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Remote sensing and soil spectroscopy applications are valuable techniques for soil property estimation. Soil organic matter (SOM) and calcium carbonate are important factors in soil quality, and although organic matter is well studied, calcium carbonates require more investigation. In this study, we validated the performance of laboratory soil spectroscopy for estimating the aforementioned properties with referenced in situ data. We also examined the performance of imaging spectroscopy sensors, such as the airborne HySpex and the spaceborne PRISMA. For this purpose, we applied four commonly used machine learning algorithms and six preprocessing methods for the evaluation of the best fitting algorithm.. The study took place over crop areas of Amyntaio in Northern Greece, where extensive soil sampling was conducted. This is an area with a very variable mineralogical environment (from lignite mine to mountainous area). The SOM results were very good at the laboratory scale and for both remote sensing sensors with R2 = 0.79 for HySpex and R2 = 0.76 for PRISMA. Regarding the calcium carbonate estimations, the remote sensing accuracy was R2 = 0.82 for HySpex and R2 = 0.36 for PRISMA. PRISMA was still in the commissioning phase at the time of the study, and therefore, the acquired image did not cover the whole study area. Accuracies for calcium carbonates may be lower due to the smaller sample size used for the modeling procedure. The results show the potential for using quantitative predictions of SOM and the carbonate content based on soil and imaging spectroscopy at the air and spaceborne scales and for future applications using larger datasets.

The main aim of this scientific work was to develop a theoretical model for calculation of nitrogen (N) fertilization of crops. The model is based on the N fertilization plans which had been prepared for the requirements of the Technical Assistance Project entitled “Implementation of Nitrates Directive in Turkey”. It was compiled and tested in irrigated corn crop. This has the possibility to calculate the amount of nitrogen which is required in a wide range of mean annual rainfall. It was tested for areas which receive mean annual rainfall 500-1500 mm and nitrates content in irrigation waters was 10 mg/L. Crop requirements in water are not covered by rainfall in the growing period of crops, due to uneven annual distribution during growing period and additional water is needed by mean of irrigation. Soil texture affects strongly the required quantity of soil nutrients and irrigation water and for this reason, the following textural soil classes were used: light, moderate, heavy texture and soils with organic matter 6% and clay 30%.This model is the basis for the development of auser friendly graphic environment which was built in Python 3.5. This tool can calculate the required Nitrogen for all possible triplets(soil class, annual rainfall, nitrogen from irrigation water) required for every annual crop. The user has simply to choose the crop type, the soil class, then to type the annual rainfall and the N content of irrigation water. The model uses as inputs three variables, namely the qualitative soil class and two quantitative annual rainfall (in mm/y) and nitrates inputs from irrigation (content in mg/L) and returns as an output the required Nitrogen in kg/da (1 da=1000 m2) for the described instance. Results have indicated that the requirements of nitrogen fertilization for corn varied among soil classes although irrigation water had the same nitrates concentration. This can be attributed mainly to different potential of nitrates leaching and N mineralization.