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Leaf-level gas exchange data support the mechanistic understanding of plant fluxes of carbon and water. These fluxes inform our understanding of ecosystem function, are an important constraint on parameterization of terrestrial biosphere models, are necessary to understand the response of plants to global environmental change, and are integral to efforts to improve crop production. Collection of these data using gas analyzers can be both technically challenging and time consuming, and individual studies generally focus on a small range of species, restricted time periods, or limited geographic regions. The high value of these data is exemplified by the many publications that reuse and synthesize gas exchange data, however the lack of metadata and data reporting conventions make full and efficient use of these data difficult. Here we propose a reporting format for leaf-level gas exchange data and metadata to provide guidance to data contributors on how to store data in repositories to maximize their discoverability, facilitate their efficient reuse, and add value to individual datasets. For data users, the reporting format will better allow data repositories to optimize data search and extraction, and more readily integrate similar data into harmonized synthesis products. The reporting format specifies data table variable naming and unit conventions, as well as metadata characterizing experimental conditions and protocols. For common data types that were the focus of this initial version of the reporting format, i.e., survey measurements, dark respiration, carbon dioxide and light response curves, and parameters derived from those measurements, we took a further step of defining required additional data and metadata that would maximize the potential reuse of those data types. To aid data contributors and the development of data ingest tools by data repositories we provided a translation table comparing the outputs of common gas exchange instruments. Extensive consultation with data collectors, data users, instrument manufacturers, and data scientists was undertaken in order to ensure that the reporting format met community needs. The reporting format presented here is intended to form a foundation for future development that will incorporate additional data types and variables as gas exchange systems and measurement approaches advance in the future. The reporting format is published in the U.S. Department of Energy's ESS-DIVE data repository, with documentation and future development efforts being maintained in a version control system. publishedVersion

Continuous monitoring of vegetation indices (VIs) the fraction of absorbed photosynthetically active radiation (fPAR) and leaf area index (LAI) through satellite remote sensing has advanced our understanding of biosphere–atmosphere interactions. Substantial efforts have been put into monitoring individual variables in the field, but options to concurrently monitor VIs, fPAR, and LAI in-situ have been lacking. In this paper, we present the Smart Surface Sensing System (4S), which automatically collects, transfers and processes VIs, fPAR and LAI data streams. The 4S consists of a microcomputer, controller and camera, a multi-spectral spectrometer built in with a light-emitting diode (LED) and an internet connection. Lab testing and field observations in a rice paddy site that experiences wet summer monsoon seasons confirmed the linear response of 4S to light intensities in the blue, green, red and near-infrared spectral channels, with wide ranging temperatures and humidity having only a minor impact on 4S throughout the growing season. Applied over an entire rice growing season (day of year [DOY] 120 - 248), VIs and fPAR from 4S were linearly related to corresponding VIs from a reference spectrometer (R2 = 0.98; NDVI, R2 = 0.96; EVI) and the LAI-2200 instrument (R2 = 0.76), respectively. Integration of gap fraction-based LAI from LED sensors and a green index from the micro-camera allowed tracking of the seasonality of green LAI. The continuous and diverse nature of 4S observations highlights its potential for evaluating satellite remote sensing products. We believe that 4S will be useful for the expansion of ecological sensing networks across multiple spatial and temporal scales.

The ECOsystem Space Thermal Radiometer Experiment on the Space Station (ECOSTRESS) was launched to the International Space Station on June 29, 2018 by the National Aeronautics and Space Administration (NASA). The primary scientific focus of ECOSTRESS is on evapotranspiration (ET), which occurs as Level 3 (L3) latent heat flux (LE) data products. This data is generated from the Level 2 Earth Surface Temperature and Emissivity Product (L2_LSTE), along with the atmospheric and ancillary surface data. Here, we provide the first validation (Stage 1, preliminary) of the global product ECOSTRESS clear - sky ET (L3_ET_PT - JPL, Version 6.0) against LE measurements at 82 eddy covariance sites around the world. Overall, the ECOSTRESS ET product performs well against site measurements (instant clear sky / flyover time: r 2 = 0.88; overall bias = 8%; normalized root mean square error, RMSE = 6%) . ET uncertainty was generally constant across climate zones, biome types, and times of day (ECOSTRESS samples the diurnal cycle), although temperate sites are over-represented. ECOSTRESS 70 m high spatial resolution improved correlations by 85% and RMSE by 62%, relative to 1 km pixels. This document serves as a reference for the accuracy of ECOSTRESS L3 ET and the Stage 1 validation status for subsequent science that continues to use this data. Fil: Fisher, Joshua B. California Institute of Technology. Jet Propulsion Laboratory; Estados Unidos Fil: Lee, Brian. California Institute of Technology. Jet Propulsion Laboratory; Estados Unidos Fil: Purdy, Adam J. California Institute of Technology. Jet Propulsion Laboratory; Estados Unidos Fil: Halverson, Gregory H. California Institute of Technology. Jet Propulsion Laboratory; Estados Unidos Fil: Dohlen, Matthew B. California Institute of Technology. Jet Propulsion Laboratory; Estados Unidos Fil: Cawse - Nicholson, Kerry. California Institute of Technology. Jet Propulsion Laboratory; Estados Unidos Fil: Wang, Audrey. California Institute of Technology. Jet Propulsion Laboratory; Estados Unidos Fil: Anderson, Ray G. U.S. Department of Agriculture; Estados Unidos Fil: Aragon, Bruno. King Abdullah University of Science and Technology. Division of Biological and Environmental Science and Engineering. Hydrology, Agriculture and Land Observation Group (HALO); Arabia Saudita Fil: Arain, M. Alfat. McMaster University. School of Geography and Earth Sciences; Canadá Fil: Baldocchi, Dennis D. University of California, Berkeley. Department of Environmental Science, Policy and Management; Estados Unidos Fil: Baker, John M. U.S. Department of Agriculture; Estados Unidos Fil: Barral, Helene. University Montpellier. HydroSciences Montpellier (HSM); Francia Fil: Bernacchi, Carl J. University of Illinois Urbana‐Champaign. Institute for Sustainability, Energy, and Environment and Carl R. Woese Institute for Genomic Biology. Center for Advanced Bioenergy and Bioproducts Innovation; Estados Unidos. U.S. Department of Agriculture, Global Change and Photosynthesis Research Unit; Estados Unidos Fil: Bernhofer, Christian. Technische Universität Dresden, Institute of Hydrology and Meteorology; Alemania Fil: Biraud, Sebastien C. Lawrence Berkeley National Laboratory; Estados Unidos Fil: Bohrer, Gil. Ohio State University. Department of Civil, Environmental and Geodetic Engineering; Estados Unidos Fil: Brunsell, Nathaniel. University of Kansas. Department of Geography and Atmospheric Science; Estados Unidos Fil: Cappelaere, Bernard. University Montpellier. HydroSciences Montpellier (HSM); Francia Fil: Castro - Contreras, Saulo. University of Alberta. Earth and Atmospheric Sciences Department. Centre for Earth Observation Sciences (CEOS); Canadá Fil: Chum, Junghwa. National Institute of Forest Science; Corea del Sur Fil: Conrad, Bryan. University of Kansas. Department of Geography and Atmospheric Science; Estados Unidos Fil: Cremonese, Edoardo. Environmental Protection Agency of Aosta Valley; Italia Fil: Demarty, Jerome. University Montpellier. HydroSciences Montpellier (HSM); Francia Fil: Desai, Ankur R. University of Wisconsin‐Madison. Department of Atmospheric and Oceanic Sciences; Estados Unidos Fil: De Ligne, Anne. University of Liege. TERRA Teaching and Research Centre. Gembloux Agro‐Bio Tech; Bélgica Fil: Foltynova, Lenka. Czech Academy of Sciences. Global Change Research Institute ; República Checa Fil: Goulden, Michael L. University of California Irvine. Department of Earth System Science; Estados Unidos Fil: Griffis, Timothy J. University of Minnesota. Department of Soil, Water, and Climate; Estados Unidos Fil: Grunwald, Thomas. Technische Universität Dresden, Institute of Hydrology and Meteorology; Alemania Fil: Johnson, Mark S. University of British Columbia. Department of Earth, Ocean, and Atmospheric Sciences; Canadá Fil: Kang, Minseok. National Center for AgroMeteorology; Corea del Sur Fil: Kelbe, Dave. Xerra Earth Observation Institute; Nueva Zelanda Fil: Kowalska, Natalia. Czech Academy of Sciences. Global Change Research Institute ; República Checa Fil: Jong - Hwan, Lim. National Institute of Forest Science; Corea del Sur Fil: Mainassara, Ibrahim. University Montpellier. HydroSciences Montpellier (HSM); Francia Fil: McCabe, Matthew F. King Abdullah University of Science and Technology. Division of Biological and Environmental Science and Engineering. Hydrology, Agriculture and Land Observation Group (HALO); Arabia Saudita Fil: Missik, Justine E.C. Washington State University. Department of Civil and Environmental Engineering. Laboratory for Atmospheric Research; Estados Unidos Fil: Mohanty, Binayak P. Texas A&M University. Texas Water Observatory; Estados Unidos Fil: Moore, Caitlin E. University of Illinois Urbana‐Champaign. Institute for Sustainability, Energy, and Environment and Carl R. Woese Institute for Genomic Biology. Center for Advanced Bioenergy and Bioproducts Innovation; Estados Unidos Fil: Morillas, Laura. University of British Columbia. Department of Earth, Ocean, and Atmospheric Sciences; Canadá Fil: Morrison, Ross. Centre for Ecology and Hydrology; Reino Unido Fil: Munger, J. Willians. Harvard University. School of Engineering and Applied Sciences; Estados Unidos Fil: Posse, Gabriela. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina Fil: Richardson, Andrew D. Northern Arizona University Center for Ecosystem Science and Society; Estados Unidos Fil: Russell, Eric. S. Washington State University. Department of Civil and Environmental Engineering. Laboratory for Atmospheric Research; Estados Unidos Fil: Ryu, Youngryel. Seoul National University . Department of Landscape Architecture and Rural Systems Engineering; Corea del Sur Fil: Sanchez - Azofeifa, Arturo. University of Alberta. Earth and Atmospheric Sciences Department. Centre for Earth Observation Sciences (CEOS); Canadá Fil: Schmidt, Marius. IInstitute of Bio‐ and Geosciences: Agrosphere (IBG‐3) Forschungszentrum Jülich IBG‐3; Alemania Fil: Schwartz, Efrat. Weizmann Institute of Science. Department of Earth and Planetary Sciences, ; Israel Fil: Sharp, Iain. University of Alberta. Earth and Atmospheric Sciences Department. Centre for Earth Observation Sciences (CEOS); Canadá Fil: Sigut, Ladislav. Czech Academy of Sciences. Global Change Research Institute ; República Checa Fil: Tang, Yao. Georgia Institute of Technology. School of Civil and Environmental Engineering; Estados Unidos Fil: Hulley, Glynn. California Institute of Technology. Jet Propulsion Laboratory; Estados Unidos Fil: Anderson, Martha. U.S. Department of Agriculture‐Agricultural Research Service; Estados Unidos Fil: Hain, Christopher. NASA Marshall Space Flight Center; Estados Unidos Fil: French, Andrew. U.S. Department of Agriculture; Estados Unidos Fil: Wood, Eric. Princeton University. Department of Civil and Environmental Engineering; Estados Unidos Fil: Hook, Simón. California Institute of Technology. Jet Propulsion Laboratory; Estados Unidos

