Satellite-derived LAI products exhibit large discrepancies and can lead to substantial uncertainty in simulated carbon and water fluxes
Understanding the terrestrial carbon and water cycles is crucial for mitigation and adaptation for climate change. Leaf area index (LAI) is a key biophysical parameter in process-based ecosystem models for simulating gross primary productivity (GPP) and evapotranspiration (ET). The uncertainty in sa...
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| Veröffentlicht in: | Remote sensing of environment 2018-03, Vol.206, p.174-188 |
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| creator | Liu, Yibo Xiao, Jingfeng Ju, Weimin Zhu, Gaolong Wu, Xiaocui Fan, Weiliang Li, Dengqiu Zhou, Yanlian |
| description | Understanding the terrestrial carbon and water cycles is crucial for mitigation and adaptation for climate change. Leaf area index (LAI) is a key biophysical parameter in process-based ecosystem models for simulating gross primary productivity (GPP) and evapotranspiration (ET). The uncertainty in satellite-derived LAI products and their effects on the simulation of carbon and water fluxes at regional scales remain unclear. We evaluated three satellite-derived LAI products - MODIS (MCD15), GLASS, and Four-Scale Geometric Optical Model based LAI (FSGOM) over the period 2003–2012 using fine-resolution (30m) LAI data and field LAI measurements. GLASS had higher accuracy than FSGOM and MCD15 for forests, while FSGOM had higher accuracy than MCD15 and GLASS for grasslands. The three LAI products differed in magnitude, spatial patterns, and trends in LAI. We then examined the resulting discrepancies in simulated annual GPP and ET over China using a process-based, diagnostic terrestrial biosphere model. Mean annual total GPP for China's terrestrial ecosystems based on GLASS (6.32PgCyr−1) and FSGOM (6.15PgCyr−1) was 22.5% and 19.2% higher than that based on MCD15 (5.16PgCyr−1), respectively. Annual GPP based on GLASS and MCD15 increased over larger fractions of China's vegetated area (15.9% and 17.3%, respectively) than that based on FSGOM (12.6%). National annual ET based on GLASS (379.9mmyr−1) and FSGOM (374.4mmyr−1) was 7.9% and 6.3% higher than that based on MCD15 (352.1mmyr−1), respectively. Simulated ET increased in larger fractions of the vegetated area for GLASS (5.7%) and MCD15 (5.8%) than for FSGOM (3.9%). Our study shows that there were large discrepancies in LAI among satellite-derived LAI products and the biases of the LAI products could lead to substantial uncertainties in simulated carbon and water fluxes.
•Satellite-derived LAI products (MCD15, GLASS, FSGOM) exhibited large discrepancies.•These LAI products had substantial differences in magnitude, patterns, and trends.•These LAI products led to large uncertainty and discrepancies in modeled GPP and ET.•GLASS and FSGOM led to in much higher annual GPP and ET estimates compared to MCD15.•More accurate LAI products will improve regional carbon and water flux simulations. |
| doi_str_mv | 10.1016/j.rse.2017.12.024 |
| format | Article |
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•Satellite-derived LAI products (MCD15, GLASS, FSGOM) exhibited large discrepancies.•These LAI products had substantial differences in magnitude, patterns, and trends.•These LAI products led to large uncertainty and discrepancies in modeled GPP and ET.•GLASS and FSGOM led to in much higher annual GPP and ET estimates compared to MCD15.•More accurate LAI products will improve regional carbon and water flux simulations.