DEVELOPMENT OF A LINEAR REGRESSION MODEL BASED ON VEGETATION INDICES OF AGRICULTURAL CROPS

Authors

DOI:

https://doi.org/10.37943/15EMUB4283

Keywords:

Normalized difference water index, NWVI coefficient, soil moisture, remote sensing data, vegetation indices, time series analysis

Abstract

The article is devoted to the study of vegetation indices for assessing the productivity of agricultural crops of the North Kazakhstan Agricultural Experimental Station (NKAES) LLP. The research was carried out using a modern software package for processing satellite images, EOS Land Viewer. The work used images from the Landsat 8 (USA) and Sentitel 2 (European Space Agency) spacecraft. Digitized Earth remote sensing data for the last 3 years are presented, showing changes in the amount of moisture reserves on the territory of NKAES LLP. Time series of distribution of the studied coefficients were constructed according to different phases of active vegetation biomass in the study area. The resulting time series made it possible to identify annually repeating patterns, a linear trend of increasing and decreasing NDWI and NDVI on the territory of the NKAES LLP.

Review of studies over the past 5 years, published in highly rated foreign journals, on various vegetation indices, including indices designed to assess moisture content in vegetation and soil. It is noted that the first normalized water index, NDWI, using the SWIR infrared channel, unlike the widely used NDVI vegetation index, actually penetrates 80% of the atmosphere.

Analysis of the obtained NDWI allowed us to identify dry, moderately dry and fairly humid periods on the territory of the NKAES LPP from 2020 to 2023. Based on the research carried out, the feasibility of using normalized difference water indicators and normalized vegetation indices for further use in forecasting yields in the conditions of the North Kazakhstan region is substantiated. Next, using vegetation indices and additional agrometeorological factors, a linear model for predicting crop yields was developed. The coefficient of determination of the resulting model is 0.90 which indicates that the selected trend line reliably approximates the process under study.

References

Berveglieri, A., Imai, N.N., Christovam, L., Galo, M., Tommaselli, A., & Honkavaara, E. (2021). Analysis of trends and changes in the successional trajectories of tropical forest using the Landsat NDVI time series. Remote Sensing Applications: Society and Environment, 24, 100622. https://data.europa.eu/doi/10.2760/487249

Mimenbayeva, A., & Zhukabayeva, T. (2020). A review of free resources for processing and analyzing geospatial data. ICEMIS'20: Proceedings of the 6th International Conference on Engineering & MIS 2020, 64, 1-4. https://doi.org /10.1145/3410352.3410800

Li, C., Xue, J., & Su, B. (2017). Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications. Journal of Sensors, 2017, 1353691. https://doi.org/10.1155 /2017/1353691

Jinru, X., & Baofeng, S. (2017). Significant Remote Sensing Vegetation Indices: A Re-view of Developments and Applications. Journal of Sensors, 2017, 1-17. https://doi.org/10.1155/2017/1353691

Mutanga, O., Masenyama, A., & Sibanda, M. (2023). Spectral saturation in the remote sensing of high-density vegetation traits: A systematic review of progress, challenges, and prospects. ISPRS Journal of Photogrammetry and Remote Sensing, 198, 297-309. https://doi.org/10.1016/j.isprsjprs.2023.03.010.

Aharon-Rotman, Y., McEvoy, J., Zheng, Z., Yu, H., Wang, X., Si, Y.& Fox, A. (2017). Water level affects availability of optimal feeding habitats for threatened migratory waterbirds. Ecology and Evolution, 7(23), 10440-10450. https://doi.org/10.1002 /ece3.3566T

Teng, J., Xia, S., Liu, Y., Yu, X., Duan, H., Xiao, H., & Zhao, C. (2021). Assessing habitat suitability for wintering geese by using Normalized Difference Water Index (NDWI) in a large floodplain wetland. Ecological Indicators, 122, 107260. https://doi.org/10.1016/j.ecolind.2020.107260.