Changes in vegetation structure are expected to influence the redistribution of heat and moisture; however, how variations in the leaf area index (LAI) affect this global energy partitioning is not yet quantified. Here, we estimate that a unit change in LAI leads to 3.66 ± 0.45 and −3.26 ± 0.41 W m−2 in latent (LE) and sensible (H) fluxes, respectively, over the 1982–2016 period. Analysis of an ensemble of data-driven products shows that these sensitivities increase by about 20% over the observational period, prominently in regions with a limited water supply, probably because of an increased transpiration/evaporation ratio. Global greening has caused a decrease in the Bowen ratio (B = H/LE) of −0.010 ± 0.002 per decade, which is attributable to the increased evaporative surface. Such a direct LAI effect on energy fluxes is largely modulated by plant functional types (PFTs) and background climate conditions. Land surface models (LSMs) misrepresent this vegetation control, possibly due to underestimation of the biophysical responses to changes in the water availability and poor representation of LAI dynamics. Changes in the leaf area index alter the distribution of heat and moisture. The change in energy partitioning related to leaf area, increasing latent and decreasing sensible fluxes over the observational period 1982–2016, is moderated by plant functional type and background climate.

The maximum rate of carboxylation (Vcmax ) is an essential leaf trait determining the photosynthetic capacity of plants. Existing approaches for estimating Vcmax at large scale mainly rely on empirical relationships with proxies such as leaf nitrogen/chlorophyll content or hyperspectral reflectance, or on complicated inverse models from gross primary production or solar-induced fluorescence. A novel mechanistic approach based on the assumption that plants optimize resource investment coordinating with environment and growth has been shown to accurately predict C3 plant Vcmax based on mean growing season environmental conditions. However, the ability of optimality theory to explain seasonal variation in Vcmax has not been fully investigated. Here, we adapt an optimality-based model to simulate daily Vcmax,25C (Vcmax at a standardized temperature of 25°C) by incorporating the effects of antecedent environment, which affects current plant functioning, and dynamic light absorption, which coordinates with plant functioning. We then use seasonal Vcmax,25C field measurements from 10 sites across diverse ecosystems to evaluate model performance. Overall, the model explains about 83% of the seasonal variation in C3 plant Vcmax,25C across the 10 sites, with a medium root mean square error of 12.3 μmol m-2 s-1 , which suggests that seasonal changes in Vcmax,25C are consistent with optimal plant function. We show that failing to account for acclimation to antecedent environment or coordination with dynamic light absorption dramatically decreases estimation accuracy. Our results show that optimality-based approach can accurately reproduce seasonal variation in canopy photosynthetic potential, and suggest that incorporating such theory into next-generation trait-based terrestrial biosphere models would improve predictions of global photosynthesis.

Sun-induced chlorophyll fluorescence (SiF) is increasingly used as a proxy for vegetation canopy photosynthesis. While ground-based, airborne, and satellite observations have demonstrated a strong linear relationship between SiF and gross primary production (GPP) at seasonal scales, their relationships at high temporal resolution across diurnal to seasonal scales remain unclear. In this study, far-red canopy SiF, GPP, and absorbed photosynthetically active radiation (APAR) were continuously monitored using automated spectral systems and an eddy flux tower over an entire growing season in a rice paddy. At half-hourly resolution, strong linear relationships between SiF and GPP (R2 = 0.76) and APAR and GPP (R2 = 0.76) for the whole growing season were observed. We found that relative humidity, diffuse PAR fraction, and growth stage influenced the relationships between SiF and GPP, and APAR and GPP, and incorporating those factors into multiple regression analysis led to improvements up to R2 = 0.83 and R2 = 0.88, respectively. Relationships between LUEp (=GPP/APAR) and LUEf (=SiF/APAR) were inconsistent at half-hourly and weak at daily resolutions (R2 = 0.24). Both at diurnal and seasonal time scales with half-hourly resolution, we found considerably stronger linear relationships between SiF and APAR than between either SiF and GPP or APAR and GPP. Overall, our results indicate that for subdiurnal temporal resolution, canopy SiF in the rice paddy is above all a very good proxy for APAR at diurnal and seasonal time scales and that therefore SiF-based GPP estimation needs to take into account relevant environmental information to model LUEp. These findings can help develop mechanistic links between canopy SiF and GPP across multiple temporal scales.

Conventional process-based crop simulation models and agro-land surface models require numerous forcing variables and input parameters. The regional application of these crop simulation models is complicated by factors concerning input data requirements and parameter uncertainty. In addition, the empirical remotely sensed regional scale crop yield estimation method does not enable growth process modeling. In this study, we developed a process-based rice yield estimation model by integrating an assimilate allocation module into the satellite remote sensing-derived and biophysical process-based Breathing Earth System Simulator (BESS). Normalized accumulated gross primary productivity ( G P P n o r m - a c c u ) was used as a scaler for growth development, and the relationships between G P P n o r m - a c c u and dry matter partitioning coefficients were determined from the eddy covariance and biometric measurements at the Cheorwon Rice paddy KoFlux site. Over 95% of the variation in the dry matter allocation coefficients of rice grain could be explained by G P P n o r m - a c c u . The dynamics of dry matter distribution among different rice components were simulated, and the annual grain yields were estimated. BESS-Rice simulated GPP and dry matter partitioning dynamics, and rice yields were evaluated against in-situ measurements at three paddy rice sites registered in KoFlux. The results showed that BESS-Rice performed well in terms of rice productivity estimation, with average root mean square error (RMSE) value of 2.2 g C m−2 d−1 (29.5%) and bias of –0.5 g C m−2 d−1 (–7.1%) for daily GPP, and an average RMSE value of 534.8 kg ha−1 (7.7%) and bias of 242.1 kg ha−1 (3.5%) for the annual yield, respectively. BESS-Rice is much simpler than conventional crop models and this helps to reduce the uncertainty related to the forcing variables and input parameters and can result in improved regional yield estimation. The process-based mechanism of BESS-Rice also enables an agronomic diagnosis to be made and the potential impacts of climate change on rice productivity to be investigated.

Sun-induced chlorophyll fluorescence (SIF) is a promising new tool for remotely estimating photosynthesis. However, the degree to which incoming solar radiation and the structure of the canopy rather than leaf physiology contribute to SIF variations is still not well characterized. Therefore, we investigated relationships between SIF and variables that at least partly capture the canopy structure component of SIF. For this, we relied on high-quality SIF observations from ground-based instruments, high-resolution airborne SIF imagery and the most recent satellite SIF products to cover large ranges in spatial and temporal resolution and diverse ecosystems. We found that the canopy structure-related near-infrared reflectance of vegetation multiplied by incoming sunlight (NIRVP) is a robust proxy for far-red SIF across a wide range of spatial and temporal scales. Our findings indicate that contributions from leaf physiology to SIF variability are small compared to the structure and radiation components. Also, NIRVP captured spatio-temporal patterns of canopy photosynthesis better than SIF, which seems to be mostly due to the greater retrieval noise of SIF. Compared to other relevant structural SIF proxies, NIRVP showed more robust relationships to SIF, especially at the global scale. Our results highlight the promise of using widely available NIRVP data for vegetation monitoring and also indicate the potential of using SIF and NIRVP in combination to extract physiological information from SIF.

