Canopy Greenness Research Articles

Daily dynamic thresholds of different agricultural drought grades for summer maize based on the Vegetation Water Index

Effective daily drought monitoring for summer maize (Zea mays L.) is critical to prevent crop yield reduction. Most of the vegetation indicators used for drought identification have been based on changes in canopy greenness that may not change immediately under drought stress, and have relatively long-time intervals (e.g., eight or 16 days) that are limited by available remote sensing products. Moreover, drought thresholds, may be potentially different during different crop growth stages, rather than constant during the entire growing season, as has been widely used in most existing drought indicators. We used the orthorectified reflectance in the short-wave infrared (SWIR) band, extracted from MODIS MCD43A4 because of its high sensitivity to variations in vegetation water content due to this waveband’s liquid water absorption characteristics. The Vegetation Water Index (VWI), calculated with the Normalized Difference Water Index (NDWI), was proposed for daily drought monitoring of summer maize in order to minimize the effects of geographical location, soil conditions, and ecological systems in different areas. Additionally, daily dynamic thresholds for drought-free, mild drought, moderate drought, and severe drought were determined with double logistic fitting functions having the highest R2 (0.79), based on the observed drought disaster and phenophase records of summer maize in the Huang-Huai-Hai region (HHH). Drought samples constructed from regional drought records and the Yearbook of Meteorological Disasters in China were used for drought validations. The results exhibited high accuracy (78.04% of total drought samples correctly showing complete correspondence with historical records regarding drought grades, and 96.90% of drought samples being within one drought grade level). Additionally, compared with the Crop Water Deficit Index (CWDI), the temporal and spatial evolution of typical drought processes were more accurately characterized by VWI. The results of this study provide a scientific basis and achieve quantitative monitoring for daily drought assessment of summer maize in HHH, and also provide a new perspective in dynamic and quantitative monitoring of agricultural disasters over large regions based on actual disaster records and remotely sensed images.

Using high frequency digital repeat photography to quantify the sensitivity of a semi-arid grassland ecosystem to the temporal repackaging of precipitation

Across large portions of the western United States, drought intervals are increasing, often accompanied by larger-magnitude rainfalls. Semi-arid ecosystems are expected to be especially responsive to such temporal repackaging of rainfall because of their high sensitivity to variation in soil moisture. We conducted a field manipulation experiment to evaluate the impacts of summer rainfall repackaging (small/many events to large/few events with a fixed total seasonal amount) on a semi-arid mixed annual/perennial bunchgrass ecosystem. We monitored the sensitivity of plant greenness and productivity to soil moisture under rainfall pulses by combining automated, high-frequency repeat digital images at both plot- and plant functional type- level, whole plot CO2 uptake measurements, and continuous in-situ soil moisture data. We found that plot greenness was closely correlated with gross primary productivity across all rainfall repackaging treatments (R = 0.82). Plot greenness was weakly correlated to soil moisture at the beginning of the growing season but showed a significant positive correlation during peak and late growing season. Notably, we found a significant lag time of ∼5 days between changes in soil moisture and canopy greenness under large/few events, while there was no lag under small/many events. This time lag was found to be driven by perennial grass greenness and its sensitivity to the relatively deep soil water infiltration observed with large/few rainfall events. Predicting semi-arid grassland responses to soil moisture dynamics under ongoing rainfall repackaging, and subsequent impacts on the regional to global carbon budget, should consider plant communities’ phenological characteristics and functional type composition.

Autumn canopy senescence has slowed down with global warming since the 1980s in the Northern Hemisphere

Climate change strongly impact vegetation phenology, with considerable potential to alter land-atmosphere carbon dioxide exchange and terrestrial carbon cycle. In contrast to well-studied spring leaf-out, the timing and magnitude of autumn senescence remains poorly understood. Here, we use monthly decreases in Normalized Difference Vegetation Index satellite retrievals and their trends to surrogate the speed of autumn senescence during 1982–2018 in the Northern Hemisphere (>30°N). We find that climate warming accelerated senescence in July, but this influence usually reversed in later summer and early autumn. Interestingly, summer greening causes canopy senescence to appear later compared to an advancing trend after eliminating the greening effect. This finding suggests that summer canopy greening may counteract the intrinsic changes in autumnal leaf senescence. Our analysis of autumn vegetation behavior provides reliable guidance for developing and parameterizing land surface models that contain an interactive dynamic vegetation module for placement in coupled Earth System Models.

