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. 2022 Jul 8;13:3959. doi: 10.1038/s41467-022-31667-9

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© The Author(s) 2022

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 2 — a Response functions of LAI sensitivity ( $\frac{\partial L A I}{\partial S M n e a r}$ ) to growing-season mean near-surface soil moisture (growing-season mean SMnear) from observations (Obs) and land surface models (Model). b Similar to a but for LAI sensitivity to sub-surface soil moisture ( $\frac{\partial L A I}{\partial S M s u b}$ ). In a, b, global grid cells with significant LAI sensitivities to soil moisture are included. Two-sided significance tests are done for each grid cell at the p < 0.01 level as assessed with Theil-sen regressions. The solid line and shaded areas show the median and interquartile ranges of LAI sensitivity. Probability distributions of near-surface soil moisture in observations and models are shown at the bottom of each plot. Results here are based on ensemble-product means, while results from individual products or models are presented in Supplementary Fig. 4.