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
$R_{n}$
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 process of
$R_{ns}$
estimation simple and efficient but also has high accuracy in estimating instantaneous all-sky
$R_{ns}$
. Benefiting from high spatio-temporal resolutions, our daily
$R_{ns}$
estimates using GOSE-16 data exhibited superior performance compared to using the 1-km Moderate Resolution Imaging Spectroradiometer (MODIS) and 1° Clouds and the Earth’s Radiant Energy System (CERES) product. Using accurate daily
$R_{ns}$
estimates and LRD as inputs, the EHM model shows reasonably good results for estimating
$R_{n}$
(
$R^{2}$
, RMSE, and bias of 0.91, 20.95 W/m
²
, and −0.05 W/m
²
, respectively). Maps of 1-km
$R_{ns}$
and
$R_{n}$
exhibit similar spatial patterns to those from the 1° CERES product, but with substantially more spatial details. Overall, the proposed
$R_{n}$
retrieval scheme can accurately estimate all-sky 1-km
$R_{ns}$
and
$R_{n}$
at mid- to low-latitudes and can be easily adapted and applied to other GOES- 16-like satellites, such as Himawari-8, Meteosat Third Generation (MTG), and Fenyun-4. This study demonstrates the advantages of estimating
$R_{n}$
using geostationary satellites with improved accuracy and resolutions.