Calibrating vegetation phenology from Sentinel-2 using eddy covariance, PhenoCam, and PEP725 networks across Europe

Tian, Feng; Cai, Zhanzhang; Jin, Hongxiao; Hufkens, Koen; Scheifinger, Helfried; Tagesson, Torbern; Smets, Bruno; Hoolst, Roel Van; Bonte, Kasper; Ivits, Éva; Tong, Xiaoye; Ardö, Jonas; Eklundh, Lars

doi:10.1016/j.rse.2021.112456

Cited by 79 publications

(60 citation statements)

References 76 publications

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“…The summer/early fall season (May-September) was the most informative period for delineating areas of plant diversity (Figure 11). Previously, remote sensing-based researches have also shown that this season is vital for the study of characteristics such as plant phenology [50] and vegetation cover [84]. This period is also regarded as "the optimal moment" because precipitation falls quickly and abundantly within weeks [45], and forest plant growth reaches the peak [85].…”

Section: Influence Of Different Featuresmentioning

confidence: 99%

“…The satellites might be an enormously powerful tool since they allow for coverage of large geographical scales in a short period of time, having the potential for ecologists to provide a critical information about the drivers of the spatial and temporal distribution of biodiversity [34,44,45]. For example, S-2 imagery has provided valuable results in the monitoring of forest ecosystems distribution [46,47], plant species classification [48], mapping of forest vegetation dominant leaf types [49], monitoring plant phenology [50], predicting above-ground biomass [51,52] as well as in distinguishing temperate tree species [53]. However, the feasibility of all these vegetation parameter maps is often limited because large-wide estimations are particularly challenging in areas with irregular vegetation, complex terrain and frequent cloud shadows, such as subtropical mountainous areas [49,54].…”

Section: Introductionmentioning

confidence: 99%

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Mapping Plant Diversity Based on Combined SENTINEL-1/2 Data—Opportunities for Subtropical Mountainous Forests

et al. 2022

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Plant diversity is an important parameter in maintaining forest ecosystem services, functions and stability. Timely and accurate monitoring and evaluation of large-area wall-to-wall maps on plant diversity and its spatial heterogeneity are crucial for the conservation and management of forest resources. However, traditional botanical field surveys designed to estimate plant diversity are usually limited in their spatiotemporal resolutions. Using Sentinel-1 (S-1) and Sentinel-2 (S-2) data at high spatiotemporal scales, combined with and referenced to botanical field surveys, may be the best choice to provide accurate plant diversity distribution information over a large area. In this paper, we predicted and mapped plant diversity in a subtropical forest using 24 months of freely and openly available S-1 and S-2 images (10 m × 10 m) data over a large study area (15,290 km2). A total of 448 quadrats (10 m × 10 m) of forestry field surveys were captured in a subtropical evergreen-deciduous broad-leaved mixed forest to validate a machine learning algorithm. The objective was to link the fine Sentinel spectral and radar data to several ground-truthing plant diversity indices in the forests. The results showed that: (1) The Simpson and Shannon-Wiener diversity indices were the best predicted indices using random forest regression, with ȓ2 of around 0.65; (2) The use of S-1 radar data can enhance the accuracy of the predicted heterogeneity indices in the forests by approximately 0.2; (3) As for the mapping of Simpson and Shannon-Wiener, the overall accuracy was 67.4% and 64.2% respectively, while the texture diversity’s overall accuracy was merely 56.8%; (4) From the evaluation and prediction map information, the Simpson, Shannon-Wiener and texture diversity values (and its confidence interval values) indicate spatial heterogeneity in pixel level. The large-area forest plant diversity indices maps add spatially explicit information to the ground-truthing data. Based on the results, we conclude that using the time-series of S-1 and S-2 radar and spectral characteristics, when coupled with limited ground-truthing data, can provide reasonable assessments of plant spatial heterogeneity and diversity across wide areas. It could also help promote forest ecosystem and resource conservation activities in the forestry sector.

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Section: Influence Of Different Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mapping Plant Diversity Based on Combined SENTINEL-1/2 Data—Opportunities for Subtropical Mountainous Forests

et al. 2022

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“…An important requirement thereby is that the temporal resolution should be high enough to cover periods with phenological change. Furthermore, we recommend investigating the use of indices other than NDVI, such as the two-band enhanced vegetation index (EVI2) or the plant phenology index (PPI), as these may saturate less quickly at higher biomass levels and, therefore, might perform better in predicting vegetation phenology [91] and potential vertical mismatch. Furthermore, especially when moving away from temperate deciduous forests, it is important to carefully choose the method to derive the SOS, as White et al [43] found that the ordinal rank of SOS methods differs among ecoregions.…”

