Accurate and timely estimation of spatiotemporal dynamics of farmland soil respiration rate is crucial for revealing carbon emission patterns during agricultural production. Traditional methods can simulate soil respiration dynamics at small scales, but they remain insufficient in characterizing spatial heterogeneity at the farmland scale. To achieve accurate estimation of soil respiration at high spatial resolution, it was focused on summer maize in a typical region of central Inner Mongolia. The experiment included one control treatment (Tr1: irrigation at 100% ET, where ET represents evapotranspiration) and three deficit irrigation treatments (Tr2, Tr3, Tr4). Soil respiration rates were monitored at different growth stages by using the static chamber method, while vegetation indices and soil surface temperature (TUAV) were retrieved from UAV-based multispectral and thermal infrared data. The TUAV, together with the simple pigment ratio index (SRPI), the green-blue normalized difference vegetation index (NDVIg-b), and the normalized pigment chlorophyll index (NPCI), were incorporated into the Lloyd-Taylor model to develop an improved vegetation-heat index (VHI) model for soil respiration rate estimation. The performance of this model was further compared with that of a back propagation neural network (BPNN) model. The results showed that under Tr1~Tr4 treatments, temperature of the soil surface (TSF) was significantly correlated with seasonal soil respiration rate, with correlation coefficients of 0.946, 0.886, 0.898 and 0.766, respectively. Among the nine vegetation indices indicative of crop photosynthetic capacity, SRPI exhibited the strongest correlation with seasonal soil respiration rate. The VHI model based on SRPI and TUAV achieved the best fitting performance (R2=0.73), which was comparable to the BPNN model (R2=0.81). Overall, it was demonstrated that integrating UAV multispectral and thermal infrared data with the VHI model enabled high-resolution characterization and mapping of soil respiration rate heterogeneity at the farmland scale, thereby improving estimation accuracy.