The persistent monitoring of evapotranspiration (ET) over the regions suffering from water scarcity is critical for sustainable agricultural water management. Remote sensing provides time- and cost-effective capability to investigate daily ET rates at large scales. Satellite-based actual evapotranspiration (ET_a) algorithms typically rely on specifying the upper and lower boundaries of ET_a rate over agricultural and pasture fields, commonly known as hot (dry) and cold (wet) pixels selection. These boundaries are to be recognized by an expert through a subjective and labor-intensive task. In this study, a method has been introduced to automatically select appropriate anchor pixels (i.e., hot and cold pixels) independent from land use/cover maps with the simplest possible way, quickly applied even by an inexperienced operator. Subsequently, ET_a was calculated using Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC), Surface Energy Balance Algorithm for Land (SEBAL), and Surface Energy Balance System (SEBS) algorithms and evaluated against measured data. In this method, the mountains and foothills were removed using the Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM) and the subsequent product was a slope mask. Then, filters were applied based on the Normalized Difference Vegetation Index (NDVI), Albedo, and Land Surface Temperature (LST) images to identify potential candidate pixels for hot and cold pixels. In the end, the best-conditioned pixel being closest to the meteorological station was selected. The method was assessed in five different regions with different topographic and climatic conditions. The selected pixels were first visually validated in Landsat images, and then the fluctuations and values were discussed in time series of anchor pixels and LST histograms. The visual interpretation was indicative of selecting the anchor pixels in fallow (hot pixel) and densely vegetated (cold pixel) surfaces. Also, the hot and cold pixels were suitably situated in the upper and lower quartiles of the LST histogram, respectively. The range of cold pixels variations throughout the study periods was lower compared with the hot pixels (44.2, 55.3, 35.5, 66.5, and 25.2 K for hot pixels against 34.2, 45.7, 25.6, 52.2, and 17 K for cold pixels) as expected, which emanated from the lower fluctuations of temperature over vegetation against the soil. The results were indicative of the better performance of METRIC compared with SEBAL and SEBS with greater values of R² in all the regions. Therefore, using the introduced method, the expert subjective interference was eliminated and processing time reduced significantly from about 1 h per image to a few minutes.

持续监测缺水地区的蒸散量对可持续农业水管理至关重要。遥感提供了节省时间和成本的功能，可以大规模研究每日的ET率。基于卫星的实际蒸散量（ET _a）算法通常依赖于指定ET _a的上下边界农业和牧场的速率，通常称为热（干）和冷（湿）像素选择。这些界限应由专家通过主观且费力的任务来识别。在这项研究中，引入了一种方法，可以以最简单的方法自动选择与土地使用/覆盖图无关的合适锚点像素（即，热像素和冷像素），即使没有经验的操作员也可以快速应用。随后，ET_一使用具有内部校准（METRIC）的高分辨率制图蒸发蒸腾量，陆地表面能平衡算法（SEBAL）和表面能平衡系统（SEBS）算法来计算并根据测量数据进行评估。在这种方法中，使用航天飞机雷达地形任务数字高程模型（SRTM DEM）去除了山脉和山麓小丘，随后的产品是防坡面罩。然后，根据归一化植被指数（NDVI），反照率和陆地表面温度（LST）图像应用滤镜，以识别热像素和冷像素的潜在候选像素。最后，选择最接近气象站的条件最佳的像素。该方法在五个具有不同地形和气候条件的不同区域进行了评估。首先在Landsat图像中对所选像素进行视觉验证，然后在锚点像素和LST直方图的时间序列中讨论波动和值。视觉解释表明在休耕（热像素）和植被茂密（冷像素）表面中选择锚点像素。同样，热像素和冷像素分别位于LST直方图的上四分位数和下四分位数中。整个研究期间，冷像素变化的范围比热像素要低（热像素分别为44.2、55.3、35.5、66.5和25.2 K，冷像素则为34.2、45.7、25.6、52.2和17 K） ，这是由于植被对土壤的温度波动较低所致。在所有区域中为²。因此，使用引入的方法，消除了专家的主观干扰，处理时间从每张图像大约1小时显着减少到几分钟。