Sun-Induced chlorophyll Fluorescence (SIF) is the most promising remote-sensing proxy of Gross Primary Production (GPP) in terrestrial ecosystems. However, the estimation of GPP using SIF is challenging when plants experience stress, particularly during extreme climatic events whose frequency is projected to increase in the future. Recently, the feasibility of canopy-level active chlorophyll fluorescence measurements (LED-induced chlorophyll fluorescence), which directly measure the apparent fluorescence yield (FyieldLIF), has provided new perspectives on detecting the responses of plants to stress. This study was conducted during the summer 2022 European heat waves in a mixed temperate deciduous broadleaf forest, located in the French Fontainebleau-Barbeau station. Continuous measurements of carbon dioxide (CO2) and energy exchanges, SIF, FyieldLIF, and ancillary environmental variables were acquired. We investigated how heat-wave induced high atmospheric dryness, measured as Vapor Pressure Deficit, affected canopy chlorophyll fluorescence (both SIF and FyieldLIF) and GPP, as well as their relationships. At the half-hourly scale, our results revealed a decrease of the correlation between SIF and GPP (R2 decreased from 0.49 to 0.17) at high atmospheric dryness. In contrast, the correlation between FyieldLIF and GPP increased significantly under high atmospheric dryness (R2 increased from 0.07 to 0.43). However, at the daily scale, the correlations between SIF and GPP and between FyieldLIF and GPP showed an overall increase compared to the half-hourly scale, suggesting a time-scale-dependent response of these relationships to atmospheric dryness. This study also highlighted FyieldLIF's advantage in detecting plant responses

Data from satellite, aircraft, drone, and ground-based measurements have already shown that canopy-scale sun-induced chlorophyll fluorescence (SIF) is tightly related to photosynthesis, which is linked to vegetation carbon assimilation. However, our ability to effectively use those findings are hindered by confounding factors, including canopy structure, fluctuations in solar radiation, and sun–canopy geometry that highly affect the SIF signal. Thus, disentangling these factors has become paramount in order to use SIF for monitoring vegetation functioning at the canopy scale and beyond. Active chlorophyll fluorescence measurements (FyieldLIF), which directly measures the apparent fluorescence yield, have been widely used to detect physiological variation of the vegetation at the leaf scale. Recently, the measurement of FyieldLIF has become feasible at the canopy scale, opening up new opportunities to decouple structural, biophysical, and physiological components of SIF at the canopy scale. In this study, based on top-of-canopy measurements above a mature deciduous forest, reflectance (R), SIF, SIF normalized by incoming photosynthetically active radiation (SIFy), FyieldLIF, and the ratio between SIFy and FyieldLIF (named Φk) were used to investigate the effects of canopy structure and shadows on the diurnal and seasonal dynamics of SIF. Further, random forest (RF) models were also used to not only predict FyieldLIF and Φk, but also provide an interpretation framework by considering additional variables, including the R in the blue, red, green, red-edge, and near-infrared bands; SIF; SIFy; and solar zenith angle (SZA) and solar azimuth angle (SAA). Results revealed that the SIF signal is highly affected by the canopy structure and sun–canopy geometry effects compared to FyieldLIF. This was evidenced by the weak correlations obtained between SIFy and FyieldLIF at the diurnal timescale. Furthermore, the daily mean SIF‾y captured the seasonal dynamics of daily mean F‾yieldLIF and explained 58 % of its variability. The findings also revealed that reflectance in the near-infrared (R-NIR) and the NIRv (the product of R-NIR and normalized difference vegetation index (NDVI)) are good proxies of Φk at the diurnal timescale, while their correlations with Φk decrease at the seasonal timescale. With FyieldLIF and Φk as outputs and the abovementioned variables as predictors, this study also showed that the RF models can explain between 86 % and 90 % of FyieldLIF, as well as 60 % and 70 % of Φk variations under clear-sky conditions. In addition, the predictor importance estimates for FyieldLIF RF models revealed that R at 410, 665, 740, and 830 nm; SIF; SIFy; SZA; and SAA emerged as the most useful and influential factors for predicting FyieldLIF, while R at 410, 665, 705, and 740 nm; SZA; and SAA are crucial for predicting Φk. This study highlighted the complexity of interpreting diurnal and seasonal dynamics of SIF in forest canopies. These dynamics are highly dependent on the complex interactions between the structure of the canopy, the vegetation biochemical properties, the illumination angles (SZA and SAA), and the light conditions (ratio of diffuse to direct solar radiation). However, such measurements are necessary to better separate the variability in SIF attributable to radiation and measurement conditions from the subtler variability attributable to plant physiological processes.

