting error became.
Results: M5 tree model was more accurate than MLR in estimating cotton water stress; In Landsat satellite, the explanation coefficient increased from 0.51 to 0.79, and the error value decreased from 4.2% to 2.9%. Also, the Landsat satellite was more accurate than the Sentinel 2 satellite. Thus, in Sentinel 2, the maximum explanation coefficient was 0.46 and the error was 4.9%. In the Landsat satellite, LST thermal index showed a great influence of water stress changes and the combination of 3 LST thermal indices, NDVI vegetation and SI2 salinity with an explanation coefficient of 0.76 and an error percentage of 3.3, provided acceptable results.
Conclusion: The effect of water stress on reflection, in the infrared and thermal range, caused thermal and water indices such as LST, NMDI, NDWI and WI to have a significant effect in the step-by-step implementation of the M5 tree model. Thus, LST thermal index in Landsat satellite and water indices NDWI and NMDI in Sentinel 2 satellite played a more effective role in estimating water stress. On the other hand, the lack of a thermal band in Sentinel 2 has reduced its accuracy compared to the Landsat satellite.

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References

 
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Journal of Water and Soil Conservation
Volume 31, Issue 2
July 2024
Pages 99-117

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