Evaluating Bare Soil Properties and Vegetation Indices for Digital Farming Applications from UAV-based Multispectral Images

Sinan Demir; Levent Başyiğit

Evaluating Bare Soil Properties and Vegetation Indices for Digital Farming Applications from UAV-based Multispectral Images

Abstract

The possibilities of using unmanned aerial vehicles (UAVs)-based on multispectral sensors and data produced from images taken from agricultural areas in digital agriculture applications are being investigated. This research is to determine the effect of bare soil reflection on vegetation indices produced from UAV-based multispectral images in the sustainable management of agricultural lands and to reveal the relationship between soil texture and vegetation indices. In the study, clay, silt, and sand contents were determined by making texture analyses in soil samples obtained by using a random stratified sampling method. A multi-band orthophoto image was created from the UAV-based multispectral data for the study area. Visible Atmospheric Resistant Index (VARI), Normalized difference vegetation index (NDVI), Normalized Difference Red Edge Index (NDRE), Leaf Chlorophyll Index (LCI), Green-Red Vegetation Index (GRVI), which are widely used in digital agriculture, from the multispectral image of the study area. Soil Adjusted Vegetation Index (SAVI), and Green Normalized Difference Vegetation Index (GNDVI) vegetation indices were calculated. The relationships between vegetation indices data set and soil clay, silt, and sand contents were determined statistically (p < 0.001). It was determined that the highest correlated vegetation index GNDVI with soil texture. It was determined that there were 0.62, -0.72, and 0.73 correlation coefficients between the GNDVI vegetation index and clay, silt, and sand, respectively. The data produced from UAV-based multispectral images between the bare soil reflection and vegetation indices have been shown to have potential at the farmland scale.

Keywords

Digital Agriculture , UAV-Based Remote Sensing , Multispectral Image , Plant Vegetation Indices

Kaynakça

1. Candiago, S., Remondino, F., De Giglio, M., Dubbini, M., & Gattelli, M. (2015). Evaluating multispectral images and vegetation indices for precision farming applications from UAV images. *Remote sensing, 7*(4), 4026-4047.
2. Boursianis, A. D., Papadopoulou, M. S., Diamantoulakis, P., Liopa-Tsakalidi, A., Barouchas, P., Salahas, G., ... Goudos, S. K. (2022). Internet of things (IoT) and agricultural unmanned aerial vehicles (UAVs) in smart farming: A comprehensive review. *Internet of Things, 18*, 100187.
3. Perez-Ruiz, M., Martínez-Guanter, J., Upadhyaya, S. K. (2021). High-precision GNSS for agricultural operations. In *GPS and GNSS Technology in Geosciences*, pp. 299-335, Elsevier.
4. Demir, S., Başayiğit, L. (2021). The effect of restricted irrigation applications on vegetation index based on UAV multispectral sensing. *Yuzuncu Yıl University Journal of Agricultural Sciences, 31*(3), 629-643.
5. Tsouros, D. C., Bibi, S., Sarigiannidis, P. G. (2019). A review on UAV-based applications for precision agriculture. *Information, 10*(11), 349.
6. Bhatt, R., Kaur, R., & Ghosh, A. (2019). Strategies to practice climate-smart agriculture to improve the livelihoods under the rice-wheat cropping system in South Asia. In *Sustainable Management of Soil and Environment*, pp. 29-71, Springer, Singapore.
7. Deng, L., Mao, Z., Li, X., Hu, Z., Duan, F., Yan, Y. (2018). UAV-based multispectral remote sensing for precision agriculture: A comparison between different cameras. *ISPRS Journal of Photogrammetry and Remote Sensing, 146*, 124-136.
8. Di Gennaro, S. F., Toscano, P., Gatti, M., Poni, S., Berton, A., Matese, A. (2022). Spectral Comparison of UAV-Based Hyper and Multispectral Cameras for Precision Viticulture. *Remote Sensing, 14*(3), 449.
9. Vega, F. A., Ramirez, F. C., Saiz, M. P., Rosua, F. O. (2015). Multi-temporal imaging using an unmanned aerial vehicle for monitoring a sunflower crop. *Biosystems Engineering, 132*, 19-27.
10. Wójtowicz, M., Wójtowicz, A., Piekarczyk, J. (2016). Application of remote sensing methods in agriculture. *Communications in Biometry and Crop Science, 11*(1), 31-50.

