Temperature separation via eliminating shadow-pixel influence based on high-resolution sUAS image in California vineyards

The images generated by high-resolution spectral and thermal sensors equipped on small unmanned aerial vehicles (sUAV) make possible estimation of energy flux for California vineyards via the two-source energy balance (TSEB) model. Temperature (thermal) image plays an important role in the TSEB model, and the high-resolution provides an opportunity for temperature separation, which may better delineate the energy flux between canopy and soil. However, with the exception of shadow effects, outliers are another major concern during data processing with a previous temperature separation algorithm that uses the relationship between the normalized difference vegetation index (NDVI) and the corresponding temperature pixel for temperature separation. An upgraded algorithm for temperature separation was introduced in the paper titled “The suitability of the TSEB model as a tool to estimate ET partitioning using improved LAI considering the difference of climate, soil, vine variety, and seasons for research areas across California,” and this research provides example data and the upgraded algorithm (a python programmed function) to demonstrate how we finished the temperature separation process.

搭载于小型无人机（small unmanned aerial vehicles, sUAV）的高光谱与热红外传感器所生成的图像，使得借助二源能量平衡模型（two-source energy balance, TSEB）估算加州葡萄园能量通量成为可能。温度（热红外）图像在TSEB模型中发挥着关键作用，高分辨率图像为温度分离提供了条件，可更精准地划分冠层与土壤间的能量通量。然而，除阴影效应外，异常值亦是此前温度分离算法在数据处理过程中的核心问题之一——该算法借助归一化植被指数（normalized difference vegetation index, NDVI）与对应温度像素的关联关系完成温度分离。题为《考虑加州全域研究区气候、土壤、葡萄品种与季节差异，结合优化后叶面积指数（leaf area index, LAI）评估TSEB模型作为蒸散分配估算工具的适用性》的论文中提出了一种升级后的温度分离算法；本研究附带示例数据与该算法（Python编程实现的函数），以完整演示温度分离流程。