Estimation of all-sky all-wave daily net radiation at high latitudes from MODIS data

REMOTE SENSING OF ENVIRONMENT

卷:245

Article

Chen, Jiang¹  He, Tao¹  Jiang, Bo²  Liang, Shunlin³ 

[1] Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Peoples R China

[2] Beijing Normal Univ, Fac Geog Sci, Beijing 100875, Peoples R China

[3] Univ Maryland, Dept Geog Sci, College Pk, MD 20742 USA

关键词: Net radiation;    High latitudes;    Length ratio of daytime;    MODIS;    High spatial resolution;   

DOI  :  10.1016/j.rse.2020.111842

来源: Elsevier

【 摘 要 】

Surface all-wave net radiation (R-n) plays an important role in various land surface processes, such as agricultural, ecological, hydrological, and biogeochemical processes. Recently, remote sensing of R-n at regional and global scales has attracted considerable attention and has achieved significant advances. However, there are many issues in estimating all-sky daily average R-n at high latitudes, such as posing greater uncertainty by surface and atmosphere satellite products at high latitudes, and unavailability of real-time and accurate cloud base height and temperature parameters. In this study, we developed the LRD (length ratio of daytime) classification model using the genetic algorithm-artificial neural network (GA-ANN) to estimate all-sky daily average R-n at high latitudes. With a very high temporal repeating frequency (similar to 6 to 20 times per day) at high latitudes, data from the Moderate Resolution Imaging Spectroradiometer (MODIS) were used to test the proposed method. R-n measurements at 82 sites and top-of-atmosphere (TOA) data of MODIS from 2000 to 2017 were matched for model training and validation. Two models for estimating daily average R-n were developed: model I based on instantaneous daytime MODIS observation and model II based on instantaneous nighttime MODIS observation. Validation results of model I showed an R-2 of 0.85, an RMSE of 23.66 W/m(2), and a bias of 0.27 W/m(2), whereas these values were 0.51, 15.04 W/m(2), and -0.08 W/m(2) for model II, respectively. Overall, the proposed machine learning algorithm with the LRD classification can accurately estimate the all-sky daily average R-n at high latitudes. Mapping of R-n over the high latitudes at 1 km spatial resolution showed a similar spatial distribution to R-n estimates from the Clouds and the Earth's Radiant Energy System (CERES) product. This method has the potential for operational monitoring of spatio-temporal change of R-n at high latitudes with a long-term coverage of MODIS observations.

【 授权许可】

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