In this research, an approach to monitor crop growth and development is presented using time series satellite data of high spatial resolution. Monitoring of winter wheat phenology based on images of PlanetScope constellations is considered. By applying various PlanetScope data processing types and ground based GreenSeeker data, differences of NDVI values at two variants of crop fertilization are determined. In particular, the following approaches were used in the research: obtaining the Top of Atmosphere Reflectance (TOA), the Planet Surface Reflectance (SR), and receiving NDVI image in Python using a Rasterio module. It was estimated that NDVI values derived from the surface reflectance imagery were significantly correlated to the ground data of a manual active GreenSeeker optical sensor (p < 0.05). The proposed simplified technique, based on PlanetScope NDVI time series, demonstrates the possibilities to monitor temporal changes in crop growth.

In this study, the winter wheat aboveground biomass (AGB), leaf area index (LAI) and leaf nitrogen concentration (LNC) were estimated using the vegetation indices, derived from a high spatial resolution Pleiades imagery. The AGB, LAI and LNC estimation equations were established between the selected VIs, such as NDVI, EVI and SAVI. Regression models (linear and exponential) were examined to determine the best empirical regression equations for estimating the crop characteristics. The results showed that all three vegetation indices provide the AGB, LAI and LNC estimations. The application of NDVI showed the smallest value of RMSE for the aboveground biomass estimation at stem elongation and heading of winter wheat. EVI gave the best significant estimation of LNC and showed better results to quantify winter wheat vegetation characteristics at stem elongation phase. This study demonstrated that Pleiades high spatial resolution imagery provides in-situ crop monitoring.

The study describes the stages of conceptual modeling to provide a crop monitoring system based on the multisource spatial data to assess the state of agricultural crops. The process of developing geodatabase models, which is the basis of the crop monitoring system, considered the construction of a set of diagrams of the Unified Modeling Language (UML). The UML Sequence diagrams were developed to describe the specific properties of crop monitoring system components and their behavior. The developed data flow diagram showed the data flow in the crop monitoring system and described the processes involved in the system for the transfer of data from the source files to the geodatabase. The approach presented in the study can be suggested as a methodology that is suitable for a wide range of developers of monitoring systems.