Applied Research in Geographical Sciences

Applied Research in Geographical Sciences

Estimation of Phenology Stages of Rapeseed Using Remote Sensing Vegetation Indices and Digital Camera Images Index

Authors
Akbar Mirahmadi 1
Hojjatollah Yazdan Panah 2
Mehdi Momeni 2
1 Phd student, Isfahan University
2 Associate Professor, Isfahan University
10.52547/jgs.24.72.231
Abstract
In recent years, the technology of crop production has been greatly expanded using satellite data. Today, Landsat 8 and OLI sensor data, with a spatial resolution of 30 meters, allow the discovery of factors that control phenology on a local scale. In this study, the remote sensing indices - NDVI, EVI, Greenness, and Brightness - obtained from the OLI sensor and the GCC index obtained from digital camera images were used to estimate the phenological stages of the rapeseed plant. The Savitzky-Goli filter was used to remove outlier data and to produce smooth curves of time series of plant indices. The results showed that the curves obtained from the indices of NDVI, EVI, GCC show all four stages of remote sensing phenology – green-up, dormancy, maturity, and senescence - well, but the Greenness index did not show the dormancy stage well. The Brightness index curve shows the inverse behavior to other curves. According to Pearsonchr('39')s correlation test, GCC index data are correlated with NDVI and Brightness index data .we used the ratio threshold, rate of change and first derivative methods, to estimate "start of season" and "end of season" and the results showed that the first derivative and ratio threshold methods with an average difference of 18 and 19 days in the "start of the season" and the rate of change method, with an average difference of 8 days, has the best performance in estimating the “end of the season”. Also, the Brightness index with an average difference of 16 days and the EVI index with an average difference of 7 days have the best performance in estimating "start of season" and "end of season", respectively.
Keywords
Rapeseed
Phenology
remote sensed vegetation indices
Landsat 8
digital camera images
Ahmad, S., Abbas, G., Fatima, Z., Khan, R. J., Anjum, M., Ahmed, M., Khan, M.A., Porter, C. H., Hoogenboom, G., (2017), Quantification of the impacts of climate warming and crop management on canola phenology in Punjab, Pakistan, Journal of Agronomy and Crop Science, 203(5): 442-452.
Baig, M. H. A., Zhang, L., Shuai, T., Tong, Q., (2014), Derivation of a tasseled cap transformation based on Landsat 8 at-satellite reflectance, Remote Sensing Letters, 5(5): 423-431.
Butt, B., Turner, M. D., Singh, A., Brottem, L., (2011), Use of MODIS NDVI to evaluate changing latitudinal gradients of rangeland phenology in Sudano-Sahelian West Africa, Remote Sensing of Environment, 115(12): 3367-3376.
Cleland, E. E., Chuine, I., Menzel, A., Mooney, H. A., Schwartz, M. D., (2007), Shifting plant phenology in response to global change, Trends in Ecology and Evolution, 22(7): 357-365.
Dash, J., Jeganathan, C., Atkinson, P. M., (2010), The use of MERIS Terrestrial Chlorophyll Index to study spatio-temporal variation in vegetation phenology over India, Remote Sensing of Environment, 114(7):1388-1402.
Duncan, J., Dash, J., Atkinson, P. M., (2015), The potential of satellite-observed crop phenology to enhance yield gap assessments in smallholder landscapes, Frontiers in Environmental Science, 3(56): 1-16.
Guo, L., An, N., Wang, K., (2016), Reconciling the discrepancy in ground‐and satellite‐observed trends in the spring phenology of winter wheat in China from 1993 to 2008, Journal of Geophysical Research: Atmospheres, 121(3): 1027-1042.
Han, H., Bai, J., Ma, G., Yan, J., (2020), Vegetation Phenological Changes in Multiple Landforms and Responses to Climate Change, ISPRS International Journal of Geo-Information, 9(2): 111.
Harper, F. R., Berkenkamp, B., (1975), Revised growth-stage key for Brassica campestris and B. napus, Canadian Journal of Plant Science, 55(2): 657-658.
He, Di., Enli, Wang., Jing, Wang., Julianne, Lilley., Zhongkui, Luo., Xuebiao, Pan., Zhihua, Pan., Ning, Yang., (2017), Uncertainty in canola phenology modelling induced by cultivar parameterization and its impact on simulated yield, Agricultural and forest meteorology, 232: 163-175.
Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., Ferreira, L. G., (2002), Overview of the radiometric and biophysical performance of the MODIS vegetation indices, Remote Sensing of Environment, 83(2): 195-213.
