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

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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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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recordid cdi_proquest_journals_2637441323
title Estimation of Daily All-Wave Surface Net Radiation With Multispectral and Multitemporal Observations From GOES-16 ABI
format Article
creator Chen, Jiang
He, Tao
Liang, Shunlin
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
ispartof IEEE transactions on geoscience and remote sensing, 2022, Vol.60, p.1-16
description 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
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fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2637441323</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>9668946</ieee_id><sourcerecordid>2637441323</sourcerecordid><originalsourceid>FETCH-LOGICAL-c245t-7650d889727141ec103e48eea67d4774db85a4e038aa866e57b9163187399e613</originalsourceid><addsrcrecordid>eNo9kF1LwzAUhoMoOD9-gHgT8LozJ0nzcTl1m8J0sClelqw9xUq3ziQV9u9trXh14OV53wMPIVfAxgDM3r7OV-sxZ5yPBUgmRHpERpCmJmFKymMyYmBVwo3lp-QshE_GQKagR6SdhlhtXayaHW1K-uCq-kAndZ28u2-k69aXLkf6gpGuXFEN3HsVP-hzW8cq7DGP3tXU7YohibjdN32y3AT037-FQGe-2dL5crpOQNHJ3dMFOSldHfDy756Tt9n09f4xWSznT_eTRZJzmcZEq5QVxljNNUjAHJhAaRCd0oXUWhYbkzqJTBjnjFKY6o0FJcBoYS0qEOfkZtjd--arxRCzz6b1u-5lxpXQUoLgoqNgoHLfhOCxzPa-c-IPGbCst5v1drPebvZnt-tcD50KEf95q5SxUokf_-F0ag</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><isCDI>true</isCDI><recordtype>article</recordtype><pqid>2637441323</pqid></control><display><type>article</type><title>Estimation of Daily All-Wave Surface Net Radiation With Multispectral and Multitemporal Observations From GOES-16 ABI</title><source>IEEE Electronic Library (IEL) Journals</source><creator>Chen, Jiang ; He, Tao ; Liang, Shunlin</creator><creatorcontrib>Chen, Jiang ; He, Tao ; Liang, Shunlin</creatorcontrib><description><![CDATA[As a vital parameter describing the Earth surface energy budget, surface all-wave net radiation (<inline-formula> <tex-math notation="LaTeX">R_{n} </tex-math></inline-formula>) drives many physical and biological processes. Remote estimation of <inline-formula> <tex-math notation="LaTeX">R_{n} </tex-math></inline-formula> using satellite data is an effective approach to monitor the spatial and temporal dynamics of <inline-formula> <tex-math notation="LaTeX">R_{n} </tex-math></inline-formula>. Accurate daily <inline-formula> <tex-math notation="LaTeX">R_{n} </tex-math></inline-formula> estimation typically depends on the spatio-temporal resolutions of satellite data. There are currently few high-spatial-resolution daily <inline-formula> <tex-math notation="LaTeX">R_{n} </tex-math></inline-formula> 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 <inline-formula> <tex-math notation="LaTeX">R_{n} </tex-math></inline-formula> data with improved spatial resolution and accuracy, an operational approach was proposed in this study to derive daily 1-km <inline-formula> <tex-math notation="LaTeX">R_{n} </tex-math></inline-formula>, 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 (<inline-formula> <tex-math notation="LaTeX">R_{ns} </tex-math></inline-formula>). Then, another RF model was developed to estimate the daily <inline-formula> <tex-math notation="LaTeX">R_{n} </tex-math></inline-formula> from <inline-formula> <tex-math notation="LaTeX">R_{ns} </tex-math></inline-formula>, 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 <inline-formula> <tex-math notation="LaTeX">R_{ns} </tex-math></inline-formula> estimation simple and efficient but also has high accuracy in estimating instantaneous all-sky <inline-formula> <tex-math notation="LaTeX">R_{ns} </tex-math></inline-formula>. Benefiting from high spatio-temporal resolutions, our daily <inline-formula> <tex-math notation="LaTeX">R_{ns} </tex-math></inline-formula> 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 <inline-formula> <tex-math notation="LaTeX">R_{ns} </tex-math></inline-formula> estimates and LRD as inputs, the EHM model shows reasonably good results for estimating <inline-formula> <tex-math notation="LaTeX">R_{n} </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">R^{2} </tex-math></inline-formula>, RMSE, and bias of 0.91, 20.95 W/m 2 , and −0.05 W/m 2 , respectively). Maps of 1-km <inline-formula> <tex-math notation="LaTeX">R_{ns} </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">R_{n} </tex-math></inline-formula> exhibit similar spatial patterns to those from the 1° CERES product, but with substantially more spatial details. Overall, the proposed <inline-formula> <tex-math notation="LaTeX">R_{n} </tex-math></inline-formula> retrieval scheme can accurately estimate all-sky 1-km <inline-formula> <tex-math notation="LaTeX">R_{ns} </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">R_{n} </tex-math></inline-formula> 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 <inline-formula> <tex-math notation="LaTeX">R_{n} </tex-math></inline-formula> using geostationary satellites with improved accuracy and resolutions.]]