##plugins.themes.academic_pro.article.sidebar##

Download
PDF

##plugins.themes.academic_pro.article.main##

Abstract

The reason we focus on this concept in this book is because information is an important part of
GAT, or without it, GAT would be meaningless. We can call it information in our ordinary language, but if
we say it in the language of GAT, then this concept has a slightly wider meaning. We know that in the world
of information there are 2 different concepts, one is information and the other is information. Information
is the event we see, and information is the image of that event processed in the human mind. In GAT,
information is derived from the processing of primary data obtained from the place, unlike the above
definition, this information or primary information is not in the human mind, it is processed using special
programs of GAT and stored in the database, and in the future, depending on the user's desire, it is presented
in the form of electronic or ordinary paper.

Keywords

Geodata model photogrammetry modeling grid

##plugins.themes.academic_pro.article.details##

Creative Commons License

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

How to Cite
Shamuratova Maftuna Gulimmat qizi, Duschanova Oygul Farxodovna, & Saparbayev Zakir Yusubboyevich. (2023). Importance of using GIS technologies in soil science. Texas Journal of Agriculture and Biological Sciences, 22, 57–59. Retrieved from https://zienjournals.com/index.php/tjabs/article/view/4777

References

  1. ALAM, S., GEBREMICHAEL, M., & LI, R. (2019). Remote sensing-based assessment of the crop, energy and water nexus in the Central Valley, California. Remote Sensing, 11(14), 1701.
  2. CAO, J., ZHANG, Z., TAO, F., ZHANG, L., LUO, Y., HAN, J., & LI, Z. (2020). Identifying the contributions of multi-source data for winter wheat yield prediction in China. Remote Sensing, 12(5), 750.
  3. CHENG, T., ZHANG, J., ZHANG, S., BAI, Y., WANG, J., LI, S., & SHARMA, T. P. P. (2022). Monitoring soil salinization and its spatiotemporal variation at different depths across the Yellow River Delta based on remote sensing data with multi-parameter optimization. Environmental Science and Pollution Research, 1-17
  4. CONGALTON, R. G., & GREEN, K. (2019). Assessing the accuracy of remotely sensed data: principles and practices. CRC press.
  5. EL-SHIRBENY, M. A., ALI, A., & SALEH, N. H. (2014). Crop water requirements in Egypt using remote sensing techniques. Journal of Agricultural Chemistry and Environment, 3(2), 57-65.
  6. FOODY, G. M., LING, F., BOYD, D. S., LI, X., & WARDLAW, J. (2019). Earth observation and machine learning to meet Sustainable Development Goal 8.7: Mapping sites associated with slavery from space. Remote Sensing, 11(3), 266.
  7. HUANG, C., CHEN, Y., ZHANG, S., & WU, J. (2018). Detecting, extracting, and monitoring surface water from space using optical sensors: A review. Reviews of Geophysics, 56(2), 333-360.
  8. IJAZ, Z., ZHAO, C., IJAZ, N., REHMAN, Z. U., & IJAZ, A. (2022). Novel application of Google earth engine interpolation algorithm for the development of geotechnical soil maps: a case study of megadistrict. Geocarto International, 1-21