[
Aceves, E.Á., Guevara, H.J.P., Enríquez, A.C., Gaxiola, J.C., Cervantes, M.P., Barrientos, J.H., Herrera, L.E., Guevara, V.M.P., Samuel, C.L. (2019). Determining Salinity and Ion Soil Using Satellite Image Processing. Polish Journal of Environmental Studies, 28(3), 1549-1560.
]Search in Google Scholar
[
Allbed, A. and 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.
]Search in Google Scholar
[
Almeida-Ñauñay, A.F., Benito, R.M., Quemada, M., Losada, J.C., Tarquis, A.M. (2020). Complexity of the Vegetation-Climate System Through Data Analysis. International Conference on Complex Networks and Their Applications, 943, 609-619.
]Search in Google Scholar
[
Almeida-Ñauñay, A.F., Tarquisab, A.M., López-Herreraa, J., Pérez-Martína, E., Pancorboac, J.L., Raya-Serenoac, M.D., Quemada, M. (2023). Optimization of soil background removal to improve the prediction of wheat traits with UAV imagery. Computers and Electronics in Agriculture, 205, 107559.
]Search in Google Scholar
[
Boiarskii, B., Hasegawa, H. (2019). Comparison of NDVI and NDRE Indices to Detect Differences in Vegetation and Chlorophyll Content. International Conference on Applied Science, Technology and Engineering. Journal of Mathematical and Computational Science, 4, 20-29.
]Search in Google Scholar
[
Daldegan, A., Gonzalez-Roglich, G., Noon, M., Zvoleff, A.I. (2020). Assessing the performance of NDVI, 2-band EVI and MSAVI vegetation indices for land degradation monitoring across variable biomass cover at global scale [Absract]. American Geophysical Union, Fall Meeting. Retrieved December 6, 2022, https://ui.adsabs.harvard.edu/abs/2020AGUFMSY0030008A/abstract
]Search in Google Scholar
[
Fabijańczyk, P., Zawadzki, J. (2022). Spatial correlations of NDVI and MSAV2 Iindices of green and forested areas of urban agglomeration, case study Warsaw, Poland. Remote Sensing Applications: Society and Environment, 26, 100721.
]Search in Google Scholar
[
Hassan, M.A., Rasheed, A., Yang, G., Reynolds, M., Xia, X., Xiao, Y., He, Z. (2019). A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform. Plant Science, 282, 95-103.
]Search in Google Scholar
[
Havrylenko Y., Kholodniak Y., Halko S., Vershkov O., Bondarenko L., Suprun O., Miroshnyk O Shchur T., Śrutek M., Gackowska M. (2021). Interpolation with specified error of a point series belonging to a monotone curve. Entropy, 23(5), 493.
]Search in Google Scholar
[
Herbei, M.V., Popescu, C.A., Sala, F. (2022). Characterization of vegetation and prediction of biomass production in silage corn crop based on satellite images. Life Science and Sustainable Development, 3(2), 33-40.
]Search in Google Scholar
[
Huang, S., Tang, L., Hupy, J.P., Wang, Y., Shao, G. (2021). A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. Journal of Forestry Research, 32, 1-6.
]Search in Google Scholar
[
Kayad, A., Sozzi, M., Gatto, S., Marinello, F., Pirotti, F. (2019). Monitoring Within-Field Variability of Corn Yield using Sentinel-2 and Machine Learning Techniques. Remote Sensing, 11(23), 2873.
]Search in Google Scholar
[
Karaiev, O., Bondarenko, L., Halko, S., Miroshnyk, O., Vershkov, O., Karaieva, T., Shchur, T., Findura, P., Prístavka, M. (2021). Mathematical modelling of the fruit-stone culture seeds calibration process using flat sieves. Acta Technologica Agriculturae, 24(3), 119-123.
]Search in Google Scholar
[
Meivel, S., Maheswari, S. (2022). Monitoring of potato crops based on multispectral image feature extraction with vegetation indices. Multidimensional Systems and Signal Processing, 33(3), 1-27.
]Search in Google Scholar
[
Naguib, N.S., Daliman, S. (2022). Analysis of NDVI and NDRE Indices Using Satellite Images for Crop Identification at Kelantan. 4th International Conference on Tropical Resources and Sustainable Sciences 2022 IOP Conf. Series: Earth and Environmental Science, 1102, 012054.
]Search in Google Scholar
[
Nguyen, K., Liou, Y., Tran, H., Hoang, P., Nguyen, T. (2020). Soil salinity assessment by using near-infrared channel and Vegetation Soil Salinity Index derived from Landsat 8 OLI data: a case study in the Tra Vinh Province, Mekong Delta, Vietnam. Progress in Earth and Planetary Science, 7/1, 1-16.
]Search in Google Scholar
[
Phung, H., Nguyen, L., Nguyen-Huy, T., Le-Toan, T., Apan, A. (2020). Monitoring rice growth status in the Mekong Delta, Vietnam using multitemporal Sentinel-1 data. Journal of Applied Remote Sensing, 14(1), 014518.
]Search in Google Scholar
[
Qawaqzeh, M.Z., Miroshnyk, O., Shchur, T., Kasner, R., Idzikowski, A., Kruszelnicka, W., Tomporowski, A., Bałdowska-Witos, P., Flizikowski, J., Zawada, M., Doerffer, K. (2021). Research of emergency modes of wind power plants using computer simulation. Energies, 14(16), 4780.
]Search in Google Scholar
[
Sagan, V., Sidike, P., Daloye, A., Erkbol, H., Fritschi, F., Maimaitijiang, M. (2020). Crop Monitoring Using Satellite/UAV Data Fusion and Machine Learning. Remote Sens, 12(9), 1357.
]Search in Google Scholar
[
Shammi, S.A., Meng, Q. (2021). Use time series NDVI and EVI to develop dynamic crop growth metrics for yield modeling. Ecological Indicators, 121, 107124.
]Search in Google Scholar
[
Solangi, K.A., Siyal, A.A., Wu, Y., Abbasi, B., Solangi, F., Lakhiar, I.A., Zhou, G. (2019). An Assessment of the Spatial and Temporal Distribution of Soil Salinity in Combination with Field and Satellite Data: A Case Study in Sujawal District. Agronomy, 9(12), 869.
]Search in Google Scholar
, 