></description><identifier>ISSN: 0196-2892</identifier><identifier>EISSN: 1558-0644</identifier><identifier>DOI: 10.1109/TGRS.2022.3140335</identifier><identifier>CODEN: IGRSD2</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>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</subject><ispartof>IEEE transactions on geoscience and remote sensing, 2022, Vol.60, p.1-16</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c245t-7650d889727141ec103e48eea67d4774db85a4e038aa866e57b9163187399e613</cites><orcidid>0000-0003-2708-9183 ; 0000-0002-7631-2591 ; 0000-0003-2079-7988</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9668946$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>315,787,791,4046,27984,27985,27986,55496</link.rule.ids></links><search><creatorcontrib>Chen, Jiang</creatorcontrib><creatorcontrib>He, Tao</creatorcontrib><creatorcontrib>Liang, Shunlin</creatorcontrib><title>Estimation of Daily All-Wave Surface Net Radiation With Multispectral and Multitemporal Observations From GOES-16 ABI</title><title>IEEE transactions on geoscience and remote sensing</title><addtitle>TGRS</addtitle><description><![CDATA[As a vital parameter describing the Earth surface energy budget, surface all-wave net radiation (<inline-formula> <tex-math notation="LaTeX">R_{n} </tex-math></inline-formula>) drives many physical and biological processes. Remote estimation of <inline-formula> <tex-math notation="LaTeX">R_{n} </tex-math></inline-formula> using satellite data is an effective approach to monitor the spatial and temporal dynamics of <inline-formula> <tex-math notation="LaTeX">R_{n} </tex-math></inline-formula>. Accurate daily <inline-formula> <tex-math notation="LaTeX">R_{n} </tex-math></inline-formula> estimation typically depends on the spatio-temporal resolutions of satellite data. There are currently few high-spatial-resolution daily <inline-formula> <tex-math notation="LaTeX">R_{n} </tex-math></inline-formula> 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 <inline-formula> <tex-math notation="LaTeX">R_{n} </tex-math></inline-formula> data with improved spatial resolution and accuracy, an operational approach was proposed in this study to derive daily 1-km <inline-formula> <tex-math notation="LaTeX">R_{n} </tex-math></inline-formula>, 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 (<inline-formula> <tex-math notation="LaTeX">R_{ns} </tex-math></inline-formula>). Then, another RF model was developed to estimate the daily <inline-formula> <tex-math notation="LaTeX">R_{n} </tex-math></inline-formula> from <inline-formula> <tex-math notation="LaTeX">R_{ns} </tex-math></inline-formula>, 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 <inline-formula> <tex-math notation="LaTeX">R_{ns} </tex-math></inline-formula> estimation simple and efficient but also has high accuracy in estimating instantaneous all-sky <inline-formula> <tex-math notation="LaTeX">R_{ns} </tex-math></inline-formula>. Benefiting from high spatio-temporal resolutions, our daily <inline-formula> <tex-math notation="LaTeX">R_{ns} </tex-math></inline-formula> 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 <inline-formula> <tex-math notation="LaTeX">R_{ns} </tex-math></inline-formula> estimates and LRD as inputs, the EHM model shows reasonably good results for estimating <inline-formula> <tex-math notation="LaTeX">R_{n} </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">R^{2} </tex-math></inline-formula>, RMSE, and bias of 0.91, 20.95 W/m 2 , and −0.05 W/m 2 , respectively). Maps of 1-km <inline-formula> <tex-math notation="LaTeX">R_{ns} </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">R_{n} </tex-math></inline-formula> exhibit similar spatial patterns to those from the 1° CERES product, but with substantially more spatial details. Overall, the proposed <inline-formula> <tex-math notation="LaTeX">R_{n} </tex-math></inline-formula> retrieval scheme can accurately estimate all-sky 1-km <inline-formula> <tex-math notation="LaTeX">R_{ns} </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">R_{n} </tex-math></inline-formula> 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 <inline-formula> <tex-math notation="LaTeX">R_{n} </tex-math></inline-formula> using geostationary satellites with improved accuracy and resolutions.]]