Using structure to model function: incorporating canopy structure improves estimates of ecosystem carbon flux in arctic dry heath tundra

Most tundra carbon flux modeling relies on leaf area index (LAI), generally estimated from measurements of canopy greenness using the normalized difference vegetation index (NDVI), to estimate the direction and magnitude of fluxes. However, due to the relative sparseness and low stature of tundra canopies, such models do not explicitly consider the influence of variation in tundra canopy structure on carbon flux estimates. Structure from motion (SFM), a photogrammetric method for deriving three-dimensional (3D) structure from digital imagery, is a non-destructive method for estimating both fine-scale canopy structure and LAI. To understand how variation in 3D canopy structure affects ecosystem carbon fluxes in Arctic tundra, we adapted an existing NDVI-based tundra carbon flux model to include variation in SFM-derived canopy structure and its interaction with incoming sunlight to cast shadows on canopies. Our study system consisted of replicate plots of dry heath tundra that had been subjected to three herbivore exclosure treatments (an exclosure-free control [CT], large mammals exclosure), and a large and small mammal exclosure [ExLS]), providing the range of 3D canopy structures employed in our study. We found that foliage within the more structurally complex surface of CT canopies received significantly less light over the course of the day than canopies within both exclosure treatments. This was especially during morning and evening hours, and was reflected in modeled rates of net ecosystem exchange (NEE) and gross primary productivity (GPP). We found that in the ExLS treatment, SFM-derived estimates of GPP were significantly lower and NEE significantly higher than those based on LAI alone. Our results demonstrate that the structure of even simple tundra vegetation canopies can have significant impacts on tundra carbon fluxes and thus need to be accounted for.

Satellite-derived forest canopy greenness shows differential drought vulnerability of secondary forests compared to primary forests in Peru

Understanding tropical secondary forest canopy greenness and responses to climatic conditions is important for climate change mitigation, particularly in the tropics where secondary forest growth is a substantial carbon sink and a promoted natural climate solution. We here test three hypotheses: (a) forest canopy greenness is higher in younger, secondary forests than in older, primary or mature forests, (b) secondary forests are more vulnerable to climatic pressures and (c) there are significant differences between forest types regarding primary–secondary canopy greenness and their differential responses to drought anomalies. To explore these relationships, we monitored wet and dry seasonal greenness from 2001 to 2020, estimated through the enhanced vegetation index (EVI), of Peruvian tropical dry, montane and lowland secondary forests and compared it to nearby primary forests. We developed predictive models of seasonal EVI using remotely sensed variables, including land surface temperature (LST), evapotranspiration (ET), potential evapotranspiration (PET), ratio of ET and PET (ETn), and the standard precipitation index (SPI). Overall, there was a higher change in annual and seasonal EVI for secondary forests compared to primary forests. However, primary forests maintained relatively stable EVI levels during the wet season despite drought anomalies. When decoupling forest type canopy greenness and drought response, primary forest greenness in dry and lowland ecosystems were temporally more stable. Secondary montane had a lower increase in greenness when drought anomalies held during different seasons. Stepwise multiple linear regression models indicated that LST and ETn, a plant water use index, were the most significant factors to predict greening fluctuations in dry and montane forest types. ET and SPI mostly drove wet season mean EVI across all forest types. Predictors of dry season mean EVI varied, but mostly including water availability. Our results suggest that tropical secondary forests are more productive overall yet more vulnerable to prolonged drought.

The Effects of Fertilizer Sources and Site Location on Greenhouse Gas Emissions from Creeping Bentgrass Putting Greens and Kentucky Bluegrass Roughs

Understanding greenhouse gas (GHG) emissions from turfgrass allows managers to make cultural management decisions to reduce GHG emissions. The objective of this study was to evaluate fertilizer source [urea (URE), polymer-encapsulated urea (POL), and milorganite (MIL)] and site location (green, wet rough, and dry rough) on GHG [carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O)] emissions. Greenhouse gas data, soil temperature, soil moisture, canopy greenness, and turfgrass quality were collected. High soil temperature and moisture were correlated with soil CO2 and N2O flux. The wet rough fluxed more soil CH4 across the 2-year study. The POL fluxed the highest amount of soil CO2, while POL and MIL fluxed the largest amount of soil N2O on the wet rough. Milorganite and POL increased canopy greenness in both roughs during the spring. On the green, URE produced greater canopy greenness in the spring and fall. Our results indicate that when soil moisture and temperature are high, turfgrass managers should employ methods of reducing soil temperatures that do not increase soil moisture to reduce GHG emissions. Under warm and wet conditions, gaseous losses of GHGs are accelerated with slow-release fertilizers.

