Estimation of Daily All-Wave Surface Net Radiation With Multispectral and Multitemporal Observations From GOES-16 ABI

As a vital parameter describing the Earth surface energy budget, surface all-wave net radiation ( R_{n} ) drives many physical and biological processes. Remote estimation of R_{n} using satellite data is an effective approach to monitor the spatial and temporal dynamics of R_{n} . Accurate daily...

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Bibliographic Details
Published in:	IEEE transactions on geoscience and remote sensing 2022, Vol.60, p.1-16
Main Authors:	Chen, Jiang, He, Tao, Liang, Shunlin
Format:	Article
Language:	eng
Subjects:	Accuracy Albedo Albedo (solar) Algorithms All-sky hybrid model (AHM) Atmospheric modeling Biological activity Clouds Daily daily net radiation Diurnal Earth surface Energy budget Estimates Estimation extended hybrid model (EHM) geostationary satellite Geostationary satellites GOES satellites length ratio of daytime (LRD) Lookup tables Machine learning Meteorological satellites MODIS Net radiation Polar orbiting satellites Radiation Radiation balance Radiative transfer Remote sensing Resolution Satellite observation Satellites Short wave radiation Sky Spatial data Spatial discrimination Spatial resolution Spectroradiometers Surface energy Surface properties Synchronous satellites Temporal resolution Transfer learning
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Summary:	As a vital parameter describing the Earth surface energy budget, surface all-wave net radiation ( R_{n} ) drives many physical and biological processes. Remote estimation of R_{n} using satellite data is an effective approach to monitor the spatial and temporal dynamics of R_{n} . Accurate daily R_{n} estimation typically depends on the spatio-temporal resolutions of satellite data. There are currently few high-spatial-resolution daily R_{n} products from polar-orbiting satellite data, and they exhibit limited accuracy due to sparse diurnal observations. In addition, traditional estimation approaches typically require cloud mask and clear-sky albedo as inputs and ignore the length ratio of daytime (LRD), which may lead to large errors. To overcome these challenges and obtain R_{n} data with improved spatial resolution and accuracy, an operational approach was proposed in this study to derive daily 1-km R_{n} , which takes the advantages from a radiative transfer model, a machine learning algorithm, and multispectral and dense diurnal temporal information of geostationary satellite observations. An improved all-sky hybrid model (AHM) coupling radiative transfer simulations with a random forest (RF) model was first developed to estimate the shortwave net radiation ( R_{ns} ). Then, another RF model was developed to estimate the daily R_{n} from R_{ns} , incorporating the LRD, which is called extended hybrid model (EHM). Data from the Advanced Baseline Imager (ABI) onboard the new-generation Geostationary Operational Environmental Satellite (GOES)-16 with a 5-min temporal resolution and a 1-km spatial resolution were used to test the proposed method. Compared to traditional lookup table (LUT) algorithms, the results show that AHM not only makes the p
ISSN:	0196-2892 1558-0644