Assessing consistency of spring phenology of snow-covered forests as estimated by vegetation indices, gross primary production, and solar-induced chlorophyll fluorescence

Chang, Qing; Xiao, Xiangming; Jiao, Wenzhe; Wu, Xiaocui; Wang, Jie; Du, Liting; Qin, Yuanwei

doi:10.1016/j.agrformet.2019.06.002

Cited by 67 publications

(34 citation statements)

References 76 publications

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“…Compared with the vegetation indices, which reflect the greenness of vegetation, SIF, which reflects the characteristics of vegetation photosynthesis quickly and without damage, has become a 'probe' for monitoring the use of light energy by vegetation and can also reflect the true growth status of vegetation (Zarco-Tejada et al, 2013). In this study, SIF was used to extract phenology across the whole area and the three sub-areas and showed that the growing season length of SIF was significantly shorter than NDVI and EVI in phenological inversion, which is consistent with previous research (Chang et al, 2019). SIF has a strong physiological basis compared to NDVI and EVI and is directly linked to the biochemical processes of vegetation.…”

Section: Advantages Of Sif In Extracting Phenologysupporting

confidence: 89%

Vegetation Phenology in Permafrost Regions of Northeastern China Based on MODIS and Solar-induced Chlorophyll Fluorescence

et al. 2021

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Vegetation phenology is an indicator of vegetation response to natural environmental changes and is of great significance for the study of global climate change and its impact on terrestrial ecosystems. The normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), extracted from the Moderate Resolution Imaging Spectrometer (MODIS), are widely used to monitor phenology by calculating land surface reflectance. However, the applicability of the vegetation index based on 'greenness' to monitor photosynthetic activity is hindered by poor observation conditions (e.g., ground shadows, snow, and clouds). Recently, satellite measurements of solar-induced chlorophyll fluorescence (SIF) from OCO-2 sensors have shown great potential for studying vegetation phenology. Here, we tested the feasibility of SIF in extracting phenological metrics in permafrost regions of the northeastern China, exploring the characteristics of SIF in the study of vegetation phenology and the differences between NDVI and EVI. The results show that NDVI has obvious SOS advance and EOS lag, and EVI is closer to SIF. The growing season length based on SIF is often the shortest, while it can represent the true phenology of vegetation because it is closely related to photosynthesis. SIF is more sensitive than the traditional remote sensing indices in monitoring seasonal changes in vegetation phenology and can compensate for the shortcomings of traditional vegetation indices. We also used the time series data of MODIS NDVI and EVI to extract phenological metrics in different permafrost regions. The results show that the length of growing season of vegetation in predominantly continuous permafrost (zone I) is longer than in permafrost with isolated taliks (zone II). Our results have certain significance for understanding the response of ecosystems in cold regions to global climate change.

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Section: Advantages Of Sif In Extracting Phenologysupporting

confidence: 89%

Vegetation Phenology in Permafrost Regions of Northeastern China Based on MODIS and Solar-induced Chlorophyll Fluorescence

et al. 2021

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“…Here, we would like to emphasize that our current results do not necessarily mean that these previous GUD estimations have larger errors, but imply large uncertainty of these GUD estimations due to possible background VIs values. To estimate GUD in the northern high-latitude areas, two novel alternatives to vegetation greenness indices may be the photochemical reflectance index (PRI) [42,43] and the solar-induced vegetation chlorophyll fluorescence [44]. However, it must keep in mind that GUD estimated from PRI and fluorescence is related to the initial increase of photosynthetic efficiency, which has different ecological meanings compared with GUD estimated from vegetation greenness indices.…”

Section: Discussionmentioning

confidence: 99%

Uncertainty of Vegetation Green-Up Date Estimated from Vegetation Indices Due to Snowmelt at Northern Middle and High Latitudes