Canopy greenness, atmospheric aridity, and large rain events jointly regulate evapotranspiration partitioning in a temperate semiarid shrubland

Canopy greenness, atmospheric aridity, and large rain events jointly regulate evapotranspiration partitioning in a temperate semiarid shrubland

Drought constrains acorn production and tree growth in the Mediterranean holm oak and triggers weak legacy effects

Droughts are becoming more frequent in the Mediterranean basin due to warmer conditions. Droughts negatively impact forests growth for several years, often generating negative legacies or carryover effects. However, these legacies differ among tree species, sites and drought characteristics and have been mainly studied considering tree growth or canopy greenness, but ignoring reproductive phenomena. Here, we compare the legacy effects of drought on acorn and male inflorescence production and radial growth by using a 19-year series of 150 Quercus ilex individuals in three stands located in north-eastern Spain. We evaluate the relationships between monthly climate variables, tree-ring width, acorn production and male inflorescence production. For the two driest years considered (2005 and 2012), when very few acorns were produced, we did not find negative legacy effects on acorn production in the three years following droughts. The production of male inflorescences did not show any significant legacy after drought, although its annual variation was related to the climatic conditions of the year before acorn ripening. Acorn production was higher than expected for some of these years, apparently following the pattern of tree growth recovery with a certain lag. This compensatory response of acorn production differed between the two analysed droughts, in accordance with different conditions of drought timing and post-drought climate conditions. Even though few negative legacy effects of growth and acorn production were found, we confirmed the negative effect of drought stress on tree growth and acorn production, linked to dry winter conditions. Our findings confirm that drought features (timing, duration, intensity) and post-drought climate conditions influence tree growth and reproduction legacies.

Negative relationship between photosynthesis and late-stage canopy development and senescence over Tibetan Plateau.

Canopy greening, which is associated with significant canopy structure changes, is the most notable signal of ecosystem changes in response to anthropogenic climate change. However, our knowledge of the changing pattern of canopy development and senescence, and its endogenous and climatic drivers is still limited. Here, we used the Normalized Difference Vegetation Index (NDVI) to quantify the changes in the speed of canopy development and senescence over the Tibetan Plateau (TP) during 2000-2018, and used a solar-induced chlorophyll fluorescence dataset as a proxy for photosynthesis, in combination with climate datasets to decipher the endogenous and climatic drivers of the interannual variation in canopy changes. We found that the canopy development during the early green-up stage (April-May) is accelerating at a rate of 0.45-0.8 × 10-3 month-1 year-1 . However, this accelerating canopy development was largely offset by a decelerating canopy development during June and July (-0.61 to -0.51 × 10-3 month-1 year-1 ), leading to the peak NDVI over the TP increasing at a rate of only one fifth of that in northern temperate regions, and less than one tenth of that in the Arctic and boreal regions. During the green-down period, we observed a significant accelerating canopy senescence during October. Photosynthesis was found to be the dominant driver for canopy changes over the TP. Increasing photosynthesis stimulates canopy development during the early green-up stage. However, slower canopy development and accelerated senescence was found with larger photosynthesis in late growth stages. This negative relationship between photosynthesis and canopy development is probably linked to the source-sink balance of plants and shifts in the allocation regime. These results suggest a sink limitation for plant growth over the TP. The impact of canopy greening on the carbon cycle may be more complicated than the source-oriented paradigm used in current ecosystem models.

