Emerging opportunities and challenges in phenology: a review

Plant phenology research has gained increasing attention because of the sensitivity of phenology to climate change and its consequences for ecosystem function. Recent technological development has made it possible to gather invaluable data at a variety of spatial and ecological scales. Despite our ability to observe phenological change at multiple scales, the mechanistic basis of phenology is still not well understood. Integration of multiple disciplines, including ecology, evolutionary biology, climate science, and remote sensing, with long-term monitoring data across multiple spatial scales are needed to advance understanding of phenology. We review the mechanisms and major drivers of plant phenology, including temperature, photoperiod, and winter chilling, as well as other factors such as competition, resource limitation, and genetics. Shifts in plant phenology have significant consequences on ecosystem productivity, carbon cycling, competition, food webs, and other ecosystem functions and services. We summarize recent advances in observation techniques across multiple spatial scales, including digital repeat photography, other complementary optical measurements, and solar induced fluorescence, to assess our capability to address the importance of these scale-dependent drivers. Then we review phenology models as an important component of earth system modeling. We find that the lack of species-level knowledge and observation data lead to difficulties in the development of vegetation phenology models at ecosystem or community scales. Finally, we recommend further research to advance understanding of the mechanisms governing phenology and the standardization of phenology observation methods across networks. With the opportunity for “big data” collection for plant phenology, we envision a breakthrough in process-based phenology modeling

A Novel Cloud Removal Method Based on Ihot and the Cloud Trajectories for Landsat Imagery

Cloud removal is significantly needed for enhancing the further utilization of Landsat imagery, since such optical remote sensing satellite images are inevitably contaminated by clouds. Clouds dynamically affect the signal transmission due to their different shapes, heights, and distribution. Generally, pixel replacement is the only and common method used to remove thick opaque clouds, and radiometric correction techniques has been widely adopted to remove the thin clouds. However, no methods can remove both thick and thin clouds at the same time. In this paper, a new method is proposed based on fitting “trajectory” of cloudy pixels with the help of IHOT spatially charactering clouds for pixel correction, which considers signal transmission including not only the additive reflectance from the clouds but also the energy attenuation when solar radiation passes through them. The experimental results show that the proposed approach performs effective removal for thick and thin clouds, and possesses the highest accuracy with the reference image, which can restore land cover information accurately

Earlier Vegetation Green-Up Has Reduced Spring Dust Storms

The observed decline of spring dust storms in Northeast Asia since the 1950s has been attributed to surface wind stilling. However, spring vegetation growth could also restrain dust storms through accumulating above ground biomass and increasing surface roughness. To investigate the impacts of vegetation spring growth on dust storms, we examine the relationships between recorded spring dust storm outbreaks and satellite-derived vegetation green-up date in Inner Mongolia, Northern China from 1982 to 2008. We find a significant dampening effect of advanced vegetation growth on spring dust storms (r = 0.49, p = 0.01), with a one-day earlier green-up date corresponding to a decrease in annual spring dust storm outbreaks by 3%. Moreover, the higher correlation (r = 0.55, p \u3c 0.01) between green-up date and dust storm outbreak ratio (the ratio of dust storm outbreaks to times of strong wind events) indicates that such effect is independent of changes in surface wind. Spatially, a negative correlation is detected between areas with advanced green-up dates and regional annual spring dust storms (r = −0.49, p = 0.01). This new insight is valuable for understanding dust storms dynamics under the changing climate. Our findings suggest that dust storms in Inner Mongolia will be further mitigated by the projected earlier vegetation green-up in the warming world

An Overview of Ecosystem Changes in Tibetan and Other Alpine Regions from Earth Observation

Alpine ecosystems have shown sensitive responses to climate change during the past few decades [...

The Mixed Pixel Effect in Land Surface Phenology: A Simulation Study

Because of the limited spatiotemporal resolutions in vegetation index(VI) products, land surface phenology (LSP) results may not well capture ground-based phenological changes. This is likely the result of the mixed pixel effect: (1) a pixel in VI products may contain an unknown composition of vegetation species or land cover types; and (2) these species differ in their sensitivity to climatic variations. The mixed pixel effect has induced inconsistent findings in LSP with in situ observations of spring phenology. To this end, this study has designed a series of simulation experiments to initiate the methodological exploration of how the green-up date (GUD) of a mixed pixel could be altered by the endmember GUDs and different non-GUD variables, including the endmember composition, minimum and maximum normalized difference vegetation index (NDVI), and the length of the growth period. The study has also compared the sensitivity of two generally adopted GUD identification methods, the relative threshold method and the curvature method (also known as the inflection-point method). The simulations with two endmembers show that even if there is no change in the endmember GUDs, the GUD of the mixed pixel could be substantially altered by the changes in non-GUD variables. In addition, the study has also developed a simulation toolkit for the GUD identification with cases of three or more endmembers. The results of the study provide insights into effective strategies for analyzing spring phenology using VI products: the mixed pixel effect can be alleviated by selecting pixels that are relatively stable in the land cover or species composition. This simulation study calls for in situ phenological observations to validate the LSP, such as conducting climate-controlled experiments on few mixed species at a small spatial scale. The paper also argues for the necessity of isolating GUD trends caused by non-phenological changes in the study of spring phenology

Coarse-Resolution Satellite Images Overestimate Urbanization Effects on Vegetation Spring Phenology

