The effect of pixel heterogeneity for remote sensing based retrievals of evapotranspiration in a semi-arid tree-grass ecosystem
Many satellite missions rely on modeling approaches to acquire global or regional evapotranspiration (ET) products. However, a current challenge in ET modeling lies in dealing with sub-pixel heterogeneity, as models often assume homogeneous conditions at the pixel level. This is particularly an issu...
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| description | Many satellite missions rely on modeling approaches to acquire global or regional evapotranspiration (ET) products. However, a current challenge in ET modeling lies in dealing with sub-pixel heterogeneity, as models often assume homogeneous conditions at the pixel level. This is particularly an issue for heterogeneous landscapes, such as tree-grass ecosystems (TGE). In these areas, while appearing homogeneous at larger spatial scales pertaining to a single land cover type, the separation of the spectral signals of the main landscape features (e.g. trees and grasses) may not be achieved at the conventional satellite sensor resolution (e.g. 10–1000 m). This leads to important heterogeneity within the pixel grid that may not be accounted for in traditional modeling frameworks. This study examined the effect of pixel heterogeneity on ET simulations over a complex TGE in central Spain. High resolution hyperspectral imagery from five airborne campaigns forced the two-source energy balance (TSEB) model at 1.5–1000 m spatial resolutions. Along with this, the sharpened (20 m) and original (1000 m) Sentinels for Evapotranspiration (Sen-ET) products were evaluated over the study site for 2017. Results indicated that TSEB accurately simulated ET (RMSD: ~60 W/m2) when the pixel scale was able to robustly discriminate between grass and tree pixels ( |
| doi_str_mv | 10.1016/j.rse.2021.112440 |
| format | Article |
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•Energy balance models have important scaling effects in tree-grass ecosystems.•ET was well modeled when pixels could separate tree and grass elements (<5 m).•Errors vastly increased at grid sizes >10 m, but remained constant from 30 to 1000 m.•Errors at coarser scales linked to poor estimation of roughness due to pixel mixing.•TSEB-2S improved ET estimations for coarser (>30 m) model runs.</description><identifier>ISSN: 0034-4257</identifier><identifier>EISSN: 1879-0704</identifier><identifier>DOI: 10.1016/j.rse.2021.112440</identifier><language>eng</language><publisher>New York: Elsevier Inc</publisher><subject>Aerodynamics ; Airborne imagery ; Aridity ; Energy balance ; Environment models ; Evapotranspiration ; Fluxes ; Grasses ; Heterogeneity ; Hyperspectral imaging ; Image resolution ; Land cover ; Land surface temperature ; Land use ; Latent heat flux ; Pixels ; Remote sensing ; Satellites ; Sensible heat flux ; Sentinels for evapotranspiration ; Spatial discrimination ; Spatial resolution ; Surface energy balance ; Surface roughness ; Tree-grass ecosystems ; Turbulent fluxes ; Uncertainty</subject><ispartof>Remote sensing of environment, 2021-07, Vol.260, p.112440, Article 112440</ispartof><rights>2021 The Authors</rights><rights>Copyright Elsevier BV Jul 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c368t-4f9c45d7a7093c4961d50cacf16a605300884c3d5e2273f67195d3baaa382d633</citedby><cites>FETCH-LOGICAL-c368t-4f9c45d7a7093c4961d50cacf16a605300884c3d5e2273f67195d3baaa382d633</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0034425721001589$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids></links><search><creatorcontrib>Burchard-Levine, Vicente</creatorcontrib><creatorcontrib>Nieto, Héctor</creatorcontrib><creatorcontrib>Riaño, David</creatorcontrib><creatorcontrib>Migliavacca, Mirco</creatorcontrib><creatorcontrib>El-Madany, Tarek S.