Notre connaissance du cycle du carbone est déterminante pour prédire la trajectoire du réchauffement climatique. La photosynthèse est à l’origine du flux de carbone le plus important entre l’atmosphère et les surfaces continentales. Pour autant, de nombreuses incertitudes subsistent sur les processus en jeu. L’observation de la fluorescence de la chlorophylle depuis l’espace apporte potentiellement l’information pour contraindre les modèles de fonctionnement carboné des écosystèmes. Nous retraçons ici les efforts méthodologiques, instrumentaux, et expérimentaux entrepris au Laboratoire de Météorologie Dynamique pour faire passer la fluorescence de la chlorophylle de donnée de laboratoire à observable climatique globale.

Abstract. Far-red Sun-Induced chlorophyll Fluorescence (SIF) is increasingly used as a proxy of vegetation Gross Primary Production (GPP) across different ecosystems and at spatiotemporal resolutions going from proximal to satellite-based remote sensing measurements. However, the use of SIF to probe variations in GPP in forests is challenged by (1) confounding factors such as canopy structure and sun-canopy geometry, and by (2) leaf physiological and biochemical properties along with abiotic factors (light intensity, temperature, soil water content, atmospheric vapour pressure deficit, etc.) that can influence SIF and GPP in a different way. To provide insights into understanding the complex drivers of GPP and SIF variations and of their relationships, we examined how SIF and GPP changed at daily and seasonal scales and how canopy structure and environmental conditions affected SIF and GPP relationships in a deciduous oak forest. To do so, we combined canopy scale SIF measurements, spectral vegetation indices, environmental variables measurements, including diffuse and direct radiations in the spectral range of the Photosynthetically Active Radiation (PAR), air and canopy temperature, soil water content (SWC), atmospheric Vapour Pressure Deficit (VPD), and GPP estimated from eddy covariance measurements. Canopy chlorophyll fluorescence was also measured using an active system with an artificial light source, referred to as LIF (LED Induced chlorophyll Fluorescence) hereafter. Further, Random Forest (RF) models were used to predict SIF and GPP and to analyse the responses of SIF and GPP to environmental drivers. The results show that both SIF and GPP variations and their relationships were dependent on the temporal scale considered. At the seasonal scale, The data show that leaf and canopy properties variations, seasonal cycle of PAR, and other abiotic factors such as VPD and SWC control not only SIF and GPP variations, but also their relationships. Further, during extreme weather conditions (heatwaves observed in 2022 in: mid-June (DOY: 166-169), mid-July (DOY: 196-199), and early August (DOY: 218-224)), we observed that SIF and reflectance-based Vegetation Indices (VIs), such as Normalized Difference Vegetation Index (NDVI) and Near-Infrared Reflectance of vegetation index (NIRv), and also SIF and PAR are uncorrelated, while GPP, SIF, passive SIF yield (SIFy) and active chlorophyll fluorescence yield (FyieldLIF) strongly decreased. This indicates that during these severe abiotic conditions SIF stayed a usable proxy of GPP, while VIs cannot be used to track changes in vegetation physiology. This specific response of SIF compared to VIs underlined the interest of SIF to monitor GPP under severe abiotic conditions. At the diurnal timescale, the results also revealed that the saturation of the relationship between GPP and SIF was not only dependent on PAR, but also on the fraction of diffuse to total PAR, as well as on VPD, SWC, and air and canopy temperature. The other key finding was that sun geometry angles had strong effects on GPP and SIF variations. This result highlights that using ground-based SIF measurements to validate satellite measurements at coarse spatial and temporal resolutions can therefore be very difficult, due to confounding factors whose effects are significant and may vary from one site to another, especially in forest canopies.

