Instituto Federal Goiano, km 01, Rodovia Sul Goiana, Rio Verde 75901-970, Brazil.
Physics Institute, Universidade Federal de Mato Grosso, 2367 Av. Fernando Corrêa da Costa, Cuiabá 78060-900, Brazil.
Sensors (Basel). 2021 Oct 29;21(21):7196. doi: 10.3390/s21217196.
The determination of the surface energy balance fluxes (SEBFs) and evapotranspiration (ET) is fundamental in environmental studies involving the effects of land use change on the water requirement of crops. SEBFs and ET have been estimated by remote sensing techniques, but with the operation of new sensors, some variables need to be parameterized to improve their accuracy. Thus, the objective of this study is to evaluate the performance of algorithms used to calculate surface albedo and surface temperature on the estimation of SEBFs and ET in the Cerrado-Pantanal transition region of Mato Grosso, Brazil. Surface reflectance images of the Operational Land Imager (OLI) and brightness temperature (Tb) of the Thermal Infrared Sensor (TIRS) of the Landsat 8, and surface reflectance images of the MODIS MOD09A1 product from 2013 to 2016 were combined to estimate SEBF and ET by the surface energy balance algorithm for land (SEBAL), which were validated with measurements from two flux towers. The surface temperature (Ts) was recovered by different models from the Tb and by parameters calculated in the atmospheric correction parameter calculator (ATMCORR). A model of surface albedo (asup) with surface reflectance OLI Landsat 8 developed in this study performed better than the conventional model (acon) SEBFs and ET in the Cerrado-Pantanal transition region estimated with asup combined with Ts and Tb performed better than estimates with acon. Among all the evaluated combinations, SEBAL performed better when combining asup with the model developed in this study and the surface temperature recovered by the Barsi model (Tsbarsi). This demonstrates the importance of an asup model based on surface reflectance and atmospheric surface temperature correction in estimating SEBFs and by SEBAL.
确定地表能量平衡通量（SEBFs）和蒸散量（ET）是环境研究的基础，涉及土地利用变化对作物需水量的影响。SEBFs 和 ET 已通过遥感技术进行估算，但随着新型传感器的运行，需要对某些变量进行参数化以提高其准确性。因此，本研究的目的是评估用于计算地表反照率和地表温度的算法在巴西马托格罗索州塞拉多-潘塔纳尔过渡区估算 SEBFs 和 ET 的性能。结合了陆地表面能量平衡算法（SEBAL）来估算 SEBF 和 ET，该算法结合了来自 2013 年至 2016 年的陆地成像仪（OLI）的地表反射率图像和热红外传感器（TIRS）的亮度温度（Tb）以及 MODIS MOD09A1 产品的地表反射率图像，并用来自两个通量塔的测量值进行了验证。通过不同模型从 Tb 恢复地表温度（Ts），并通过大气校正参数计算器（ATMCORR）计算的参数进行恢复。本研究中开发的基于陆地卫星 8 OLI 地表反射率的地表反照率模型（asup）比传统模型（acon）表现更好，与 asup 结合 Ts 和 Tb 估算的 SEBFs 和 ET 表现更好。在所评估的所有组合中，当将 asup 与本研究开发的模型以及 Barsi 模型（Tsbarsi）恢复的地表温度相结合时，SEBAL 的表现更好。这表明基于地表反射率和大气地表温度校正的 asup 模型在估算 SEBFs 和 ET 方面的重要性。