</creatorcontrib><creatorcontrib>Guzinski, Radoslaw</creatorcontrib><creatorcontrib>Carrara, Arnaud</creatorcontrib><creatorcontrib>Martín, M. Pilar</creatorcontrib><title>The effect of pixel heterogeneity for remote sensing based retrievals of evapotranspiration in a semi-arid tree-grass ecosystem</title><title>Remote sensing of environment</title><description>Many satellite missions rely on modeling approaches to acquire global or regional evapotranspiration (ET) products. However, a current challenge in ET modeling lies in dealing with sub-pixel heterogeneity, as models often assume homogeneous conditions at the pixel level. This is particularly an issue for heterogeneous landscapes, such as tree-grass ecosystems (TGE). In these areas, while appearing homogeneous at larger spatial scales pertaining to a single land cover type, the separation of the spectral signals of the main landscape features (e.g. trees and grasses) may not be achieved at the conventional satellite sensor resolution (e.g. 10–1000 m). This leads to important heterogeneity within the pixel grid that may not be accounted for in traditional modeling frameworks. This study examined the effect of pixel heterogeneity on ET simulations over a complex TGE in central Spain. High resolution hyperspectral imagery from five airborne campaigns forced the two-source energy balance (TSEB) model at 1.5–1000 m spatial resolutions. Along with this, the sharpened (20 m) and original (1000 m) Sentinels for Evapotranspiration (Sen-ET) products were evaluated over the study site for 2017. Results indicated that TSEB accurately simulated ET (RMSD: ~60 W/m2) when the pixel scale was able to robustly discriminate between grass and tree pixels (<5 m). However, model uncertainty drastically increased at spatial resolution greater than 10 m (RMSD: ~115 W/m2). Model performance remains relatively constant between 30 and 1000 m spatial resolutions, with within pixel heterogeneity being similar at all these scales. For mixed pixels (≥30 m), forcing an effective landscape roughness into TSEB (RMSD: ~80 W/m2) or applying a seasonally changing TSEB (TSEB-2S; RMSD: ~65 W/m2) improved the modeling performance. The Sen-ET products behaved similarly at both scales with RMSD of ET roughly 80 W/m2. The non-linear relationship between input parameters and flux output, along with the poor representation of aerodynamic surface roughness, were the main drivers for the increased uncertainties at coarser scales. These results suggest that care should be taken when using global ET products over TGE and similarly heterogeneous landscapes. The modeling procedure should inherently account for the presence of vastly different vegetation roughness elements within the pixel, to achieve reliable estimates of turbulent fluxes over a TGE.
•Energy balance models have important scaling effects in tree-grass ecosystems.•ET was well modeled when pixels could separate tree and grass elements (<5 m).•Errors vastly increased at grid sizes >10 m, but remained constant from 30 to 1000 m.•Errors at coarser scales linked to poor estimation of roughness due to pixel mixing.•TSEB-2S improved ET estimations for coarser (>30 m) model runs.</description><subject>Aerodynamics</subject><subject>Airborne imagery</subject><subject>Aridity</subject><subject>Energy balance</subject><subject>Environment models</subject><subject>Evapotranspiration</subject><subject>Fluxes</subject><subject>Grasses</subject><subject>Heterogeneity</subject><subject>Hyperspectral imaging</subject><subject>Image resolution</subject><subject>Land cover</subject><subject>Land surface temperature</subject><subject>Land use</subject><subject>Latent heat flux</subject><subject>Pixels</subject><subject>Remote sensing</subject><subject>Satellites</subject><subject>Sensible heat flux</subject><subject>Sentinels for evapotranspiration</subject><subject>Spatial discrimination</subject><subject>Spatial resolution</subject><subject>Surface energy balance</subject><subject>Surface roughness</subject><subject>Tree-grass ecosystems</subject><subject>Turbulent fluxes</subject><subject>Uncertainty</subject><issn>0034-4257</issn><issn>1879-0704</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp9kMtKQzEQhoMoWC8P4C7g-tTczg1XIt6g4KauQ5pM2pT25DhJxa58dVPq2tUMw__NDB8hN5xNOePN3XqKCaaCCT7lXCjFTsiEd21fsZapUzJhTKpKibo9JxcprRnjddfyCfmZr4CC92AzjZ6O4Rs2dAUZMC5hgJD31EekCNuYgSYYUhiWdGESuDLMGODLbNIBLc0YM5ohjQFNDnGgYaCmMNtQGQyOZgSolmhSomBj2qcM2yty5ssCuP6rl-Tj-Wn--FrN3l_eHh9mlZVNlyvle6tq15qW9dKqvuGuZtZYzxvTsFoy1nXKSleDEK30Tcv72smFMUZ2wjVSXpLb494R4-cOUtbruMOhnNSiVko0fS95SfFjymJMCcHrEcPW4F5zpg-e9VoXz_rgWR89F-b-yEB5_ysA6mQDDBZcwGJVuxj-oX8Br5uHVA</recordid><startdate>202107</startdate><enddate>202107</enddate><creator>Burchard-Levine, Vicente</creator><creator>Nieto, Héctor</creator><creator>Riaño, David</creator><creator>Migliavacca, Mirco</creator><creator>El-Madany, Tarek S.</creator><creator>Guzinski, Radoslaw</creator><creator>Carrara, Arnaud</creator><creator>Martín, M. Pilar</creator><general>Elsevier Inc</general><general>Elsevier BV</general><scope>6I.