Improving field-scale crop actual evapotranspiration monitoring with Sentinel-3, Sentinel-2, and Landsat data fusion
© 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
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Elsevier
2023-12
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| Subjects: | Irrigated agriculture, Remote sensing, Surface energy balance, Land surface temperature, |
| Online Access: | http://hdl.handle.net/10261/346799 |
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Irrigated agriculture Remote sensing Surface energy balance Land surface temperature Irrigated agriculture Remote sensing Surface energy balance Land surface temperature |
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Irrigated agriculture Remote sensing Surface energy balance Land surface temperature Irrigated agriculture Remote sensing Surface energy balance Land surface temperature Guzinski, Radoslaw Nieto, Héctor Ramo Sánchez, Rubén Sánchez, Juan Manuel Jomaa, Ihab Zitouna-Chebbi, Rim Roupsard, Olivier López-Urrea, Ramón Improving field-scale crop actual evapotranspiration monitoring with Sentinel-3, Sentinel-2, and Landsat data fusion |
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© 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
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European Space Agency |
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European Space Agency Guzinski, Radoslaw Nieto, Héctor Ramo Sánchez, Rubén Sánchez, Juan Manuel Jomaa, Ihab Zitouna-Chebbi, Rim Roupsard, Olivier López-Urrea, Ramón |
| format |
artículo |
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Irrigated agriculture Remote sensing Surface energy balance Land surface temperature |
| author |
Guzinski, Radoslaw Nieto, Héctor Ramo Sánchez, Rubén Sánchez, Juan Manuel Jomaa, Ihab Zitouna-Chebbi, Rim Roupsard, Olivier López-Urrea, Ramón |
| author_sort |
Guzinski, Radoslaw |
| title |
Improving field-scale crop actual evapotranspiration monitoring with Sentinel-3, Sentinel-2, and Landsat data fusion |
| title_short |
Improving field-scale crop actual evapotranspiration monitoring with Sentinel-3, Sentinel-2, and Landsat data fusion |
| title_full |
Improving field-scale crop actual evapotranspiration monitoring with Sentinel-3, Sentinel-2, and Landsat data fusion |
| title_fullStr |
Improving field-scale crop actual evapotranspiration monitoring with Sentinel-3, Sentinel-2, and Landsat data fusion |
| title_full_unstemmed |
Improving field-scale crop actual evapotranspiration monitoring with Sentinel-3, Sentinel-2, and Landsat data fusion |
| title_sort |
improving field-scale crop actual evapotranspiration monitoring with sentinel-3, sentinel-2, and landsat data fusion |
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Elsevier |
| publishDate |
2023-12 |
| url |
http://hdl.handle.net/10261/346799 |
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AT guzinskiradoslaw improvingfieldscalecropactualevapotranspirationmonitoringwithsentinel3sentinel2andlandsatdatafusion AT nietohector improvingfieldscalecropactualevapotranspirationmonitoringwithsentinel3sentinel2andlandsatdatafusion AT ramosanchezruben improvingfieldscalecropactualevapotranspirationmonitoringwithsentinel3sentinel2andlandsatdatafusion AT sanchezjuanmanuel improvingfieldscalecropactualevapotranspirationmonitoringwithsentinel3sentinel2andlandsatdatafusion AT jomaaihab improvingfieldscalecropactualevapotranspirationmonitoringwithsentinel3sentinel2andlandsatdatafusion AT zitounachebbirim improvingfieldscalecropactualevapotranspirationmonitoringwithsentinel3sentinel2andlandsatdatafusion AT roupsardolivier improvingfieldscalecropactualevapotranspirationmonitoringwithsentinel3sentinel2andlandsatdatafusion AT lopezurrearamon improvingfieldscalecropactualevapotranspirationmonitoringwithsentinel3sentinel2andlandsatdatafusion |
