Optimizing data collection in precision agriculture comparing remote sensing and in situ analyses
Endalkachew Abebe Kebede, Silviya Vasileva, Bozhidar Ivanov, Orhan Dengiz, Bojin Bojinov
Abstract: Remote sensing has a potential application in assessing and monitoring the plants' biophysical properties using the spectral responses of plants and soils within the electromagnetic spectrum. However, only a few reports compare the performance of different remote sensing approaches against in-situ spectral measurement. The current study assessed potential applications of open data source satellite images (Sentinel 2 and Landsat 9) in estimating the biophysical properties of a crop on a study farm. A Landsat 9 (30 m resolution) and Sentinel-2 (10 m resolution) satellite images with less than 10% cloud cover have been extracted from the open data sources for the period of December 2021 – April 2022. In addition, SpectraVue 710s Leaf Spectrometer was used to measure the spectral response of the crop in April at five different locations within the same field. Results obtained by different data collection methods were compared to evaluate them for applicability in precision agriculture.
Keywords: NDVI; precision agriculture; vegetation indices
Citation: Kabede, E.A., Vasileva, S., Ivanov, B., Dengiz, O. & Bojinov, B. (2024). Optimizing data collection in precision agriculture – comparing remote sensing and in situ analyses. Bulg. J. Agric. Sci., 30(1), 11–16.
References: (click to open/close) | Arthur, A. M., MacLellan, C. J. & Malthus, T. (2012). The Fields of View and Directional Response Functions of Two Field Spectroradiometers. IEEE Transactions on Geoscience and Remote Sensing, 50(10), 3892-3907. Bareth, G., Bolten, A., Gnyp, M. L., Reusch, S. & Jasper, J. (2016). Comparison of uncalibrated RGBVI with spectrometer-based NDVI derived from UAV sensing systems on field scale. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 837-843. Bégué, A., Arvor, D., Lelong, C., Vintrou, E. & Simoes, M. (2015). Agricultural systems studies using remote sensing. In : Land resources monitoring, modeling, and mapping with remote sensing. In "Remote Sensing Handbook" (S. Prasad and Thenkabail, eds.), pp. 113-130. CRC Press, Boca Raton. Bojinov, B., Ivanov, B. & Vasileva, S. (2022). Current state and usage limitations of vegetation indices in precision agriculture. Bulgarian Journal of Agricultural Science, 28(3), 387-394. Croft, H., Arabian, J., Chen, J. M., Shang, J. & Liu, J. (2020). Mapping within-field leaf chlorophyll content in agricultural crops for nitrogen management using Landsat-8 imagery. Precision Agriculture, 21(4), 856-880. Darnhofer, I., Bellon, S., Dedieu, B. & Milestad, R. (2010). Adaptiveness to enhance the sustainability of farming systems. A review. Agronomy for Sustainable Development, 30(3), 545-555. Darvishzadeh, R., Skidmore, A., Abdullah, H., Cherenet, E., Ali, A., Wang, T., Nieuwenhuis, W., Heurich, M., Vrieling, A., et al. (2019). Mapping leaf chlorophyll content from Sentinel-2 and RapidEye data in spruce stands using the invertible forest reflectance model. International Journal of Applied Earth Observation and Geoinformation, 79, 58-70. Dimitrov, P., Filchev, L., Roumenina, E. & Jelev, G. (2021). CROP TYPE MAPPING IN BULGARIA USING SENTINEL-1/2 DATA. Aerospace Research in Bulgaria, 33, 40-50. Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Hoersch, B., Isola, C., Laberinti, P., et al. (2012). Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services. Remote Sensing of Environment, 120, 25-36. Gikov, A., Dimitrov, P., Filchev, L., Roumenina, E. & Jelev, G. (2019). Crop type mapping using multi-date imagery from the Sentinel-2 satellites. Comptes Rendus de L’Academie Bulgare Des Sciences, 72(6), 787–795. Ke, Y., Im, J., Lee, J., Gong, H. & Ryu, Y. (2015). Characteristics of Landsat 8 OLI-derived NDVI by comparison with multiple satellite sensors and in-situ observations. Remote Sensing of Environment, 164, 298-313. Kolev, N. & Kozelov, L. (2015). Combined remote sensing and GIS technologies for land resources management in Bulgaria. Bulgarian Journal of Agricultural Science, 21(4), 761–766. Lillesand, T., Kiefer, R. W. & Chipman, J. (2015). "Remote sensing and image interpretation," John Wiley & Sons, 1. Masek, J. G., Wulder, M. A., Markham, B., McCorkel, J., Crawford, C. J., Storey, J. & Jenstrom, D. T. (2020). Landsat 9: Empowering open science and applications through continuity. Remote Sensing of Environment, 248, 111968. Mezera, J., Lukas, V., Horniaček, I., Smutný, V. & Elbl, J. (2022). Comparison of Proximal and Remote Sensing for the Diagnosis of Crop Status in Site-Specific Crop Management. Sensors, 22(1), 19. Moussaid, A., Fkihi, S. & Zennayi, Y. (2020). Citrus Orchards Monitoring based on Remote Sensing and Artificial Intelligence Techniques: A Review of the Literature. In "2nd International Conference on Advanced Technologies for Humanity" (Brahim E. Bhiri, Ayman Mosallam and Ashraf Aboshosha, eds.), 172-178, Rabat, Morocco. Polivova, M. & Brook, A. (2021). Detailed Investigation of Spectral Vegetation Indices for Fine Field-Scale Phenotyping. In "Vegetation Index and Dynamics" (C. Eusebio Cano, O. Ana Cano, C. Riocardo Quinto and M. Carmelo Maria, eds.), pp. Ch. 7. IntechOpen, Rijeka. Shanmugapriya, P., Rathika, S., Ramesh, T. & Janaki, P. (2019). Applications of Remote Sensing in Agriculture - A Review. International Journal of Current Microbiology and Applied Sciences 8(1), 2270-2283. Stoyanov, A., Georgiev, N., Gigova, I. & Borisova, D. (2019). Application of remote sensing data for monitoring of forest vegetation on the territory of nature park “Blue Stones," Bulgaria. In "SPIE Remote Sensing", 11149, 1114927. SPIE, Strasbourg, France. Thrall, P. H., Bever, J. D. & Burdon, J. J. (2010). Evolutionary change in agriculture: the past, present and future. Evolutionary Applications, 3(5-6), 405-408. Ulfa, F., Orton, T. G., Dang, Y. P. & Menzies, N. W. (2022). Developing and Testing Remote-Sensing Indices to Represent within-Field Variation of Wheat Yields: Assessment of the Variation Explained by Simple Models. Agronomy, 12(2).
|
|
| Date published: 2024-02-26
Download full text