ABSTRACT

Evapotranspiration is one of the most important elements of the hydrological cycle. Estimation of evapotranspiration is imperative for effective forest, irrigation, rangeland and water resources management as well as to increase yields and for better crop management. This study aims to evaluate the effectiveness of the Surface Energy Balance Algorithm for Land (SEBAL) in estimating evapotranspiration and crop coefficient of corn in the Mediterranean region of Adana province, Turkey. The Landsat 8 satellite images from March to September 2018 were used to acquire the coefficients of the respective bands. Then, the net radiation flux on the earth’s surface and the earth’s heat flux is obtained using incoming-outgoing radiation fluxes from albedo, surface emissivity coefficients, land surface temperature, and plant indicators. Next, the sensible heat flux is calculated by determining the hot and cold pixels under consideration via the atmospheric stability conditions. Finally, evapotranspiration maps are plotted. The crop coefficient of corn is also estimated with the respected maps being plotted. To validate the outcomes from the SEBAL algorithm, experimental methods were employed to calculate the evapotranspiration values and evaluated using suitable performance metrics. The results showed that the SEBAL generated evapotranspiration values are in high agreement with the FAO Penman-Monteith method registering the highest correlation (R = 0.91) and the lowest error (RMSE = 1.14). In addition, the SEBAL method registered the highest correlation values of 0.89, 0.87 and 0.68 with Turk, Makkink and Hargreaves experimental methods, respectively. Moreover, the crop coefficients estimated using SEBAL also manifested an acceptable correlation with all methods. The highest correlation value registered was with the FAO Penman-Monteith method (R = 0.98). The outcomes show that since the performance of the SEBAL algorithm in estimating the actual evapotranspiration and crop coefficient using Landsat 8 satellite images is acceptable, the SEBAL algorithm could be a very convenient method. Moreover, it could easily be assimilated into farming management systems and precision agriculture for better decision-making and higher yield.

摘要

蒸散是水文循环中最重要的要素之一。蒸发蒸腾量的估算对于有效的森林、灌溉、牧场和水资源管理以及提高产量和更好的作物管理至关重要。本研究旨在评估土地表面能量平衡算法 (SEBAL) 在估算土耳其阿达纳省地中海地区玉米蒸散量和作物系数方面的有效性。利用2018年3月至2018年9月的Landsat 8卫星影像获取各波段系数。然后，使用来自反照率的进出辐射通量、地表发射系数、地表温度和植物指标，获得地球表面的净辐射通量和地球的热通量。下一个，显热通量是通过大气稳定性条件确定考虑中的热像素和冷像素来计算的。最后，绘制蒸散图。玉米的作物系数也可以通过绘制的受人尊敬的地图来估计。为了验证 SEBAL 算法的结果，采用了实验方法来计算蒸散值并使用合适的性能指标进行评估。结果表明，SEBAL 生成的蒸散值与粮农组织 Penman-Monteith 方法高度一致，相关性最高 (R = 0.91)，误差最低 (RMSE = 1.14)。此外，SEBAL 方法与 Turk、Makkink 和 Hargreaves 实验方法的相关值最高，分别为 0.89、0.87 和 0.68。而且，使用 SEBAL 估计的作物系数也表现出与所有方法的可接受的相关性。记录的最高相关值是使用 FAO Penman-Monteith 方法 (R = 0.98)。结果表明，由于 SEBAL 算法在使用 Landsat 8 卫星图像估计实际蒸散量和作物系数方面的性能是可以接受的，因此 SEBAL 算法可能是一种非常方便的方法。此外，它可以很容易地融入农业管理系统和精准农业，以实现更好的决策和更高的产量。结果表明，由于 SEBAL 算法在使用 Landsat 8 卫星图像估计实际蒸散量和作物系数方面的性能是可以接受的，因此 SEBAL 算法可能是一种非常方便的方法。此外，它可以很容易地融入农业管理系统和精准农业，以实现更好的决策和更高的产量。结果表明，由于 SEBAL 算法在使用 Landsat 8 卫星图像估计实际蒸散量和作物系数方面的性能是可以接受的，因此 SEBAL 算法可能是一种非常方便的方法。此外，它可以很容易地融入农业管理系统和精准农业，以实现更好的决策和更高的产量。