Iranian Journal of Remote Sensing and GIS

Iranian Journal of Remote Sensing and GIS

Development and application of crop and field condition indices using time-series satellite images of Sentinel-2

Document Type : علمی - پژوهشی

Authors
Hamed nematollahi 1
Davoud Ashourloo 2
Abas Alimohammadi 3
Elham Khodabandehloo 4
Soheil Radiom 5
1 M.Sc. of RS & GIS, Shahid Beheshti University
2 Assistant Prof., Dept. of RS & GIS, Shahid Beheshti University
3 Professor., Dept. of GIS Engineering, Faculty of Geodesy & Geomatic Engineering, K.N. Toosi Uniersity of Technology
4 Space Research Institute, Iranian Space Research Center
5 Assistant Prof., Iranian Space Research Center
Abstract
One of important objectives in sustainable agriculture is preservation of healthy ecosystems with focus on natural aquatic and terrestrial resources management in order to accomplish food security at local and global scales. Time-series remotely sensed datasets are precious and valuable resource of temporal and spectral information that could support researchers to access field management goals. Farm management have been always encountered some challenges such as lack of access to quantitative and qualitative information of agricultural crops. This research aims to develop crop and field condition indices using time-series of NDVI (Sentinel-2) and crop type maps of Moghan Agro-Industry (MAI) in 2016-2017 and also Shahid Rajaei Agro-Industry (SRAI) in 2017-2018. Then we tried to identify parts of the fields that are affected by Environmental factors such as disease, pest, weed, soil-related deficiencies and uneven distribution of water due to Inefficient irrigation system. To this end, Time-series of NDVI for four crops (wheat, maize, alfalfa and sugar beet) in various fields was provided. Finaly, field and crop condition indices were developed to show the variations of crop in each field and also the fields in comparison with each other. Finally, the proposed indices showed high accuracy with ground observations. The results were 88.88% for Alfalfa fields in MAI, and 94.11% for wheat fields in SRAI. After evaluation of the results of indices with ground observations, it was revealed that where field (homogeneity) index is low, growth limiting factors are activated.
Keywords
NDVI time-series
field condition index
crop condition index
farm management
Sentinel-2
  1. Bannari, A., Morin, D., Bonn, F., Huete, A.R., 1995, A review of vegetation indices, Remote Sensing Reviews. (13): 95– 120.
  2. Bannari, A., Huete, A.R., Morin, D., Zagolski, F., 1996, Effets de la couleur et de la brillance du sol sur les indices de vegetation, International Journal of Remote Sensing. (17): 1885 – 1906.
  3. Barton, C.W.M., 2012, Advances in remote sensing of plant stress, Plant and Soil (354): 41–44.
  4. Bastiaanssen, W.G.M., Ali, S., 2003, A new crop yield forecasting model based on satellite measurements applied across the Indus Basin, Pakistan, Agriculture, Ecosystems and Environment, 94:321-340.
  5. Calvão, T., Palmeirim, J.M., 2011, A comparative evaluation of spectral vegetation indices for the estimation of biophysical characteristics of Mediterranean semi-deciduous shrub communities, International Journal of Remote Sensing, 32(8): 2275-2296
  6. Clevers J.G.P.W., 1997, A simplified approach for yield prediction of sugar beet based on optical remote sensing data, Remote Sensing of Environment, 61(2): 221-228
  7. Copernicus Open Access Hub, https://scihub.copernicus.eu/
  8. Eastman, J.R., Sangermano, F., Machado, E.A., Rogan, J., Anyamba, A., 2013, Global Trends in Seasonality of Normalized Difference Vegetation Index (NDVI), 1982–2011. Remote Sensing, 5(10):4799-4818.