North American evergreen forests cover large areas and influence the global carbon cycle. Satellite remote sensing has been used to track the phenology of ecosystem photosynthesis of these forests by detecting variation in vegetation optical properties associated with physiological and structural features, and most of these methods have been closely tied to vegetation greenness. However, in evergreens, the application of satellite data to monitor photosynthetic phenology is often limited by the lack of sensitivity of greenness-based indices. In this study, we identified 47 evergreen forest flux sites in North America that had MODIS observation overlapping with the flux tower records. We then calculated four vegetation indices using MODIS MAIAC data (MCD19A1), including NDVI, CCI, NIRv, and kNDVI, for the 47 flux sites and evaluated relationships between gross primary productivity (GPP) and vegetation indices across the North American evergreen forests. Our results showed that snow had substantial effects on the performance of all vegetation indices in tracking GPP phenology, particularly in the early spring when rapid changes occurred to both GPP and snow cover. Different vegetation indices were affected differently, indicating contradictory and confounding effects of snow on these indices. After correcting for the snow effects, both CCI and NIRv performed well in tracking GPP phenology, albeit for different reasons. CCI is sensitive to seasonal changes in the relative levels of chlorophyll and carotenoid pigments, which are closely tied to GPP phenology in evergreens. NIRv is sensitive to the absorbed photosynthetically active radiation and to the contribution of deciduous components to the overall optical properties. We also found that correlations between GPP and vegetation indices varied among ecoregions and climate classes. In general, regions with pronounced seasonal GPP patterns had stronger correlations between GPP and greenness-based indices than regions with weaker seasonal GPP patterns. These biome differences were less pronounced for CCI. The snow artifacts and complementary vegetation index effects reported here should be considered in any large-scale studies of GPP using reflectance-based indices from optical satellites.

An accurate estimation of vegetation gross primary productivity (GPP), which is the amount of carbon taken up by vegetation through photosynthesis for a given time and area, is critical for understanding terrestrial–atmosphere CO2 exchange processes and ecosystem functioning, as well as ecosystem responses and adaptations to climate change. Prior studies, based on ground, airborne, and satellite sun-induced chlorophyll fluorescence (SIF) observations, have recently revealed close relationships with GPP at different spatial and temporal scales and across different plant functional types (PFTs). However, questions remain regarding whether there is a unique relationship between SIF and GPP across different sites and PFTs and how we can improve GPP estimates using solely remotely sensed data. Using concurrent measurements of daily TROPOspheric Monitoring Instrument (TROPOMI) SIF (daily SIFd); daily MODIS Terra and Aqua spectral reflectance; vegetation indices (VIs, notably normalized difference vegetation index (NDVI), near-infrared reflectance of vegetation (NIRv), and photochemical reflectance index (PRI)); and daily tower-based GPP across eight major different PFTs, including mixed forests, deciduous broadleaf forests, croplands, evergreen broadleaf forests, evergreen needleleaf forests, grasslands, open shrubland, and wetlands, the strength of the relationships between tower-based GPP and SIFd at 40 Integrated Carbon Observation System (ICOS) flux sites was investigated. The synergy between SIFd and MODIS-based reflectance (R) and VIs to improve GPP estimates using a data-driven modeling approach was also evaluated. The results revealed that the strength of the hyperbolic relationship between GPP and SIFd was strongly site-specific and PFT-dependent. Furthermore, the generalized linear model (GLM), fitted between SIFd, GPP, and site and vegetation type as categorical variables, further supported this site- and PFT-dependent relationship between GPP and SIFd. Using random forest (RF) regression models with GPP as output and the aforementioned variables as predictors (R, SIFd, and VIs), this study also showed that the spectral reflectance bands (RF-R) and SIFd plus spectral reflectance (RF-SIF-R) models explained over 80 % of the seasonal and interannual variations in GPP, whereas the SIFd plus VI (RF-SIF-VI) model reproduced only 75 % of the tower-based GPP variance. In addition, the relative variable importance of predictors of GPP demonstrated that the spectral reflectance bands in the near-infrared, red, and SIFd appeared as the most influential and dominant factors determining GPP predictions, indicating the importance of canopy structure, biochemical properties, and vegetation functioning on GPP estimates. Overall, this study provides insights into understanding the strength of the relationships between GPP and SIF and the use of spectral reflectance and SIFd to improve estimates of GPP across sites and PFTs.

