CLOUD-BASED SYSTEM FOR AGRICULTURAL LAND MONITORING USING REMOTE SENSING DATA IN NORTHERN KAZAKHSTAN

Authors

DOI:

https://doi.org/10.37943/ZACP9086

Keywords:

remote sensing, agricultural monitoring, land surface temperature, NDVI, TMDI, EVI, vegetation indices, multi-sensor fusion, Google Earth Engine, Northern Kazakhstan

Abstract

Satellite remote sensing offers strong opportunities for monitoring agricultural lands, especially in climate-sensitive regions such as Northern Kazakhstan. However, most existing agricultural monitoring platforms rely mainly on optical vegetation indices (e.g., Normalized vegetation index (NDVI) or Enhanced Vegetation Index (EVI)) and provide limited information on soil moisture and surface temperature. As a result, early signs of water and heat stress at the field level are often missed.

This study presents a cloud-based agricultural monitoring system that differs from existing solutions by integrating optical, microwave, and thermal satellite data within a unified framework. The system combines Sentinel-2 vegetation indices, SMAP SM, and land surface temperature (LST) from the MODIS MOD11A2 product, processed using Sentinel Hub and Google Earth Engine. All datasets are harmonized into consistent 10-day composites and aggregated at the field scale.

The Thermo-Moisture Drought Index (TMDI) was calculated by combining LST and soil moisture (SM), enabling improved characterization of drought conditions compared to vegetation indices alone. Integrating TMDI and EVI highlights stress conditions that are not visible in optical data only, demonstrating a clear advantage over traditional NDVI-based platforms.

The developed system provides an operational, cloud-based solution for field-level monitoring, potentially early stress detection, and interactive analysis. Compared to existing systems, it offers improved environmental interpretation by combining multiple satellite sensors, making it a practical decision-support tool for rainfed agriculture in Northern Kazakhstan.

This proposed system architecture is adaptable to other agricultural regions with similar characteristics. Furthermore, the framework can be extended with machine learning algorithms to predict crop yields and automatically assess drought risk.

References

Mimenbaeva, A., Yessen, A., Nurbekova, A., Suleimenova, R., Ospanova, T., Kasymova, A., & Niyazova, R. (2024). 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

Kabzhanova, G. R., Rakhimzhanov, B. K., & Tuleukulova, D. T. (2024). Assessment of the possibilities of remote monitoring of soil moisture in the territory of northern Kazakhstan. Herald of Science of S. Seifullin Kazakh Agrotechnical University: Multidisciplinary, 4(123), 1788. https://doi.org/10.51452/kazatu.2024.4(123).1788

Rafikov, T., & Yerbolkyzy, M. (2024). Primenenie dannykh distantsionnogo zondirovaniya zemli i analiza NDVI v Vostochno-Kazakhstanskoi oblasti [Application of remote sensing data and NDVI analysis in the East Kazakhstan Region]. Issledovaniya, rezul'taty, 1(101). https://doi.org/10.37884/1-2024/18

Vitkovskaya, I., Batyrbayeva, M., Berdigulov, N., & Mombekova, D. (2024). Prospects for drought detection and monitoring using long term vegetation indices series from satellite data in Kazakhstan. Land, 13(12), 2225. https://doi.org/10.3390/land13122225

Mimenbaeva, A. B., & Akanova, A. S. (2022). Soltüstik Qazaqstan oblysynyñ auylsharuashylyq daqyldarynyñ küiin NDVI syzyqtyq trendteri arqyly zertteu [Study of the condition of agricultural crops in the North Kazakhstan Region using NDVI linear trends]. Academic Scientific Journal of Computer Science, (3), 185–197. https://doi.org/10.32014/2022.2518-1726.146

Qin, Q., Wu, Z., Zhang, T., Sagan, V., Zhang, Z., Zhang, Y., Ren, H., Sun, Y., & Xu, W. (2021). Optical and thermal remote sensing for monitoring agricultural drought. Remote Sensing, 13(24), 5092. https://doi.org/10.3390/rs13245092

Vreugdenhil, M., Greimeister-Pfeil, I., Preimesberger, W., Camici, S., Dorigo, W., Enenkel, M., van der Schalie, R., Steele-Dunne, S., & Wagner, W. (2022). Microwave remote sensing for agricultural drought monitoring: Recent developments and challenges. Frontiers in Water, 4, Article 1045451. https://doi.org/10.3389/frwa.2022.1045451

Pilia, S.; Fontanelli, G.; Santurri, L.; Palchetti, E.; Ramat, G.; Baroni, F.; Santi, E.; Lapini, A.; Pettinato, S.; Paloscia, S. Integration of Optical and Microwave Satellite Data for Monitoring Vegetation Status in Sorghum Fields. (2025). Remote Sensing, 17(9), 1591. https://doi.org/10.3390/rs17091591

Li, M. Q., Wang, P. X., Tansey, K., & Sun, Y. F. (2025). Improved field scale drought monitoring using MODIS and Sentinel 2 data for vegetation temperature condition index generation through a fusion framework. Computers and Electronics in Agriculture, 234, 110256. https://doi.org/10.1016/j.compag.2025.110256

Cai, Y., Fan, P., Lang, S., Li, M., Muhammad, Y., & Liu, A. (2022). Downscaling of SMAP SM data by using a deep belief network. Remote Sensing, 14(22), 5681. https://doi.org/10.3390/rs14225681

