Air temperature estimation over winter wheat fields by integrating machine learning and remote sensing techniques

Abstract

Air temperature (Ta) is significant to numerous Earth's surface and agricultural processes. For agricultural fields (e.g., winter wheat), Ta plays an important role in crop growth monitoring, crop yield estimation, and agricultural risk assessment. In this study, a revised Ta estimation model is proposed by integrating the temperature-vegetation index (TVX) method with multiple regression models. Daily Ta from 2013 to 2021 over winter wheat fields in Henan province (the Top 1 winter wheat-producing province in China) was estimated. The revised models utilized the TVX method over regions and periods featuring extensive vegetation coverage, especially during winter wheat growing seasons, then employed three regression models including multivariate linear regression (MLR) model, random forest (RF) machine learning regression model, and deep belief network (DBN) deep learning regression model. The satellite remote sensing observations, meteorological in-situ Ta measurements, and model assimilations were integrated into the revised models to retrieve Ta. Results obtained through methods proposed in this study were calibrated and validated by in-situ Ta measurements from weather networks. The statistical results reveal that the revised methods can produce Ta estimation over winter wheat-planting areas with higher accuracy compared with traditional regression methods, with R2, MAE, and RMSE ranges from 0.915 to 0.971, 1.204 to 2.097 °C, and 1.623 to 2.911 °C, respectively. Integrating the TVX with RF has the best performance with the highest R2 (0.971), lowest MAE (1.204 °C), and lowest RMSE (1.623 °C) over the winter wheat planting area.