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Global monitoring of soil multifunctionality in drylands using satellite imagery and field data

Rocio Hernandez-Clemente, A. Hornero Orcid Logo, V. Gonzalez‐Dugo Orcid Logo, M. Berdugo Orcid Logo, J. L. Quero Orcid Logo, J. C. Jiménez Orcid Logo, F. T. Maestre Orcid Logo

Remote Sensing in Ecology and Conservation, Volume: 12, Issue: 1

Swansea University Author: Rocio Hernandez-Clemente

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DOI (Published version): 10.1002/rse2.340

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 con...

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Published in: Remote Sensing in Ecology and Conservation
ISSN: 2056-3485 2056-3485
Published: Wiley
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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 &lt; 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 &lt; 0.01, NMRSE = 0.15). 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spelling v2 63584 2023-06-05 Global monitoring of soil multifunctionality in drylands using satellite imagery and field data 0b007e63ef097cd47d6bc60b58379103 Rocio Hernandez-Clemente Rocio Hernandez-Clemente true false 2023-06-05 FGSEN 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. Journal Article Remote Sensing in Ecology and Conservation 12 1 Wiley 2056-3485 2056-3485 0 0 0 0001-01-01 10.1002/rse2.340 http://dx.doi.org/10.1002/rse2.340 COLLEGE NANME Science and Engineering - Faculty COLLEGE CODE FGSEN Swansea University Field data were obtained with the support of the European Research Council (ERC) grant agreement 242658 (BIOCOM). Hernández-Clemente R. was supported by the Ramón y Cajal program (RYC2020-029187-I) and the State Subprogram for Knowledge Generation (D-Traits-PID2021-124058OA-I00) from the Spanish Ministry of Science and Innovation. Maestre F. T. acknowledges support from Generalitat Valenciana (CIDEGENT/2018/041) and the Spanish Ministry of Science and Innovation (EUR2022-134048). 2023-06-21T15:51:19.3091706 2023-06-05T13:30:55.4202589 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Geography Rocio Hernandez-Clemente 1 A. Hornero 0000-0002-8434-2168 2 V. Gonzalez‐Dugo 0000-0002-1445-923x 3 M. Berdugo 0000-0003-1053-8907 4 J. L. Quero 0000-0001-5553-506x 5 J. C. Jiménez 0000-0001-7562-4895 6 F. T. Maestre 0000-0002-7434-4856 7 63584__27711__176fe7e1b49740b9b299b0317097f198.pdf 63584.pdf 2023-06-05T13:43:16.2584734 Output 1142982 application/pdf Version of Record true 2023 The Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. true eng http://creativecommons.org/licenses/by-nc-nd/4.0/
title Global monitoring of soil multifunctionality in drylands using satellite imagery and field data
spellingShingle Global monitoring of soil multifunctionality in drylands using satellite imagery and field data
Rocio Hernandez-Clemente
title_short Global monitoring of soil multifunctionality in drylands using satellite imagery and field data
title_full Global monitoring of soil multifunctionality in drylands using satellite imagery and field data
title_fullStr Global monitoring of soil multifunctionality in drylands using satellite imagery and field data
title_full_unstemmed Global monitoring of soil multifunctionality in drylands using satellite imagery and field data
title_sort Global monitoring of soil multifunctionality in drylands using satellite imagery and field data
author_id_str_mv 0b007e63ef097cd47d6bc60b58379103
author_id_fullname_str_mv 0b007e63ef097cd47d6bc60b58379103_***_Rocio Hernandez-Clemente
author Rocio Hernandez-Clemente
author2 Rocio Hernandez-Clemente
A. Hornero
V. Gonzalez‐Dugo
M. Berdugo
J. L. Quero
J. C. Jiménez
F. T. Maestre
format Journal article
container_title Remote Sensing in Ecology and Conservation
container_volume 12
container_issue 1
institution Swansea University
issn 2056-3485
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doi_str_mv 10.1002/rse2.340
publisher Wiley
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Biosciences, Geography and Physics - Geography{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Biosciences, Geography and Physics - Geography
url http://dx.doi.org/10.1002/rse2.340
document_store_str 1
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description 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.
published_date 0001-01-01T15:51:19Z
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