Comparison of zonation approaches by means of remote sensing vegetation indices for agricultural applications

Remote sensing vegetation indices play a vital role in agricultural zoning by providing detailed insights into crop health, productivity, and environmental conditions. They enable researchers and professionals to monitor environmental changes, urban expansion, and natural events with exceptional accuracy and precision. This progress has been fueled by major technological developments in satellite sensors, data processing algorithms, and analytical methods, enabling the capture of more detailed information and increased observation frequency across expansive regions. Despite these excellent opportunities, numerous image processing techniques have been suggested, each customized for particular applications, datasets, and user needs, yet no widely recognized standard methods have been established. This absence of standardization creates difficulties of interoperability, reproducibility, and consistency in analytical results. Researchers and practitioners frequently encounter challenges choosing the most suitable methods, since the effectiveness of these techniques can fluctuate based on factors like spatial resolution, temporal frequency, and the type of landscape under examination. As a result, there is an increasing demand for the creation of thorough guidelines and uniform procedures that can facilitate the use of remote sensing instruments while ensuring dependable and comparable outcomes across various studies and fields. In this research, we analyze zonation outcomes obtained from remote sensing images captured at different times, using several vegetation indices and applying various clustering techniques. The objective is to evaluate how time-related changes, the selection of vegetation indices, and the use of different clustering methods affect the precision and dependability of land classification. Through the examination of these combination performance, this comparative examination underscores both the advantages and drawbacks of each approach while offering important insights for improving classification methods in varied and changing environments.