International audience; Disentangling the individual contributions from vegetation and soil in measured canopy reflectance is a grand challenge to the remote sensing and ecophysiology communities. Since Solar Induced chlorophyll Fluorescence (SIF) is uniquely emitted from vegetation, it can be used to evaluate how well reflectance-based vegetation indices (VIs) can separate the vegetation and soil components. Due to the residual soil background contributions, Near-infrared (NIR) reflectance of vegetation (NIRv) and Difference Vegetation index (DVI) present offsets when compared to SIF (i.e., the value of NIRv or DVI is non-zero when SIF is zero). In this study, we proposed a simple framework for estimating the true NIR reflectance of vegetation from Hyperspectral measurements (NIRvH) with minimal soil impacts. NIRvH takes advantage of the spectral shape variations in the red-edge region to minimize the soil effects. We evaluated the capability of NIRvH, NIRv and DVI in isolating the true NIR reflectance of vegetation using the data from both the model-based simulations and Hyperspectral Plant imaging spectrometer (HyPlant) measurements. Benchmarked by simultaneously measured SIF, NIRvH has the smallest offset (0–0.037), as compared to an intermediate offset of 0.047–0.062 from NIRv, and the largest offset of 0.089–0.112 from DVI. The magnitude of the offset can vary with different soil reflectance spectra across spatio-temporal scales, which may lead to bias in the downstream NIRv-based photosynthesis estimates. NIRvH and SIF measurements from the same sensor platform avoided complications due to different geometry, footprint and time of observation across sensors when studying the radiative transfer of reflected photons and SIF. In addition, NIRvH was primarily determined by canopy structure rather than chlorophyll content and soil brightness. Our work showcases that NIRvH is promising for retrieving canopy structure parameters such as leaf area index and leaf inclination angle, and for estimating fluorescence yield with current and forthcoming hyperspectral satellite measurements.

Time series of vegetation indices (e.g. normalized difference vegetation index [NDVI]) and color indices (e.g. green chromatic coordinate [GCC]) based on radiometric measurements are now available at different spatial and temporal scales ranging from weekly satellite observations to sub-hourly in situ measurements by means of near-surface remote sensing (e.g. spectral sensors or digital cameras). In situ measurements are essential for providing validation data for satellite-derived vegetation indices. In this study we used a recently developed method to calculate NDVI from near-infrared (NIR) enabled digital cameras (NDVIC) at 17 sites (for a total of 74 year-sites) encompassing six plant functional types (PFT) from the PhenoCam network. The seasonality of NDVIC was comparable to both NDVI measured by ground spectral sensors and by the moderate resolution imaging spectroradiometer (MODIS). We calculated site- and PFT-specific scaling factors to correct NDVIC values and recommend the use of site-specific NDVI from MODIS in order to scale NDVIC. We also compared GCC extracted from red-green-blue images to NDVIC and found PFT-dependent systematic differences in their seasonalities. During senescence, NDVIC lags behind GCC in deciduous broad-leaf forests and grasslands, suggesting that GCC is more sensitive to changes in leaf color and NDVIC is more sensitive to changes in leaf area. In evergreen forests, NDVIC peaks later than GCC in spring, probably tracking the processes of shoot elongation and new needle formation. Both GCC and NDVIC can be used as validation tools for the MODIS Land Cover Dynamics Product (MCD12Q2) for deciduous broad-leaf spring phenology, whereas NDVIC is more comparable than GCC with autumn phenology derived from MODIS. For evergreen forests, we found a poor relationship between MCD12Q2 and camera-derived phenology, highlighting the need for more work to better characterize the seasonality of both canopy structure and leaf biochemistry in those ecosystems. Our results demonstrate that NDVIC is in excellent agreement with NDVI obtained from spectral measurements, and that NDVIC and GCC can complement each other in describing ecosystem phenology. Additionally, NDVIC allows the detection of structural changes in the canopy that cannot be detected by visible-wavelength imagery.

Landscape fragmentation has created large areas of forest edge. Understanding how soil organic carbon (SOC) stocks within forest edges respond to fragmentation is essential to assess carbon budgets; however, the causes and magnitude of edge effects on SOC stocks have been poorly characterized. Here, we quantify the edge effects on SOC stocks within and across urban to rural forest patches from three fragmented urban forests to a large patch of rural forest. The SOC stocks within 20 m of the rural forest edge (1.86 kgC m−2) is on average 80% lower than the interiors of rural forest (10.47 kgC m−2). We found that biotic factors, including annual litterfall mass (R2 > 0.94), peak leaf area index (R2 > 0.92), and fine-root mass density (R2 > 0.77), explained the spatial variation in SOC stocks within the rural forest. In urban forests, human activities at forest edges led to contrasting edge effects on SOC stocks, for instance, the SOC stocks at the east edges (4.74 kgC m−2) were 56% greater than at the west edges (3.03 kgC m−2) explained by the adjacent land uses (e.g., paved roads vs. non-paved soils) and in-situ litterfall management. We also found significant differences in summer soil temperature (ΔTS > 2.8 °C) and soil moisture (ΔVWC > 0.05 m3 m−3) between the east and west forest edges. Our results reveal that the factors responsible for the edge effects on SOC stocks in rural forests are biotic factors, while heterogeneous human activities at the local scale lead to complex edge effects on urban forest SOC stocks.

Soil respiration (Rs) determines land surface carbon balance; however, there have been few studies that measured Rs in heterogeneous urban landscapes. Here, we investigated the spatial and temporal variations in Rs in six land cover types (mixed forest, deciduous broadleaf forest, evergreen needleleaf forest, lawn, wetland, and bare land) in Seoul Forest Park, Republic of Korea, between March 2013 and September 2014, which included a wet (2013) and an extremely dry (2014) summer. Spatially, there was a three-fold difference (0.48–1.45 kgC m−2) in annual Rs among the six land cover types. The soil organic carbon stock at a depth of 0.1 m explained 72% of the spatial variation in the annual Rs across the land cover types. During the entire study period, the soil temperature explained 82–97% of the temporal variation in Rs among different land cover types. Comparing the two summers, the 2014 drought only resulted in a decrease in Rs in the lawn plots (25%), which was driven by a reduction in the leaf area index and the fine root density. The temperature sensitivity of Rs in 2014 (dry summer) compared to 2013 (wet summer) was significantly lower in mixed forest, deciduous broadleaf forest, and lawn, and did not change in evergreen needleleaf forest, wetland, or bare land. The differences in Rs in these drought responses highlight the importance of the careful selection of land cover type during park planning to better manage carbon cycles.

The savanna complex is a highly diverse global biome that occurs within the seasonally dry tropical to sub-tropical equatorial latitudes and are structurally and functionally distinct from grasslands and forests. Savannas are open-canopy environments that encompass a broad demographic continuum, often characterised by a changing dominance between C3-tree and C4-grass vegetation, where frequent environmental disturbances such as fire modulates the balance between ephemeral and perennial life forms. Climate change is projected to result in significant changes to the savanna floristic structure, with increases to woody biomass expected through CO2 fertilisation in mesic savannas and increased tree mortality expected through increased rainfall interannual variability in xeric savannas. The complex interaction between vegetation and climate that occurs in savannas has traditionally challenged terrestrial biosphere models (TBMs), which aim to simulate the interaction between the atmosphere and the land surface to predict responses of vegetation to changing in environmental forcing. In this review, we examine whether TBMs are able to adequately represent savanna fluxes and what implications potential deficiencies may have for climate change projection scenarios that rely on these models. We start by highlighting the defining characteristic traits and behaviours of savannas, how these differ across continents and how this information is (or is not) represented in the structural framework of many TBMs. We highlight three dynamic processes that we believe directly affect the water use and productivity of the savanna system: phenology, root-water access and fire dynamics. Following this, we discuss how these processes are represented in many current-generation TBMs and whether they are suitable for simulating savanna fluxes.Finally, we give an overview of how eddy-covariance observations in combination with other data sources can be used in model benchmarking and intercomparison frameworks to diagnose the performance of TBMs in this environment and formulate road maps for future development. Our investigation reveals that many TBMs systematically misrepresent phenology, the effects of fire and root-water access (if they are considered at all) and that these should be critical areas for future development. Furthermore, such processes must not be static (i.e. prescribed behaviour) but be capable of responding to the changing environmental conditions in order to emulate the dynamic behaviour of savannas. Without such developments, however, TBMs will have limited predictive capability in making the critical projections needed to understand how savannas will respond to future global change.

Understanding the long-term performance of global satellite leaf area index (LAI) products is important for global change research. However, few effort has been devoted to evaluating the long-term time-series consistencies of LAI products. This study compared four long-term LAI products (GLASS, GLOBMAP, LAI3g, and TCDR) in terms of trends, interannual variabilities, and uncertainty variations from 1982 through 2011. This study also used four ancillary LAI products (GEOV1, MERIS, MODIS C5, and MODIS C6) from 2003 through 2011 to help clarify the performances of the four long-term LAI products. In general, there were marked discrepancies between the four long-term LAI products. During the pre-MODIS period (1982-1999), both linear trends and interannual variabilities of global mean LAI followed the order GLASS>LAI3g>TCDR>GLOBMAP. The GLASS linear trend and interannual variability were almost 4.5 times those of GLOBMAP. During the overlap period (2003-2011), GLASS and GLOBMAP exhibited a decreasing trend, TCDR no trend, and LAI3g an increasing trend. GEOV1, MERIS, and MODIS C6 also exhibited an increasing trend, but to a much smaller extent than that from LAI3g. During both periods, the R2 of detrended anomalies between the four long-term LAI products was smaller than 0.4 for most regions. Interannual variabilities of the four long-term LAI products were considerably different over the two periods, and the differences followed the order GLASS>LAI3g>TCDR>GLOBMAP. Uncertainty variations quantified by a collocation error model followed the same order. Our results indicate that the four long-term LAI products were neither intraconsistent over time nor interconsistent with each other. These inconsistencies may be due to NOAA satellite orbit changes and MODIS sensor degradation. Caution should be used in the interpretation of global changes derived from the four long-term LAI products.

