Global monitoring of soil multifunctionality in drylands using satellite imagery and field data
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.
| Main Authors: | , , , , , , |
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| Other Authors: | |
| Format: | artículo biblioteca |
| Language: | English |
| Published: |
John Wiley & Sons
2023-12-01
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| Subjects: | Soil multifunctionality, Artificial intelligence, Drylands, Global monitoring, Satellite data, |
| Online Access: | http://hdl.handle.net/10261/354308 http://dx.doi.org/10.13039/501100004837 http://dx.doi.org/10.13039/501100000781 http://dx.doi.org/10.13039/501100003359 https://api.elsevier.com/content/abstract/scopus_id/85160098692 |
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