Numerous investigations of urbanization effects on vegetation spring phenology using satellite images have reached a consensus that vegetation spring phenology in urban areas occurs earlier than in surrounding rural areas. Nevertheless, the magnitude of this rural–urban difference is quite different among these studies, especially for studies over the same areas, which implies large uncertainties. One possible reason is that the satellite images used in these studies have different spatial resolutions from 30 m to 1 km. In this study, we investigated the impact of spatial resolution on the rural–urban difference of vegetation spring phenology using satellite images at different spatial resolutions. To be exact, we first generated a dense 10 m NDVI time series through harmonizing Sentinel-2 and Landsat-8 images by data fusion method, and then resampled the 10 m time series to coarser resolutions from 30 m to 8 km to simulate images at different resolutions. Afterwards, to quantify urbanization effects, vegetation spring phenology at each resolution was extracted by a widely used tool, TIMESAT. Last, we calculated the difference between rural and urban areas using an urban extent map derived from NPP VIIRS nighttime light data. Our results reveal: (1) vegetation spring phenology in urban areas happen earlier than rural areas no matter which spatial resolution from 10 m to 8 km is used, (2) the rural–urban difference in vegetation spring phenology is amplified with spatial resolution, i.e., coarse satellite images overestimate the urbanization effects on vegetation spring phenology, and (3) the underlying reason of this overestimation is that the majority of urban pixels in coarser images have higher diversity in terms of spring phenology dates, which leads to spring phenology detected from coarser NDVI time series earlier than the actual dates. This study indicates that spatial resolution is an important factor that affects the accuracy of the assessment of urbanization effects on vegetation spring phenology. For future studies, we suggest that satellite images with a fine spatial resolution are more appropriate to explore urbanization effects on vegetation spring phenology if vegetation species in urban areas is very diverse

Reconstructing High-Spatiotemporal-Resolution (30 m and 8-Days) NDVI Time-Series Data for the Qinghai–Tibetan Plateau from 2000–2020

As the largest and highest alpine ecoregion in the world, the Qinghai–Tibetan Plateau (QTP) is extremely sensitive to climate change and has experienced extraordinary warming during the past several decades; this has greatly affected various ecosystem processes in this region such as vegetation production and phenological change. Therefore, numerous studies have investigated changes in vegetation dynamics on the QTP using the satellite-derived normalized-difference vegetation index (NDVI) time-series data provided by the Moderate-Resolution Imaging Spectroradiometer (MODIS). However, the highest spatial resolution of only 250 m for the MODIS NDVI product cannot meet the requirement of vegetation monitoring in heterogeneous topographic areas. In this study, therefore, we generated an 8-day and 30 m resolution NDVI dataset from 2000 to 2020 for the QTP through the fusion of 30 m Landsat and 250 m MODIS NDVI time-series data. This dataset, referred to as QTP-NDVI30, was reconstructed by employing all available Landsat 5/7/8 images (>100,000 scenes) and using our recently developed gap-filling and Savitzky–Golay filtering (GF-SG) method. We improved the original GF-SG approach by incorporating a module to process snow contamination when applied to the QTP. QTP-NDVI30 was carefully evaluated in both quantitative assessments and visual inspections. Compared with reference Landsat images during the growing season in 100 randomly selected subregions across the QTP, the reconstructed 30 m NDVI images have an average mean absolute error (MAE) of 0.022 and a spatial structure similarity (SSIM) above 0.094. We compared QTP-NDVI30 with upscaled cloud-free PlanetScope images in some topographic areas and observed consistent spatial variations in NDVI between them (averaged SSIM = 0.874). We further examined an application of QTP-NDVI30 to detect vegetation green-up dates (GUDs) and found that QTP-NDVI30-derived GUD data show general agreement in spatial patterns with the 250 m MODIS GUD data, but provide richer spatial details (e.g., GUD variations at the subpixel scale). QTP-NDVI30 provides an opportunity to monitor vegetation and investigate land-surface processes in the QTP region at fine spatiotemporal scales

Remotely Sensed Vegetation Green-Up Onset Date on the Tibetan Plateau: Simulations and Future Predictions

Vegetation green-up onset date (VGD) is a key indicator of ecosystem structure and processes. As the largest and highest alpine ecoregion, the Tibetan plateau (TP) has experienced markable climate warming during the past decades and showed substantial changes in VGD. However, the existing process-based phenology models still cannot simulate interannual variations in satellite-derived VGD. In this study, we developed a data-driven VGD model for the TP based on the Long short-term memory neural network (called VGD-LSTM). VGD-LSTM considers the complicated nonlinear relationship between VGD and multiple climatic and environmental drivers, including the time series of temperature (daytime, daily minimum, and daily mean) and precipitation, as well as nonsequential variables (elevation and geolocation). Compared with the benchmark process-based VGD model for the TP (i.e., the hierarchical model), VGD-LSTM greatly improved the simulation of interannual VGD variations. We calculated the correlation coefficients (R) between satellite-derived VGDs and VGD simulations during 2000–2018; the percentages of pixels with R values above 0.5 increased from 15% for the hierarchical model to 41% for VGD-LSTM. The advanced trend in the satellite-derived VGD on the entire TP during 2000–2018 (−0.37 day/year) was captured well by VGD-LSTM (−0.33 day/year) but was underestimated by the hierarchical model (−0.08 day/year). According to VGD-LSTM simulations, VGDs on the TP are projected to advance by 8–10 days by 2100 relative to 2015–2020 under high shared socioeconomic pathway scenarios. This study suggests the potential of artificial intelligence in phenology modeling for which the physiological processes are difficult to be fully represented