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dig-cide-es-10261-3467992024-02-12T13:32:49Z Improving field-scale crop actual evapotranspiration monitoring with Sentinel-3, Sentinel-2, and Landsat data fusion Guzinski, Radoslaw Nieto, Héctor Ramo Sánchez, Rubén Sánchez, Juan Manuel Jomaa, Ihab Zitouna-Chebbi, Rim Roupsard, Olivier López-Urrea, Ramón European Space Agency Agencia Estatal de Investigación (España) Ministerio de Ciencia e Innovación (España) European Commission Fondation Total LEAP-Agri Nieto, Héctor [0000-0003-4250-6424] Irrigated agriculture Remote sensing Surface energy balance Land surface temperature © 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). One of the primary applications of satellite Land Surface Temperature (LST) observations lies in their utilization for modeling of actual evapotranspiration (ET) in agricultural crops, with the primary goals of monitoring and enhancing irrigation practices and improving crop water use productivity, as stipulated by Sustainable Development Goal (SDG) indicator 6.4.1. Evapotranspiration is a complex and dynamic process, both temporally and spatially, necessitating LST observations with high spatio-temporal resolution. Presently, none of the existing spaceborne thermal sensors can provide quasi-daily field-scale LST observations, prompting the development of methods for data fusion (thermal sharpening) of observations from various shortwave and thermal sensors to meet this spatio-temporal requirement. Previous research has demonstrated the effectiveness of combining shortwave-multispectral Sentinel-2 observations with thermal-infrared Sentinel-3 observations to derive daily, field-scale LST and ET estimates. However, these studies also highlighted limitations in capturing the distinct thermal contrast between cooler LST in irrigated agricultural areas and the hotter, adjacent dry regions. In this study, we aim to address this limitation by incorporating information on thermal spatial variability observed by Landsat satellites into the data fusion process, without being constrained by infrequent or cloudy Landsat thermal observations and while retaining the longwave radiance emission captured by the Sentinel-3 thermal sensor at its native resolution. Two approaches are evaluated, both individually and as a complementary combination, and validated against in situ LST measurements. The best performing approach, which leads to reduction in root mean square error of up to 1.5 K when compared to previous research, is subsequently used to estimate parcel-level actual evapotranspiration. The ET modeling process has also undergone various improvements regarding the gap-filling of input and output data, input datasets and code implementation. The resulting ET is validated using lysimeters and eddy covariance towers in Spain, Lebanon, Tunisia, and Senegal resulting in minimal overall bias (systematic underestimation of less than 0.07 mm/day) and a low root mean square error (down to 0.84 mm/day) when using fully global input datasets. The enhanced LST sharpening methodology is sensor agnostic and should remain relevant for the upcoming thermal missions while the accuracy of the modeled ET fluxes is encouraging for further utilization of observations from Sentinel satellites, and other Copernicus data, for monitoring SDG indicator 6.4.1. The authors would like to thank European Space Agency (ESA) for funding this study through ET4FAO project (contract no. 4000130120/20/I-DT). Additional funding was provided by Sat-ET4Drought project PID2021-127345OR-C32, funded by MCIN/AEI DOI:10.13039/501100011033 and FEDER. Field instrumentation and measurements from Spain were supported by the Spanish Ministry of Science and Innovation, MCIN/AEI, together with Next Generation EU/PRTR funds (projects PID2020-113498RB-C21, PID2021-123305OB-C31 and TED2021-130405B-I00). Faidherbia-Flux site in Senegal received supports from EU-LEAP-Agri (RAMSES II), EU-DESIRA (CASSECS), EU-H2020 (SUSTAINSAHEL), AGROPOLIS and TOTAL Foundations (DSCATT), CGIAR (GLDC) . Peer reviewed 2024-02-12T13:32:49Z 2024-02-12T13:32:49Z 2023-12 artículo International Journal of Applied Earth Observation and Geoinformation 125: 103587 (2023) 1569-8432 http://hdl.handle.net/10261/346799 10.1016/j.jag.2023.103587 1872-826X en Publisher's version https://doi.org/10.1016/j.jag.2023.103587 Sí open application/pdf Elsevier |