估算沿海地区红树林林总初级生产力的环境胁迫量化方法。

Estimating mangrove forest gross primary production by quantifying environmental stressors in the coastal area.

机构信息

Institute of Industrial Science, The University of Tokyo, Tokyo, 1538505, Japan.

出版信息

Sci Rep. 2022 Feb 9;12(1):2238. doi: 10.1038/s41598-022-06231-6.

DOI:10.1038/s41598-022-06231-6

PMID:35140321

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8828879/

Abstract

Mangrove ecosystems play an important role in global carbon budget, however, the quantitative relationships between environmental drivers and productivity in these forests remain poorly understood. This study presented a remote sensing (RS)-based productivity model to estimate the light use efficiency (LUE) and gross primary production (GPP) of mangrove forests in China. Firstly, LUE model considered the effects of tidal inundation and therefore involved sea surface temperature (SST) and salinity as environmental scalars. Secondly, the downscaling effect of photosynthetic active radiation (PAR) on the mangrove LUE was quantified according to different PAR values. Thirdly, the maximum LUE varied with temperature and was therefore determined based on the response of daytime net ecosystem exchange and PAR at different temperatures. Lastly, GPP was estimated by combining the LUE model with the fraction of absorbed photosynthetically active radiation from Sentinel-2 images. The results showed that the LUE model developed for mangrove forests has higher overall accuracy (RMSE = 0.0051, R = 0.64) than the terrestrial model (RMSE = 0.0220, R = 0.24). The main environmental stressor for the photosynthesis of mangrove forests in China was PAR. The estimated GPP was, in general, in agreement with the in-situ measurement from the two carbon flux towers. Compared to the MODIS GPP product, the derived GPP had higher accuracy, with RMSE improving from 39.09 to 19.05 g C/m/8 days in 2012, and from 33.76 to 19.51 g C/m/8 days in 2015. The spatiotemporal distributions of the mangrove GPP revealed that GPP was most strongly controlled by environmental conditions, especially temperature and PAR, as well as the distribution of mangroves. These results demonstrate the potential of the RS-based productivity model for scaling up GPP in mangrove forests, a key to explore the carbon cycle of mangrove ecosystems at national and global scales.

摘要

红树林生态系统在全球碳预算中发挥着重要作用，然而，人们对这些森林的环境驱动因素与生产力之间的定量关系仍知之甚少。本研究提出了一种基于遥感（RS）的生产力模型，以估计中国红树林的光能利用率（LUE）和总初级生产力（GPP）。首先，LUE 模型考虑了潮汐淹没的影响，因此将海面温度（SST）和盐度作为环境标量。其次，根据不同的光合有效辐射（PAR）值量化了 PAR 对红树林 LUE 的降尺度效应。第三，最大 LUE 随温度变化而变化，因此根据不同温度下白天净生态系统交换和 PAR 的响应来确定。最后，根据 Sentinel-2 图像的吸收光合有效辐射分数，将 LUE 模型与 GPP 相结合进行估算。结果表明，与陆地模型（RMSE=0.0220，R=0.24）相比，开发的红树林 LUE 模型具有更高的整体精度（RMSE=0.0051，R=0.64）。中国红树林光合作用的主要环境胁迫因子是 PAR。估算的 GPP 通常与两个碳通量塔的原位测量值一致。与 MODIS GPP 产品相比，该方法的 GPP 具有更高的精度，2012 年 RMSE 从 39.09 提高到 19.05 g C/m/8 days，2015 年 RMSE 从 33.76 提高到 19.51 g C/m/8 days。红树林 GPP 的时空分布表明，GPP 主要受到环境条件的控制，特别是温度和 PAR 以及红树林的分布。这些结果表明，基于 RS 的生产力模型具有在红树林中扩展 GPP 的潜力，这是探索红树林生态系统碳循环的关键，对于国家和全球尺度都具有重要意义。