></description><subject>Accuracy</subject><subject>Albedo</subject><subject>Albedo (solar)</subject><subject>Algorithms</subject><subject>All-sky hybrid model (AHM)</subject><subject>Atmospheric modeling</subject><subject>Biological activity</subject><subject>Clouds</subject><subject>Daily</subject><subject>daily net radiation</subject><subject>Diurnal</subject><subject>Earth surface</subject><subject>Energy budget</subject><subject>Estimates</subject><subject>Estimation</subject><subject>extended hybrid model (EHM)</subject><subject>geostationary satellite</subject><subject>Geostationary satellites</subject><subject>GOES satellites</subject><subject>length ratio of daytime (LRD)</subject><subject>Lookup tables</subject><subject>Machine learning</subject><subject>Meteorological satellites</subject><subject>MODIS</subject><subject>Net radiation</subject><subject>Polar orbiting satellites</subject><subject>Radiation</subject><subject>Radiation balance</subject><subject>Radiative transfer</subject><subject>Remote sensing</subject><subject>Resolution</subject><subject>Satellite observation</subject><subject>Satellites</subject><subject>Short wave radiation</subject><subject>Sky</subject><subject>Spatial data</subject><subject>Spatial discrimination</subject><subject>Spatial resolution</subject><subject>Spectroradiometers</subject><subject>Surface energy</subject><subject>Surface properties</subject><subject>Synchronous satellites</subject><subject>Temporal resolution</subject><subject>Transfer learning</subject><issn>0196-2892</issn><issn>1558-0644</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNo9kF1LwzAUhoMoOD9-gHgT8LozJ0nzcTl1m8J0sClelqw9xUq3ziQV9u9trXh14OV53wMPIVfAxgDM3r7OV-sxZ5yPBUgmRHpERpCmJmFKymMyYmBVwo3lp-QshE_GQKagR6SdhlhtXayaHW1K-uCq-kAndZ28u2-k69aXLkf6gpGuXFEN3HsVP-hzW8cq7DGP3tXU7YohibjdN32y3AT037-FQGe-2dL5crpOQNHJ3dMFOSldHfDy756Tt9n09f4xWSznT_eTRZJzmcZEq5QVxljNNUjAHJhAaRCd0oXUWhYbkzqJTBjnjFKY6o0FJcBoYS0qEOfkZtjd--arxRCzz6b1u-5lxpXQUoLgoqNgoHLfhOCxzPa-c-IPGbCst5v1drPebvZnt-tcD50KEf95q5SxUokf_-F0ag</recordid><startdate>2022</startdate><enddate>2022</enddate><creator>Chen, Jiang</creator><creator>He, Tao</creator><creator>Liang, Shunlin</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H8D</scope><scope>H96</scope><scope>KR7</scope><scope>L.G</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0003-2708-9183</orcidid><orcidid>https://orcid.org/0000-0002-7631-2591</orcidid><orcidid>https://orcid.org/0000-0003-2079-7988</orcidid></search><sort><creationdate>2022</creationdate><title>Estimation of Daily All-Wave Surface Net Radiation With Multispectral and Multitemporal Observations From GOES-16 ABI</title><author>Chen, Jiang ; He, Tao ; Liang, Shunlin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c245t-7650d889727141ec103e48eea67d4774db85a4e038aa866e57b9163187399e613</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Accuracy</topic><topic>Albedo</topic><topic>Albedo (solar)</topic><topic>Algorithms</topic><topic>All-sky hybrid model (AHM)</topic><topic>Atmospheric modeling</topic><topic>Biological activity</topic><topic>Clouds</topic><topic>Daily</topic><topic>daily net radiation</topic><topic>Diurnal</topic><topic>Earth surface</topic><topic>Energy budget</topic><topic>Estimates</topic><topic>Estimation</topic><topic>extended hybrid model (EHM)</topic><topic>geostationary satellite</topic><topic>Geostationary satellites</topic><topic>GOES satellites</topic><topic>length ratio of daytime (LRD)</topic><topic>Lookup tables</topic><topic>Machine learning</topic><topic>Meteorological satellites</topic><topic>MODIS</topic><topic>Net radiation</topic><topic>Polar orbiting satellites</topic><topic>Radiation</topic><topic>Radiation balance</topic><topic>Radiative transfer</topic><topic>Remote sensing</topic><topic>Resolution</topic><topic>Satellite observation</topic><topic>Satellites</topic><topic>Short wave radiation</topic><topic>Sky</topic><topic>Spatial data</topic><topic>Spatial discrimination</topic><topic>Spatial resolution</topic><topic>Spectroradiometers</topic><topic>Surface energy</topic><topic>Surface properties</topic><topic>Synchronous satellites</topic><topic>Temporal resolution</topic><topic>Transfer