Development of Cropping Pattern Product Using Sentinel-2 Satellite Data

Understanding cropland utilization is essential for improving agricultural productivity and efficiently managing cropland resources. Analyzing region-specific cropping systems enables the establishment of sustainable agricultural policies tailored to environmental conditions. However, conducting field surveys over extensive agricultural areas presents significant challenges. Satellite data for agricultural monitoring provides continuous and large-scale information for cropland. The purpose of this study is to develop a cropping pattern product for annual crops using satellite data. The study area is ‘Gimje-si’ in the Republic of Korea. Sentinel-2 Level-2 data was acquired from 2022 to 2024. The normalized difference vegetation index (NDVI) was calculated after eliminating cloud and contaminated pixels, and then the monthly mean NDVI was computed. Cropland was extracted using a farmland boundary map in vector file format. Types of cropping patterns were classified into single and sequential (e.g., double, triple) cropping, and non-cultivated land, based on the number of peaks in the time-series NDVI data. The threshold for NDVI peaks was set to 0.4, and the minimum distance between NDVI peaks was set to 3. The final product was generated in vector format and includes monthly NDVI values, cropping patterns, and peak month information for each field. The annual map for 3 years showed changes in cropping patterns. These products were useful for detecting changes in cropland and confirming whether it was being cultivated. There was an increasing trend in the number of fields with sequential cropping from 2022 to 2024. Our results help comprehend the use and change of cropland spatiotemporally.

Acknowledgments: This research was funded by RDA, grant number PJ01676802.