Generation of High-Resolution Time-Series NDVI Images for Monitoring Heterogeneous Crop Fields

Various fusion methods of optical satellite images were proposed for monitoring heterogeneous farmlands requiring high spatial and temporal resolution. In this study, 3m normalized difference vegetation index (NDVI) was generated by applying spatiotemporal fusion method to simultaneously generate full-length normalized difference vegetation index time series (SSFIT) and enhanced spatial and temporal adaptive reflectance fusion method (ESTARFM), to NDVI of Sentinel-2 (S2) and PlanetScope (PS) from 2019 to 2021 for rice paddy and heterogeneous cabbage fields. Before fusion, S2 was applied with the maximum NDVI composite (MNC) and the spatio-temporal gap-filling technique to minimize the cloud effects. The fused NDVI image had a spatial resolution similar to PS, enabling more accurate monitoring of small and heterogeneous fields. In particular, the SSFIT technique showed higher accuracy than ESTARFM with a root mean square error of less than 0.16 and correlation of more than 0.8 compared to PS NDVI, and was processed faster than the ESTARFM code. In some images where ESTARFM was applied, outliers originating from the S2 were still present and heterogeneous NDVI distributions were also shown. This spatio-temporal fusion (STF) technique can be used to produce high-resolution NDVI images for any date during the rainy season required for time-series analysis.