</description><identifier>ISSN: 0034-4257</identifier><identifier>EISSN: 1879-0704</identifier><identifier>DOI: 10.1016/j.rse.2017.12.024</identifier><language>eng</language><publisher>New York: Elsevier Inc</publisher><subject>Atmospheric models ; Biosphere ; Carbon ; Carbon cycle ; Climate change ; Climatic indexes ; Computer simulation ; Diagnostic systems ; Ecosystem model ; Ecosystem models ; Environment models ; Environmental changes ; Environmental monitoring ; Evapotranspiration ; Fluxes ; Four-scale geometric optical model ; Glass ; Grasslands ; Gross primary productivity ; Hydrologic cycle ; Leaf area ; Leaf area index ; Mitigation ; MODIS ; Process parameters ; Regional analysis ; Satellites ; Terrestrial ecosystems ; Terrestrial environments ; Uncertainty</subject><ispartof>Remote sensing of environment, 2018-03, Vol.206, p.174-188</ispartof><rights>2017 Elsevier Inc.</rights><rights>Copyright Elsevier BV Mar 1, 2018</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c473t-e688cafa99f6ddef320d743fa5392a76727d49324cbcf15bec7befce3c4cd583</citedby><cites>FETCH-LOGICAL-c473t-e688cafa99f6ddef320d743fa5392a76727d49324cbcf15bec7befce3c4cd583</cites><orcidid>0000-0002-4345-0138</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.rse.2017.12.024$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3536,27903,27904,45974</link.rule.ids></links><search><creatorcontrib>Liu, Yibo</creatorcontrib><creatorcontrib>Xiao, Jingfeng</creatorcontrib><creatorcontrib>Ju, Weimin</creatorcontrib><creatorcontrib>Zhu, Gaolong</creatorcontrib><creatorcontrib>Wu, Xiaocui</creatorcontrib><creatorcontrib>Fan, Weiliang</creatorcontrib><creatorcontrib>Li, Dengqiu</creatorcontrib><creatorcontrib>Zhou, Yanlian</creatorcontrib><title>Satellite-derived LAI products exhibit large discrepancies and can lead to substantial uncertainty in simulated carbon and water fluxes</title><title>Remote sensing of environment</title><description>Understanding the terrestrial carbon and water cycles is crucial for mitigation and adaptation for climate change. Leaf area index (LAI) is a key biophysical parameter in process-based ecosystem models for simulating gross primary productivity (GPP) and evapotranspiration (ET). The uncertainty in satellite-derived LAI products and their effects on the simulation of carbon and water fluxes at regional scales remain unclear. We evaluated three satellite-derived LAI products - MODIS (MCD15), GLASS, and Four-Scale Geometric Optical Model based LAI (FSGOM) over the period 2003–2012 using fine-resolution (30m) LAI data and field LAI measurements. GLASS had higher accuracy than FSGOM and MCD15 for forests, while FSGOM had higher accuracy than MCD15 and GLASS for grasslands. The three LAI products differed in magnitude, spatial patterns, and trends in LAI. We then examined the resulting discrepancies in simulated annual GPP and ET over China using a process-based, diagnostic terrestrial biosphere model. Mean annual total GPP for China's terrestrial ecosystems based on GLASS (6.32PgCyr−1) and FSGOM (6.15PgCyr−1) was 22.5% and 19.2% higher than that based on MCD15 (5.16PgCyr−1), respectively. Annual GPP based on GLASS and MCD15 increased over larger fractions of China's vegetated area (15.9% and 17.3%, respectively) than that based on FSGOM (12.6%). National annual ET based on GLASS (379.9mmyr−1) and FSGOM (374.4mmyr−1) was 7.9% and 6.3% higher than that based on MCD15 (352.1mmyr−1), respectively. Simulated ET increased in larger fractions of the vegetated area for GLASS (5.7%) and MCD15 (5.8%) than for FSGOM (3.9%). Our study shows that there were large discrepancies in LAI among satellite-derived LAI products and the biases of the LAI products could lead to substantial uncertainties in simulated carbon and water fluxes.
