USE OF REMOTE SENSING IMAGES IN CROP MONITORING CASE STUDY: SOYBEAN CROP PUBLISHED

S. LANGHE, M. V. HERBEI, F. SALA None mihai_herbei@yahoo.com
The study used remote sensing images in order to analyze and describe the monitoring process for soybean crop. Soybean, which has been the subject of this study, is a culture of particular economic importance due to its many uses. It is used in human nutrition, in the production of animal feed, but also as a raw material for some industries Worldwide in the production of soybean vegetable oil is the second largest after the palm. The satellite images were achieved by the PlanetScope satellite system, images in 4 spectral bands: RED, GREEN, BLUE and NIR, with a 3 m spatial resolution, that show the dynamics of this crop over the period analyzed. The study was carried out over a total time interval (T) of 121 days, from 27.03.2020 to 24.07.2020. The parcel analyzed in this research is part of Experimental Didactic Station of BUASVM Timisoara and it is located in Timis County and it has approx. 55 hectares. Eight satellite scenes were taken over and processed for the 2 combination of spectral bands: Red Green Blue and Near Infrared Red Green and for the calculation and interpretation of 2 useful vegetation indices in such monitoring, namely, the Normalized Difference Vegetation Index (NDVI) and The modified soil-adjusted vegetation index (MSAVI2). Processing of the acquired images was performed with specialized software: ERDAS Image 2014 and ArcGIS v. 10.3, and mathematical processing was done with the Past software. The variation of NDVI index in relation to time, over the study period, a 3rd degree polynomial equation, under statistical safety, R2=0.966, p=0.00208. The NDVI index variation in relation with MSAVI2 has been described by a 2nd degree polynomial equation under statistical security conditions, R2=0.999, p<0.001. Cluster analysis led to grouping of variants according to time of remote sensing (D) acquisition based on affinity under high statistical accuracy (Coph.corr. = 0,955).
agricultural crops, vegetation indices, monitoring, remote sensing
Sustainable Development, Environmental Engineering and Geomatics
Presentation: poster

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