Solar-induced chlorophyll fluorescence captures photosynthetic phenology better than traditional vegetation indices

Zhang, Jingru; Gonsamo, Alemu; Tong, Xiaochong; Xiao, Jingfeng; Rogers, Cheryl; Qin, Shuhong; Liu, Peirong; Yu, Peiyang; Ma, Ping

doi:10.1016/j.isprsjprs.2023.07.021

Cited by 12 publications

(4 citation statements)

References 73 publications

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“…Another new perspective of vegetation phenology has emerged in recent years, which suggests it is possible to minimize the uncertainties in SOS estimates by using solar‐induced chlorophyll fluorescence (SIF; Zhang et al., 2022, 2023), especially for evergreen vegetation with high productivity such as subtropical evergreens (Li et al., 2021; Ren et al., 2021) and coniferous and broadleaf forests (Huang et al., 2023; Rogers & Chen, 2022). The mechanism that allows SIF to capture phenology more accurately relative to the traditional NDVI is that SIF involves the reemission of a fraction of observed radiation by vegetation chlorophyll molecules, mostly at wavelengths between 600 and 800 nm, which is physiologically related to photosynthesis (Zhang et al., 2022).…”

Section: Discussionmentioning

confidence: 99%

Climatic Constraints of Spring Phenology and Its Variability on the Mongolian Plateau From 1982 to 2021

Yuan,

Bao,

Dorjsuren

et al. 2024

JGR Biogeosciences

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The start of vegetation growing season (SOS) plays an important role in the energy cycle between the land and atmosphere. Due to the limited temporal span of a single satellite sensor through time, the continuous variation of the SOS over 40 years has not been adequately quantified. Using the overlapping periods (2001–2015) between the Global Inventory Modeling and Mapping Studies (GIMMS) (1982–2015) and Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) (2001–2021) data sets, we construct an NDVI data set covering the period 1982–2021 on the Mongolian Plateau and further construct a map of relative climatic constraint on the SOS (divided into “temperature‐constrained,” “precipitation‐constrained,” and “other” regions) for quantifying SOS variability. We show that the constructed NDVI data set has high consistency and continuity with earlier GIMMS NDVI data. Regions with the SOS constrained by temperature account for 55.3% of the plateau and are located in northwestern and northeastern cold areas, while regions with the SOS constrained by precipitation constitute over 34.7% and are located in central and southwestern drier regions. Importantly, the temperature‐constrained SOS has continuously and significantly advanced, with a total advance of 4.8 days over 40 years. In contrast, the precipitation‐constrained SOS reversed from advancing to delaying in 2005. This suggests that differentiating the climatic constraint on the SOS might be a practical treatment for reducing the uncertainties in the SOS trends in previous studies. Interestingly, the precipitation‐constrained SOS does not significantly correlate to both the chilling and forcing temperatures, indicating less dependency of the SOS on chilling, which may not have been well considered previously.

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Section: Discussionmentioning

confidence: 99%

Climatic Constraints of Spring Phenology and Its Variability on the Mongolian Plateau From 1982 to 2021

Yuan,

Bao,

Dorjsuren

et al. 2024

JGR Biogeosciences

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“…where r is the correlation coefficient of two variables, X and Y are the average values of the two variables in the time series, and n is the number of years. In addition, a t-test was used to test the significance of the correlations [57]. The correlation coefficients are significant at the 0.05 level with p values < 0.05, and are significant at the 0.01 level with p values < 0.01.…”

Section: Pearson Correlation Analysismentioning

confidence: 99%

Earlier Spring-Summer Phenology and Higher Photosynthetic Peak Altered the Seasonal Patterns of Vegetation Productivity in Alpine Ecosystems

