气候变化领域集成服务门户(CCPortal): Estimation of all-sky all-wave daily net radiation at high latitudes from MODIS data

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DOI	10.1016/j.rse.2020.111842
	Estimation of all-sky all-wave daily net radiation at high latitudes from MODIS data
	Chen J.; He T.; Jiang B.; Liang S.
发表日期	2020
ISSN	00344257
卷号	245
英文摘要	Surface all-wave net radiation (Rn) plays an important role in various land surface processes, such as agricultural, ecological, hydrological, and biogeochemical processes. Recently, remote sensing of Rn 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 Rn 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 Rn at high latitudes. With a very high temporal repeating frequency (~6 to 20 times per day) at high latitudes, data from the Moderate Resolution Imaging Spectroradiometer (MODIS) were used to test the proposed method. Rn 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 Rn 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 R2 of 0.85, an RMSE of 23.66 W/m2, and a bias of 0.27 W/m2, whereas these values were 0.51, 15.04 W/m2, and −0.08 W/m2 for model II, respectively. Overall, the proposed machine learning algorithm with the LRD classification can accurately estimate the all-sky daily average Rn at high latitudes. Mapping of Rn over the high latitudes at 1 km spatial resolution showed a similar spatial distribution to Rn 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 Rn at high latitudes with a long-term coverage of MODIS observations. © 2020 The Authors
英文关键词	High latitudes; High spatial resolution; Length ratio of daytime; MODIS; Net radiation
语种	英语
scopus关键词	Agricultural robots; Genetic algorithms; Learning algorithms; Machine learning; Neural networks; Remote sensing; Uncertainty analysis; Biogeochemical process; Classification models; Clouds and the Earth's radiant energy systems; Land-surface process; Moderate resolution imaging spectroradiometer; Operational monitoring; Spatio-temporal changes; Temperature parameters; Radiometers; algorithm; clear sky; estimation method; latitude; latitudinal gradient; machine learning; model validation; MODIS; net radiation; remote sensing; satellite data; spatial distribution; top of atmosphere
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179311
作者单位	School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China; Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China; Department of Geographical Sciences, University of Maryland, College ParkMD 20742, United States
推荐引用方式 GB/T 7714	Chen J.,He T.,Jiang B.,et al. Estimation of all-sky all-wave daily net radiation at high latitudes from MODIS data[J],2020,245.
APA	Chen J.,He T.,Jiang B.,&Liang S..(2020).Estimation of all-sky all-wave daily net radiation at high latitudes from MODIS data.Remote Sensing of Environment,245.
MLA	Chen J.,et al."Estimation of all-sky all-wave daily net radiation at high latitudes from MODIS data".Remote Sensing of Environment 245(2020).