Spatiotemporal Data Fusion of Index-Based VTCI Using Sentinel-2 and -3 Satellite Data for Field-Scale Drought Monitoring

Due to climate change, the impact of drought on field crop production is extremely important. This study focuses on the vegetation temperature condition index (VTCI), an index-based drought monitoring index that can characterize drought conditions in near-real time (at 10-day intervals), and explores the applicability of different spatial and temporal data fusion schemes to it. It also proposes a field-scale VTCI fusion framework based on the Sentinel-3 VTCI calculation and the LST downscaling. First, based on analyzing the computational characteristics of VTCI, multi-year VTCI based on Sentinel data sources was obtained, which further expands the diversity of data sources for VTCI. On this basis, a combination of qualitative and quantitative methods was used to compare the applicability of two schemes: Scheme 1, based on the “blend-then-index” (BI) strategy, which firstly fuses NDVI and LST, and then calculated the fused VTCIs; and Scheme 2, based on the “index-then-blend” (IB) strategy, which directly fuses the VTCIs based on the calculated VTCIs. It was found that all the fused VTCIs remained highly correlated with the 10-day cumulative precipitation. Compared with the fused VTCIs obtained by Scheme 2, the VTCIs obtained by Scheme 1 were able to display more spatial details. In addition, the VTCIs of Scheme 1 were more consistent with the Sentinel-3 VTCIs, and the accuracy of field yield estimation using the fused VTCIs was higher (r of 0.58, RMSE of 783.27 kg/ha).