Remote sensing of far-red sun-induced chlorophyll fluorescence (SIF) has emerged as an important tool for studying gross primary productivity (GPP) at the global scale. However, the relationship between SIF and GPP at the canopy scale lacks a clear mechanistic explanation. This is largely due to the poorly characterized role of the relative contributions from canopy structure and leaf physiology to the variability of the top-of-canopy, observed SIF signal. In particular, the effect of the canopy structure beyond light absorption is that only a fraction (fesc) of the SIF emitted from all leaves in the canopy can escape from the canopy due to the strong scattering of near-infrared radiation. We combined rice, wheat and corn canopy-level in-situ datasets to study how the physiological and structural components of SIF individually relate to measures of photosynthesis. At seasonal time scales, we found a considerably strong positive correlation (R2 = 0.4–0.6) of fesc to the seasonal dynamics of the photosynthetic light use efficiency (LUEP), while the estimated physiological SIF yield was almost entirely uncorrelated to LUEP both at seasonal and diurnal time scales, with the partial exception of wheat. Consistent with these findings, the canopy structure and radiation component of SIF, defined as the product of APAR and fesc, explained the relationship of observed SIF to GPP and even outperformed GPP estimation based on observed SIF at two of the three sites investigated. These results held for both half-hourly and daily mean values. In contrast, the total emitted SIF, obtained by normalizing observed SIF for fesc, improved only the relationship to APAR but considerably decreased the correlation to GPP for all three crops. Our findings demonstrate the dominant role of canopy structure in the SIF-GPP relationship and establish a strong, mechanistic link between the near-infrared reflectance of vegetation (NIRV) and the relevant canopy structure information contained in the SIF signal. These insights are expected to be useful in improving remote sensing based GPP estimates.

Global vegetation greening trend stalled after the late 1990s due to increased atmospheric water demand., Atmospheric vapor pressure deficit (VPD) is a critical variable in determining plant photosynthesis. Synthesis of four global climate datasets reveals a sharp increase of VPD after the late 1990s. In response, the vegetation greening trend indicated by a satellite-derived vegetation index (GIMMS3g), which was evident before the late 1990s, was subsequently stalled or reversed. Terrestrial gross primary production derived from two satellite-based models (revised EC-LUE and MODIS) exhibits persistent and widespread decreases after the late 1990s due to increased VPD, which offset the positive CO2 fertilization effect. Six Earth system models have consistently projected continuous increases of VPD throughout the current century. Our results highlight that the impacts of VPD on vegetation growth should be adequately considered to assess ecosystem responses to future climate conditions.

With increasing crop water demands and drought threats, mapping and monitoring of cropland evapotranspiration (ET) at high spatial and temporal resolutions becomes increasingly critical for water management and sustainability. However, estimating ET from satellite for precise water resources management is still challenging due to the limitations in both existing ET models and satellite input data. Specifically, the process of ET is complex and difficult to model, and existing satellite remote sensing data could not fulfill high resolutions in both space and time. To address the above two issues, this study presented a new high spatiotemporal resolution ET mapping framework, i.e., BESS-STAIR, which integrates a satellite-driven water-carbon-energy coupled biophysical model BESS (Breathing Earth System Simulator) with a generic and fully-automated fusion algorithm STAIR (SaTallite dAta IntegRation). In this framework, STAIR provides daily 30-meter multispectral surface reflectance by fusing Landsat and MODIS satellite data to derive fine-resolution leaf area index and visible/near-infrared albedo, all of which, along with coarse-resolution meteorological and CO2 data, are used to drive BESS to estimate gap-free 30-m resolution daily ET. We applied BESS-STAIR from 2000 through 2017 in six areas across the U.S. Corn Belt, and validated BESS-STAIR ET estimations using flux tower measurements over 12 sites (85 site-years). Results showed that BESS-STAIR daily ET achieved an overall R2 = 0.75, with RMSE = 0.93 mm d−1 and relative error = 27.9 % when benchmarked with the flux measurements. In addition, BESS-STAIR ET estimations well captured the spatial patterns, seasonal cycles, and interannual dynamics in different sub-regions. The high performance of the BESS-STAIR framework is primarily resulted from: (1) the implementation of coupled constraints on water, carbon, and energy in BESS, (2) high-quality daily 30-m data from STAIR fusion algorithm, and (3) BESS’s applicability under all-sky conditions. BESS-STAIR is calibration-free and has great potentials to be a reliable tool for water resources management and precision agriculture applications for the U.S. Corn Belt, and even for worldwide given the global coverage of its input data.

Solar-induced chlorophyll fluorescence (SIF) has emerged as a leading approach for remote sensing of gross primary productivity (GPP). While SIF has an intrinsic, underlying relationship with canopy light capture and light use efficiency, these physiological relationships are obscured by the fact that satellites observe a small and variable fraction of total emitted canopy SIF. Upon emission, most SIF photons are reabsorbed or scattered within the canopy, preventing their observation remotely. The complexities of the radiative transfer process, which vary across time and space, limit our ability to reliably infer physiological processes from SIF observations. Here, we propose an approach for estimating the fraction of total emitted near-infrared SIF (760 nm) photons that escape the canopy by combining the near-infrared reflectance of vegetation (NIRV) and the fraction of absorbed photosynthetically active radiation (fPAR), two widely available remote sensing products. Our approach relies on the fact that NIRV is resilient against soil background contamination, allowing us to reliably calculate the bidirectional reflectance factor of vegetation, which in turn conveys information about the escape ratio of SIF photons. Our NIRV-based approach explains variations in the escape ratio with an R2 of 0.91 and an RMSE of 1.48% across a series of simulations where canopy structure, soil brightness, and sun-sensor-canopy geometry are varied. The approach is applicable to conditions of low leaf area index and fractional vegetation cover. We show that correcting for the escape ratio of SIF using NIRV provides robust estimates of total emitted SIF, providing for the possibility of studying physiological variations of fluorescence yield at the global scale.

Solar-induced chlorophyll fluorescence (SIF) provides us with new opportunities to understand the physiological and structural dynamics of vegetation from leaf to global scales. However, the relationships between SIF and gross primary productivity (GPP) are not fully understood, which is mainly due to the challenges of decoupling structural and physiological factors that control the relationships. Here, we report the results of continuous observations of canopy-level SIF, GPP, absorbed photosynthetically active radiation (APAR), and chlorophyll: carotenoid index (CCI) in a temperate evergreen needleleaf forest. To understand the mechanisms underlying the relationship between GPP and SIF, we investigated the relationships of light use efficiency (LUEp), chlorophyll fluorescence yield (ΦF), and the fraction of emitted SIF photons escaping from the canopy (fesc) separately. We found a strongly non-linear relationship between GPP and SIF at diurnal and seasonal time scales (R2 = 0.91 with a hyperbolic regression function, daily). GPP saturated with APAR, while SIF did not. Also, there were differential responses of LUEp and ΦF to air temperature. While LUEp reached saturation at high air temperatures, ΦF did not saturate. We found that the canopy-level chlorophyll: carotenoid index was strongly correlated to canopy-level ΦF (R2 = 0.84) implying that ΦF could be more closely related to pigment pool changes rather than LUEp. In addition, we found that the fesc contributed to a stronger SIF-GPP relationship by partially capturing the response of LUEp to diffuse light. These findings can help refine physiological and structural links between canopy-level SIF and GPP in evergreen needleleaf forest.

It is assumed that the ratio of actual evapotranspiration (AET) to potential evapotranspiration (PET) is mostly controlled by the soil water content available for ET. This control is formulated using the soil moisture stress function (SSF), where the evaporative fraction (EF) or the fraction of the AET to PET (fPET) is assumed to be either a linear or a non-linear function of soil moisture. We examine the effectiveness of the soil moisture stress function to quantify soil moisture control on EF or fPET over a dryland wheat field in Victoria, Australia. Micrometeorological observations from two cropping seasons were used for the analysis. The efficacy of a root-density-weighted soil moisture estimate in predicting EF and fPET was investigated as against the commonly assumed fixed-depth root zone soil moisture. However, results indicate a strong relationship between EF and available soil water fraction (AWF) in the root zone only when solar radiation is higher than 5 MJ/m2/day. As the rooting depth increases with vegetation growth, SSF exhibits the strongest correlation with AWF for increasing soil profile depth. In the early and harvesting crop growth stages, ET is constrained mostly by surface soil moisture (0–5 cm). In the mid-growth stages, ET is strongly influenced by soil moisture in the root zone (0–60 cm). The shape of SSF, however, changes significantly between the two years (2012 and 2013). It is inferred that different temporal rainfall patterns between the years caused wheat’s different response to water stress.