Section: Future Research Directionsmentioning

confidence: 99%

Climate Effects on Vertical Forest Phenology of Fagus sylvatica L., Sensed by Sentinel-2, Time Lapse Camera, and Visual Ground Observations

et al. 2021

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Contemporary climate change leads to earlier spring phenological events in Europe. In forests, in which overstory strongly regulates the microclimate beneath, it is not clear if further change equally shifts the timing of leaf unfolding for the over- and understory of main deciduous forest species, such as Fagus sylvatica L. (European beech). Furthermore, it is not known yet how this vertical phenological (mis)match—the phenological difference between overstory and understory—affects the remotely sensed satellite signal. To investigate this, we disentangled the start of season (SOS) of overstory F.sylvatica foliage from understory F. sylvatica foliage in forests, within nine quadrants of 5.8 × 5.8 km, stratified over a temperature gradient of 2.5 °C in Bavaria, southeast Germany, in the spring seasons of 2019 and 2020 using time lapse cameras and visual ground observations. We explained SOS dates and vertical phenological (mis)match by canopy temperature and compared these to Sentinel-2 derived SOS in response to canopy temperature. We found that overstory SOS advanced with higher mean April canopy temperature (visual ground observations: −2.86 days per °C; cameras: −2.57 days per °C). However, understory SOS was not significantly affected by canopy temperature. This led to an increase of vertical phenological mismatch with increased canopy temperature (visual ground observations: +3.90 days per °C; cameras: +2.52 days per °C). These results matched Sentinel-2-derived SOS responses, as pixels of higher canopy height advanced more by increased canopy temperature than pixels of lower canopy height. The results may indicate that, with further climate change, spring phenology of F. sylvatica overstory will advance more than F. sylvatica understory, leading to increased vertical phenological mismatch in temperate deciduous forests. This may have major ecological effects, but also methodological consequences for the field of remote sensing, as what the signal senses highly depends on the pixel mean canopy height and the vertical (mis)match.

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“…Along with the availability of high resolution satellite time series and proven methodologies to extract temporal information from satellite acquisitions, comes the need for procedures to generate high resolution Earth observation derived phenological metrics that could serve a wide range of applications. In the last decade, Sentinel-2 MSI satellite sensors have contributed significantly in encouraging vegetation investigations which have led to develop methods and products [34,54,55]. In particular, the availability of high spatial resolution and dense revisit time satellite observations, opened the way to high resolution phenological metrics estimation, representing a promising tool for the study of terrestrial ecosystems and species-specific phenology.…”

Section: Introductionmentioning

confidence: 99%

Earth Observation for Phenological Metrics (EO4PM): Temporal Discriminant to Characterize Forest Ecosystems

2022

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The study of vegetation phenology has great relevance in many fields since the importance of knowing timing and shifts in periodic plant life cycle events to face the consequences of global changes in issues such as crop production, forest management, ecosystem disturbances, and human health. The availability of high spatial resolution and dense revisit time satellite observations, such as Sentinel-2 satellites, allows high resolution phenological metrics to be estimated, able to provide key information from time series and to discriminate vegetation typologies. This paper presents an automated and transferable procedure that combines validated methodologies based on local curve fitting and local derivatives to exploit full satellite Earth observation time series to produce information about plant phenology. Multivariate statistical analysis is performed for the purpose of demonstrating the capacity of the generated smoothed vegetation curve, temporal statistics, and phenological metrics to serve as temporal discriminants to detect forest ecosystems processes responses to environmental gradients. The results show smoothed vegetation curve and temporal statistics able to highlight seasonal gradient and leaf type characteristics to discriminate forest types, with additional information about forest and leaf productivity provided by temporal statistics analysis. Furthermore, temporal, altitudinal, and latitudinal gradients are obtained from phenological metrics analysis, which also allows to associate temporal gradient with specific phenophases that support forest types distinction. This study highlights the importance of integrated data and methodologies to support the processes of vegetation recognition and monitoring activities.

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Calibrating vegetation phenology from Sentinel-2 using eddy covariance, PhenoCam, and PEP725 networks across Europe

Cited by 79 publications

References 76 publications

Mapping Plant Diversity Based on Combined SENTINEL-1/2 Data—Opportunities for Subtropical Mountainous Forests

Mapping Plant Diversity Based on Combined SENTINEL-1/2 Data—Opportunities for Subtropical Mountainous Forests

Climate Effects on Vertical Forest Phenology of Fagus sylvatica L., Sensed by Sentinel-2, Time Lapse Camera, and Visual Ground Observations

Earth Observation for Phenological Metrics (EO4PM): Temporal Discriminant to Characterize Forest Ecosystems

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