Key Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China; University of Chinese Academy of Sciences, Beijing 100049, China.
Key Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China; Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, Chinese Academy of Sciences, Lanzhou 730000, China.
Sci Total Environ. 2020 Aug 10;729:138724. doi: 10.1016/j.scitotenv.2020.138724. Epub 2020 Apr 19.
The accurate quantification of surface heat and water vapor fluxes is significantly essential for understanding water balance dynamics. In this study, 15-m spatial resolution turbulent fluxes (H and LE) in the Zhangye oasis situated the middle reaches of the Heihe River Basin (HRB) were estimated by the remote sensing-based two-source energy balance model (TSEB). The TSEB model uses temperature including land surface temperature (LST) and air temperature (T) as the main input variable to compute turbulent fluxes but their spatial resolution is rather limited. To overcome this shortcoming, the 15-m spatial resolution LST and T were obtained by using the back-propagation neural network (BPNN). The results indicated that the BPNN was able to obtain finer spatial resolution and LST and T; the root mean square error (RMSE) values of LST and T are 1.99 K and 0.50 K, respectively. The remotely sensed H and LE predicted by TSEB model utilizing the LST and T modeled by BPNN. The results showed that H and LE agreed well with the flux observations from multi-set eddy covariance (EC) systems installed at a number of sites and covering all representative land cover types; particularly for the latent heat flux, its estimates produced mean absolute percent errors (MAPE) of 8.76% for maize, 20.17% for vegetable, 29.06% for residential area, and 16.12% for orchard. This study obtained surface heat and water vapor fluxes at finer spatial resolution than the other flux estimates from the remote sensing models that have been used in the Zhangye oasis. The results produced by combining the TSEB model and BPNN can provide more information for drafting reliable sustainable water resource management schemes and improving the irrigation water use efficiency in arid and semi-arid regions.
准确量化地表热量和水汽通量对于理解水平衡动态具有重要意义。本研究采用基于遥感的两源能量平衡模型（TSEB）估算了位于黑河流域中游的张掖绿洲 15m 空间分辨率湍流通量（H 和 LE）。TSEB 模型使用包括陆面温度（LST）和气温（T）在内的温度作为主要输入变量来计算湍流通量，但它们的空间分辨率相当有限。为了克服这一缺点，使用反向传播神经网络（BPNN）获得了 15m 空间分辨率的 LST 和 T。结果表明，BPNN 能够获得更精细的空间分辨率和 LST 和 T；LST 和 T 的均方根误差（RMSE）值分别为 1.99K 和 0.50K。TSEB 模型利用 BPNN 模拟的 LST 和 T 遥感预测 H 和 LE。结果表明，H 和 LE 与安装在多个站点并覆盖所有代表性土地覆盖类型的多套涡度相关（EC）系统的通量观测值吻合较好；特别是对于潜热通量，其估算值产生的平均绝对百分比误差（MAPE）分别为玉米 8.76%、蔬菜 20.17%、居民区 29.06%和果园 16.12%。本研究获得了比在张掖绿洲使用的其他遥感模型估算的更精细空间分辨率的地表热量和水汽通量。结合 TSEB 模型和 BPNN 的结果可以为制定可靠的可持续水资源管理方案和提高干旱半干旱地区的灌溉水利用效率提供更多信息。
