Comparison of NDVI, NDRE, MSAVI and NDSI Indices for Early Diagnosis of Crop Problems
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Abstract
In precision agriculture, it is possible to use satellite monitoring of fields. Satellite monitoring systems allow you to get free images with a resolution of up to 10 m per pixel, which is sufficient to determine the state of vegetation of plants on such indicators as the normalized vegetation index NDVI. However, the NDVI indicator already indicates the existing problems of correction which will not help to restore the lost yield of crops, but only helps to prevent further losses. Using the NDSI soil salinity index, it is possible to determine the difference in its properties from spectral images. Also, you can study the vegetation of plants in the early stages of their development, in fact immediately after germination. Soil-adjusted vegetation index, such as MSAVI, is used for this purpose. Studies indicate the possibility of using NDSI and MSAVI indicators for early diagnosis of confirmed crops NDVI and NDRE (indicating chlorophyll activity in plants) at later stages of their development. Studies conducted on soybean, spring barley and maize crops sown in the spring of 2021 indicate a correspondence between raster field maps show-ing the above indices made from March to July. Statistical analysis of raster images of field maps using specialized software showed a correlation between NDSI and MSAVI in March and May, respectively, with NDVI and NDRE indexes in June and July. Therefore, it is possible to judge the expediency of using NDSI and MSAVI indicators for early diagnosis of possible problems with plant vegetation, as well as for the creation of maps of differential fertilization.
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References
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), 373-385.10.4236/ars.2013.24040
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.
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, 107559.10.1016/j.compag.2022.107559
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, 20-29.10.26782/jmcms.spl.4/2019.11.00003
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
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.
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, 95-103.10.1016/j.plantsci.2018.10.02231003615
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), 493.10.3390/e23050493814299833919030
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), 33-40.10.58509/lssd.v3i2.204
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, 1-6.10.1007/s11676-020-01155-1
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), 2873.10.3390/rs11232873
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), 119-123.10.2478/ata-2021-0020
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), 1-27.10.1007/s11045-021-00809-5
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.
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, 1-16.10.1186/s40645-019-0311-0
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), 014518.10.1117/1.JRS.14.014518
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), 4780.10.3390/en14164780
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), 1357.10.3390/rs12091357
Shammi, S.A., Meng, Q. (2021). Use time series NDVI and EVI to develop dynamic crop growth metrics for yield modeling. Ecological Indicators, 121, 107124.10.1016/j.ecolind.2020.107124
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), 869.10.3390/agronomy9120869