The atmospheric mixing layer height (MLH) is a critical variable for understanding and constraining ecosystem and climate dynamics. Past MLH estimation efforts have largely relied on data with low temporal (radiosondes) or spatial (reanalysis) resolutions. This study is unique in that it utilized continuous point-based ceilometer- and radiosonde-derived measurements of MLH at surface flux tower sites to identify the surface influence on MLH dynamics. We found a strong correlation (R2 = 0.73-0.91) between radiosonde MLH and ceilometer MLH at two sites with co-located observations. Seasonally, mean MLH was the highest at all sites during the summer, while the highest annual mean MLH was found at the warm and dry sites, dominated by high sensible heat fluxes. At daily time scales, surface fluxes of sensible heat, latent heat, and vapor pressure deficit had the largest influence on afternoon MLH. However, at best, the identified forcing variables and surface fluxes only accounted for -3865% of the variability in MLH under all sky conditions, and -53-76% of the variability under clear skies. These results highlight the difficulty in using single-point observations to explain MLH dynamics but should encourage the use of ceilometers or similar atmospheric measurements at surface flux sites in future studies.

Vegetation phenology is a key control on water, energy, and carbon fluxes in terrestrial ecosystems. Because vegetation canopies are heterogeneous, spatially explicit information related to seasonality in vegetation activity provides valuable information for studies that use eddy covariance measurements to study ecosystem function and land-atmosphere interactions. Here we present a land surface phenology (LSP) dataset derived at 3 m spatial resolution from PlanetScope imagery across a range of plant functional types and climates in North America. The dataset provides spatially explicit information related to the timing of phenophase changes such as the start, peak, and end of vegetation activity, along with vegetation index metrics and associated quality assurance flags for the growing seasons of 2017–2021 for 10 × 10 km windows centred over 104 eddy covariance towers at AmeriFlux and National Ecological Observatory Network (NEON) sites. These LSP data can be used to analyse processes controlling the seasonality of ecosystem-scale carbon, water, and energy fluxes, to evaluate predictions from land surface models, and to assess satellite-based LSP products.

Projected changes in temperature and precipitation are expected to influence spring and autumn vegetation phenology and hence the length of the growing season in many ecosystems. However, the sensitivity of green‐up and senescence to climate remains uncertain. We analyzed 488 site years of canopy greenness measurements from deciduous forest broadleaf forests across North America. We found that the sensitivity of green‐up to temperature anomalies increases with increasing mean annual temperature, suggesting lower temperature sensitivity as we move to higher latitudes. Furthermore, autumn senescence is most sensitive to moisture deficits at dry sites, with decreasing sensitivity as mean annual precipitation increases. Future projections suggest North American deciduous forests will experience higher sensitivity to temperature in the next 50 years, with larger changes expected in northern regions than in southern regions. Our study highlights how interactions between long‐term and short‐term changes in the climate system influence green‐up and senescence.

Digital repeat photography and near-surface remote sensing have been used by environmental scientists to study environmental change for nearly a decade. However, a user-friendly, reliable, and robust platform to extract color-based statistics and time series from a large stack of images is still lacking. Here, we present an interactive open-source toolkit, called xROI, that facilitates the process of time series extraction and improves the quality of the final data. xROI provides a responsive environment for scientists to interactively (a) delineate regions of interest (ROI), (b) handle field of view (FOV) shifts, and (c) extract and export time series data characterizing color-based metrics. The software gives user the opportunity to adjust mask files or draw new masks, every time an FOV shift occurs. Utilizing xROI can significantly facilitate data extraction from digital repeat photography and enhance the accuracy and continuity of extracted data.

