Distinguishing Growth Stages of Wheat Crop by Remote Sensing Techniques and Time Series Analysis

Document Type : Original Research

Authors

1 Biosystems Engineering Department. Shahid Bahonar University of Kerman, Kerman, Iran

2 Biosystem Engineering Department, Shahid Bahonar University of Kerman, Kerman, Iran

3 Agricultural Engineering Research Department, Fars Agricultural and Natural Resource Research and Education Center, AREEO, Shiraz, Iran

4 Department of Plant production and Genetics, Shiraz University, Shiraz, Iran

Abstract

Remote sensing has attracted the attentions by providing a broad and comprehensive view of the world. The use of remote sensing in various fields such as agriculture is constantly expanding. Spectral bands in visible and infrared ranges can be used to discriminate between phenomena and ground cover by computing various spectral indices. Investigating plant physiology is essential to know the physiological and ecological aspects of plant functions. In this study, images of Sentinel-2 satellite were used to compute spectral indices and correlate them with phenological stages of wheat crop in two agricultural centers in Fars province, Iran. Zadoks scale is one of the most reputed methods to state growth stages of wheat crop. The Zadoks scale uses two-digit codes to demonstrate different phenological processes. In this study, nine growing stages were carefully identified using ground truth method. After calculating two spectral indices of normalized difference vegetation index (NDVI) and soil adjusted vegetation index (SAVI) on satellite images of various dates during the growing season, NDVI and SAVI time series were generated. Each time series image consisted of nine bands, each band being an image obtained from a wheat growing stage. Study the trend between NDVI and SAVI indices and the Zadoks scale showed that the phenological stages of wheat can be identified using remote sensing technology.

Keywords

Main Subjects

References

Baillarin, S, Meygret, A, Dechoz, C, Petrucci, B, Lacherade, S, Trémas, T, Isola, C, Martimort, P and Spoto, F. (2012). Sentinel-2 level 1 products and image processing performances.  2012 IEEE international geoscience and remote sensing symposium. IEEE 7003-7006.
Balla, K, Karsai, I, Bónis, P, Kiss, T, Berki, Z, Horváth, Á, Mayer, M, Bencze, S and Veisz, O (2019). Heat stress responses in a large set of winter wheat cultivars (Triticum aestivum L.) depend on the timing and duration of stress. PLoS One, 14, e0222639.
Bounoua, L, Collatz, G, Los, S, Sellers, P, Dazlich, D, Tucker, C and Randall, D (2000). Sensitivity of climate to changes in NDVI. Journal of Climate, 13, 2277-2292.
Decuyper, M, Chávez, RO, Lohbeck, M, Lastra, JA, Tsendbazar, N, Hackländer, J, Herold, M and Vågen, T-G (2022). Continuous monitoring of forest change dynamics with satellite time series. Remote Sensing of Environment, 269, 112829.
Eva, H and Lambin, EF (1998). Remote sensing of biomass burning in tropical regions: Sampling issues and multisensor approach. Remote sensing of environment, 64, 292-315.
Farg, E, Arafat, S, Abd El-Wahed, M and El-Gindy, A (2012). Estimation of evapotranspiration ETc and crop coefficient Kc of wheat, in south Nile Delta of Egypt using integrated FAO-56 approach and remote sensing data. The Egyptian Journal of Remote Sensing and Space Science, 15, 83-89.
Guo, C, Zhang, L, Zhou, X, Zhu, Y, Cao, W, Qiu, X, Cheng, T and Tian, Y (2018). Integrating remote sensing information with crop model to monitor wheat growth and yield based on simulation zone partitioning. Precision agriculture, 19, 55-78.
Hu, P, Sharifi, A, Tahir, MN, Tariq, A, Zhang, L, Mumtaz, F and Shah, SHIA (2021). Evaluation of Vegetation Indices and Phenological Metrics Using Time-Series MODIS Data for Monitoring Vegetation Change in Punjab, Pakistan. Water, 13, 2550.
Huete, AR (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25, 295-309.
Hunt, R (2012). Basic growth analysis: plant growth analysis for beginners, Springer Science & Business Media.
Jawak, SD and Luis, AJ (2013). A spectral index ratio-based Antarctic land-cover mapping using hyperspatial 8-band WorldView-2 imagery. Polar Science, 7, 18-38.
Johnson, SJ (2009). An evaluation of land change modeler for ARCGIS for the ecological analysis of landscape composition, Southern Illinois University at Carbondale.
Kogan, FN (1995). Application of vegetation index and brightness temperature for drought detection. Advances in Space Research, 15, 91-100.
Malingreau, J, Tucker, C and Laporte, N (1989). AVHRR for monitoring global tropical deforestation. International Journal of Remote Sensing, 10, 855-867.
Mao, J, Shi, X, Thornton, PE, Hoffman, FM, Zhu, Z and Myneni, RB (2013). Global latitudinal-asymmetric vegetation growth trends and their driving mechanisms: 1982–2009. Remote Sensing, 5, 1484-1497.
Myneni, RB, Asrar, G, Tanre, D and Choudhury, BJ (1992). Remote sensing of solar radiation absorbed and reflected by vegetated land surfaces. IEEE Transactions on Geoscience and Remote Sensing, 30, 302-314.
Nouri, H, Anderson, S, Sutton, P, Beecham, S, Nagler, P, Jarchow, CJ and Roberts, DA (2017). NDVI, scale invariance and the modifiable areal unit problem: An assessment of vegetation in the Adelaide Parklands. Science of the Total Environment, 584, 11-18.
Rahnama, S, Maharlooei, M, Rostami, M and Maghsoudi, H. (2018). Date palm identification using Sentinel and Landsat satellites imagery.  2018 ASABE Annual International Meeting. American Society of Agricultural and Biological Engineers, 1.
Reynolds, M, Pask, A and Mullan, D (2012). Physiological breeding I: interdisciplinary approaches to improve crop adaptation, CIMMYT.
Senay, G and Elliott, R (2000). Combining AVHRR-NDVI and landuse data to describe temporal and spatial dynamics of vegetation. Forest Ecology and Management, 128, 83-91.
Slafer, G, Calderini, D and Miralles, D (1996). Yield components and compensation in wheat: opportunities for further increasing yield potential. Increasing yield potential in wheat: Breaking the Barriers, 101-133.
Xue, J and Su, B (2017). Significant remote sensing vegetation indices: A review of developments and applications. Journal of Sensors, 2017.
Yuping, M, Shili, W, Li, Z, Yingyu, H, Liwei, Z, Yanbo, H and Futang, W (2008). Monitoring winter wheat growth in North China by combining a crop model and remote sensing data. International Journal of Applied Earth Observation and Geoinformation, 10, 426-437.
Zubair, AO. (2006). Change detection in land use and Land cover using remote sensing data and GIS (A case study of Ilorin and its environs in Kwara State). Unpublished MSc. thesis
Biomechanism and Bioenergy Research
Volume 1, Issue 1
June 2022
Pages 16-22

Files

History

  • Receive Date: 11 February 2022
  • Revise Date: 20 March 2022
  • Accept Date: 21 March 2022

Share

How to cite

Statistics