Sharma, S. S., Mukherjee, J., & Dell’Acqua, F. (2025). Leveraging Sentinel-2 data and machine learning for drought detection in India: The Process of Ground truth Construction and a case study. Remote Sensing, 17(18), 3159. https://doi.org/10.3390/rs17183159

Pérez-Cutillas, P., Pérez-Navarro, A., Conesa-García, C., Zema, D. A., & Amado-Álvarez, J. P. (2022). What is going on within google earth engine? A systematic review and meta-analysis. Remote Sensing Applications Society and Environment, 29, 100907. https://doi.org/10.1016/j.rsase.2022.100907

Alshehri, B., Zhang, Z., &Liu, X. (2025). A review of Google Earth Engine for Land Use and Land Cover Change Analysis: Trends, Applications, and Challenges. ISPRS International Journal of Geo-Information, 14 (11), 416. https://doi.org/10.3390/ijgi14110416

Chen, S., Zhang, L., Hu, X., Meng, Q., Qian, J., & Gao, J. (2023). A spatiotemporal fusion model of land surface temperature based on pixel long time-series regression: expanding inputs for efficient generation of robust fused results. Remote Sensing, 15(11), 5211. https://doi.org/10.3390/rs15215211

Li, J., Wei, Y., Lin, L., Yuan, Q., & Shen, H. (2025). Two-stage downscaling and correction cascade learning framework for generating long-time series seamless soil moisture. Remote Sensing of Environment, 321, 114684. https://doi.org/10.1016/j.rse.2025.114684

Qin, Q., Wu, Z., Zhang, T., Sagan, V., Zhang, Z., Zhang, Y., Zhang, C., Ren, H., Sun, Y., Xu, W., et al. (2021). Optical and thermal remote sensing for monitoring agricultural drought. Remote Sensing, 13, 5092. https://doi.org/10.3390/rs13245092

Gao, G., Qi, J., Lin, S., Hu, R., & Huang, H. (2023). Estimating plant area density of individual trees from discrete airborne laser scanning data using intensity information and path length distribution. International Journal of Applied Earth Observation and Geoinformation, 118, Article 103281. https://doi.org/10.1016/j.jag.2023.103281

Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031

Guria, R., Mishra, M., da Silva, R. M., dos Santos, C. A. C., & Santos, C. A. G. (2025). Multisensor Integrated Drought Severity Index (IDSI) for assessing agricultural drought in Odisha, India. Remote Sensing Applications: Society and Environment, 37, Article 101399. https://doi.org/10.1016/j.rsase.2024.101399

Khafizova, Z., Umarov, N., Gulmurodov, F., Raximov, U., Yoqubov, A., & Yarkulov, Z. (2025). Drought analysis using cropland surface temperature and NDVI data. AIP Conference Proceedings, 3286(1), 040023. https://doi.org/10.1063/5.0280104

Andure, N., Kadam, A., Jadhav, S., Raut, S., Shafiyoddin, S., & Fulani, A. (2025). Real-time drought monitoring and prediction using multispectral remote sensing and machine learning. International Journal of Environmental Sciences, 11(8), 3921–3928. https://doi.org/10.64252/y0bxsk22

Qin, Q., Wu, Z., Zhang, T., Sagan, V., Zhang, Z., Zhang, Y., Zhang, C., Ren, H., Sun, Y., Xu, W., & Zhao, C. (2021). Optical and Thermal Remote Sensing for Monitoring Agricultural Drought. Remote Sensing, 13(24), 5092. https://doi.org/10.3390/rs13245092

Peng, L., Sheffield, J., Wei, Z., Ek, M., & Wood, E. F. (2024). An enhanced Standardized Precipitation–Evapotranspiration Index (SPEI) drought-monitoring method integrating land surface characteristics. Earth System Dynamics, 15(5), 1277–1300. https://doi.org/10.5194/esd-15-1277-2024

Le, M. S., & Liou, Y. (2021). Spatio-Temporal assessment of surface moisture and evapotranspiration variability using remote sensing techniques. Remote Sensing, 13(9), 1667. https://doi.org/10.3390/rs13091667

Sun, D., Li, Y., Zhan, X., Yang, C., & Yang, R. (2021). Integrating optical and microwave satellite observations for high resolution soil moisture estimation and applications in drought analyses. Journal of Geospatial Science, 4(1), 1–13. https://doi.org/10.24294/jgc.v4i1.1313

Lakshmi, V., Kir, E. G., Kir, A., & Fang, B. (2025). Remote Sensing-Based Monitoring of Agricultural Drought and Irrigation Adaptation Strategies in the Antalya Basin, Türkiye. Hydrology, 12(11), 288. https://doi.org/10.3390/hydrology12110288

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Published

2026-06-30

How to Cite

Mimenbayeva, A., Zhukabayeva, T., Turaev, S. ., Solovyov, O., Naizagarayeva, A. ., & Kudabayeva, R. . (2026). CLOUD-BASED SYSTEM FOR AGRICULTURAL LAND MONITORING USING REMOTE SENSING DATA IN NORTHERN KAZAKHSTAN. Scientific Journal of Astana IT University, 26(2), 6–24. https://doi.org/10.37943/ZACP9086

Issue

Vol. 26 No. 2 (2026): Volume 26

Section

Information Technologies

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