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Global monitoring of soil multifunctionality in drylands using satellite imagery and field data
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V. Gonzalez‐Dugo ,
M. Berdugo ,
J. L. Quero ,
J. C. Jiménez ,
F. T. Maestre
Remote Sensing in Ecology and Conservation, Volume: 12, Issue: 1
Swansea University Author: Rocio Hernandez-Clemente
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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.
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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...
| Published in: | Remote Sensing in Ecology and Conservation |
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| ISSN: | 2056-3485 2056-3485 |
| Published: |
Wiley
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| Online Access: |
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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. |
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| 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 |