利用增强的植被指数和地表温度重建不同纬度和物候的森林和草地的太阳诱导叶绿素荧光

IF 2.7 3区 农林科学 Q2 ECOLOGY
Peipei Zhang, Haiqiu Liu, Hangzhou Li, Jianen Yao, Xiu Chen, Jinying Feng
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引用次数: 0

摘要

森林和草地是两种主要的陆地碳收集生态系统,探测它们的太阳诱导叶绿素荧光(SIF)可以评估它们的光合强度和碳收集能力。由于直接从卫星观测中获取的SIF存在低空间分辨率、不连续或低时间分辨率的问题,因此利用一些植被指数和气象因子作为预测因子来重建SIF产品。然而,与VIs不同的是,某些气象因子的空间分辨率相对较低,它们的观测结果并不总是可获得的。本研究旨在探索从更少的预测因子中重建SIF的潜力,这些预测因子的高分辨率观测结果很容易获得。方法选取低、中、高纬度地区的6个森林和草原区,比较常用预测因子归一化植被指数(NDVI)、增强植被指数(EVI)和地表温度(LST)与SIF的相关性。结果表明,EVI和LST的组合与SIF的相关性更强,但在森林和草地的不同生长阶段,它们对SIF的贡献不同。因此,我们提出了考虑地理位置和物候阶段的组合采样方法,探讨时间和空间覆盖样本跨度对特定区域特定生长阶段EVI数据差异的放大程度。为此,提出了三种样本组合方法:全球尺度的月回归、区域尺度的季节回归和区域尺度的月回归。在此基础上,利用Sentinel-3 EVI和MODIS LST数据重建了6个地区的500 m SIF。结果与讨论结果表明,重建的SIF与MODIS GPP (gross primary productivity)的r2值≥0.90,与GOME-2 SIF的r2值为0.70,与GOSIF的r2值为0.77,表明该方法可以获得可靠的500 m SIF重建结果。该方法绕过了传统SIF重建模型对众多难以获得的预测因子的依赖,可以作为未来更高效、更准确的高分辨率SIF重建的更好选择。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using enhanced vegetation index and land surface temperature to reconstruct the solar-induced chlorophyll fluorescence of forests and grasslands across latitude and phenology
Introduction Forest and grassland are the two main carbon-collecting terrestrial ecosystems, and detecting their solar-induced chlorophyll fluorescence (SIF) enables evaluation of their photosynthetic intensity and carbon-collecting capacity. Since SIF that is retrieved directly from satellite observations suffers from low spatial resolution, discontinuity, or low temporal resolution, some vegetation indexes (VIs) and meteorological factors are used as predictors to reconstruct SIF products. Yet, unlike VIs, certain meteorological factors feature a relatively low space resolution and their observations are not always accessible. This study aimed to explore the potential of reconstructing SIF from fewer predictors whose high-resolution observations are easily accessible. Methods A total of six forest and grassland regions across low, mid, and high latitudes were selected, and the commonly used predictors-normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and land surface temperature (LST)—were compared for their correlation with SIF. Results show that the combination of EVI and LST is more strongly correlated with SIF, but each contributed differently to SIF at differing growth stages of forest and grassland. Accordingly, we proposed the idea of a combined sampling approach that considers both location and phenological phase, to explore the extent to which time and space coverage samples' span could enlarge the disparity of EVI data in particular regions at specific growth stages. To do that, three kinds of sample combination methods were proposed: monthly regression at a global scale, seasonal regression at a regional scale, and monthly regression at a regional scale. Following this, Sentinel-3 EVI and MODIS LST data were used to reconstruct 500 m SIF in the six regions by implementing the proposed methodology. Results and discussion These results showed that the R 2 values were ≥0.90 between the reconstructed SIF and MODIS GPP (gross primary productivity), 0.70 with GOME-2 SIF and 0.77 with GOSIF, thus proving the proposed methodology could produce reliable results for reconstruction of 500 m SIF. This proposed approach, which bypasses dependence of traditional SIF reconstruction model on numerous predictors not easy to obtain, can serve as a better option for more efficient and accurate high-resolution SIF reconstructions in the future.
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来源期刊
CiteScore
4.50
自引率
6.20%
发文量
256
审稿时长
12 weeks
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