11. Purwanto, A. D., Asriningrum, W. (2019). Identification of mangrove forests using multispectral satellite imageries. *International Journal of Remote Sensing and Earth Sciences (IJReSES), 16*(1), 63-86.
12. Xue, J., Su, B. (2017). Significant remote sensing vegetation indices: A review of developments and applications. *Journal of Sensors*, pp. 2-17.
13. Ollinger, S. V. (2011). Sources of variability in canopy reflectance and the convergent properties of plants. *New Phytologist, 189*(2), 375-394.
14. Şenol, H., Alaboz, P., Demir, S., Dengiz, O. (2020). Computational intelligence applied to soil quality index using GIS and geostatistical approaches in semiarid ecosystem. *Arabian Journal of Geosciences, 13*(23), 1-20.
15. Erinç, S. (1949). The climates of Turkey according to Thornthwaite's classifications. *Annals of the Association of American Geographers, 39*(1), 26-46.
16. Meteoroloji Genel Müdürlüğü (MGM). General Directorate of Meteorology. Retrieved from https://www.mgm.gov.tr/veridegerlendirme/il-ve-ilceler-istatistik.aspx?k=undefined&m=ISPARTA. Accessed 21 August 2022.
17. Rouse Jr, J. W., Haas, R. H., Schell, J. A., Deering, D. W. (1973). Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation. *NASA Special Publication, 351*, 309.
18. Gitelson, A., Merzlyak, M. N. (1994). Quantitative estimation of chlorophyll-a using reflectance spectra: Experiments with autumn chestnut and maple leaves. *Journal of Photochemistry and Photobiology B: Biology, 22*(3), 247-252.
19. Datt, B., McVicar, T. R., Van Niel, T. G., Jupp, D. L., Pearlman, J. S. (2003). Preprocessing EO-1 Hyperion hyperspectral data to support the application of agricultural indexes. *IEEE Transactions on Geoscience and Remote Sensing, 41*(6), 1246-1259.
20. Gitelson, A. A., Stark, R., Grits, U., Rundquist, D., Kaufman, Y., Derry, D. (2002). Vegetation and soil lines in visible spectral space: A concept and technique for remote estimation of vegetation fraction. *International Journal of Remote Sensing, 23*(13), 2537-2562.
21. Gitelson, A. A., Kaufman, Y. J., Merzlyak, M. N. (1996). Use of a green channel in remote sensing of global vegetation from EOS-MODIS. *Remote sensing of Environment, 58*(3), 289-298.
22. Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. *Remote Sensing of Environment, 8*(2), 127-150.
23. Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). *Remote Sensing of Environment, 25*(3), 295-309.
24. Bouyoucos, G. H. (1951). Determination of particle sizes in soils. *Agro. J, 43*, 434-438.
25. R Core Team. (2016). R: A Language and Environment for Statistical Computing. *R Foundation for Statistical Computing, Vienna, Austria*.
26. Vanlear, M. (2019). Soil Texture Calculator. *Natural Resource Conservation Service, US Department of Agriculture (NRCS USDA)*. Retrieved from https://www
27. Lilliefors, H. W. (1967). On the Kolmogorov-Smirnov test for normality with mean and variance unknown. *Journal of the American statistical Association, 62*(318), 399-402.
28. Yıldırım, F., Esen, M., Binici, S., Çelik, C., Yıldırım, A., Karakurt, Y. (2021). Antioxidant enzymes activities of walnut nursery trees to drought stress progression. *International Journal of Agriculture Forestry and Life Sciences, 5*(2), 217-225. Retrieved from https://dergipark.org.tr/en/pub/ijafls/issue/66205/1034779.
29. Taylor, J. A., Anastasiou, E., Fountas, S., Tisseyre, B., Molin, J. P., Trevisan, R. G., ... & Travers, M. (2021). Applications of optical sensing of crop health and vigour. In *Sensing Approaches for Precision Agriculture*, 333-367.
30. Pluer, E. M., Robinson, D. T., Meinen, B. U., & Macrae, M. L. (2020). Pairing soil sampling with very-high resolution UAV imagery: An examination of drivers of soil and nutrient movement and agricultural productivity in southern Ontario. *Geoderma, 379*, 114630.
31. Lipovac, A., Bezdan, A., Moravčević, D., Djurović, N., Ćosić, M., Benka, P., & Stričević, R. (2022). Correlation between ground measurements and UAV sensed vegetation indices for yield prediction of common bean grown under different irrigation treatments and sowing periods. *Water, 14*(22), 3786.
32. Demir, S., & Başayiğit, L. (2021). The effect of physiographical change on profile development and soil properties. *Turkish Journal of Agricultural Research, 8*(3), 261-272.

Ayrıntılar

Birincil Dil

İngilizce

Konular

Makine Öğrenmesi Algoritmaları

Bölüm

Araştırma Makalesi

Yazarlar

Sinan Demir ^*
Türkiye

Levent Başyiğit Bu kişi benim
Türkiye

Yayımlanma Tarihi

21 Kasım 2023

Gönderilme Tarihi

21 Nisan 2022

Kabul Tarihi

7 Haziran 2023

Yayımlandığı Sayı

Yıl 1970 Cilt: 6 Sayı: 1

IZ

https://izlik.org/JA95MD54WY

IEEE

[1]S. Demir ve L. Başyiğit, “Evaluating Bare Soil Properties and Vegetation Indices for Digital Farming Applications from UAV-based Multispectral Images”, DataSCI, c. 6, sy 1, ss. 5–10, Kas. 2023, [çevrimiçi]. Erişim adresi: https://izlik.org/JA95MD54WY