Modeling seasonal vegetation phenology from hydroclimatic drivers for contrasting plant functional groups within drylands of the Southwestern USA

In dryland ecosystems, vegetation within different plant functional groups exhibits distinct seasonal phenologies that are affected by the prevailing hydroclimatic forcing. The seasonal variability of precipitation, atmospheric evaporative demand, and streamflow influences root-zone water availability to plants in water-limited environments. Increasing interannual variations in climate forcing of the local water balance and uncertainty regarding climate change projections have raised the potential for phenological shifts and changes to vegetation dynamics. This poses significant risks to plant functional types across large areas, especially in drylands and within riparian ecosystems. Due to the complex interactions between climate, water availability, and seasonal plant water use, the timing and amplitude of phenological responses to specific hydroclimate forcing cannot be determined a priori, thus limiting efforts to dynamically predict vegetation greenness under future climate change. Here, we analyze two decades (1994–2021) of remote sensing data (soil adjusted vegetation index (SAVI)) as well as contemporaneous hydroclimate data (precipitation, potential evapotranspiration, depth to groundwater, and air temperature), to identify and quantify the key hydroclimatic controls on the timing and amplitude of seasonal greenness. We focus on key phenological events across four different plant functional groups occupying distinct locations and rooting depths in dryland SE Arizona: semi-arid grasses and shrubs, xeric riparian terrace and hydric riparian floodplain trees. We find that key phenological events such as spring and summer greenness peaks in grass and shrubs are strongly driven by contributions from antecedent spring and monsoonal precipitation, respectively. Meanwhile seasonal canopy greenness in floodplain and terrace vegetation showed strong response to groundwater depth as well as antecedent available precipitation (aaP = P − PET) throughout reaches of perennial and intermediate streamflow permanence. The timings of spring green-up and autumn senescence were driven by seasonal changes in air temperature for all plant functional groups. Based on these findings, we develop and test a simple, empirical phenology model, that predicts the timing and amplitude of greenness based on hydroclimate forcing. We demonstrate the feasibility of the model by exploring simple, plausible climate change scenarios, which may inform our understanding of phenological shifts in dryland plant communities and may ultimately improve our predictive capability of investigating and predicting climate-phenology interactions in the future.

Effects of Seed Treatments and Yield Potential on Infected Soybean Seed

Diaporthe seed decay is an endemic disease throughout most soybean-growing regions. A major outbreak of Diaporthe seed decay occurred in 2018 in Iowa and other parts of the United States. The most prevalent fungi associated with this outbreak in Iowa were Diaporthe longicolla and D. caulivora. We established field experiments in 2019 and 2020 in two central Iowa locations to evaluate the effects of seed treatments and yield potential of Diaporthe-infected seed lots. Seed of the same variety but originating from different sources were categorized as “good” or “bad” seed depending on the percentage of Diaporthe-infected seed. Four seed treatments were tested, and results indicated that there were differences in plant stand, plant health (normalized difference vegetation index and canopy coverage) (both years), and yield (2019) between both seed lots. The good seed lot had about 9% greater yield than the bad seed lot ( P < 0.001), whereas the seed lots had similar yield in 2020 ( P = 0.822). Seed treatments improved stand, canopy greenness, and canopy coverage but did not affect yield in either year. For the yield potential experiment, there were minimal differences in plant health and no differences in yield between good and bad seed lots thinned to the same plant population in both years. These results suggest that Diaporthe-infected seed lots with lower germination and plant stand resulted in lower yield, and seed treatments can improve soybean stand, canopy greenness, and coverage. Additionally, Diaporthe-infected seed did not affect soybean growth and development or yield once plant stand was established.

Effects of Disease Resistance and Fungicide Application on Management of Tar Spot on Corn in the North Central United States

Tar spot, caused by Phyllachora maydis, is the most significant disease of corn in the North Central United States. Elucidating an integrated management approach to protect grain yield is a high priority. The integration of tillage, hybrid, and fungicide application on tar spot severity, canopy greenness, and grain yield was assessed in Indiana in 2019, 2020, and 2021. A split-plot arrangement of two tillage treatments as main plots with factorial arrangement of subplots with three hybrids (tar spot susceptible and two partially resistant) and fungicide application (propiconazole + benzovindiflupyr + azoxystrobin) were evaluated. Furthermore, a multistate study comparing two hybrids (susceptible and partially resistant) and fungicide application (propiconazole + benzovindiflupyr + azoxystrobin) was conducted in Indiana, Michigan, and Wisconsin in 2020. This research demonstrates that partially resistant hybrids with a standardized area under the disease progress curve of 2.5 and 3.0 had significantly less tar spot than a susceptible hybrid with a standardized area under the disease progress curve of 10.1 and increased canopy greenness ratings of 48.1 and 51.5%, which were significantly higher than the susceptible hybrid at 13.0% at maturity. Tar spot severity was further reduced and canopy greenness increased with a fungicide application in the susceptible hybrid. These results suggest that partial resistance alone may be used to manage tar spot. Yields did not differ significantly across hybrids with or without a fungicide application. However, a general trend of preserved yield potential was observed in the treatments with fungicide, indicating that knowledge of genetics and yield potential will be necessary to achieve the greatest benefit from partially resistant hybrids and judicious fungicide applications.