Hufkens, K., Melaas, E. K., Mann, M. L., Foster, T., Ceballos, F., Robles, M., Kramer, B, (2019), Monitoring crop phenology using a smartphone based near-surface remote sensing approach, Agricultural and forest meteorology, 265: 327-337.
Ide, R., Oguma, H., (2010), Use of digital cameras for phenological observations, Ecological Informatics, 5(5): 339-347.
Liang, Y., Abbott, D., Howard, N., Lim, K., Ward, R., Elgendi, M., (2019), How Effective Is Pulse Arrival Time for Evaluating Blood Pressure? Challenges and Recommendations from a Study Using the MIMIC Databas, Journal of Clinical Medicine, 8(3): 337.
Lloyd, D., (1990), A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery, Title Remote Sensing, 11(12): 2269-2279.
Ma, X., Huete, A., Yu, Q., Coupe, N.R., Davies, K., Broich, M., Ratana, P., Beringer, J., Hutley, L.B., Cleverly, J. Boulain, N., (2013), Spatial patterns and temporal dynamics in savanna vegetation phenology across the North Australian Tropical Transect, Remote Sensing of Environment, 139: 97-115.
Matsushita, B., Yang, W., Chen, J., Onda, Y., Qiu, G., (2007), Sensitivity of the enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) to topographic effects: a case study in high-density cypress forest, Sensors, 7(11): 2636-2651.
Melaas, E. K., Friedl, M. A., Zhu, Z., (2013), Detecting interannual variation in deciduous broadleaf forest phenology using Landsat TM/ETM+ data, Remote Sensing of Environment, 132: 176-185.
Pettorelli, N., Laurance, W. F., O'Brien, T. G., Wegmann, M., Nagendra, H., Turner, W., (2014), Satellite remote sensing for applied ecologists: opportunities and challenges, Journal of Applied Ecology, 51(4): 839-848.
Raza, S. M. H., Mahmood, S. A., Khan, A. A., Liesenberg, V., (2018), Delineation of potential sites for rice cultivation through multi-criteria evaluation (MCE) using remote sensing and GIS, International Journal of Plant Production, 12(1): 1-11.
Samarawickrama, U., Piyaratne, D., Ranagalage, M., (2017), Relationship between NDVI with Tasseled cap Indices: A Remote Sensing based Analysis, IJIRT, 3(12): 13-19.
St Peter, J., Hogland, J., Hebblewhite, M., Hurley, M. A., Hupp, N., Proffitt, K., (2018), Linking phenological indices from digital cameras in Idaho and Montana to MODIS NDVI, Remote Sensing, 10(10): 1612.
Tucker, C.J, (1978), Red and photographic infrared linear combinations for monitoring vegetation, Remote Sensing of Environment, 8: 127-150.
Vrieling, A., Meroni, M., Darvishzadeh, R., Skidmore, A.K., Wang, T., Zurita-Milla, R., Oosterbeek, K., O'Connor, B., Paganini, M., (2018), Vegetation phenology from Sentinel-2 and field cameras for a Dutch barrier island, Remote Sensing of Environment, 215: 517-529.
Wang, J., Huang, J.F., Wang, X.Z., Jin, M.T., Zhou, Z., Guo, Q.Y., Zhao, Z.W., Huang, W.J., Zhang, Y., Song, X.D., (2015), Estimation of rice phenology date using integrated HJ-1 CCD and Landsat-8 OLI vegetation indices time-series images, Journal of Zhejiang University-SCIENCE B, 16(10): 832-844.
White, M. A., Nemani, R. R., (2006), Real-time monitoring and short-term forecasting of land surface phenology, Remote Sensing of Environment, 104(1): 43-49.
Woźniak, E., Waszkowska, E., Zimny, T., Sowa, S., Twardowski, T., (2019), The Rapeseed Potential in Poland and Germany in the Context of Production, Legislation and Intellectual Property Rights, Frontiers in plant science, 10:1423.
Wu, C., Hou, X., Peng, D., Gonsamo, A., Xu, S., (2016), Land surface phenology of China's temperate ecosystems over 1999–2013: Spatial–temporal patterns, interaction effects, covariation with climate and implications for productivity, Agricultural and forest meteorology, 216:177-187.
Wu, C., Peng, D., Soudani, K., Siebicke, L., Gough, C.M., Arain, M.A., Bohrer, G., Lafleur, P.M., Peichl, M., Gonsamo, A., Xu, S., Fang, B., Ge, Q., (2017), Land surface phenology derived from normalized difference vegetation index (NDVI) at global FLUXNET sites, Agricultural and Forest Meteorology, 233:171-182.
You, X., Meng, J., Zhang, M., Dong, T., (2013), Remote sensing based detection of crop phenology for agricultural zones in China using a new threshold method, Remote Sensing, 5(7):3190-3211.
Zhang, X., Friedl, M. A., Schaaf, C. B., Strahler, A. H., Hodges, J. C., Gao, F., Reed, B. C., Huete, A., (2003), Monitoring vegetation phenology using MODIS, Remote Sensing of Environment, 84(3): 471-475.