•Satellite-derived LAI products (MCD15, GLASS, FSGOM) exhibited large discrepancies.•These LAI products had substantial differences in magnitude, patterns, and trends.•These LAI products led to large uncertainty and discrepancies in modeled GPP and ET.•GLASS and FSGOM led to in much higher annual GPP and ET estimates compared to MCD15.•More accurate LAI products will improve regional carbon and water flux simulations.</description><subject>Atmospheric models</subject><subject>Biosphere</subject><subject>Carbon</subject><subject>Carbon cycle</subject><subject>Climate change</subject><subject>Climatic indexes</subject><subject>Computer simulation</subject><subject>Diagnostic systems</subject><subject>Ecosystem model</subject><subject>Ecosystem models</subject><subject>Environment models</subject><subject>Environmental changes</subject><subject>Environmental monitoring</subject><subject>Evapotranspiration</subject><subject>Fluxes</subject><subject>Four-scale geometric optical model</subject><subject>Glass</subject><subject>Grasslands</subject><subject>Gross primary productivity</subject><subject>Hydrologic cycle</subject><subject>Leaf area</subject><subject>Leaf area index</subject><subject>Mitigation</subject><subject>MODIS</subject><subject>Process parameters</subject><subject>Regional analysis</subject><subject>Satellites</subject><subject>Terrestrial ecosystems</subject><subject>Terrestrial environments</subject><subject>Uncertainty</subject><issn>0034-4257</issn><issn>1879-0704</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNp9kE1LxDAQhoMouK7-AG8Bz635atPiScQvWPCg95AmE81S0zVJ_fgF_m2zrmdPA8M88848CJ1SUlNC2_N1HRPUjFBZU1YTJvbQgnayr4gkYh8tCOGiEqyRh-gopTUhtOkkXaDvR51hHH2GykL072Dx6vIeb-JkZ5MThs8XP_iMRx2fAVufTISNDsZDwjpYbHTAI2iL84TTPKSsQ_Z6xHMwELP2IX9hH3Dyr_NYkrZAHKbwy36URsRunD8hHaMDp8cEJ391iZ5urp-u7qrVw-391eWqMkLyXEHbdUY73feutRYcZ8RKwZ1ueM-0bCWTVvScCTMYR5sBjBzAGeBGGNt0fInOdmvLg28zpKzW0xxDSVSMtLxraSubMkV3UyZOKUVwahP9q45fihK11a3WquhWW92KMlV0F-Zix0C5_t1DVKlIKhasj2CyspP_h_4BErCL8g</recordid><startdate>20180301</startdate><enddate>20180301</enddate><creator>Liu, Yibo</creator><creator>Xiao, Jingfeng</creator><creator>Ju, Weimin</creator><creator>Zhu, Gaolong</creator><creator>Wu, Xiaocui</creator><creator>Fan, Weiliang</creator><creator>Li, Dengqiu</creator><creator>Zhou, Yanlian</creator><general>Elsevier Inc</general><general>Elsevier BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SN</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7TG</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>C1K</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>JG9</scope><scope>JQ2</scope><scope>KL.</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><orcidid>https://orcid.org/0000-0002-4345-0138</orcidid></search><sort><creationdate>20180301</creationdate><title>Satellite-derived LAI products exhibit large discrepancies and can lead to substantial uncertainty in simulated carbon and water fluxes</title><author>Liu, Yibo ; Xiao, Jingfeng ; Ju, Weimin ; Zhu, Gaolong ; Wu, Xiaocui ; Fan, Weiliang ; Li, Dengqiu ; Zhou, Yanlian</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c473t-e688cafa99f6ddef320d743fa5392a76727d49324cbcf15bec7befce3c4cd583</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Atmospheric models</topic><topic>Biosphere</topic><topic>Carbon</topic><topic>Carbon cycle</topic><topic>Climate change</topic><topic>Climatic indexes</topic><topic>Computer simulation</topic><topic>Diagnostic systems</topic><topic>Ecosystem model</topic><topic>Ecosystem models</topic><topic>Environment models</topic><topic>Environmental changes</topic><topic>Environmental monitoring</topic><topic>Evapotranspiration</topic><topic>Fluxes</topic><topic>Four-scale geometric optical model</topic><topic>Glass</topic><topic>Grasslands</topic><topic>Gross primary productivity</topic><topic>Hydrologic cycle</topic><topic>Leaf area</topic><topic>Leaf area index</topic><topic>Mitigation</topic><topic>MODIS</topic><topic>Process parameters</topic><topic>Regional analysis</topic><topic>Satellites</topic><topic>Terrestrial ecosystems</topic><topic>Terrestrial