Shifts in vegetation phenology are a key example of the biological effects of climate change1–3. However, there is substantial uncertainty about whether these temperature-driven trends will continue, or whether other factors—for example, photoperiod—will become more important as warming exceeds the bounds of historical variability4,5. Here we use phenological transition dates derived from digital repeat photography6 to show that experimental whole-ecosystem warming treatments7 of up to +9 °C linearly correlate with a delayed autumn green-down and advanced spring green-up of the dominant woody species in a boreal Picea–Sphagnum bog. Results were confirmed by direct observation of both vegetative and reproductive phenology of these and other bog plant species, and by multiple years of observations. There was little evidence that the observed responses were constrained by photoperiod. Our results indicate a likely extension of the period of vegetation activity by 1–2 weeks under a ‘CO2 stabilization’ climate scenario (+2.6 ± 0.7 °C), and 3–6 weeks under a ‘high-CO2 emission’ scenario (+5.9 ± 1.1 °C), by the end of the twenty-first century. We also observed severe tissue mortality in the warmest enclosures after a severe spring frost event. Failure to cue to photoperiod resulted in precocious green-up and a premature loss of frost hardiness8, which suggests that vulnerability to spring frost damage will increase in a warmer world9,10. Vegetation strategies that have evolved to balance tradeoffs associated with phenological temperature tracking may be optimal under historical climates, but these strategies may not be optimized for future climate regimes. These in situ experimental results are of particular importance because boreal forests have both a circumpolar distribution and a key role in the global carbon cycle11. Experimental whole-ecosystem warming treatments of a Picea–Sphagnum peat bog reveal the likely phenological consequences of future temperature increases that exceed those of historical climate regimes.

Phenology, or the seasonality of recurring biological events such as vegetation canopy http://www.agu.org/pubs/crossref/2006/2005JD006588.shtml (1 of 2)9/1/2006 9:19:03 AM Evaluation of the SeaWinds scatterometer for regional monitoring of vegetation phenology development and senescence, is a primary constraint on global carbon, water and energy cycles. We analyzed multiseason Ku-band radar backscatter measurements from the SeaWinds-on-QuikSCAT scatterometer to determine canopy phenology and growing season vegetation dynamics from 2000 to 2002 at 27 sites representing major global land cover classes and regionally across most of North America. We compared these results with similar information derived from the MODIS leaf area index (LAI) data product (MOD-15A2). In site-level linear regression analysis, the correspondence between radar backscatter and LAI was significant (p < 0.05) at most but not all sites and was generally higher (R2 > 0.5) for sites with relatively low LAI or where the seasonal range in LAI was large (e.g., >3 m^ m~2). The SeaWinds instrument also detected generally earlier onset of vegetation canopy growth in spring than the optical/near-infrared (NIR) based LAI measurements from MODIS, though the timing of canopy senescence and the end of the growing season were more similar. Over North America, the correlation between the two time series was stratified largely by land cover class, with higher correlations (R ~ 0.70.9) for most cropland, deciduous broadieaf forest, crop/natural vegetation mosaic land cover, and some grassland. Lower correlations were observed for open shrubiand and evergreen needieieaf forest. Overall, the results indicate that SeaWinds backscatter is sensitive to growing season canopy dynamics across a range of broadieaf vegetation types and provides a quantitative view that is independent of optieal/NIR remote sensing

Green leaf phenology is known to be sensitive to climate variation. Phenology is also important because it exerts significant control on terrestrial carbon cycling and sequestration. High-quality measurements of green leaf phenology are therefore increasingly important for understanding the effects of climate change on ecosystem function and biosphere–atmosphere interactions. In this paper, we compare “near-surface” and satellite remote sensing-based observations of vegetation phenology at four deciduous forest sites. Specifically, we addressed three questions related to how observations of plant phenology measured by red–green–blue (RGB) cameras mounted on towers above forest canopies are related to measurements of phenology acquired by moderate resolution sensors on satellites. First, how are estimated phenophase transition dates — or the observable stages in the life cycle of plants — influenced by the choice of vegetation index (VI) measured by remote sensing? Second, are VIs and phenological metrics derived from near-surface and satellite remote sensing comparable, and what is the nature and magnitude of covariation between near-surface and satellite-remote sensing-based estimates of phenology at seasonal and interannual time scales? Third, does near-surface remote sensing data provide a basis for validating satellite-derived land surface phenology products and what are the requirements for achieving this goal? Our study provides substantial support for future efforts linking satellite and near-surface remote sensing. We show significant agreement between phenological time series and metrics derived from these two data sources. However, issues of scale and representation strongly influence the relationship between near surface and satellite remote sensing measures of phenology. In particular, intra- and interannual correlation between time series from each source are dependent on how representative the camera FOV is of the regional landscape. Further, our results show that the specific VI used to monitor phenology exerts substantial influence on satellite VI derived phenological metrics, and by extension, how they compare to VI time series and metrics obtained from near-surface remote sensing. These results improve understanding of how near-surface and satellite remote sensing complement each other. However, more work is required to develop formal protocols for evaluating, calibrating and validating satellite remote sensing phenology products using near surface remote sensing at a regional to continental scale.

