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In agricultural systems, rapid information from data collection and processing is an important factor for stakeholders and researchers to correctly account for the spatial and temporal variability of crop and soil factors. The aim of the present study was to investigate soil-plant-water systems and interactions using manual and remote sensing techniques in a small agricultural catchment. Four land use types of forest, grassland, vineyard, and cropland (sunflower) were investigated in different slope positions. At the same time, three different tillage practices were applied in the vineyard between the rows: grassed (NT), cover cropped (CC), and tilled (T) inter rows. We evaluated NDVI measurements from three different sources (PlantPen - PP, Meter Group - MG, Sentinel-2 - S2) representing different scales (leaves, 0.33m2, and 100m2). We also compared ground and satellite measurements of varying vegetation indices.Spectral reflectance sensors were used on the slopes of grassland, cropland, and three vineyard sites. The Normalized Difference Vegetation Index (NDVI) and Photochemical Reflectance Index (PRI) sensors were used to measure leaf reflectance. A hemispherical sensor set was used for each measurement. Hand-held instruments were used to measure the topsoil soil water content (SWC) and temperature, leaf NDVI and chlorophyll concentrations, and Leaf Area Index (LAI) every two weeks. Satellite data, such as NDVI, green (GCI) and red edge (RECI) chlorophyll indices, and soil-adjusted vegetation index (SAVI), were obtained from the Sentinel-2 database on days when both ground and satellite overpass occurred within 24 hours.Land use types and slope position have a strong influence on vegetation growth. The highest overall NDVI and leaf chlorophyll values were observed in vineyard and forest samples, and the lowest in grassland. SWC and temperature were the lowest in the forest and vineyards. SWCs were significantly different for T and CC samples (p0.05). For the other three land use types, there were no significant differences in values between slope positions. Chlorophyll data showed a very strong correlation between Sentinel-2 retrieved data and hand-held measurements, with r=0.84 for grassland (GCI), r=0.83 for NT (GCI), and r=0.87 for T (RECI). Strong correlations were found between the different sources of NDVI for the grassland samples (e.g. r=0.97, p |