environments</topic><topic>Uncertainty</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Yibo</creatorcontrib><creatorcontrib>Xiao, Jingfeng</creatorcontrib><creatorcontrib>Ju, Weimin</creatorcontrib><creatorcontrib>Zhu, Gaolong</creatorcontrib><creatorcontrib>Wu, Xiaocui</creatorcontrib><creatorcontrib>Fan, Weiliang</creatorcontrib><creatorcontrib>Li, Dengqiu</creatorcontrib><creatorcontrib>Zhou, Yanlian</creatorcontrib><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Ecology Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Remote sensing of environment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Yibo</au><au>Xiao, Jingfeng</au><au>Ju, Weimin</au><au>Zhu, Gaolong</au><au>Wu, Xiaocui</au><au>Fan, Weiliang</au><au>Li, Dengqiu</au><au>Zhou, Yanlian</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Satellite-derived LAI products exhibit large discrepancies and can lead to substantial uncertainty in simulated carbon and water fluxes</atitle><jtitle>Remote sensing of environment</jtitle><date>2018-03-01</date><risdate>2018</risdate><volume>206</volume><spage>174</spage><epage>188</epage><pages>174-188</pages><issn>0034-4257</issn><eissn>1879-0704</eissn><abstract>Understanding the terrestrial carbon and water cycles is crucial for mitigation and adaptation for climate change. Leaf area index (LAI) is a key biophysical parameter in process-based ecosystem models for simulating gross primary productivity (GPP) and evapotranspiration (ET). The uncertainty in satellite-derived LAI products and their effects on the simulation of carbon and water fluxes at regional scales remain unclear. We evaluated three satellite-derived LAI products - MODIS (MCD15), GLASS, and Four-Scale Geometric Optical Model based LAI (FSGOM) over the period 2003–2012 using fine-resolution (30m) LAI data and field LAI measurements. GLASS had higher accuracy than FSGOM and MCD15 for forests, while FSGOM had higher accuracy than MCD15 and GLASS for grasslands. The three LAI products differed in magnitude, spatial patterns, and trends in LAI. We then examined the resulting discrepancies in simulated annual GPP and ET over China using a process-based, diagnostic terrestrial biosphere model. Mean annual total GPP for China's terrestrial ecosystems based on GLASS (6.32PgCyr−1) and FSGOM (6.15PgCyr−1) was 22.5% and 19.2% higher than that based on MCD15 (5.16PgCyr−1), respectively. Annual GPP based on GLASS and MCD15 increased over larger fractions of China's vegetated area (15.9% and 17.3%, respectively) than that based on FSGOM (12.6%). National annual ET based on GLASS (379.9mmyr−1) and FSGOM (374.4mmyr−1) was 7.9% and 6.3% higher than that based on MCD15 (352.1mmyr−1), respectively. Simulated ET increased in larger fractions of the vegetated area for GLASS (5.7%) and MCD15 (5.8%) than for FSGOM (3.9%). Our study shows that there were large discrepancies in LAI among satellite-derived LAI products and the biases of the LAI products could lead to substantial uncertainties in simulated carbon and water fluxes.
•Satellite-derived LAI products (MCD15, GLASS, FSGOM) exhibited large discrepancies.•These LAI products had substantial differences in magnitude, patterns, and trends.•These LAI products led to large uncertainty and discrepancies in modeled GPP and ET.•GLASS and FSGOM led to in much higher annual GPP and ET estimates compared to MCD15.•More accurate LAI products will improve regional carbon and water flux simulations.</abstract><cop>New York</cop><pub>Elsevier Inc</pub><doi>10.1016/j.rse.2017.12.024</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0002-4345-0138</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Atmospheric models Biosphere Carbon Carbon cycle Climate change Climatic indexes Computer simulation Diagnostic systems Ecosystem model Ecosystem models Environment models Environmental changes Environmental monitoring Evapotranspiration Fluxes Four-scale geometric optical model Glass Grasslands Gross primary productivity Hydrologic cycle Leaf area Leaf area index Mitigation MODIS Process parameters Regional analysis Satellites Terrestrial ecosystems Terrestrial environments Uncertainty |
| title | Satellite-derived LAI products exhibit large discrepancies and can lead to substantial uncertainty in simulated carbon and water fluxes |
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