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

Abstract

Ratoon rice, which refers to a second harvest of rice obtained from the regenerated tillers originating from the stubble of the first harvested crop, plays an important role in both food security and agroecology while requiring minimal agricultural inputs. However, accurately identifying ratoon rice crops is challenging due to the similarity of its 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 that frequently exhibit cloudy and rainy weather. In this study, taking Qichun County in Hubei Province, China as an example, we developed 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 incorporated the red, near-infrared, and shortwave infrared 1 bands was developed based on the analysis of spectro-phenological separability and feature selection. Based on actual 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). The 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 NDVI, EVI, LSWI and their combination at the 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.