</scope><scope>AAFTH</scope><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></search><sort><creationdate>202107</creationdate><title>The effect of pixel heterogeneity for remote sensing based retrievals of evapotranspiration in a semi-arid tree-grass ecosystem</title><author>Burchard-Levine, Vicente ; Nieto, Héctor ; Riaño, David ; Migliavacca, Mirco ; El-Madany, Tarek S. ; Guzinski, Radoslaw ; Carrara, Arnaud ; Martín, M. 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Pilar</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The effect of pixel heterogeneity for remote sensing based retrievals of evapotranspiration in a semi-arid tree-grass ecosystem</atitle><jtitle>Remote sensing of environment</jtitle><date>2021-07</date><risdate>2021</risdate><volume>260</volume><spage>112440</spage><pages>112440-</pages><artnum>112440</artnum><issn>0034-4257</issn><eissn>1879-0704</eissn><abstract>Many satellite missions rely on modeling approaches to acquire global or regional evapotranspiration (ET) products. However, a current challenge in ET modeling lies in dealing with sub-pixel heterogeneity, as models often assume homogeneous conditions at the pixel level. This is particularly an issue for heterogeneous landscapes, such as tree-grass ecosystems (TGE). In these areas, while appearing homogeneous at larger spatial scales pertaining to a single land cover type, the separation of the spectral signals of the main landscape features (e.g. trees and grasses) may not be achieved at the conventional satellite sensor resolution (e.g. 10–1000 m). This leads to important heterogeneity within the pixel grid that may not be accounted for in traditional modeling frameworks. This study examined the effect of pixel heterogeneity on ET simulations over a complex TGE in central Spain. High resolution hyperspectral imagery from five airborne campaigns forced the two-source energy balance (TSEB) model at 1.5–1000 m spatial resolutions. Along with this, the sharpened (20 m) and original (1000 m) Sentinels for Evapotranspiration (Sen-ET) products were evaluated over the study site for 2017. Results indicated that TSEB accurately simulated ET (RMSD: ~60 W/m2) when the pixel scale was able to robustly discriminate between grass and tree pixels (<5 m). However, model uncertainty drastically increased at spatial resolution greater than 10 m (RMSD: ~115 W/m2). Model performance remains relatively constant between 30 and 1000 m spatial resolutions, with within pixel heterogeneity being similar at all these scales. For mixed pixels (≥30 m), forcing an effective landscape roughness into TSEB (RMSD: ~80 W/m2) or applying a seasonally changing TSEB (TSEB-2S; RMSD: ~65 W/m2) improved the modeling performance. The Sen-ET products behaved similarly at both scales with RMSD of ET roughly 80 W/m2. The non-linear relationship between input parameters and flux output, along with the poor representation of aerodynamic surface roughness, were the main drivers for the increased uncertainties at coarser scales. These results suggest that care should be taken when using global ET products over TGE and similarly heterogeneous landscapes. The modeling procedure should inherently account for the presence of vastly different vegetation roughness elements within the pixel, to achieve reliable estimates of turbulent fluxes over a TGE.
•Energy balance models have important scaling effects in tree-grass ecosystems.•ET was well modeled when pixels could separate tree and grass elements (<5 m).•Errors vastly increased at grid sizes >10 m, but remained constant from 30 to 1000 m.•Errors at coarser scales linked to poor estimation of roughness due to pixel mixing.•TSEB-2S improved ET estimations for coarser (>30 m) model runs.</abstract><cop>New York</cop><pub>Elsevier Inc</pub><doi>10.1016/j.rse.2021.112440</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Aerodynamics Airborne imagery Aridity Energy balance Environment models Evapotranspiration Fluxes Grasses Heterogeneity Hyperspectral imaging Image resolution Land cover Land surface temperature Land use Latent heat flux Pixels Remote sensing Satellites Sensible heat flux Sentinels for evapotranspiration Spatial discrimination Spatial resolution Surface energy balance Surface roughness Tree-grass ecosystems Turbulent fluxes Uncertainty |
| title | The effect of pixel heterogeneity for remote sensing based retrievals of evapotranspiration in a semi-arid tree-grass ecosystem |
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