learning</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chen, Jiang</creatorcontrib><creatorcontrib>He, Tao</creatorcontrib><creatorcontrib>Liang, Shunlin</creatorcontrib><collection>CrossRef</collection><collection>Water Resources Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Aquatic Science &amp; Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy &amp; Non-Living Resources</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science &amp; Fisheries Abstracts (ASFA) Professional</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE transactions on geoscience and remote sensing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chen, Jiang</au><au>He, Tao</au><au>Liang, Shunlin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Estimation of Daily All-Wave Surface Net Radiation With Multispectral and Multitemporal Observations From GOES-16 ABI</atitle><jtitle>IEEE transactions on geoscience and remote sensing</jtitle><stitle>TGRS</stitle><date>2022</date><risdate>2022</risdate><volume>60</volume><spage>1</spage><epage>16</epage><pages>1-16</pages><issn>0196-2892</issn><eissn>1558-0644</eissn><coden>IGRSD2</coden><abstract><![CDATA[As a vital parameter describing the Earth surface energy budget, surface all-wave net radiation (<inline-formula> <tex-math notation="LaTeX">R_{n} </tex-math></inline-formula>) drives many physical and biological processes. Remote estimation of <inline-formula> <tex-math notation="LaTeX">R_{n} </tex-math></inline-formula> using satellite data is an effective approach to monitor the spatial and temporal dynamics of <inline-formula> <tex-math notation="LaTeX">R_{n} </tex-math></inline-formula>. Accurate daily <inline-formula> <tex-math notation="LaTeX">R_{n} </tex-math></inline-formula> estimation typically depends on the spatio-temporal resolutions of satellite data. There are currently few high-spatial-resolution daily <inline-formula> <tex-math notation="LaTeX">R_{n} </tex-math></inline-formula> 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 <inline-formula> <tex-math notation="LaTeX">R_{n} </tex-math></inline-formula> data with improved spatial resolution and accuracy, an operational approach was proposed in this study to derive daily 1-km <inline-formula> <tex-math notation="LaTeX">R_{n} </tex-math></inline-formula>, 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 (<inline-formula> <tex-math notation="LaTeX">R_{ns} </tex-math></inline-formula>). Then, another RF model was developed to estimate the daily <inline-formula> <tex-math notation="LaTeX">R_{n} </tex-math></inline-formula> from <inline-formula> <tex-math notation="LaTeX">R_{ns} </tex-math></inline-formula>, 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 <inline-formula> <tex-math notation="LaTeX">R_{ns} </tex-math></inline-formula> estimation simple and efficient but also has high accuracy in estimating instantaneous all-sky <inline-formula> <tex-math notation="LaTeX">R_{ns} </tex-math></inline-formula>. Benefiting from high spatio-temporal resolutions, our daily <inline-formula> <tex-math notation="LaTeX">R_{ns} </tex-math></inline-formula> 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 <inline-formula> <tex-math notation="LaTeX">R_{ns} </tex-math></inline-formula> estimates and LRD as inputs, the EHM model shows reasonably good results for estimating <inline-formula> <tex-math notation="LaTeX">R_{n} </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">R^{2} </tex-math></inline-formula>, RMSE, and bias of 0.91, 20.95 W/m 2 , and −0.05 W/m 2 , respectively). Maps of 1-km <inline-formula> <tex-math notation="LaTeX">R_{ns} </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">R_{n} </tex-math></inline-formula> exhibit similar spatial patterns to those from the 1° CERES product, but with substantially more spatial details. Overall, the proposed <inline-formula> <tex-math notation="LaTeX">R_{n} </tex-math></inline-formula> retrieval scheme can accurately estimate all-sky 1-km <inline-formula> <tex-math notation="LaTeX">R_{ns} </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">R_{n} </tex-math></inline-formula> 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 <inline-formula> <tex-math notation="LaTeX">R_{n} </tex-math></inline-formula> using geostationary satellites with improved accuracy and resolutions.]]></abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TGRS.2022.3140335</doi><orcidid>https://orcid.org/0000-0003-2708-9183</orcidid><orcidid>https://orcid.org/0000-0002-7631-2591</orcidid><orcidid>https://orcid.org/0000-0003-2079-7988</orcidid></addata></record>