Stand characteristics modulate secondary growth responses to drought and gross primary production in Pinus halepensis afforestation

Impacts of climate warming on forests vigour are forecasted to increase in magnitude. Yet it remains unclear how stand characteristics and competition modulate the relationship between tree growth and gross primary production with drought. Here, we studied how the spatial variation in stand density, basal area and height modulates tree growth (Basal Area Increment, BAI and stand growth), summer NDVI, as well as their responses to drought (Standardized Precipitation-Evapotranspiration Index, SPEI) in 56 Aleppo pine (Pinus halepensis Mill.) planted forests located in Northeast Spain. Long-term BAI responses to SPEI were strongly determined by stand density, suggesting that competition modulates Aleppo pine growth responses to drought. Along this, summer NDVI also displayed strong associations with SPEI. NDVI was mostly related with stand growth, suggesting canopy densification drives NDVI pattern and trends. Short-term BAI and NDVI responses to severe droughts were mainly independent of stand characteristics. In the studied region, drought is a universal factor limiting Aleppo pine secondary growth and canopy greening. However, the results suggest that stand density modulates Aleppo pine growth responses to drought on the long-term, reducing the growth in densest stands. Denser stands with larger trees are the ones that present higher NDVI values, suggesting that canopy activity depends more on stand canopy coverage than on secondary growth rate and its response to drought. In these Mediterranean pines, canopy activity and secondary growth are temporally coupled but spatially decoupled.

Development of a LoRaWAN IoT Node with Ion-Selective Electrode Soil Nitrate Sensors for Precision Agriculture

Crop productivity is highly dependent on the availability of soluble nitrogen (N), e.g. nitrate, in soil. When N levels are low, fertilisers are applied to replenish the soil's reserves. Typically the timing of these applications is based on paper-based guidance and sensor-based measurements of canopy greenness, which provides an indirect measure of soil N status. However this approach often means that N fertiliser is applied inappropriately or too late, resulting in excess N being lost to the environment, or too little N to meet crop demand. To promote greater N use efficiency and improve agricultural sustainability, we developed an Internet of Things (IoT) approach for the real-time measurement of soil nitrate levels using ion-selective membrane sensors in combination with digital soil moisture probes. The node incorporates state-of-the-art IoT connectivity using a LoRaWAN transceiver. The sensing platform can transfer real-time data via a cloud-connected gateway for processing and storage. In summary, we present a validated soil sensor system for real-time monitoring of soil nitrate concentrations, which can support fertiliser management decisions, improve N use efficiency and reduce N losses to the environment.

COVID-19 pandemic underscores role of green space in urban carbon dynamics

For Detroit Michigan the arrival of COVID-19 led to intensive measures to prevent further spread of the virus resulting in consequent changes in traffic and energy use. We take advantage of these different emission scenarios to explore CO2 dynamics in a postindustrial city with a declining population and increasing green space. We present atmospheric CO2 concentration and net urban ecosystem exchange of CO2 (NUE) from a typical eddy covariance system and canopy greenness from a field camera on the Wayne State University campus in midtown Detroit. We categorized our study period (January 18, 2020–July 31, 2020) into three subperiods associated with the state-wide shelter-in-place order. Our results support that the city was a net carbon source throughout the period, particularly during the shelter-in-place period, although reduced traffic lowered CO2 concentrations and NUE. However, during the post-order period when traffic was highest, atmospheric CO2 concentrations and NUE were lowest, suggesting that the greening of urban vegetation may have greater carbon mitigation potential than lowering anthropogenic carbon emissions through traffic reductions.