Several global gross primary production (GPP) and evapotranspiration (ET) remote sensing products exist, mainly provided by machine-learning (e.g. MPI-BGC) and semi-empirical (e.g. MODIS) approaches. Process-based approaches have the advantage of representing the atmosphere-vegetation-soil system and associated fluxes as an organic integration, but their sophistication results in a lack of high spatiotemporal resolution continuous products. Targeting this gap, we reported a new set of global 8-day composite 1-km resolution GPP and ET products from 2000 to 2015, using a simplified process-based model, the Breathing Earth System Simulator (BESS). BESS couples atmosphere and canopy radiative transfer, photosynthesis and evapotranspiration, and uses MODIS atmosphere and land data and other satellite data sources as inputs. We evaluated BESS products against FLUXNET observations at site scale (total of 113 sites, 742 site years), and against MPI-BGC products at global scale. At site scale, BESS 8-day products agreed with FLUXNET observations with R 2 = 0.67 and RMSE = 2.58 gC m − 2 d − 1 for GPP, and R 2 = 0.62 and RMSE = 0.78 mm d − 1 for ET, respectively, and they captured a majority of seasonal variability, about half of spatial variability, and a minority of interannual variability in FLUXNET observations. At global scale, BESS mean annual sum GPP and ET maps agreed with MPI-BGC products with R 2 = 0.93 and RMSE = 229 gC m − 2 y − 1 for GPP, and R 2 = 0.90 and RMSE = 118 mm y − 1 for ET, respectively. Over the period of 2001–2011, BESS quantified the mean global GPP and ET as 122 ± 25 PgC y − 1 and 65 × 10 3 ± 11 × 10 3 km 3 y − 1 , respectively, with a significant ascending GPP trend by 0.27 PgC y − 2 ( p

Digital cover photography (DCP) has emerged as an indirect method to measure gap fraction of vegetation canopies. However, as with other photographic methods, determining camera relative exposure value (REV) and threshold for pixel classification, cause substantial uncertainties in gap fraction estimates. Here we propose a new method to improve the measurement of gap fraction under various solar zenith angles (SZAs), sky conditions, and canopy structures. This method computes gap fractions of ambiguous vegetation or sky pixels using an unsaturated raw image from DCP and a reconstructed sky image from the raw image, thus taking full advantage of the potential of raw image processing. This is combined with pre-classification of pixels that are unambiguously canopy and sky to greatly reduce light scattering effects that are likely to be present within the canopy. To test the sensitivity of the new method, we acquired images at one-hour intervals between 20 and 85° of SZAs under closed, half-closed, and open canopies with REV settings from 0 to −5. The new method showed little variation in gap fractions across the diverse SZAs in closed, half-closed, and open canopies. A perforated panel experiment, which was used to test the accuracy of the estimated gap fractions, confirmed that the new method accurately estimated gap fractions across a range of hole size, gap fractions and SZAs. We conclude that the new method opens new opportunities to estimate gap fractions accurately regardless of solar positions from open to closed canopies, and is a significant advance for accurate and precise monitoring of canopy cover and leaf area index (LAI), and for calibrating and evaluating satellite remote sensing LAI products.

Satellite image fusion methods that improve spatial and temporal resolution have significant potential to advance understanding of ecosystem dynamics in space and time. However, systematic evaluations of image fusion methods against in situ spectral data are lacking. Here, we used a suite of in situ spectral data collected at 60 elementary sampling units (10 × 10 m) covering 15 Landsat pixel (30 × 30 m) plots and one Moderate Resolution Imaging Spectroradiometer (MODIS) pixel (250 × 250 m) throughout the entire growing season in a heterogeneous rice paddy landscape to evaluate four state-of-the-art image fusion NDVI products. They include the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), Flexible Spatiotemporal DAta Fusion (FSDAF), SaTellite dAta IntegRation (STAIR), and the CubeSat Enabled Spatio-Temporal Enhancement Method (CESTEM); the former three blended Landsat and MODIS data, whereas the latter combined CubeSats, Landsat, and MODIS observations. All fusion products showed strong linear relationships against in situ data when combining all spatial and temporal observations (R2: 0.73 to 0.93) although there were partly negative biases (–1% to –9%). These biases resulted from forcing data to image fusion algorithms, such as Landsat (–4%) and MODIS (–7%). Performance difference between fusion methods were considerably larger for spatial than for temporal variation. Furthermore, Landsat NDVI explained only 17–22% of spatial variation against in situ spectral data, which can be translated into weak performance of image fusion products to predict spatial variability in NDVI. Image fusion products that relied on spatial interpolation showed large biases (–15% to –30%) for a vegetation plot surrounded by mixed land cover plots. Our results highlight key sources of uncertainty and will be instrumental in improving satellite image fusion methods to monitor land surface phenology in space and time.

Vegetation monitoring can be used to detect CO2 leakage in carbon capture and storage (CCS) sites because it can monitor a large area at a relatively low cost. However, a rapidly responsive, sensitive, and cost-effective plant parameters must be suggested for vegetation monitoring to be practically utilized as a CCS management strategy. To screen the proper plant parameters for leakage monitoring, a greenhouse experiment was conducted by exposing kale (Brassica oleracea var. viridis), a sensitive plant, to 10%, 20%, and 40% soil CO2 concentrations. Water and water with CO2 stress treatments were also introduced to examine the parameters differentiating CO2 stress from water stresses. We tested the hypothesis that chlorophyl fluorescence parameters would be early and sensitive indicator to detect CO2 leakage. The results showed that the fluorescence parameters of effective quantum yield of photosystem II (Y(II)), detected the difference between CO2 treatments and control earlier than any other parameters, such as chlorophyl content, hyperspectral vegetation indices, and biomass. For systematic comparison among many parameters, we proposed an indicator evaluation score (IES) method based on four categories: CO2 specificity, early detection, field applicability, and cost. The IES results showed that fluorescence parameters (Y(II)) had the highest IES scores, and the parameters from spectral sensors (380–800 nm wavelength) had the second highest values. We suggest the IES system as a useful tool for evaluating new parameters in vegetation monitoring.

Rapid urbanization has created large areas of impervious surface areas globally. As there is little carbon input by plants into soils under impervious surfaces, soil organic carbon (SOC) stocks under impervious surfaces generally have been overlooked in the urban carbon budgets. Here we investigate SOC stocks under impervious concrete surfaces and vegetative surfaces across soil profiles to a depth of 5 m in urban housing complexes in Seoul, Republic of Korea. In the top 1 m of the profile, SOC stocks under vegetative surfaces were three times greater than those under impervious surfaces. However, we discovered that unexpectedly high SOC stocks appeared in deeper soil layers under both surface types, which led to comparable SOC stocks at a depth of 5 m beneath the impervious surface (16.9 ± 1.9 kgC m−2) and at the vegetative surface (22.3 ± 2.2 kgC m−2). Consequently, the ratio of SOC stocks at depths of 1 m to 5 m were 16% in impervious surfaces and 34% in vegetative surfaces, suggesting conventional soil sampling at 1 m depth could miss large SOC. Stable isotope data ( δ 13C and δ 15N) combined with historical aerial photographs revealed that cropland that existed until the 1970s formed the high SOC cultural layer in deeper soils. Our results highlight that deep soils under impervious surfaces could be overlooked carbon hotspots in urban ecosystems. We believe this finding could help city planners and policy makers to assess regional carbon budgets and to reduce carbon footprint by recycling the deep SOC excavated from various construction projects towards sustainable urban development.

Addressing the impact of vegetation architecture on shortwave radiation transfer in land surface models is important for accurate weather forecasting, carbon budget estimates, and climate predictions. This paper investigates to what extent it is possible to retrieve structural parameters of two different parameterization schemes from direct transmittance derived from digital hemispherical photography and 3D radiative transfer modeling for two study sites with different vegetation canopy architectures. Neglecting the representation of 3D canopy structure in radiative transfer schemes leads to significant errors in shortwave radiation partitioning (up to 3.5 times more direct transmittance in the 3D model). Structural parameters, referred to as whole canopy ‘clumping indices’, were obtained in order to evaluate the impact of angular variation in clumping on shortwave radiation transfer. Impacts on photosynthesis were evaluated at site level with the UKESM land surface model, JULES. A comparison between flux tower derived and modeled photosynthesis indicates that considering zenith angular variations of structural parameters in the radiative transfer scheme of the UKESM land surface model significantly improves photosynthesis prediction in light limited ecosystems (from RMSE = 2.91 μmol CO2.m − 2.s − 1 to RMSE = 1.51 μmol CO2.m − 2.s − 1, 48% smaller), typically with enhanced photosynthesis from bottom layers.

Rice is a major crop that feeds more than half of the global population. Rice paddies play a complex role in the carbon cycle by emitting CH4 to the atmosphere while either sequestering or releasing CO2 to the atmosphere. Here, we present 3.5 years of eddy covariance measurements of CO2 and CH4 fluxes over an intermittently irrigated, single crop rice paddy in South Korea in tandem with carbon stock measurements for leaves, stems, grains, and roots. The rice paddy acted as a slight sink or neutral in CO2 (−47 ± 51 g C m−2 y−1; mean ± 95% confidence interval) with 941 ± 130 g C m−2 y−1 and 894 ± 108 g C m−2 y−1 as the gross primary production (GPP) and ecosystem respiration (Reco), respectively. The mean annual net primary productivity (NPP) was 666 ± 31 g C m−2 y−1, which was partitioned to leaves (9%), stems (30%), grains (51%), and roots (10%), for a harvest index of 57%. The paddy emitted 20.6 ± 1.5 g C m−2 y−1 in the form of CH4, of which between 82 and 96% was emitted during the cultivation period (~132 days) . The CH4 flux showed a mid-season decline caused by mid-season drainage, which produced aerobic soil conditions that inhibited CH4 emissions. The proportion of estimated ebullitive flux to total CH4 flux ranged from 10 to 17% during the cultivation period. GPP, Reco, and CH4 fluxes varied by 12%, 11%, and 7%, respectively, over the 3-year period. When considering the harvest and the global warming potential of CH4, the paddy shifted from a greenhouse gas sink to a strong source (2,741 ± 468 g CO2 eq m−2 y−1). These findings will improve our understanding of carbon budgets and cycles, and help develop greenhouse gas and water management strategies for rice paddies.

