School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China; Ocean College, Zhejiang University, Zhoushan, 316021, China.
Ocean College, Zhejiang University, Zhoushan, 316021, China.
J Environ Manage. 2022 Jan 15;302(Pt B):114077. doi: 10.1016/j.jenvman.2021.114077. Epub 2021 Nov 10.
Diffuse solar radiation (Rd), known as an important component of global solar radiation (Rg), is a key parameter for solar energy related applications and ecosystem photosynthesis. Some meteorological models have been developed to estimate Rd with acceptable accuracy, but their spatial scales are often small due to the limited meteorological station number. Satellite-based models provide accurate and large-scale Rg estimates. However, remote sensing estimations of Rd are often with low spatial resolutions and large uncertainties, because their methods were based on inaccurate surface and atmospheric parameters. To address these challenges, the high-spatiotemporal-resolution (half-hourly and 1-km) Rd in the United States was estimated using the top-of-atmosphere (TOA) data from the new-generation geostationary satellite Geostationary Operational Environmental Satellites (GOES-16) and the method iterative random forest (RF). The results showed that the iterative RF model had higher accuracy than the simple RF and artificial neural network (ANN) models, and using TIR (thermal infrared) bands can improve models' accuracy. The best model can estimate half-hourly Rd with the accuracy R = 0.88, RMSE = 37.81 W/m, and MBE = 0.01 W/m. Compared with the previous 0.01-degree (∼11-km) Rd product Earth Polychromatic Imaging Camera (EPIC), the GOES-16 estimated 1-km Rd had similar spatial patterns. Moreover, based on the GOES-16 estimated half-hourly and 1-km Rd in the United States, the spatiotemporal heterogeneity of Rd was quantitatively observed. The proposed approach can be used to produce more high-spatiotemporal-resolution Rd products, and these products are very helpful for many solar-related research topics and industrial applications.
漫射太阳辐射（Rd），作为总太阳辐射（Rg）的重要组成部分，是太阳能相关应用和生态系统光合作用的关键参数。一些气象模型已经被开发出来，可以以可接受的精度来估算 Rd，但由于气象站数量有限，它们的空间尺度通常较小。基于卫星的模型可以提供准确的大尺度 Rg 估算。然而，Rd 的遥感估算通常具有较低的空间分辨率和较大的不确定性，因为它们的方法是基于不准确的地表和大气参数。为了解决这些挑战，使用新一代地球静止卫星地球静止业务环境卫星（GOES-16）的大气顶（TOA）数据和迭代随机森林（RF）方法，在美国估算了具有高时空分辨率（半小时和 1 公里）的 Rd。结果表明，迭代 RF 模型比简单 RF 和人工神经网络（ANN）模型具有更高的精度，并且使用 TIR（热红外）波段可以提高模型的精度。最佳模型可以以 R=0.88、RMSE=37.81 W/m 和 MBE=0.01 W/m 的精度估算半小时 Rd。与之前的 0.01 度（约 11 公里） Rd 产品地球多色成像相机（EPIC）相比，GOES-16 估算的 1 公里 Rd 具有相似的空间格局。此外，基于美国的 GOES-16 估算的半小时和 1 公里 Rd，定量观察了 Rd 的时空异质性。该方法可用于生成更多高时空分辨率的 Rd 产品，这些产品对许多与太阳能相关的研究课题和工业应用非常有帮助。
