Abstract
The paper aims to discuss the difficulties with vegetation indices determined using satellite imagery of Brassica napus crops. Data used in the study was registered by Sentinel-2 satellite. Differential evolution method was used for vegetation model fitting for numerous monitored fields. In specific cases that procedure allowed to determine parameters even with a very limited number of clear images. Crop vegetation modeling using only NDVI (Normalized Differential Vegetation Index) or solely on EVI (Enhanced Vegetation Index) is often problematic while these indices are not resistant to imagery issues caused by clouds and cloud shadows registered on earth surface. To overcome the limitations of those indices the novel index MSVI (Maxima Standardized Vegetation Index) was proposed. Robust regression was used to find its optimal parameters for individual fields. To verify the presented methodology a set of satellite images of Grudziądz area (northern Poland) for the period 1st to 25th of May 2018 was used, with 6373 fields of Brassica napus that were analyzed. According to the conducted experiments, the median value of absolute error of the florescence date estimated using the proposed methodology was only 0.421 days and was significantly lower to those calculated using conventional indices: NDVI or EVI - 1.191 and 14.128 days respectively.
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References
Basili, M., Rossi, M.A.: Brassica carinata-derived biodiesel production: economics, sustainability and policies. Italian case. J. Cleaner Prod. 191, 40–47 (2018). https://doi.org/10.1016/j.jclepro.2018.03.306
Szczęsna, T., Rybak-Chmielewska, H., Waś, E., Kachaniuk, K., Teper, D.: Characteristics of Polish unifloral honeys. I. Rape honey (Brassica napus L. var. oleifera Metzger). J. Apic. Sci. 55(1), 111–119 (2011)
Wang, N., et al.: Flowering time variation in oilseed rape (Brassica napus L.) is associated with allelic variation in the FRIGIDA homologue BnaA.FRI.a. J. Exp. Botany 62(15), 5641–5658 (2011). https://doi.org/10.1093/jxb/err249
Peschechera, G., Fratino, U.: Calibration of CLAIR model by means of Sentinel-2 LAI data for analysing wheat crops through Landsat-8 surface reflectance data. In: Gervasi, O., et al. (eds.) ICCSA 2018. LNCS, vol. 10964, pp. 294–304. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-95174-4_24
Sakowska, K., Juszczak, R., Gianelle, D.: Remote sensing of grassland biophysical parameters in the context of the Sentinel-2 satellite mission. J. Sens. 2016 (2016). https://doi.org/10.1155/2016/4612809
Modica, G., Pollino, M., Solano, F.: Sentinel-2 imagery for mapping cork oak (Quercus suber L.) distribution in Calabria (Italy): capabilities and quantitative estimation. In: Calabrò, F., Della Spina, L., Bevilacqua, C. (eds.) ISHT 2018. SIST, vol. 100, pp. 60–67. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-92099-3_8
European Space Agency: MultiSpectral Instrument (MSI) Overview (2018). earth.esa.int/web/sentinel/technical-guides/sentinel-2-msi/msi-instrument
Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W.: Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation. Prog. Rep. RSC 1978-1, Remote Sensing Center, Texas A&M University, College Station, nr E73-106393, 93 (NTIS No. E73-106393) (1973)
Liu, H.Q., Huete, A.: Feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE Trans. Geosci. Remote Sens. 33(2), 457–465 (1995)
TBRS team, MODIS Vegetation Indices, The University of Arizona (2002). tbrs.arizona.edu
Saputroa, D.R.S., Widyaningsih, P.: Limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) method for the parameter estimation on geographically weighted ordinal logistic regression model (GWOLR). In: AIP Conference Proceedings, vol. 1868 (2017). https://doi.org/10.1063/1.4995124
Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J. Global Opt. 11, 341–359 (1997)
Xue, J., Su, B.: Significant remote sensing vegetation indices: a review of developments and applications. J. Sens. 2017 (2017). https://doi.org/10.1155/2017/1353691
Rousseeuw, P.J., Leroy, A.M.: Robust Regression and Outlier Detection. Wiley, Hoboken (2003)
Brown, M.B., Forsythe, A.B.: Robust tests for equality of variances. J. Am. Stat. Assoc. 69, 364–367 (1974). https://doi.org/10.1080/01621459.1974.10482955
Belgiua, M., Csillik, O.: Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis. Remote Sens. Environ. 204 (2018). https://doi.org/10.1016/j.rse.2017.10.005
Garroutte, E.L., Hansen, A.J., Lawrence, R.L.: Using NDVI and EVI to map spatiotemporal variation in the biomass and quality of forage for migratory elk in the greater yellowstone ecosystem. Remote Sens. 8(5) (2016). https://doi.org/10.3390/rs8050404s
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Słapek, Smykała and Ruszczak were supported by European Regional Development Fund grant no.: RPOP.01.01.00-16-0056/16-00.
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Słapek, M., Smykała, K., Ruszczak, B. (2019). Brassica Napus Florescence Modeling Based on Modified Vegetation Index Using Sentinel-2 Imagery. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2019. Lecture Notes in Computer Science(), vol 11509. Springer, Cham. https://doi.org/10.1007/978-3-030-20915-5_8
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