International audience; Terrestrial gross primary productivity (GPP) plays an essential role in the global carbon cycle, but the quantification of the spatial and temporal variations in photosynthesis is still largely uncertain. Our work aimed to investigate the potential of remote sensing to provide new insights into plant photosynthesis at a fine spatial resolution. This goal was achieved by exploiting high-resolution images acquired with the FLuorescence EXplorer (FLEX) airborne demonstrator HyPlant. The sensor was flown over a mixed forest, and the images collected were elaborated to obtain two independent indicators of plant photosynthesis. First, maps of sun-induced chlorophyll fluorescence (F), a novel indicator of plant photosynthetic activity, were successfully obtained at both the red and far-red peaks (r2 = 0.89 and p 0.05). The spatial relationships found at high resolution provide valuable insight into the critical role of spatial heterogeneity in controlling the relationship between the far-red F and the GPP, indicating the need to consider this heterogeneity at a coarser resolution.

article Vegetation indices are important remotely sensed metrics for ecosystem monitoring and land surface process assessment, among which Normalized Difference Vegetation Index (NDVI) has been most widely used. The newly launched Landsat 8 Operational Land Imager (OLI) sensor, together with its predecessor Landsat 7 Enhanced Thematic Mapper Plus (ETM+), provides continuous earth observations with an 8-day interval. The design improvements of the new sensor, including narrower near-infrared waveband, higher signal-to-noise ratio (SNR), and greater radiometric sensitivity highlight the need for investigating the land surface observation properties, especially its consistency with data from its predecessors and other satellite sensors. This study aims to evaluate the characteristics of Landsat 8 OLI-derived NDVI against Landsat 7 ETM+ by cross-comparison and by comparing with Moderate Resolution Imaging Spectroradiometer (MODIS) and Geostationary Ocean Color Imager (GOCI)-derived NDVIs as well as in-situ NDVI measurements. Simulations of Top of Atmosphere (TOA) reflectance and surface reflectance of broadleaf trees and water were conducted for Landsat 8 OLI, Landsat 7 ETM+, and MODIS in order to evaluate the impact of band pass difference on NDVI calculation. Four consecutive pairs of Landsat 8 OLI and Landsat 7 ETM+ data over China and Korea were examined, and NDVIs derived from TOA reflectance and surface reflectance by three atmospheric correction methods were evaluated. Both simula- tions and comparisons showed that NDVIs derived from atmospherically-corrected surface reflectance had good consistency, while the simulation showed that the agreement varied with atmospheric characteristics. The four pairs of Landsat 8 OLI and Landsat 7 ETM+ NDVI had a mean bias error within ±0.05, and R 2 from 0.84 to 0.98.

Field measurements of vertical canopy structure have been challenging for decades and still important for understanding forest ecosystems. We measured the vertical canopy structure and its seasonal changes in a temperate deciduous forest canopy in Gwangneung, Korea. Time-series measurements of leaf area index (LAI) were collected in 2013 from a five story (4-m vertical intervals) tower. We evaluated crown depth and species composition by height from a vegetation survey. The vertical distribution of leaf and woody area density was described from measurements taken during the leaf-on and leaf-off seasons, and averaged 0.18 and 0.04 m2 m−3, respectively. Three strata were characterized: (1) the dense upper crowns with large trees (>16-m) of Quercus serrata, in which 29.3 % of the plant materials were distributed; (2) abundant foliage dominated by Carpinus laxiflora at about 16-m (40.8 %); and (3) a diverse and well-developed understory vegetation at about 4-m (15.5 %), consisting of C. laxiflora, Carnipus cordata, and Styrax japonica communities. Per-layer phenology of each species was successfully illustrated by the drastic increase in LAI during the leaf-out season [days of the year (DOY) 110–140], the full-leaved stage LAI of 3.4 ± 0.9 m2 m−2 (mean ±1 standard deviation), and a decrease during the leaf-fall season (DOY 280–320). The seasonal variation in gap fractions reflected different light conditions varying with canopy height. This type of vertical profile archive is valuable not only for comparing the structure of various forests but also for monitoring changes in this ecosystem in the future.

Urban parks offer valuable ecosystem services to citizens and they have long been recognized for their recreational service; however, less attention has been paid to their carbon sequestration value. Here, we report on soil organic carbon (SOC) stocks in an urban park, Seoul Forest Park, which was built in 2004. We had two objectives: (1) to estimate SOC stocks (to a depth of 1 m) in different land-cover types (wetland, forest, lawn, and bare soil) and (2) quantify the change in the SOC concentration in topsoil in different land-use types over a 10 year period (2003–2013). We found a tenfold difference in SOC stocks across the different land-cover types within the park. Wetland soils had the highest stocks of SOC (13.99 ± 1.05 kg m −2 ), followed by forest, lawn, and bare soils. We found that a “cultural layer” that preserved previous land use history located deep in the soil profile substantially increased SOC stocks in the wetland. SOC concentrations in the topsoil were approximately three times higher in 2013 than in 2003 (256 ± 130%). The normalized difference vegetation index (NDVI) derived from MODIS and Landsat satellite images revealed that land-use history, expansion of plant areas and growth of plants could explain the increase in SOC concentrations in topsoil over the 10 year period. These findings imply that urban park soils could act as a carbon sink, and understanding the land-use history and the choice of land-cover types in park planning can substantially influence the carbon budget of urban parks.

This research focuses on the development and integration of low-cost Mobile Urban Sensing Technologies (MUST) and immersive environmental data exploration mechanisms with the ambition to inform citizens about their environments and aid scientists in uncovering the relations between the surface attributes and the urban environment. We propose to use 3D immersive environmental visualization techniques to enable a user-centered interactive analysis and rationalization of the available urban environmental data in relation to further urban attributes. With this ambition, we have developed three mobile apps that explore three strategies of Augmented Reality (AR) 3D visualizations of urban environmental data. While some data could be acquired from urban Geographic Information System (GIS) and existing sensor networks, we also developed an urban sensing kit specifically designed for deployment on mobile platforms such as buses or cars.

A large number of East Asian cities, like cities in other parts of the world, are being affected by extreme heatwaves. Yet, little is known about the general urban design characteristics of sites with significant heat vulnerability within the localized context. In this study, empirical data sets were constructed describing the biomedical, social, environmental, and place-based parameters associated with the location of heat-related emergency calls in Suwon, South Korea, between 2010 and 2014. The results showed that the distribution of heat vulnerability appeared to be highly clustered within a few places, although the area did not have a high proportion of socioeconomically vulnerable populations. Here, three urban design characteristics were described in association with the location of emergency calls: (1) high-rise building complexes built on a superblock gridiron layout with large open spaces in between, (2) low-rise, high-density residential districts with a fine-scaled street layout, and (3) hilly ...

Parks account for a large proportion of green spaces in urban regions, but most previous studies have focused on the values of recreational services in urban parks-carbon uptake by plants in urban parks has been studied less extensively. Urban parks typically form complex landscapes in space and time by integrating multiple species with open canopies. Thus, to better understand canopy photosynthesis in urban park, measuring spatial and temporal variations in photosynthetic parameters and canopy structural variables is essential. Here, we report seasonal and spatial variations in two key photosynthetic parameters ( V cmax and J max which are the maximum rates of carboxylation and electron transport, respectively) and leaf area index (LAI) in Seoul Forest Park. During the peak growing season, we found an eightfold difference (20 to 149 μmol m −2 s −1 ) and fourfold difference (38 to 141 μmol m −2 s −1 ) in V cmax and J max , respectively, across 10 species. Over the seasons, two woody species ( Zelkova serrata (Thunb.) Makino and Prunus yedoensis Matsum ), respectively showed three- to fivefold differences in V cmax and two- to fivefold differences in J max . We evaluated whether leaf nitrogen contents could predict V cmax and J max , and found significant correlations among the three variables during the peak growing season across 10 species, but no significant correlations among them over the seasons in the two woody species. LAI computed using in- situ observations and satellite remote-sensing imagery showed a high spatial heterogeneity during the growing season. These results highlight the considerable spatial and temporal variability in photosynthetic parameters and LAI, which implies that individual tree-based three-dimensional canopy modeling will be essential for accurate estimation of canopy photosynthesis in urban parks.

summary Understanding basin-scale evapotranspiration (ET) is an important issue for the management of regional water resources, especially with the recent trend of intensified precipitation (P). This study assessed the spatial and temporal variations of regional ET in response to P extremes, for various types of land-cover across the Youngsan River basin in Korea. The spatial distribution of monthly P and ET from 2001 to 2009 were estimated using rainfall records from 40 weather stations located across the basin and a satellite-derived, process-based ET model Breathing Earth System Simulator (BESS) (Ryu et al., 2011), respectively. The study periods were focused on the recent years with abnormally large, small and normal P, which were identified from anomalies in the zsores of long-term (1973–2011) rainfall records. The variation of regional ET was assessed in terms of: (1) the controlling factors, using the statistics of related meteorological and geographical data, (2) a waterenergy balance, using Budyko’s framework, and (3) the water balance of four selected watersheds in the region, using the partitioning of annual P into ET and riverflow discharge (Q). The total annual ET of this region decreased in abnormally large-P year and increased in small-P year, because the ET in July to August (which accounts for more than 36% of annual ET) was limited by the available energy rather than available water due to the summer monsoon. In terms of land cover types, forests showed larger interannual variability in ET than paddy fields or cropland, with the differences in ET between large and small-P years being 108 and 82 mm yr � 1 , respectively. The sensitivity of annual ET to P extremes was significantly related to the leaf area index (LAI), rather than soil properties, topography, or specific land-cover type (p < 0.05, generalized linear model). However, the interannual variations of ET were not large (15–18%) compared to those of annual P (51–62%) and Q (108–232%) during 2002– 2009. Thus, vegetation played a consistent role in releasing water back to the regional atmosphere through ET, regardless of P extremes.

