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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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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