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.

中文翻译：

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利用卫星图像和实地数据对旱地土壤多功能性进行全球监测

需要从卫星图像数据导出的模型来监测大空间尺度的陆地生态系统的状态。然而，尽管在这一主题上做出了大量研究工作，但仍缺乏基于遥感的在全球范围内量化土壤多功能性的方法。这样做的一个主要限制是是否有合适的全球范围的现场数据来校准遥感指标（RSI），以及在较小程度上，可用卫星传感器的光谱数据对土壤背景和大气条件的敏感性。在这里，我们的目标是开发一个土壤多功能模型来监测全球旱地，将六大洲 222 个旱地地区的 14 种土壤功能的地面数据与源自时间序列（2006-2013）Landsat 数据集的 18 RSI 耦合起来。在评估的 RSI 中，叶绿素吸收率指数是单变量模型中土壤多功能性的最佳预测因子（r  = 0.66，P  < 0.01，NMRSE = 0.17）。然而，结合叶绿素吸收率指数、全球环境监测指数和冠层-空气温差的多变量RSI模型提高了量化土壤多功能性的准确性（r  = 0.73，P  < 0.01，NMRSE = 0.15）。此外，RSI 和土壤变量之间的相关性显示出广泛的准确度，分别为 AMI（r  = 0.889，NMRSE = 0.05）和 BGL（r  = 0.685，NMRSE = 0.18）获得上限和下限。我们的结果为使用 RSI 评估土壤多功能性提供了新的见解，可能有助于有效监测全球旱地功能的时间变化。