Forest Damage by Super Typhoon Rammasun and Post-Disturbance Recovery Using Landsat Imagery and the Machine-Learning Method

Typhoon Rammasun landed on the southern coastal region of Guangdong and Hainan Provinces on 18 July 2014, and is the strongest recorded typhoon since the 1970s in China. It caused enormous losses in human lives, property, and crop yields in two provinces; however, its impact on forests and subsequent recovery has not yet been assessed. Here we detected forest damage area and severity from Typhoon Rammasun using Landsat 8 OLI imagery, the Random Forest (RF) machine-learning algorithm, and univariate image differencing (UID) methods, and the controlling factors on damage severity and canopy greenness recovery were further analyzed. The accuracy evaluations against sample plot data indicated that the RF approach can more accurately detect the affected forest area and damage severity than the UID-based methods, with higher overall accuracy (94%), Kappa coefficient (0.92), and regression coefficient (R2 = 0.81; p < 0.01). The affected forest area in Guangdong and Hainan was 13,556 km2 and 3914 km2, accounting for 13.8% and 18.5% total forest area, respectively. The highest affected forest fractions reached 70% in some cities or counties. The proportions of severe damage category accounted for 20.85% and 21.31% of all affected forests in Guangdong and Hainan, respectively. Our study suggests that increasing tree density and choosing less sensitive tree species would reduce damage from typhoons in vulnerable areas such as fringe, scattered, and high-slope forests. The canopy greenness of damaged forests recovered rapidly within three months for both provinces; however, management strategies should still be applied in the severely damaged areas to sustain forest functions since the persistent forest canopy structure and biomass may require a longer time to recover.

Evergreen broadleaf greenness and its relationship with leaf flushing, aging, and water fluxes

Remote sensing capabilities to monitor evergreen broadleaved vegetation are limited by the low temporal variability in the greenness signal. With canopy greenness computed from digital repeat photography (PhenoCam), we investigated how canopy greenness related to seasonal changes in leaf age and traits as well as variation of trees’ water fluxes (characterized by sap flow and canopy conductance). The results showed that sprouting leaves are mainly responsible for the rapid increase in canopy green chromatic coordinate (GCC) in spring. We found statistically significantly differences in leaf traits and spectral properties among leaves of different leaf ages. Specifically, mean GCC of young leaves was 0.385 ± 0.010 (mean ± SD), while for mature and old leaves was 0.369 ± 0.003, and 0.376 ± 0.004, respectively. Thus, the temporal dynamics of canopy GCC can be explained by changes in leaf spectral properties and leaf age. Sap flow and canopy conductance are both well explained by a combination of environmental drivers and greenness (96% and 87% of the variance explained, respectively). In particular, air temperature and vapor pressure deficit (VPD) explained most of sap flow and canopy conductance variance, respectively. Besides, GCC is an important explanatory variable for variation of canopy conductance may because GCC can represent the leaf ontogeny information. We conclude that PhenoCam GCC can be used to identify the leaf flushing for evergreen broadleaved trees, which carries important information about leaf ontogeny and traits. Thus, it can be helpful for better estimating canopy conductance which constraints water fluxes.

Disentangling the Relative Drivers of Seasonal Evapotranspiration Across a Continental‐Scale Aridity Gradient

AbstractEvapotranspiration (ET) is a significant ecosystem flux, governing the partitioning of energy at the land surface. Understanding the seasonal pattern and magnitude of ET is critical for anticipating a range of ecosystem impacts, including drought, heat‐wave events, and plant mortality. In this study, we identified the relative controls of seasonal variability in ET, and how these controls vary among ecosystems. We used overlapping AmeriFlux and PhenoCam time series at a daily timestep from 20 sites to explore these linkages (# site‐years >100), and our study area covered a broad climatological aridity gradient in the U.S. and Canada. We focused on disentangling the most important controls of bulk surface conductance (Gs) and evaporative fraction (EF = LE/[H + LE]), where LE and H represent latent and sensible heat fluxes, respectively. Specifically, we investigated how vegetation phenology varied in importance relative to meteorological variables (vapor pressure deficit and antecedent precipitation) as a driver of Gs and EF using path analysis, a framework for quantifying and comparing the causal linkages among multiple response and explanatory variables. Our results revealed that the drivers of Gs and EF seasonality varied significantly between energy‐ and water‐limited ecosystems. Specifically, precipitation had a much higher effect in water‐limited ecosystems, while seasonal patterns in canopy greenness emerged as a stronger control in energy‐limited ecosystems. Given that phenology is expected to shift under future climate, our findings provide key information for understanding and predicting how phenology may impact 21st‐century hydroclimate regimes and the surface‐energy balance.