Yang,

Liu,

Chen

et al. 2024

Remote Sensing

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Carbon uptake of vegetation is controlled by phenology and photosynthetic carbon uptake capacity. However, our knowledge of the seasonal responses of vegetation productivity to phenological and physiological changes in alpine ecosystems is still weak. In this study, we quantified the spatio-temporal variations of vegetation phenology and gross primary productivity (GPP) across the source region of the Yellow River (SRYR) by analyzing MODIS-derived vegetation phenology and GPP from 2001 to 2019, and explored how vegetation phenology and maximum carbon uptake capacity (GPPmax) affected seasonal GPP over the region. Our results showed that the SRYR experienced significantly advanced trends (p < 0.05) for both start (SOS) and peak (POS) of the growing season from 2001 to 2019. Spring GPP (GPPspr) had a significantly increasing trend (p < 0.01), and the earlier SOS had obvious positive effects on GPPspr. Summer GPP (GPPsum) was significantly and negatively correlated to POS (p < 0.05). In addition, GPPmax had a significant and positive correlation with GPPsum and GPPann (p < 0.01), respectively. It was found that an earlier spring-summer phenology and higher photosynthetic peak enhanced the photosynthetic efficiency of vegetation in spring and summer and altered the seasonal patterns of vegetation productivity in the SRYR under warming and wetting climates. This study indicated that not only spring and autumn phenology but also summer phenology and maximum carbon uptake capacity should be regarded as crucial indicators regulating the carbon uptake process in alpine ecosystems. This research provides important information about how changes in phenology affect vegetation productivity in alpine ecosystems under global climate warming.

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“…Selecting sensitive indicators to detect the response of vegetation to drought is helpful to grasp the situation of vegetation affected by drought as early as possible, and to help understand the response process of vegetation to drought stress. A large number of studies have shown that SIF can quickly reflect the effect of water stress on the physiological growth of vegetation [15]. SIF is the light signal of red and far-red bands released by chlorophyll in the excited state after capturing light quantum in the process of photosynthesis, which is directly related to the photosynthetic rate of vegetation.…”

Section: Introductionmentioning

confidence: 99%

Response of Vegetation to Drought in the Source Region of the Yangtze and Yellow Rivers Based on Causal Analysis

Lu,

Qin,

Yan

et al. 2024

Remote Sensing

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The vegetation and ecosystem in the source region of the Yangtze River and the Yellow River (SRYY) are fragile. Affected by climate change, extreme droughts are frequent and permafrost degradation is serious in this area. It is very important to quantify the drought–vegetation interaction in this area under the influence of climate–permafrost coupling. In this study, based on the saturated vapor pressure deficit (VPD) and soil moisture (SM) that characterize atmospheric and soil drought, as well as the Normalized Differential Vegetation Index (NDVI) and solar-induced fluorescence (SIF) that characterize vegetation greenness and function, the evolution of regional vegetation productivity and drought were systematically identified. On this basis, the technical advantages of the causal discovery algorithm Peter–Clark Momentary Conditional Independence (PCMCI) were applied to distinguish the response of vegetation to VPD and SM. Furthermore, this study delves into the response mechanisms of NDVI and SIF to atmospheric and soil drought, considering different vegetation types and permafrost degradation areas. The findings indicated that low SM and high VPD were the limiting factors for vegetation growth. The positive and negative causal effects of VPD on NDVI accounted for 47.88% and 52.12% of the total area, respectively. Shrubs were the most sensitive to SM, and the response speed of grassland to SM was faster than that of forest land. The impact of SM on vegetation in the SRYY was stronger than that of VPD, and the effect in the frozen soil degradation area was more obvious. The average causal effects of NDVI and SIF on SM in the frozen soil degradation area were 0.21 and 0.41, respectively, which were twice as high as those in the whole area, and SM dominated NDVI (SIF) changes in 62.87% (76.60%) of the frozen soil degradation area. The research results can provide important scientific basis and theoretical support for the scientific assessment and adaptation of permafrost, vegetation, and climate change in the source area and provide reference for ecological protection in permafrost regions.

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Solar-induced chlorophyll fluorescence captures photosynthetic phenology better than traditional vegetation indices

Cited by 12 publications

References 73 publications

Climatic Constraints of Spring Phenology and Its Variability on the Mongolian Plateau From 1982 to 2021

Climatic Constraints of Spring Phenology and Its Variability on the Mongolian Plateau From 1982 to 2021

Earlier Spring-Summer Phenology and Higher Photosynthetic Peak Altered the Seasonal Patterns of Vegetation Productivity in Alpine Ecosystems

Response of Vegetation to Drought in the Source Region of the Yangtze and Yellow Rivers Based on Causal Analysis

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