Solar-induced chlorophyll fluorescence (SIF) has been widely used to track vegetation photosynthesis at different scales ranging from in-situ measurements to satellite products. Airborne platforms sample SIF data at a spatial scale intermediate between in-situ and satellite, matching that of ground measurement (e.g. flux tower footprints and other field sampling), enabling us to explore causes of SIF variation and validate satellite-based SIF products. However, harmonizing SIF across sensors and platforms (correcting for systematic errors to yield a consistent, comparable SIF product) is challenging because SIF can be retrieved in different absorption windows, with different instruments and methods complicating the comparison between different observational levels (i.e., ground, airborne, satellites) and between sites equipped with different instruments with varying optical prop-erties (spectral resolution and sampling intervals, spatial resolution). Additionally, the spatial and temporal variability of atmospheric properties can influence the retrieval of the weak SIF signal. Because of these com-plications, direct comparisons of airborne and ground SIF across scales are rarely attempted. In this study, we combined airborne SIF data with simultaneous 'ground truth' data collected by stationary and mobile platforms in a soybean field in Nebraska, USA. In this effort, we tested several SIF extraction methods, including Fraunhofer Line Discrimination (FLD), improved Fraunhofer Line Discrimination (iFLD), Spectral Fitting Method (SFM), SpecFit, and a Singular Vector Decomposition (SVD) method. The SpecFit method was sensitive to the 715-740 nm water bands and removing the water bands in the fitting process yielded better agreement between the airborne and ground SIF spectra. Accurate estimation of the ground level downwelling irradiance obtained by ground measurements over a calibration target improved agreement between airborne and ground SIF retrievals at the O2A band, and allowed us to derive a SIF dataset with improved agreement across platforms and sampling scales. This experimental approach provided a method for generating comparable SIF signals across instruments, methods and platforms, which is critical to understanding the SIF-GPP relationship at different scales and to cross-validate the diversity of platforms used for satellite products calibration and validation.

Annual time-series of the two satellites C-band SAR (Synthetic Aperture Radar) Sentinel-1A and 1B data over five years were used to characterize the phenological cycle of a temperate deciduous forest. Six phenological metrics of the start (SOS), middle (MOS) and end (EOS) of budburst and leaf expansion stage in spring, and the start (SOF), middle (MOF) and end (EOF) of leaf senescence in autumn were extracted using an asymmetric double sigmoid function (ADS) fitted to the time-series of the ratio (VV/VH) of backscattering at co-polarization VV (vertical–vertical) and at cross polarization VH (vertical-horizontal). Phenological metrics were also derived from other four vegetation proxies (Normalized Difference Vegetation Index NDVI time-series from Sentinel-2A and 2B images, and in situ measurements of NDVI measurements, Leaf Area Index LAI and litterfall temporal dynamics). These estimated phenological metrics were compared to phenological observations obtained by visual observations from the ground, achieved using binoculars by three inter-calibrated observers, on a bi-weekly basis during the budburst and weekly during the senescence. We observe a decrease in the backscattering coefficient (σ0) at VH cross polarization during the leaf development and the expansion phase in spring and an increase during the senescence phase, contrary to what is usually observed on various types of crops. In vertical polarization, σ0VV shows very little variation throughout the year. S-1 time-series of VV/VH ratio provide a good description of the seasonal vegetation cycle allowing the estimation of spring and autumn phenological metrics. Estimates provided by VV/VH of budburst dates using MOS criterion differ by approximately 8 days on average (mean average deviation) from phenological observations. During senescence phase, estimates using MOF criterion are later and deviate by about 20 days from phenological observations of leaf senescence while the differences are of the order of 2 to 4 days between the phenological observations and estimates based on in situ NDVI and LAI time-series, respectively. A deviation of about 7 days, comparable to that observed during budburst, is obtained between the estimates of senescence (MOF) from S-1 and those determined from the in situ monitoring of litterfall. While in spring, leaf emergence and expansion described by LAI or NDVI explain the increase of VV/VH (or the decrease of σ0VH), during senescence, S-1 VV/VH is decorrelated from LAI or NDVI and is better explained by litterfall temporal dynamics. This behavior resulted in a hysteresis phenomenon observed on the relationships between VV/VH and NDVI or LAI. For the same LAI or NDVI, the response of VV/VH is different depending on the phenological phase considered. This study shows the high potential offered by Sentinel-1 SAR C-band time-series for the detection of forest phenology, thus overcoming the limitations caused by cloud cover in optical remote sensing of vegetation phenology.