Digital repeat photography has the potential to become an important long-term data source for phenological research given its advantages in terms of logistics, continuity, consistency and objectivity over traditional assessments of vegetation status by human observers. Red-green-blue (RGB) color channel information from digital images can be separately extracted as digital numbers, and subsequently summarized through color indices such as excess green (ExG = 2G - [R + B]) or through nonlinear transforms to chromatic coordinates or other color spaces. Previous studies have demonstrated the use of ExG and the green chromatic coordinate (g(cc) = G/[R+ G + B]) from digital landscape image archives for tracking canopy development but several methodological questions remained unanswered. These include the effects of diurnal, seasonal and weather-related changes in scene illumination on ExG and g(cc), and digital camera and image file format choice.We show that g, is generally more effective than ExG in suppressing the effects of changes in scene illumination. To further reduce these effects we propose a moving window approach that assigns the 90th percentile of all daytime values within a three-day window to the center day (per90), resulting in three-day ExG and gee. Using image archives from eleven forest sites in North America, we demonstrate that per90 is able to further reduce unwanted variability in ExG and g due to changes in scene illumination compared to previously used mean mid-day values of ExG and g(cc).Comparison of eleven different digital cameras at Harvard Forest (autumn 2010) indicates that camera and image file format choice might be of secondary importance for phenological research: with the exception of inexpensive indoor webcams, autumn patterns of changes in g, and ExG from images in common JPEG image file format were in good agreement, especially toward the end of senescence. Due to its greater effectiveness in suppressing changes in scene illumination, especially in combination with per90, we advocate the use of gee for phenological research. Our results indicate that g(cc) from different digital cameras can be used for comparing the timing of key phenological events (e.g., complete leaf coloring) across sites. However, differences in how specific cameras "see" the forest canopy may obscure subtle phenological changes that could be detectable if a common protocol was implemented across sites. (C) 2011 Elsevier B.V. All rights reserved.

We analyzed 2000–2004 growing‐season SeaWinds Ku‐band microwave backscatter and MODIS leaf area index (LAI) data over North America. Large anomalies in mid‐growing‐season mean backscatter and LAI, relative to 5‐year mean values, occurred primarily in the western Great Plains; backscatter and LAI anomalies had similar spatial patterns across this region. Backscatter and LAI time series data for three ∼103 km2 regions in the western Great Plains were strongly correlated (r2 ∼ 0.6–0.8), and variability in mid‐growing season values was well‐correlated with annual precipitation (October through September). The results indicate that SeaWinds backscatter is sensitive to interannual variability in grassland biomass/productivity, and can provide an assessment that is completely independent of optical/near‐infrared remote sensing instruments.

Elastic electron scattering cross sections have been measured for Y-89 at 180-degrees for 0.72 < q < 2.67 fm-1. The M1 form factor has been extracted and a Fourier-Bessel analysis performed to obtain the ground-state magnetization density. Calculations including effects of core polarization have been performed within the framework of the finite Fermi system theory. These show a weakening of the strong repulsion in the spin-isospin channel and are in overall agreement with the data.