a b s t r a c t Estimates of the canopy gap fraction, on which calculations of leaf area index (LAI) are based, are sensi- tive to photographic exposure in upward-facing images. In this article we describe a simple, automated method of image acquisition and processing that eliminates both subjectivity and the need for the oper- ator to consider photographic exposure in the field. A key strength of our methodology was the use of a test apparatus (perforated aluminum screen) with a precisely known gap fraction; this allowed us to separate the confounding effects of gap size and gap fraction on the optimal photographic exposure for a canopy. We took photographs of the test apparatus at different photographic exposures; we varied the gap fraction by covering a proportion of the holes in the screen, and also varied gap size by varying the distance of the camera from the screen. We acquired both raw images and JPEG images. We found that the optimal exposure of JPEG images varied with both gap size and gap fraction, not just gap fraction as previously assumed. Underexposing by one stop yielded raw data that were never clipped resulting in data loss, but that used most of the 14-bit range of the raw file. We also found that it was easily possible to standardize photographic exposure during image processing by acquiring raw images in the field; thus eliminating the variation in estimated gap fraction and LAI associated with exposure variations. This result was replicated in both fisheye images and cover images that we acquired in real canopies. We recommend the following protocol for acquiring canopy images in future studies

article i nfo Emerging near-surface remote sensing techniques have advanced our ability to monitor forest canopy phenolo- gy. Thus far, however, little effort has been made to monitor the phenologies of the various canopies of multi- layer forestsseparately,despite theirimportance inregulating forestbiogeochemicalcycles. Here we reportphe- nological changesinmulti-layercanopiesofdeciduousbroadleafandevergreenneedleleaf forestsintheRepublic of Korea during the spring of 2013. We installed light-emitting diode (LED) sensors at four different canopy heights at two sites to measure the normalized difference vegetation index (NDVI) using red and near-infrared (NIR) spectral reflectance and to estimate leaf area index (LAI) using the blue band gap fraction. LED-sensors identified leaf-out dates of over- and understory canopies at both sites; leaves unfolded 8-11 days earlier in the understory canopy than the overstory canopy. At the deciduous forest site, LED-NDVI failed to capture the leaf-out date in the overstory canopy, because all four LED-sensors started to see green-up from the understory canopy while the overstory canopy was leafless. LED-LAI identified different leaf-out dates for the over- and un- derstory canopy, because the gap fraction was measured explicitly for each canopy layer. In the evergreen forest site, LED-NDVI signals between the top of the tower and beneath the overstory canopy were decoupled be- cause of the dense evergreen overstory canopy. Both LED-NDVI and LED-LAI identified new needle expan- sion in the overstory canopy and understory canopy development. MODIS NDVI agreed well with LED-NDVI data (R 2 = 0.96, RMSE = 0.04) at the deciduous forest site, and we discovered that understory canopy de- velopment determined the onset of greenness based on MODIS NDVI data. LED-LAI data agreed well with independent estimates from the other instruments, indicating that LED-sensors could be used to monitor multi-layer canopy LAI. Continuous, in-situ observation of multi-layer canopy phenology will aid in the in- terpretation of satellite remote sensing phenology products and improve land surface models that adopt a multi-layer canopy model.

Statistical gross primary productivity (GPP) estimation from remote sensing observations has mostly been attempted on the basis of multispectral observations. To make full use of the information contained in vegetation spectra, however, hyperspectral observations should be used in combination with appropriate multivariate methods. Nevertheless, only very few previous studies attempted to estimate GPP directly from hyperspectral observations and did so on the basis of reflectance, with only a limited number of temporally discontinuous observations. In this study, we used long-term, continuous, half-hourly hyperspectral observations covering the visible and near-infrared spectral range to estimate GPP directly from upwelling irradiance using partial least square (PLS) regression in a rice paddy. To gain a better understanding of processes underlying the PLS estimation, we used extensive complementary field observations to run process-based simulations using the SCOPE model. We then applied PLS regression to the simulated hyperspectral data in the same way as for the observations and disentangled contributions related to relevant physiological processes, namely sun-induced chlorophyll fluorescence (SIF) and xanthophyll cycle-related spectral changes (XC). We found that upwelling hyperspectral irradiance in the visible and near-infrared spectral range predicted GPP better than reflectance. Furthermore, PLS-based GPP estimates outperformed both far-red SIF and widely used vegetation index-based methods. However, the most relevant information for the observation-based PLS-models was not clearly related to XC or SIF as the near-infrared spectral range showed comparable performance. Also, the simple average of upwelling irradiance over the 850–900 nm range outperformed the other non-multivariate approaches, including far-red SIF. These results held for the evaluation in terms of the seasonal variation of GPP, while there was apparently a small contribution of SIF and XC for the diurnal variation. The simulation-based analysis showed that SIF and XC contributed useful information to both GPP and photosynthetic light use efficiency (LUE) estimates at both seasonal and diurnal time scales. The strongest unique contribution from either SIF or XC, however, was to the diurnal variation of GPP and XC showed considerably better performance than SIF. We did not find improvements when combining the spectral regions of XC (500–570 nm) and SIF (650–800 nm) to estimate GPP. While SIF showed improvements when combined with the remaining spectral information excluding XC, this was not the case for XC. Our approach combines the strengths of process-based modeling with multivariate statistical analysis to improve our understanding of the usable information content in vegetation spectra and is highly relevant for further developing suitable methods for GPP estimation at large scales.

Solar-induced chlorophyll fluorescence (SIF) has emerged as a leading approach for remote sensing of gross primary productivity (GPP). While SIF has an intrinsic, underlying relationship with canopy light capture and light use efficiency, these physiological relationships are obscured by the fact that satellites observe a small and variable fraction of total emitted canopy SIF. Upon emission, most SIF photons are reabsorbed or scattered within the canopy, preventing their observation remotely. The complexities of the radiative transfer process, which vary across time and space, limit our ability to reliably infer physiological processes from SIF observations. Here, we propose an approach for estimating the fraction of total emitted near-infrared SIF (760 nm) photons that escape the canopy by combining the near-infrared reflectance of vegetation (NIRV) and the fraction of absorbed photosynthetically active radiation (fPAR), two widely available remote sensing products. Our approach relies on the fact that NIRV is resilient against soil background contamination, allowing us to reliably calculate the bidirectional reflectance factor of vegetation, which in turn conveys information about the escape ratio of SIF photons. Our NIRV-based approach explains variations in the escape ratio with an R2 of 0.91 and an RMSE of 1.48% across a series of simulations where canopy structure, soil brightness, and sun-sensor-canopy geometry are varied. The approach is applicable to conditions of low leaf area index and fractional vegetation cover. We show that correcting for the escape ratio of SIF using NIRV provides robust estimates of total emitted SIF, providing for the possibility of studying physiological variations of fluorescence yield at the global scale.

Clumping index, the measure of foliage grouping relative to a random distribution of leaves in space, is a key structural parameter of plant canopies that influences canopy radiation regimes and controls canopy photosynthesis and other land–atmosphere interactions. In this study, we retrieve the clumping index using the original 275 m resolution data of the Multi-angle Imaging SpectroRadiometer (MISR) instrument over a set of sites representing diverse biomes and different canopy structures. Also for the first time, the MISR derived clumping index values are directly validated with both in-situ vertical profiles and seasonal trajectories of clumping index. Our results illustrate that MISR data with 275 m allow clumping index estimates at much more pertinent scales (both spatial and temporal) than previous maps from Polarization and Directionality of Earth Reflectances (POLDER) and Moderate Resolution Imaging Spectroradiometer (MODIS) for modeling local carbon and energy fluxes.

Correct estimates of gap fraction are essential for quantifying canopy architectural variables, such as leaf area and clumping indices, which modify land–atmosphere interactions. However, gap fraction measurements from optical sensors are contaminated by radiation that is scattered by plant elements and ground surfaces. In this study, we propose a simple one-dimensional, invertible, bidirectional transmission model to remove scattering effects from gap fraction measurements. To evaluate how well the proposed model computes scattered radiance under a variety of ecosystem conditions, we compared simulated scattered radiance by the proposed model to a more sophisticated three-dimensional model in four ecosystem types (oak-grass savanna, birch, pine, and spruce stands). The simple model showed good agreement with the three-dimensional model in the scattering factor (scattered radiation from leaves normalized by sky radiation), except for highly reflective stems such as birch. The simple model showed that the scattering factor is highest when the leaf area index (LAI) is low (1–2 m 2 m −2 ) in a non-clumped canopy, potential errors in estimating the LAI increase with an increase in LAI, and bright land surfaces (e.g., snow and bright soil) and bright stems (e.g., birch) can contribute significantly to scattering effects. By applying the simple model with LAI-2200 data collected in an oak-grass savanna woodland, we found that the scattering factor causes significant underestimation of the LAI (up to 26% for sunny conditions, 7.7% for diffuse sky conditions) and significant overestimation of the apparent clumping index (up to 14% for sunny conditions, 4.3% for diffuse sky conditions). The LAI is underestimated because of the effect of scattered radiation on gap fraction estimates, which cause overestimation of the clumping index. Even under highly diffuse sky conditions, errors in LAI estimates due to scattering effects are not always negligible (up to 7.7% underestimation). The proposed inversion scheme provides an opportunity to quantify gap fractions, LAI, and apparent clumping index even under sunny conditions.

a b s t r a c t Vegetation phenology is highly sensitive to climate change. Phenology also controls many feedbacks of vegetation to the climate system by influencing the seasonality of albedo, surface roughness length, canopy conductance, and fluxes of water, energy, CO2 and biogenic volatile organic compounds. In this review, we first discuss the environmental drivers of phenology, and the impacts of climate change on phenology, in different biomes. We then examine the vegetation-climate feedbacks that are mediated by phenology, and assess the potential impact on these feedbacks of shifts in phenology driven by climate change. We finish with an overview of phenological modeling and we suggest ways in which models might be improved using existing data sets. Several key weaknesses in our current understanding emerge from this analysis. First, we need a better understanding of the drivers of phenology, particularly in under-studied biomes (e.g. tropical forests). We do not have a mechanistic understanding of the role of photoperiod, even in well-studied biomes. In all biomes, the factors controlling senescence and dormancy are not well-documented. Second, for the most part (i.e. with the exception of phenology impacts on CO2 exchange) we have only a qualitative understanding of the feedbacks between vegetation and climate that are mediated by phenology. We need to quantify the magnitude of these feedbacks, and ensure that they are accurately reproduced by models. Third, we need to work towards a new understanding of phenological processes that enables progress beyond the modeling paradigms currently in use. Accurate representation of phenological processes in models that couple the land surface to the climate system is particularly important, especially when such models are being used to predict future climate.