  9. Elvidge, C.D., Chen, Z., 1995, Comparison of broad-band and narrowband red and near-infrared vegetation indices, Remote Sensing of Environment, (54): 38 – 48.
  10. Fensholt, R., Hostert, P., Pflugmacher, D., Udelhoven, T., Yin, H., 2012, How Normalized Difference Vegetation Index (NDVI) Trends from Advanced Very High Resolution Radiometer (AVHRR) and Système Probatoire d'Observation de la Terre VEGETATION (SPOT VGT) Time Series Differ in Agricultural Areas: An Inner Mongolian Case Study, Remote Sensing, (4): 3364-3389.
  11. Fischer, A., 1994, A model for the seasonal variations of vegetation indices in coarse resolution data and its inversion to extract crop parameters. Remote Sensing of Environment, 48(2): 220-230.
  12. Gitelson, A.A., 2004, Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation, Journal of Plant Physiology. 161: 165-173.
  13. Gu, Z., Chen, J., Shi, P., Xu, M., 2007, Correlation analysis of Normalized Different Vegetation Index (NDVI) difference series and climate variables in the Xilingole steppe, China from 1983 to 1999, Frontiers in Biology (2): 218-228.
  14. Lillesand, T., Kiefer, R., Chipman, J., 2015, Remote Sensing and Image Interpretation, 7th Edition, John Wiley And Sons Inc.
  15. Mishra, A.K., Singh, V.P., 2011, Drought modeling – A review, Journal of Hydrology.403(1):157-175
  16. Myneni, R.B., Hall, F.G., Sellers, P.J., Marshak, A.L., 1995, The interpretation of spectral vegetation indexes. IEEE Transactions on Geoscience and Remote Sensing, (33): 481–486.
  17. Pan, Z., Huang, J., Zhou, Q., Wang, L., Cheng, Y., Zhang, H., Blackburn, G.A., Yan, J., Liu, J., 2015, Mapping crop phenology using NDVI time-series derived from HJ-1 A/B data, International Journal of Applied Earth Observation and Geoinformation, (34):188-197
  18. Piao, S., Fang, J., Zhou, L., Guo, Q., Henderson, M., Ji, W., Li, Y., Tao, S., 2003, Interannual variations of monthly and seasonal normalized difference vegetation index (NDVI) in China from 1982 to 1999, Journal of Geophysical Research-Atmospheres, 108(D14): 4401
  19. Rouse, J.W., Haas, R.H., Schell, J.A. and Deering, D.W., 1973, Monitoring vegetation systems in the Great Plains with ERTS, In 3rd ERTS Symposium, NASA SP-351 I: 309–317.
  20. Sakamoto, T., Yokozawa, M., Toritani, H., Shibayama, M., Ishitsuka, N., Ohno, H., 2005, A crop phenology detection method using time-series MODIS data, Remote Sensing of Environment. 96(3): 366–374.
  21. Shoshany, M., Long, D., Bonfil, D., 2013, Remote Sensing for sustainable agriculture. International Journal of Remote Sensing, (34):6021–6023.
  22. Song, X., Zhao, C., Chen, L., Huang, W., Cui, B., 2012, Winter Wheat Growth Uniformity Monitoring Through Remote Sensed Images, International Society of Precision Agriculture proceedings.
  23. Wessels, K.J, Prince, S.D, Reshef I., 2008, Mapping land degradation by comparison of vegetation production to spatially derived estimates of potential production, Journal of Arid Environments, 72(10):1940-1949.
  24. Wu, M., Yang, C., Song, X., Hoffmann, W.C., Huang, W., Niu, Z., Wang, C., Li, W., Yu, B., 2018, Monitoring cotton root rot by synthetic Sentinel-2 NDVI time series using improved spatial and temporal data fusion, Scientific Reports.
  25. Zhihui, G.U., Chen, J., Shi, P. and Ming, X.U., 2007, Correlation analysis of Normalized Difference Vegetation Index (NDVI) difference series and climate variables in the Xilingole steppe, China from 1983 to 1999, Frontiers in Biology (2): 218-228
Iranian Journal of Remote Sensing and GIS
Volume 10, Issue 3 - Serial Number 3
Winter 2019
Pages 105-122