Tree phenology is a major driver of forest–atmosphere mass and energy exchanges. Yet, tree phenology has rarely been monitored in a consistent way throughout the life of a flux-tower site. Here, we used seasonal time series of ground-based NDVI (Normalized Difference Vegetation Index), RGB camera GCC (greenness chromatic coordinate), broadband NDVI, LAI (leaf area index), fAPAR (fraction of absorbed photosynthetic active radiation), CC (canopy closure), fRvis (fraction of reflected radiation) and GPP (gross primary productivity) to predict six phenological markers detecting the start, middle and end of budburst and of leaf senescence in a temperate deciduous forest using an asymmetric double sigmoid function (ADS) fitted to the time series. We compared them to observations of budburst and leaf senescence achieved by field phenologists over a 13-year period. GCC, NDVI and CC captured the interannual variability of spring phenology very well (R2>0.80) and provided the best estimates of the observed budburst dates, with a mean absolute deviation (MAD) of less than 4 d. For the CC and GCC methods, mid-amplitude (50 %) threshold dates during spring phenological transition agreed well with the observed phenological dates. For the NDVI-based method, on average, the mean observed date coincides with the date when NDVI reaches 25 % of its amplitude of annual variation. For the other methods, MAD ranges from 6 to 17 d. The ADS method used to derive the phenological markers provides the most biased estimates for the GPP and GCC. During the leaf senescence stage, NDVI- and CC-derived dates correlated significantly with observed dates (R2=0.63 and 0.80 for NDVI and CC, respectively), with an MAD of less than 7 d. Our results show that proximal-sensing methods can be used to derive robust phenological metrics. They can be used to retrieve long-term phenological series at eddy covariance (EC) flux measurement sites and help interpret the interannual variability and trends of mass and energy exchanges.

Phenological cameras have been used over a decade for identifying plant phenological markers (budburst, leaf senescence) and more generally the greenness dynamics of forest canopies. The analysis is usually carried out over the full camera field of view, with no particular analysis of the variability of phenological markers among trees. Here we show that images produced by phenological cameras can be used to quantify the within-population variability of budburst (WPVbb) in temperate deciduous forests. Using seven site-years of image analyses, we report a strong correlation (r(2) = 0.97) between the WPVbb determined with a phenological camera and its quantification through ground observation. We show that WPVbb varies strongly (by a factor of 4) from year to year in a given population and that those variations are linked with temperature conditions during the budburst period, with colder springs associated to a higher differentiation of budburst (higher WPVbb) among trees. Deploying our approach at the continental scale, i.e., throughout phenological cameras networks, would improve the understanding of the spatial (across populations) and temporal (across years) variations of WPVbb, which have strong implications on forest functioning, tree fitness and phenological modelling.

Abstract. Tree phenology is a major driver of forest-atmosphere mass and energy exchanges. Yet tree phenology has historically not been recorded at flux measurement sites. Here, we used seasonal time-series of ground-based NDVI (Normalized Difference Vegetation Index), RGB camera GCC (Greenness Chromatic Coordinate), broad-band NDVI, LAI (Leaf Area Index), fAPAR (fraction of Absorbed Photosynthetic Active Radiation), CC (Canopy Closure), fRvis (fraction of Reflected Radiation) and GPP (Gross Primary Productivity) to predict six phenological markers detecting the start, middle and end of budburst and of leaf senescence in a temperate deciduous forest. We compared them to observations of budburst and leaf senescence achieved by field phenologists over a 13-year period. GCC, NDVI and CC captured very well the interannual variability of spring phenology (R2 \u003e 0.80) and provided the best estimates of the observed budburst dates, with a mean absolute deviation (MAD) less than 4 days. For the CC and GCC methods, mid-amplitude (50 %) threshold dates during spring phenological transition agreed well with the observed phenological dates. For the NDVI-based method, on average, the mean observed date coincides with the date when NDVI reaches 25 % of its amplitude of annual variation. For the other methods, MAD ranges from 6 to 17 days. GPP provides the most biased estimates. During the leaf senescence stage, NDVI- and CC-derived dates correlated significantly with observed dates (R2 = 0.63 and 0.80 for NDVI and CC, respectively), with MAD less than 7 days. Our results show that proximal sensing methods can be used to derive robust phenological metrics. They can be used to retrieve long-term phenological series at flux measurement sites and help interpret the interannual variability and trends of mass and energy exchanges.