Modeling plant phenology by MODIS derived photochemical reflectance index (PRI)

Vegetation phenology is a sensitive indicator of ecosystem responses to climate change, and thus the accurate estimation of vegetation phenology is critical to evaluate the impact of climate change on terrestrial ecosystems. Common structural vegetation indices (VIs) such as the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Near-infrared Reflectance of Vegetation (NIRv) and Plant Phenology Index (PPI), are the most widely used indicators of phenology, but they have limited potential in tracking autumn phenology, especially for evergreen species with low seasonal variability of canopy greenness. Given the important role of carotenoid pigments in regulating photosynthetic activity and plant phenology, we hypothesize that satellite-based indicators of leaf pigments derived from MODIS ocean bands could be useful for phenology modeling. Using 624 site-years of flux data at 84 FLUXNET sites and 9979 ground observations at 138 PEP725 sites, we first explored the potential of different forms of scaled photochemical reflectance index (sPRIref) in monitoring photosynthetic activity, and found that band 10 and band 13 were more suitable for tracking gross primary productivity (GPP) than other reference bands. By comparing with canopy photosynthetic phenology, sPRI10 and sPRI13 showed improved representation of phenological transitions (the start and end of growing season, SOS and EOS, respectively) than structural VIs. In spring, all VIs exhibited comparable performances for estimating SOS at deciduous broadleaf forests (DBF) and grasslands (GRA) sites; however, sPRI10 and sPRI13 were better predictors of SOS than structural VIs at evergreen needleleaf forests (ENF) and mixed forests (MF) sites. In autumn, sPRI10 and sPRI13 showed improved predictive strength of EOS than structural VIs for ENF, MF and GRA sites. Further investigations using the ground observed phenological records also confirmed the improved performances of sPRI10 and sPRI13 for both SOS and EOS estimation. We also investigated the spatial patterns of sPRI10-derived SOS and EOS over the Northern Hemisphere with respect to different plant functional types. We showed that sPRI10 reliably tracked plant phenology with 83.0% and 78.8% success in detecting SOS and EOS, respectively. Spatial patterns of SOS exhibited obvious latitudinal gradients, while EOS showed a strong regional heterogeneity. In addition, sPRI10 predicted an overall earlier SOS (61.8%) and later EOS (51.2%) than the MODIS phenology product (VNP22Q2 v001) estimated from structural VI, suggesting the latter underestimated the greening potential of the Northern Hemisphere. Our results suggest that MODIS PRI could be useful to monitor vegetation phenology, and further reveal the importance of underappreciated carotenoid pigments in tracking plant seasonal changes, particularly in autumn months.

Decoupling of greenness and gross primary productivity as aridity decreases

Ecosystem primary productivity is a key ecological process influencing many ecosystem services, including carbon storage. Thus, clarifying how primary productivity in terrestrial ecosystems responds to climatic variability can reveal key mechanisms that will drive future changes in the global carbon budget. Satellite products of canopy greenness are widely used as proxies for vegetation productivity to evaluate how ecosystems respond to climate variability. However, to what degree inter-annual variations in productivity are consistent with greenness and how this relationship varies spatially remains unclear. Here we investigated the strength of the coupling between inter-annual variations in leaf area index (LAI, a measure of greenness) and ecosystem gross primary productivity (GPP) derived from eddy covariance towers, i.e., the r2 of the LAI-GPP relationship. Overall, inter-annual GPP and LAI were highly coupled (i.e., high r2) in arid grasslands, but were fully decoupled in mesic evergreen broadleaf forests, indicating that this relationship varies strongly along aridity gradients. A possible mechanism of the spatial variation in the LAI-GPP relationship is that the tradeoff between ecosystem structure (LAI) and physiology (photosynthesis per unit leaf area) becomes stronger in more humid climates. Land models overestimated the r2 of LAI-GPP correlation for most ecosystem types and failed to capture the spatial pattern along aridity gradients. We conclude that relying on greenness products for evaluating inter-annual changes in vegetation productivity may bias assessments, especially in tropical rainforest ecosystems. Our findings may also reconcile observed disparities between responses in greenness and GPP during drought in Amazon forests.