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Global monitoring of soil multifunctionality in drylands using satellite imagery and field data
dc.contributor.author | Hernández Clemente, Rocío | |
dc.contributor.author | Hornero, Alberto | |
dc.contributor.author | González Dugo, V. | |
dc.contributor.author | Berdugo, Miguel | |
dc.contributor.author | Quero, José L. | |
dc.contributor.author | Jiménez, J. C. | |
dc.contributor.author | Maestre, Fernando T. | |
dc.date.accessioned | 2024-01-31T09:50:19Z | |
dc.date.available | 2024-01-31T09:50:19Z | |
dc.date.issued | 2023 | |
dc.identifier.issn | 2056-3485 | |
dc.identifier.uri | http://hdl.handle.net/10396/26878 | |
dc.description.abstract | Models derived from satellite image data are needed to monitor the status of terrestrial ecosystems across large spatial scales. However, a remote sensing-based approach to quantify soil multifunctionality at the global scale is missing despite significant research efforts on this topic. A major constraint for doing so is the availability of suitable global-scale field data to calibrate remote sensing indicators (RSI) and, to a lesser extent, the sensitivity of spectral data of available satellite sensors to soil background and atmospheric conditions. Here, we aimed to develop a soil multifunctionality model to monitor global drylands coupling ground data on 14 soil functions of 222 dryland areas from six continents to 18 RSI derived from a time series (2006–2013) Landsat dataset. Among the RSI evaluated, the chlorophyll absorption ratio index was the best predictor of soil multifunctionality in single-variable-based models (r = 0.66, P < 0.01, NMRSE = 0.17). However, a multi-variable RSI model combining the chlorophyll absorption ratio index, the global environment monitoring index and the canopy-air temperature difference improved the accuracy of quantifying soil multifunctionality (r = 0.73, P < 0.01, NMRSE = 0.15). Furthermore, the correlation between RSI and soil variables shows a wide range of accuracy with upper and lower values obtained for AMI (r = 0.889, NMRSE = 0.05) and BGL (r = 0.685, NMRSE = 0.18) respectively. Our results provide new insights on assessing soil multifunctionality using RSI that may help to monitor temporal changes in the functioning of global drylands effectively. | es_ES |
dc.format.mimetype | application/pdf | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Zoological Society of London | es_ES |
dc.rights | https://creativecommons.org/licenses/by-nc-nd/4.0/ | es_ES |
dc.source | Remote Sensing in Ecology and Conservation, Vol 9, Issue 6 p. 743-758 (2023) | es_ES |
dc.subject | Soil multifunctionality | es_ES |
dc.subject | Global monitoring | es_ES |
dc.subject | Satellite data | es_ES |
dc.subject | Drylands | es_ES |
dc.subject | Artificial intelligence. | es_ES |
dc.title | Global monitoring of soil multifunctionality in drylands using satellite imagery and field data | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.relation.publisherversion | https://doi.org/10.1002/rse2.340 | es_ES |
dc.relation.projectID | Unión Europea. Grant agreement 242658 (BIOCOM) | es_ES |
dc.relation.projectID | Gobierno de España. PID2021-124058OA-I00 | es_ES |
dc.relation.projectID | Gobierno de España. RYC2020-029187-I | es_ES |
dc.relation.projectID | Gobierno de España. EUR2022-134048 | es_ES |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es_ES |