Sun‐induced fluorescence (SIF) has been found to be strongly correlated with gross primary production (GPP) in a quasi‐linear pattern at the scales beyond leaves. However, the causes of the GPP:SIF relationship deviating from a linear pattern remain unclear. In the current study conducted at two maize sites in Nebraska in 2017 summer growing season, we investigated the relationship between GPP and SIF at 760 nm (F760) at two temporal scales and quantified the contributions of incoming photosynthetically active radiation (PARin), fraction of absorbed PAR (fPAR), light use efficiency (LUE), and F760 yield (F760,y, defined as F760/(PARin×fPAR)) to GPP and F760 variabilities to further understand the linearity and deviations in the GPP:F760 relationship. We found the following: (1) For individual growth stages when canopy structure and chlorophyll content were stable, GPP and F760 were strongly controlled by PARin, while LUE and F760,y had much lower contributions to the GPP:F760 relationship; during this period, LUE and F760,y had either a slightly negative or no clear relationship, which explained some deviations in the GPP:SIF relationship. (2) At the seasonal scale, the contribution of LUE to GPP variability as well as the contribution of F760,y to F760 variability increased and was comparable to the contribution of PARin; the LUE:F760,y relationship showed a strong linear relationship, which strengthened the linear GPP:F760 relationship. Both maize sites showed similar patterns. A framework was applied to estimate LUE at individual stages and as a result, significantly improved the GPP estimation, thus enhancing the SIF potential for inferring photosynthesis.

Leaf mass per area (LMA) and leaf equivalent water thickness (EWT) are key leaf functional traits providing information for many applications including ecosystem functioning modeling and fire risk management. In this paper, we investigate two common conclusions generally made for LMA and EWT estimation based on leaf optical properties in the near-infrared (NIR) and shortwave infrared (SWIR) domains: (1) physically-based approaches estimate EWT accurately and LMA poorly, while (2) statistically-based and machine learning (ML) methods provide accurate estimates of both LMA and EWT. Using six experimental datasets including broadleaf species samples of >150 species collected over tropical, temperate and boreal ecosystems, we compared the performances of a physically-based method (PROSPECT model inversion) and a ML algorithm (support vector machine regression, SVM) to infer EWT and LMA based on leaf reflectance and transmittance. We assessed several merit functions to invert PROSPECT based on iterative optimization and investigated the spectral domain to be used for optimal estimation of LMA and EWT. We also tested several strategies to select the training samples used by the SVM, in order to investigate the generalization ability of the derived regression models. We evidenced that using spectral information from 1700 to 2400 nm leads to strong improvement in the estimation of EWT and LMA when performing a PROSPECT inversion, decreasing the LMA and EWT estimation errors by 55% and 33%, respectively. The comparison of various sampling strategies for the training set used with SVM suggests that regression models show limited generalization ability, particularly when the regression model is applied on data fully independent from the training set. Finally, our results demonstrate that, when using an appropriate spectral domain, the PROSPECT inversion outperforms SVM trained with experimental data for the estimation of EWT and LMA. Thus we recommend that estimation of LMA and EWT based on leaf optical properties should be physically-based using inversion of reflectance and transmittance measurements on the 1700 to 2400 nm spectral range.

