COMPARATIVE ANALYSIS OF TWO SATELLITE SYSTEMS IN SERVICES FOR AGRICULTURE. CASE STUDY: THE USAMVBT TEACHING AND EXPERIMENTAL RESORT PUBLISHEDA.M. Stăncescu1*, F. Sala1 firstname.lastname@example.org
Monitoring crops using satellite imagery has been proven to be particularly useful in agriculture, especially in precision agriculture, crop management and agricultural production. The present study performed a comparative analysis of two satellite systems: Landsat 8 OLI (produced by the United States of America through NASA) and the Sentinel 2 MSI Constellation (produced by the European Space Agency under the European Union supervision) in the context of normalized differential indices of vegetation, soil adjusted vegetation index and differential moisture index. In order to carry out the study, satellite images were acquired from 5 different periods - January 2018 to November 2018 - from the USGS website. The 2 mentioned satellite systems offer to the general public medium resolution satellite images: 30 m for Landsat and 10 m in three visible bands and one near-infrared band, respectively 20 m in red edge band and shortwave infrared bands for Sentinel 2 MSI. Based on spectral information from the satellite images provided by the two satellite systems, 4 normalized vegetation indices were calculated: NDVI (Normalized Difference Vegetation Index), NDBR (Normalized Burn Ratio), NDMI (Normalized Difference Moisture Index), and SAVI (Soil Adjusted Vegetation Index). Based on these indices, a characterization of an agricultural area within the USAMVBT Teaching and Experimental Resort was achieved, and it created a general analysis model to characterize the vegetation land cover. In this study the analysis was focused on one agricultural crop, the analysis of different vegetation stages and crop dynamics as well. For the accuracy assessment, the data obtained was correlated with information obtained from the land, referring to the agricultural crops that occupied the studied land. Different study models that describe the dynamics crop vegetation cover are helpful in the estimation of the biomass production and determining the optimal harvesting time corelated with the purpose of the crops.
agriculture, NDBR, NDVI, NDMI, SAVI, satellite imagery, Landsat 8 OLI, Sentinel 2 MSI