The world is experiencing remarkable climate change, which alters vegetation structural and functional status, and the biosphere feedback to the climate might amplify or dampen regional and global climate change. The exchange of energy and mass across the land-atmosphere interface is an essential indicator of the interaction between ecosystem and environment, and it has been widely measured at landscape scale since the establishment of FLUXNET, a global network of micrometeorological flux measurement sites (Baldocchi, 2008; Baldocchi et al., 2001). Furthermore, these site observations have been upscaled to global scale in conjunction with satellite remote sensing data, providing opportunities to investigate the spatial and temporal variations of carbon and water cycles from a macro perspective (Beer et al., 2010; Martin Jung et al., 2010). However, such upscaling approaches are based on data-driven models, which need to be well calibrated using site observations (M. Jung, Reichstein, & Bondeau, 2009; Papale & Valentini, 2003; Xiao et al., 2010; Yang et al., 2007). Consequently, those models are limited by the representativeness, quantity and quality of the training datasets (Sundareshwar et al., 2006), as well as the lack of independent reference datasets for validation. Furthermore, by using the upscaling approach predictors are directly linked with target fluxes through machine learning, while the intermediate variables along with intrinsic mechanisms are hidden.

Savanna ecosystems are one of the most dominant and complex terrestrial biomes that derives from a distinct vegetative surface comprised of co-dominant tree and grass populations. While these two vegetation types co-exist functionally, demographically they are not static, but are dynamically changing in response to environmental forces such as annual fire events and rainfall variability. Modelling savanna environments with the current generation of terrestrial biosphere models (TBMs) has presented many problems, particularly describing fire frequency and intensity, phenology, leaf biochemistry of C3 and C4 photosynthesis vegetation, and root water uptake. In order to better understand why TBMs perform so poorly in savannas, we conducted a model inter-comparison of 6 TBMs and assessed their performance at simulating latent energy (LE) and gross primary productivity (GPP) for five savanna sites along a rainfall gradient in northern Australia. Performance in predicting LE and GPP was measured using an empirical benchmarking system, which ranks models by their ability to utilise meteorological driving information to predict the fluxes. On average, the TBMs performed as well as a multi-linear regression of the fluxes against solar radiation, temperature and vapour pressure deficit, but were outperformed by a more complicated nonlinear response model that also included the leaf area index (LAI). This identified that the TBMs are not fully utilising their input information effectively in determining savanna LE and GPP, and highlights that savanna dynamics cannot be calibrated into models and that there are problems in underlying model processes. We identified key weaknesses in a model's ability to simulate savanna fluxes and their seasonal variation, related to the representation of vegetation by the models and root water uptake. We underline these weaknesses in terms of three critical areas for development. First, prescribed tree-rooting depths must be deep enough, enabling the extraction of deep soil water stores to maintain photosynthesis and transpiration during the dry season. Second, models must treat grasses as a co-dominant interface for water and carbon exchange, rather than a secondary one to trees. Third, models need a dynamic representation of LAI that encompasses the dynamic phenology of savanna vegetation and its response to rainfall interannual variability. We believe this study is the first to assess how well TBMs simulate savanna ecosystems, and that these results will be used to improve the representation of savannas ecosystems in future global climate model studies.

The savanna complex is a highly diverse global biome that occurs within the seasonally dry tropical to sub-tropical equatorial latitudes. Savannas are open-canopy environments that encompass a broad demographic continuum, often characterised by a dynamically changing dominance between C3-tree and C4-grass vegetation, where frequent environmental disturbances such as fire modulates the balance between ephemeral and perennial life forms. Climate change is projected to result in significant changes to the savanna floristic structure, with increases to woody biomass expected through CO2 fertilisation in mesic savannas and increased tree mortality expected through increased rainfall interannual variability in xeric savannas. The complex interaction between vegetation and climate that occurs in savannas has traditionally challenged current-generation terrestrial biosphere models (TBMs), which aim to simulate the interaction between the atmosphere and the land-surface to predict responses of vegetation to changing in environmental forcing. In this review, we examine whether TBMs are able to adequately represent savanna dynamics and what implications potential deficiencies may have for climate change projection scenarios that rely on these models. We start by highlighting the defining characteristic traits and behaviours of savanna, how these differ across continents, and how this information is (or is not) represented in the structural framework of many TBMs. We highlight three dynamic processes that we believe directly affect the water-use and productivity of the savanna system, namely: phenology; root-water access; and fire dynamics. Following this, we discuss how these processes are represented in many current generation TBMs and whether they are suitable for simulating savanna dynamics. Finally, we give an overview of how eddy-covariance observations in combination with other data sources, can be used in model benchmarking and inter-comparison frameworks to diagnose the performance of TBMs in this environment and formulate roadmaps for future development. Our investigation reveals that many TBMs systematically misrepresent phenology, effects of fire and root-water access (if they are considered at all) and that these should be critical areas for future development. Furthermore, such processes must not be static (i.e. prescribed behaviour), but be capable of responding to the changing environmental conditions in order to emulate the dynamic behaviour of savannas. Without such developments, however, TBMs will have limited predictive capability in making the critical projections needed to understand how savannas will respond to future global change.

Most land surface and ecosystem models assume that a vegetated canopy can be abstracted as a turbid medium when such models compute mass, energy, and carbon exchange. However, those models fail to simulate radiation environments in heterogeneous landscapes. This study aims to couple a spatially explicit three-dimensional (3D) shortwave and longwave radiative transfer model with a soil and canopy energy balance and canopy physiology model (CANOAK-FLiES), to investigate how well the 3D model performs in a heterogeneous landscape compared to a 1D model. The canopy structural parameters were extracted using airborne-based Light Detection and Ranging (LiDAR) data and digital cover photographs. The developed model was compared with a wide variety of field and remote sensing data (e.g., hyper-spectral remote sensing images, overstory and understory radiation, and tower-based energy and CO2 fluxes) in a woody oak savanna in CA, USA. Overall, the simulated spatial and diurnal patterns of the radiation environment were consistent with the measurements of shortwave and longwave wavebands. The 3D approach worked better than the 1D approach from the wet mild spring to the dry hot summer while the relative importance of using the 3D approach depends on climate and canopy physiological conditions as well as the canopy structure. With low leaf area index in the oak woodland, the woody elements absorbed 12%–39% of the PAR and 20%–52% of the NIR radiation. Consequently, 12% of the daytime energy flux was used as woody heat storage at our study site. This novel 3D model has the potential to serve as a useful tool for analyzing the spatio-temporal variability of radiation and energy fluxes in evaluating footprints of radiation sensors and eddy covariance fluxes, and serve as a standard in evaluating the performance of a hierarchy of simpler land surface models to compute mass and energy exchange of heterogeneous landscapes.

The continued spread of invasive weeds is threatening ecosystem health throughout North America. Understanding the relationships between invasive weeds’ key phenological phases and structural and/or functional canopy development is an essential step for making informed decisions regarding their management. We analyzed a three-year image archive obtained from an inexpensive webcam overlooking a perennial pepperweed (Lepidium latifoliumL.) infestation in California to explore the ability of red (R)‐green (G)‐blue (B) color space information to track the structural and functional development of the pepperweed.Wecharacterizedstructuralandfunctionalcanopydevelopmentthroughsurfaceroughness length (z 0m ; a proxy for canopy height and leaf area index) and canopy photosynthesis (F A ), respectively, both of which we derived from eddy covariance measurements. Here we demonstrate the use of cross-correlation functions to determine the temporal lags between chromatic coordinates and two color indices, all calculated fromRGBbrightness levels, withz 0m andF A . We found that these color metrics fail to represent the structural and/or functional state of the canopy. In contrast,relative luminance(CIE Y) appears to be a better indicator forz 0m and especially forF A . We calculatedCIE Yfrom pepperweedRGB brightness levels in relation to hypothetical horizontal referenceRGBbrightness levels. We obtained the latter by applying the ratio between horizontally measured and hypothetical incoming solar radiation on a vertical surface toRGBbrightness levels of a vertically oriented reference of invariant light-grey color. We conclude that webcam image archives may provide an inexpensive tool for making informed decisions regarding the timing but not for assessing the effectiveness of invasive plant control measures such as mowing.