A phenology-based vegetation index for improving ratoon rice mapping using harmonized Landsat and Sentinel-2 data

Ratoon rice, which refers to a second harvest of rice obtained from regenerated tillers originating from the stubbles of the first harvested crop, plays an important role in both food security and agroecology while requiring minimal agricultural inputs. However, it is challenging to accurately identify ratoon rice crops due to the similar spectral features with other rice cropping systems (e.g., double rice). Moreover, images with a high spatiotemporal resolution are essential since ratoon rice is generally cultivated in fragmented croplands within regions frequently exhibiting cloudy and rainy weather. In this study, adopting Qichun county in Hubei province as an example, we proposed a new phenology-based ratoon rice vegetation index (PRVI) for the purpose of ratoon rice mapping at a 30-m spatial resolution using a robust time series generated from harmonized Landsat and Sentinel-2 (HLS) images. The PRVI that ingested the red, near-infrared, and shortwave infrared 1 bands was developed based on the analysis of spectro-phenological separability and feature selection. Based on the field samples, the performance of the PRVI for ratoon rice mapping was carefully evaluated by comparing it to several vegetation indices, including normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and land surface water index (LSWI). Results suggested that the PRVI could sufficiently capture the specific characteristics of ratoon rice, leading to a favorable separability between ratoon rice and other land cover types. Furthermore, the PRVI showed the best performance for identifying ratoon rice in the phenological phases characterized by grain filling and harvesting to tillering of the ratoon crop (GHS-TS2), indicating that only several images are required to obtain an accurate ratoon rice map. Finally, the PRVI performed better than the NDVI, EVI, LSWI and their combination at GHS-TS2 stages, with producer's accuracy and user's accuracy of 92.22 and 89.30%, respectively. These results demonstrate that the proposed PRVI based on HLS data can effectively identify ratoon rice in fragmented croplands at crucial phenological stages, which is promising for identifying the earliest timing of ratoon rice planting and can provide a fundamental dataset for crop management activities.