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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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URI: https://cronfa.swan.ac.uk/Record/cronfa63584
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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.
College: Faculty of Science and Engineering
Funders: 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).
Issue: 1