et al. 2020

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Vegetation green-up date (GUD), an important phenological characteristic, is usually estimated from time-series of satellite-based normalized difference vegetation index (NDVI) data at regional and global scales. However, GUD estimates in seasonally snow-covered areas suffer from the effect of spring snowmelt on the NDVI signal, hampering our realistic understanding of phenological responses to climate change. Recently, two snow-free vegetation indices were developed for GUD detection: the normalized difference phenology index (NDPI) and normalized difference greenness index (NDGI). Both were found to improve GUD detection in the presence of spring snowmelt. However, these indices were tested at several field phenological camera sites and carbon flux sites, and a detailed evaluation on their performances at the large spatial scale is still lacking, which limits their applications globally. In this study, we employed NDVI, NDPI, and NDGI to estimate GUD at northern middle and high latitudes (north of 40° N) and quantified the snowmelt-induced uncertainty of GUD estimations from the three vegetation indices (VIs) by considering the changes in VI values caused by snowmelt. Results showed that compared with NDVI, both NDPI and NDGI improve the accuracy of GUD estimation with smaller GUD uncertainty in the areas below 55° N, but at higher latitudes (55°N-70° N), all three indices exhibit substantially larger GUD uncertainty. Furthermore, selecting which vegetation index to use for GUD estimation depends on vegetation types. All three indices performed much better for deciduous forests, and NDPI performed especially well (5.1 days for GUD uncertainty). In the arid and semi-arid grasslands, GUD estimations from NDGI are more reliable (i.e., smaller uncertainty) than NDP-based GUD (e.g., GUD uncertainty values for NDGI vs. NDPI are 4.3 d vs. 7.2 d in Mongolia grassland and 6.7 d vs. 9.8 d in Central Asia grassland), whereas in American prairie, NDPI performs slightly better than NDGI (GUD uncertainty for NDPI vs. NDGI is 3.8 d vs. 4.7 d). In central and western Europe, reliable GUD estimations from NDPI and NDGI were acquired only in those years without snowfall before green-up. This study provides important insights into the application of, and uncertainty in, snow-free vegetation indices for GUD estimation at large spatial scales, particularly in areas with seasonal snow cover.

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“…Satellite vegetation indices (VIs) have been widely used in recent decades to identify photosynthetic phenology and its response to climate change (Chang et al, 2019; D'Odorico et al, 2015; Gonsamo et al, 2012; Middleton et al, 2016; Wu et al, 2017). The information contents of existing VIs can be categorized into structure and physiology.…”

Section: Introductionmentioning

confidence: 99%

Divergent Estimates of Forest Photosynthetic Phenology Using Structural and Physiological Vegetation Indices

Yin

Verger

Filella

et al. 2020

Geophysical Research Letters

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The accurate estimation of photosynthetic phenology using vegetation indices (VIs) is important for measuring the interannual variation of atmospheric CO 2 concentrations, but the relative performances of structural and physiological VIs remain unclear. We found that structural VIs (normalized difference VI, enhanced VI, and near-infrared reflectance of vegetation) were suitable for estimating the start of the photosynthetically active season in deciduous broadleaf forests using gross primary production measured by FLUXNET as a benchmark, and a physiological VI (chlorophyll/carotenoid index) was better at identifying the end of the photosynthetically active season for deciduous broadleaf forests and both the start and end of season for evergreen needleleaf forests. The divergent performances were rooted in the combined control of structural and physiological regulations of carbon uptake by plants. Most existing studies of photosynthetic phenology have been based on structural VIs, so we suggest revisiting the dynamics of photosynthetic phenology using physiological VIs, which has significant implications on global plant phenology and carbon uptake studies. Plain Language Summary The uptake of photosynthetic carbon by forests is strongly seasonal, which can be characterized by photosynthetic phenology, e.g., the start and end of the photosynthetically active season (SOS and EOS, respectively). Satellite vegetation indices (VIs) can detect photosynthesis in canopies either structurally or physiologically. Clarifying the convergence or divergence of the performance of structural and physiological VIs is therefore crucial. This study compared the capacity of three structural VIs and one physiological VI for estimating SOS and EOS. Their performances were jointly controlled by structural and physiological regulations of carbon uptake by plants. The structural and physiological controls for deciduous broadleaf forests (DBFs) were nearly synchronous during green-up, and canopy structural changes were visible, so structural VIs were reliable for estimating SOS. Canopies, however, change slowly in evergreen needleleaf forests (ENFs) throughout the year and in DBFs during autumn, and the capacity to take up carbon is mainly limited by physiological stress, so physiological VIs outperformed structural VIs. Our study highlights the unique advantage of physiological VIs for estimating photosynthetic phenology. These findings constitute a step toward improving our understanding of the roles of the structural and physiological regulations of the dynamics of terrestrial carbon.

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Assessing consistency of spring phenology of snow-covered forests as estimated by vegetation indices, gross primary production, and solar-induced chlorophyll fluorescence

Cited by 67 publications

References 76 publications

Vegetation Phenology in Permafrost Regions of Northeastern China Based on MODIS and Solar-induced Chlorophyll Fluorescence

Vegetation Phenology in Permafrost Regions of Northeastern China Based on MODIS and Solar-induced Chlorophyll Fluorescence

Uncertainty of Vegetation Green-Up Date Estimated from Vegetation Indices Due to Snowmelt at Northern Middle and High Latitudes

Divergent Estimates of Forest Photosynthetic Phenology Using Structural and Physiological Vegetation Indices

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