Teng, J., Xia, S., Liu, Y., Cui, P., Chen, J., Si, W., Duan, H., & Yu, X. (2020). Differences of regulative flexibility between hydrological isolated and connected lakes in a large floodplain: insight from inundation dynamics and landscape heterogeneity. Water, 12, 991. https://doi.org/10.3390/w12040991

Chandrasekar, K., Srikanth, P., Chakraborty, A., Choudhary, K., & Ramana, K. (2022). Response of crop water indices to soil wetness and vegetation water content. Advances in Space Research. https://doi.org/10.1016/j.asr.2022.11.019

Damm, A., Cogliati, S., Colombo, R., Fritsche, L., Genangeli, A., Genesio, L., Hanus, J., Peressotti, A., Rademske, P., Rascher, U., Schuettemeyer, D., Siegmann, B., Sturm, J., & Miglietta, F. (2022). Response times of remote sensing measured sun-induced chlorophyll fluorescence, surface temperature and vegetation indices to evolving soil water limitation in a crop canopy. Remote Sensing of Environment, 273, 112957. https://doi.org/10.1016/j.rse.2022.112957

Ding, С., Liu, X., Huang, F., Li, Y., & Zou, X. (2017). Onset of drying and dormancy in relation to water dynamics of semi-arid grasslands from MODIS NDWI. Agricultural and Forest Meteorology, 234, 22-30. https://doi.org/10.1016/j.agrformet. 2016.12.006

Ren, S., Guo, B., Wang, Z., Wang, J., Fang, Q., & Wang, J. (2022). Optimized spectral index models for accurately retrieving soil moisture (SM) of winter wheat under water stress. Agricultural Water Management, 261, 107333. https://doi.org/10.1016j .agwat. 2021.107333

Jahromi, M., Shahrokh Zand-Parsa, S., Razzaghi, F., Sajad Jamshidi, S., Didari, S., Doosthosseini, A., & Pourghasemi, H. (2023). Developing machine learning models for wheat yield prediction using ground-based data, satellite-based actual evapotranspiration and vegetation indices. European Journal of Agronomy, 146, 126820. https://doi.org/10.1016/j.eja.2023.126820

Marusig, D. & Napolitano, R. (2020). Correlation of Field-Measured and Remotely Sensed Plant Water Status as a Tool to Monitor the Risk of Drought-Induced Forest Decline. Forests, 11 (5), 77-80. https://doi.org/10.3390/f11010077

Acharya, U., Daigh, A., & Oduor, P. (2021). Machine Learning for Predicting Field Soil Moisture Using Soil, Crop, and Nearby Weather Station Data in the Red River Valley of the North. Soil Systems, 5, 57. https://doi.org/10.3390/soilsystems5040057

Huang, X., Cao, H., & Jia, B. (2023). Optimization of Levenberg Marquardt Algorithm Applied to Nonlinear Systems. Processes, 11, 1794. https://doi.org/10.3390/pr11061794

Downloads

Published

2023-09-30

How to Cite

Mimenbayeva, A., Yessen, A., Nurbekova, A. ., Suleimenova, R. ., Ospanova, T. ., Kasymova, A. ., & Niyazova, R. . (2023). DEVELOPMENT OF A LINEAR REGRESSION MODEL BASED ON VEGETATION INDICES OF AGRICULTURAL CROPS. Scientific Journal of Astana IT University, 15(15), 101–110. https://doi.org/10.37943/15EMUB4283

Issue

2023: Volume 15

Section

Articles

License

Copyright (c) 2023 Articles are open access under the Creative Commons License

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Authors who publish a manuscript in this journal agree to the following terms:

  • The authors reserve the right to authorship of their work and transfer to the journal the right of first publication under the terms of the Creative Commons Attribution License, which allows others to freely distribute the published work with a mandatory link to the the original work and the first publication of the work in this journal.
  • Authors have the right to conclude independent additional agreements that relate to the non-exclusive distribution of the work in the form in which it was published by this journal (for example, to post the work in the electronic repository of the institution or publish as part of a monograph), providing the link to the first publication of the work in this journal.
  • Other terms stated in the Copyright Agreement.
betpas