Assessing crop yield trends over years is a key step in site specific management, in view of improving the economic and environmental profile of agriculture. This study was conducted in a 11.07 ha area under Mediterranean climate in Northern Italy to evaluate the spatial variability and the relationships between six remotely sensed vegetation indices (VIs) and grain yield (GY) in five consecutive years. A total of 25 satellite (Landsat 5, 7, and 8) images were downloaded during crop growth to obtain the following VIs: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Green Normalized Difference Vegetation Index (GNDVI), Green Chlorophyll Index (GCI), and Simple Ratio (SR). The surveyed crops were durum wheat in 2010, sunflower in 2011, bread wheat in 2012 and 2014, and coriander in 2013. Geo-referenced GY and VI data were used to generate spatial trend maps across the experimental field through geostatistical analysis. Crop stages featuring the best correlations between VIs and GY at the same spatial resolution (30 m) were acknowledged as the best periods for GY prediction. Based on this, 2–4 VIs were selected each year, totalling 15 VIs in the five years with r values with GY between 0.729** and 0.935**. SR and NDVI were most frequently chosen (six and four times, respectively) across stages from mid vegetative to mid reproductive growth. Conversely, SAVI never had correlations high enough to be selected. Correspondence analysis between remote VIs and GY based on quantile ranking in the 126 (30 m size) pixels exhibited a final agreement between 64% and 86%. Therefore, Landsat imagery with its spatial and temporal resolution proved a good potential for estimating final GY over different crops in a rotation, at a relatively small field scale.

Availability of georeferenced yield data involving different crops over years, and their use in future crop management, are a subject of growing debate. In a 9 hectare field in Northern Italy, seven years of yield data, including wheat (3 years), maize for biomass (2 years), sunflower, and sorghum, and comprising remote (Landsat) normalized difference vegetation index (NDVI) data during central crop stages, and soil analysis (grid sampling), were subjected to geostatistical analysis (semi-variogram fitting), spatial mapping (simple kriging), and Pearson’s correlation of interpolated data at the same resolution (30 m) as actual NDVI values. Management Zone Analyst software indicated two management zones as the optimum zone number in multiple (7 years) standardized yield data. Three soil traits (clay content, total limestone, total nitrogen) and five dates within the NDVI dataset (acquired in different years) were shown to be best correlated with multiple- and single-year yield data, respectively. These eight parameters were normalized and combined into a two-zone multiple soil and NDVI map to be compared with the two-zone multiple yield map. This resulted in 83% pixel agreement in the high and low zone (89 and 10 respective pixels in the soil and NDVI map; 73 and 26 respective pixels in the yield map) between the two maps. The good agreement, which is due to data buffering across different years and crop types, is a good premise for differential management of the soil- and NDVI-based two zones in future cropping seasons.