Prairie productivity is largely affected by temperature and precipitation and is vulnerable to a changing climate. In this study, we used 5-years of growing season eddy covariance and satellite data (MODIS MAIAC) from two adjacent field sites in southern Alberta (Canada) prairie to monitor intra- and inter-annual variation in prairie productivity using remote sensing. Three MODIS vegetation indices were examined to track seasonal variation of Gross Primary Productivity (GPP): the normalized difference vegetation index (NDVI), the NIRv, and the chlorophyll/carotenoid index (CCI). The productivity of these prairie ecosystems was mainly driven by precipitation, with temperature affecting the starting time of the growing season. The three vegetation indices captured distinct aspects of GPP phenology. CCI, which is sensitive to chlorophyll and carotenoid pigment ratios, followed seasonal GPP dynamics more closely than NDVI and NIRv. Consequently, less hysteresis occurred with the seasonal CCI-GPP relationship than with the NDVI-GPP or NIRv-GPP relationships. Relative to 16-day composite data, daily MODIS data provided a more detailed GPP phenology. However, relationships between all vegetation indices and GPP improved with temporal aggregation up to one month, demonstrating that the degree of data aggregation affects the ability of reflectance-based indices to track GPP. Results from a multivariable regression revealed a strong relationship between GPP and a linear combination of 3 MODIS bands (B1, B2 and B11), which indicates that additional spectral information provided by the MODIS ocean band (band 11) can help track grassland GPP better than typical 2-band broad-band indices (e.g. NDVI or NIRv) only. Improved monitoring of prairie ecosystems using these enhanced approaches, can lead to a better understanding of the effects of changing weather and climate on the productivity of prairie ecosystems.

Remotely sensed vegetation indices (RSVIs) can be used to efficiently estimate terrestrial primary productivity across space and time. Terrestrial productivity, however, has many facets (e.g., spatial and temporal variability, including seasonality, interannual variability, and trends), and different vegetation indices may not be equally good at predicting them. Their accuracy in monitoring productivity has been mostly tested in single-ecosystem studies, but their performance in different ecosystems distributed over large areas still needs to be fully explored. To fill this gap, we identified the facets of terrestrial gross primary production (GPP) that could be monitored using RSVIs. We compared the temporal and spatial patterns of four vegetation indices (NDVI, EVI, NIRV, and CCI), derived from the MODIS MAIAC data set and of GPP derived from data from 58 eddy-flux towers in eight ecosystems with different plant functional types (evergreen needle-leaved forest, evergreen broad-leaved forest, deciduous broad-leaved forest, mixed forest, open shrubland, grassland, cropland, and wetland) distributed throughout Europe, covering Mediterranean, temperate, and boreal regions. The RSVIs monitored temporal variability well in most of the ecosystem types, with grasslands and evergreen broad-leaved forests most strongly and weakly correlated with weekly and monthly RSVI data, respectively. The performance of the RSVIs monitoring temporal variability decreased sharply, however, when the seasonal component of the time series was removed, suggesting that the seasonal cycles of both the GPP and RSVI time series were the dominant drivers of their relationships. Removing winter values from the analyses did not affect the results. NDVI and CCI identified the spatial variability of average annual GPP, and all RSVIs identified GPP seasonality well. The RSVI estimates, however, could not estimate the interannual variability of GPP across sites or monitor the trends of GPP. Overall, our results indicate that RSVIs are suitable to track different facets of GPP variability at the local scale, therefore they are reliable sources of GPP monitoring at larger geographical scales.

This study assessed the application of airborne imaging spectroscopy to monitor α-diversity in restored grassland plots. The study site was located within the Central Platte River ecosystem, south of Wood River in Central Nebraska, USA, and consisted of two sets of plots (young and old). Exotic species had recently invaded the old plots, confounding the original study design, while the young plots did not have significant invasion by weeds, reflecting the original study design. We used spectral variation (i.e. spectral diversity, expressed as the coefficient of variation) as a proxy for α-diversity (expressed as species richness and Shannon index). Airborne data collected at two flight altitudes and two flight directions tested the validity of “spectral diversity-α-diversity” relationship at different sampling scales and flight directions. Our results showed a strong relationship between spectral diversity and α-diversity in young, non-invaded plots exhibiting strong differences in α-diversity. However, in the old, invaded plots, the spectral diversity-α-diversity relationship was non-significant. Factors likely contributing to this failure in the old plots included the spatial mismatch between airborne and field-based sampling, the convergence in diversity levels over time, and the unique reflectance signatures of the invasive species related to their different structural and phenological properties. Unlike previous airborne studies in manipulated experimental prairie plots, but similar to results in more natural settings, the strong spectral diversity-α-diversity relationship in the young plots remained even at the spatial resolution of 1 m, demonstrating the potential of airborne remote sensing to assess diversity patterns in prairie grasslands. These findings demonstrate the importance of experimental remote sensing in evaluating spectral diversity, and provide insight for the development of operational airborne methods to assess biodiversity.