Abstract
The environmental, social, and economic problems confronting agriculture today are symptoms of agricultural industrialization. In this study, the agricultural field is analyzed using satellite surface reflectance data. This technology facilitates monitoring of crop vegetation by spectral analysis of satellite images of different sites and crops which can track positive and negative dynamics of crop development. Using this analysis, the field can be categorized into different categories rating its potency to grow crops, which helps the user to get detailed information about the current condition of the field. For the analysis, we have used Landsat 8 data. We have used the Google Earth Engine to import the data from the ground station. The indices we have used for this study are Normalized Difference Vegetation Index (NDVI), Modified Soil Adjusted Vegetative Index (MSAVI) and Normalized Difference Water Index (NDWI) and average rainfall data. For clustering the data, we have implemented k-means clustering algorithm. We have collected data from over 6 years and by taking mean values we classified the agricultural fields into different categories according to their quality.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Yang, C., Anderson, G.L.: mapping grain sorghum yield variability using airborne digital videography. Precis. Agric. 2, 7–23 (2000). https://doi.org/10.1023/A:1009928431735
Chang, L., et al.: A review of plant spectral reflectance response to water physiological changes. Chin. J. Plant Ecol. 40(1), 80–91 (2016). https://doi.org/10.17521/cjpe.2015.0267
Üstuner, M., Sanli, F.B., Abdikan, S., Esetlili, M.T., Kurucu, Y.: Crop type classification using vegetation indices of rapideye imagery. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. XL-7, 195–198 (2014). https://doi.org/10.5194/isprsarchives-xl-7-195-2014
Burchfield, E., Nay, J.J., Gilligan, J.: Application of machine learning to the prediction of vegetation health. Int. Arch. Photogramm. Remote Sensing Spatial Inf. Sci. XLI-B2 (2016). https://doi.org/10.5194/isprsarchives-xli-b2-465-2016
Han, H., Bai, J., Yan, J., Yang, H., Ma, G.: A combined drought monitoring index based on multi-sensor remote sensing data and machine learning. Geocarto Int. 1–16 (2019). https://doi.org/10.1080/10106049.2019.1633423
Alexander, C.: Normalized difference spectral indices and urban land cover as indicators of land surface temperature (LST). Int. J. Appl. Earth Observ. Geoinf. 86, 102013 (2020). https://doi.org/10.1016/j.jag.2019.102013, ISSN 0303-2434
Veeraswamy, G., Nagaraju, A., Balaji, E., Sridhar, Y.: land use land cover studies of using remotesensing and gis a case study in gudur area nellore district, andhrapradesh. Int. J. Res. 4 (2017)
Kobayashi, N., Tani, H., Wang, X., Sonobe, R.: Crop classification using spectral indices derived from Sentinel-2A imagery. J. Inf. Telecommun. 4(1), 67–90 (2020). https://doi.org/10.1080/24751839.2019.1694765
Romero, M., Luo, Y., Su, B., Fuentes, S.: Vineyard water status estimation using multispectral imagery from an UAV platform and machine learning algorithms for irrigation scheduling management, Comput. Electron. Agric. 147, 109–117 (2018). https://doi.org/10.1016/j.compag.2018.02.013, ISSN 0168-1699
Schwalbert, R.A., Amado, T., Corassa, G., Pott, L.P., Prasad, P.V., Ciampitti, I.A.: Satellite-based soybean yield forecast: integrating machine learning and weather data for improving crop yield prediction in southern Brazil. Agric. Forest Meteorol. 284, 107886 (2020). https://doi.org/10.1016/j.agrformet.2019.107886, ISSN 0168-1923
Fu, W., Ma, J., Chen, P., Chen, F.: Remote sensing satellites for digital earth. In: Guo, H., Goodchild, M.F., Annoni, A. (eds.) Manual Digit. Earth, pp. 55–123. Springer, Singapore (2020). https://doi.org/10.1007/978-981-32-9915-3_3
Anyamba, A., Tucker, C.: Historical perspectives on AVHRR NDVI and vegetation drought monitoring. Remote Sensing of Drought: Innovative Monitoring Approaches (2012). https://doi.org/10.1201/b11863
Brecht. Remote Sensing Indices (2018). https://medium.com/regen-network/remote-sensing-indices-389153e3d947
Jena, J., Misra, S., Tripathi, K.: Normalized Difference Vegetation Index (NDVI) and its role in Agriculture (2019)
Xue, J., Baofeng, S.: Significant remote sensing vegetation indices: a review of developments and applications. J. Sensors 2017, 1–17 (2017). https://doi.org/10.1155/2017/1353691
Google earth engine website. https://earthengine.google.com/
Barsi, J.A., Schott, J.R., Hook, S.J., Raqueno, N.G., Markham, B.L., Radocinski, R.G.: Landsat-8 thermal infrared sensor (TIRS) vicarious radiometric calibration. Remote Sensing 6, 11607–11626 (2014). https://doi.org/10.3390/rs61111607
Bao, W., Lianju, N., Yue, K.: Integration of unsupervised and supervised machine learning algorithms for credit risk assessment. Exp. Syst. Appl. 128, 301–315 (2019). https://doi.org/10.1016/j.eswa.2019.02.033, ISSN 0957-4174
Li, Y., Wu, H.: A clustering method based on k-means algorithm. Phys. Proc. 1104–1109 (2012). https://doi.org/10.1016/j.phpro.2012.03.206
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wyawahare, M., Kulkarni, P., Kulkarni, A., Lad, A., Majji, J., Mehta, A. (2020). Agricultural Field Analysis Using Satellite Surface Reflectance Data and Machine Learning Technique. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T., Valentino, G. (eds) Advances in Computing and Data Sciences. ICACDS 2020. Communications in Computer and Information Science, vol 1244. Springer, Singapore. https://doi.org/10.1007/978-981-15-6634-9_40
Download citation
DOI: https://doi.org/10.1007/978-981-15-6634-9_40
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-6633-2
Online ISBN: 978-981-15-6634-9
eBook Packages: Computer ScienceComputer Science (R0)