The study used the remote sensing method (Sentinel 2) to analyze the sunflower crop and to estimate the production. The study area was within the DES, ULS 'King Mihai I' from Timisoara, Romania. Eight series of images were taken (April 06 - August 07, 2022). Based on the spectral information, the NDMI, NDVI, NPCRI and NBR indexes were calculated. Spline models best described the variation of index values in relation to time (t, days) during the study period, ε = −0.04286 for NDMI, ε = 0.01172 for NDVI, ε = 0.00537 for NPCRI, respectively ε = −0.08481 for NBR. Very strong correlations were found between NDVI and NDMI (r=0.975), between NBR and NDMI (r=0.997), and between NBR and NDVI (r=0.967), p<0.001. Strong correlation was recorded between NDVI and NPCRI (r=-0.881), p<0.01. Moderate correlations were found between NDMI and t (r=0.729), between NBR and t (r=0.752), between NPCRI and NDMI (r=-0.776), and between NBR and NPCRI (r=-0.762), p<0.05. The regression analysis facilitated the estimation of the production based on calculated indices, under conditions of statistical safety.
The study evaluated the interdependence relationship between certain agrochemical indices of soil fertility. Soil reaction (pH), organic matter (H), nitrogen index (NI), base saturation degree (V), phosphorus (P) and potassium content (K) were analyzed. The variation of NI in relation to H was described by a polynomial equation of degree 2, inconditions of R2 = 0.876, p = 0.0151. Mobile phosphorus soil content (P) in relation to soil pH was described by a polynomial equation of degree 2, and from the derivation of the equation was obtained the value of pH = 6.77 at which P had high mobility in soil, and high accessibility for plants. The P variation was analyzed by regression analysis in relation to pH and V, under conditions of R<sup<2</sup< = 0.817. The distribution in 3D form and in the form of isoquants of the P values in relation to soil pH and V was obtained. The variation of K was analyzed by regression analysis in relation to pH and P (R<sup<2</sup< = 0.776), and in relation to pH and NI (R<sup<2</sup< = 0.949) respectively. The graphical distribution in 3D and isoquants form of the K values in relation to pH and P, respectively in relation to pH and NI was obtained.
The present study evaluated the interdependence relationship between agrochemical soil indexes, agricultural crops, NDVI and SAVI indices, respectively Red Edge band, based on RapidEye remote sensing images. The NDVI, SAVI, and Red Edge were used to characterize the crops. Agrochemical indices of pH, humus (H), saturation in bases (V), nitrogen index (NI), phosphorus (P) and potassium (K) content, were used for the soil characterization. Kendall's correlation analysis revealed a very strong correlation between P and Red Edge (r = -0.905), moderate correlations between P and NDVI respectively SAVI (r = 0.619), and weak correlations between K and Red Edge (r = -0.524). The regression analysis facilitated the achievement of 2nd degree polynomial equations for the P prediction based on NDVI under the conditions of R 2 = 0.724, p <0.01, the P prediction based on SAVI under conditions of R 2 = 0.718, p <0.01, respectively P prediction based on Red Edge, under conditions of R 2 = 0.985, p <<0.001. Prediction of K was possible based on NDVI, under the conditions of R 2 = 0.774, based on SAVI under the conditions of R 2 = 0.768, and respectively based on Red Edge under condition of R 2 = 0.889. For the other agrochemical indices, the predictive relations in condition of low statistical safety were obtained (e.g. for pH based on Red Edge, R 2 = 0.696; for H based on Red Edge, R 2 = 0.538). For all agrochemicals, the safety predictions achieved through regression analysis were higher based on the Red Edge band compared to NDVI or SAVI indices.
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